HK40062217B - Ancestry-specific genetic risk scores - Google Patents

Description

Lineage-specific genetic risk score

Cross-referencing

This application claims the benefit of U.S. Provisional Application No. 62/772,565, filed November 28, 2018, and U.S. Patent Application No. 16/216,940, filed December 11, 2018, which are incorporated herein by reference in their entirety.

sequence list

The immediate request contains a sequence list, which is incorporated herein in its entirety by reference. The ASCII copy was created on October 28, 2019, named 55075-701_601_SL.txt, and is 47.9KB in size.

Summary of the Invention

Genome-wide association studies (GWAS) enable scientists to identify genetic variations associated with a wide range of phenotypic traits. Genetic risk scores (GRS) are used to predict whether an individual will develop a particular trait based on the detection of certain genetic variants in samples taken from that individual. However, data show that the underlying genetic variations and patterns differ across discrete lineages. Therefore, whether a detected genetic variant gives an individual a risk of developing that trait depends largely on that individual's ancestry. Current genetic risk prediction methods either do not consider an individual's ancestry at all or use consumer questionnaires to account for ancestry, leading to imprecise and often inaccurate genetic risk predictions.

In some embodiments, this document discloses methods, media, and systems for determining an individual's lineage by analyzing their genotype and calculating GRS based on lineage-specific genetic risk variants derived from GWAS of subjects sharing the same lineage as the individual. In some embodiments, the genetic variants (multiple) considered in the GRS may include single nucleotide variants (SNVs), nucleotide base insertions or deletions (indels), or copy number variants (CNVs). In some embodiments, if a genetic variant detected in a sample obtained from the individual does not correspond to a genetic variant (unknown genetic variant) reported in a GWAS of a lineage-specific subject group, a proxy genetic variant is selected based on a non-random association (referred to as linkage disequilibrium (LD)) with the unknown genetic variant within the specific lineage population used as the basis for risk prediction. Studies have shown that patterns of LD in the human genome differ across different lineage populations.

In some embodiments, this document discloses a computer-implemented method comprising: (a) assigning an individual’s lineage using a distance-based or model-based computer program to analyze the individual’s genotype, the genotype including one or more individual-specific genetic variants; and (b) detecting in the individual’s genotype a lineage-specific variant associated with a particular phenotypic trait, the lineage-specific variant corresponding to: (i) an individual-specific genetic variant detectable in the individual’s genotype; or (ii) a genetic variant in linkage disequilibrium (LD) with the individual-specific genetic variant determined by imputation from an individual-specific variant missing from ancestry-specific phased haplotypes, the ancestry-specific phased haplotypes being determined using a reference group of individuals with the same lineage as the individual; and (c) calculating a genetic risk score (GRS) for the individual based on the lineage-specific variants detected in (b), wherein the GRS indicates the likelihood that the individual has or will develop the particular phenotypic trait. In some embodiments, lineage-specific genetic variants and individual-specific genetic variants are selected from single nucleotide variants (SNVs), copy number variants (CNVs), and indels. In some embodiments, the interpolation in step (ii) includes: (1) typing untyped genotype data from the individual to generate a lineage-specific typing haplotype based on the individual's lineage; and (2) interpolating individual-specific genotypes not present in the lineage-specific typing haplotype using typing haplotype data from a reference group with the same lineage as the individual to select genetic variants at the LD (Lower Pathway). In some embodiments, the LD is defined by a D' value including at least about 0.80 or an ^r² value including at least 0.80. In some embodiments, a specific trait includes nutritional traits, clinical traits, subclinical traits, physical traits, skin traits, allergic traits, or mental traits, or combinations thereof. In some embodiments, the individual's genotype is obtained by or has been obtained through genotyping of genetic material obtained from the individual. In some embodiments, genotyping assays include deoxyribonucleic acid (DNA) arrays, ribonucleic acid (RNA) arrays, sequencing assays, or combinations thereof. In some embodiments, the distance-based computer program is principal component analysis, and the model-based computer program is a maximum likelihood or Bayesian method. In some embodiments, the individual's GRS based on the pedigree-specific variant is more accurate than the individual's corresponding GRS based on non-pedigree-specific variants. In some embodiments, the computer-implemented method further includes providing notification including the individual's GRS for a specific phenotypic trait. In some embodiments, the notification also includes behavioral recommendations for the individual based on the GRS for a specific phenotypic trait. In some embodiments, behavioral changes associated with a specific phenotypic trait include increasing, decreasing, or avoiding activities including physical exercise, ingestion of drugs, vitamins, or supplements, exposure to products, use of products, dietary changes, sleep changes, alcohol consumption, or caffeine consumption. In some embodiments, subclinical traits include phenotypes of diseases or conditions. In some implementations, physical activity traits include aversion to exercise, aerobic capacity, difficulty losing weight, endurance, strength, physical fitness benefits, decreased heart rate response to exercise, lean body mass, muscle soreness, risk of muscle injury, impaired muscle repair, stress fractures, overall injury risk, likelihood of obesity, or impaired resting metabolic rate. In some implementations, skin traits include collagen breakdown, dryness, antioxidant deficiency, impaired detoxification, skin glycation, pigmentation spots, premature aging, photoaging, dermal sensitivity, or sensitivity to sunlight. In some implementations, hair traits include hair thickness, thinning hair, hair loss, baldness, oiliness, dryness, dandruff, or hair volume. In some implementations, nutritional traits include vitamin deficiencies, mineral deficiencies, antioxidant deficiencies, fatty acid deficiencies, metabolic imbalances, impaired metabolism, metabolic sensitivity, allergies, satiety, or effectiveness of a healthy diet. In some embodiments, vitamin deficiency includes deficiencies in vitamins A, B1, B2, B3, B5, B6, B7, B8, B9, B12, C, D, E, and K. In some embodiments, mineral deficiency includes deficiencies in minerals including calcium, iron, magnesium, zinc, or selenium. In some embodiments, antioxidant deficiency includes deficiencies in antioxidants including glutathione or coenzyme Q10 (CoQ10). In some embodiments, fatty acid deficiency includes deficiencies in polyunsaturated or monounsaturated fatty acids. In some embodiments, metabolic imbalance includes glucose imbalance. In some embodiments, metabolic impairment includes metabolic impairment due to caffeine or drug treatment. In some embodiments, metabolic sensitivity includes gluten sensitivity, glycan sensitivity, or lactose sensitivity. In some embodiments, allergy includes allergies to food (food allergy) or allergies to environmental factors (environmental allergy). In some embodiments, the method further includes treatment to effectively improve or prevent a specific trait in an individual, provided that a genetic risk score indicates a high probability that the individual has or will develop the specific trait. In some embodiments, the treatment includes supplements or pharmacological therapy. In some embodiments, supplements include vitamins, minerals, probiotics, antioxidants, anti-inflammatory agents, or combinations thereof. In some embodiments, behavioral changes associated with the specific trait include increasing, decreasing, or avoiding activities including physical exercise, ingestion of drugs, vitamins, or supplements, exposure to products, use of products, dietary changes, sleep changes, alcohol consumption, or caffeine consumption.

In some embodiments, this document discloses a system comprising: a computing device including at least one processor, memory, and software program, the software program including instructions executable by at least one processor to assess the likelihood that an individual has or will develop a particular phenotypic trait, the instructions including the steps of: (a) assigning a pedigree of an individual using a distance-based or model-based computer program to analyze the individual's genotype, the genotype including one or more individual-specific genetic variants; and (b) detecting pedigree-specific variants associated with a particular phenotypic trait in the individual's genotype, the pedigree-specific variants... The variant corresponds to: (i) an individual-specific genetic variant detectable in the individual's genotype; or (ii) a genetic variant in linkage disequilibrium (LD) with the individual-specific genetic variant determined by interpolating the individual-specific variant missing from the pedigree-specific haplotype, the pedigree-specific haplotype being determined using a reference group of individuals of the same pedigree as the individual; and (c) a genetic risk score (GRS) calculated for the individual based on the pedigree-specific variant detected in (b), wherein the GRS indicates the likelihood that the individual has or will develop the particular phenotypic trait. In some embodiments, the pedigree-specific genetic variant and the individual-specific genetic variant are selected from single nucleotide variants (SNVs), copy number variants (CNVs), and indels. In some embodiments, the interpolation in step (2) includes: (1) typing untyped genotype data from the individual to generate pedigree-specific haplotypes based on the individual's lineage; and (2) interpolating individual-specific genotypes not present in the pedigree-specific haplotypes using haplotype data from a reference group with the same lineage as the individual, to select genetic variants at the LD (Lower Pathway) with respect to the individual-specific genetic variant. In some embodiments, the LD is defined by a D' value including at least about 0.80 or an ^r² value including at least 0.80. In some embodiments, the specific trait includes nutritional traits, clinical traits, subclinical traits, physical traits, skin traits, allergic traits, or mental traits. In some embodiments, the system further includes genotyping assays. In some embodiments, genotyping assays include deoxyribonucleic acid (DNA) arrays, ribonucleic acid (RNA) arrays, sequencing assays, or combinations thereof. In some embodiments, the distance-based computer program is principal component analysis, and the model-based computer program is maximum likelihood or Bayesian methods. In some embodiments, the individual's GRS based on the pedigree-specific variant is more accurate than the corresponding GRS based on non-pedigree-specific variants. In some embodiments, the system further includes a reporting module configured to generate a report including the individual's GRS for a specific phenotypic trait. In some embodiments, the system further includes an output module configured to present the report to the individual. In some embodiments, the report includes the risk that the individual has or will develop a specific trait. In some embodiments, the report further includes recommendations for the individual's behavior based on the GRS for the specific phenotypic trait. In some embodiments, behavioral changes associated with a specific phenotypic trait include increasing, decreasing, or avoiding activities including physical exercise, ingestion of medications, vitamins, or supplements, exposure to products, use of products, dietary changes, sleep changes, alcohol consumption, or caffeine consumption. In some embodiments, subclinical traits include phenotypes of diseases or conditions. In some implementations, physical activity traits include aversion to exercise, aerobic capacity, difficulty losing weight, endurance, strength, physical fitness benefits, decreased heart rate response to exercise, lean body mass, muscle soreness, risk of muscle injury, impaired muscle repair, stress fractures, overall injury risk, likelihood of obesity, or impaired resting metabolic rate. In some implementations, skin traits include collagen breakdown, dryness, antioxidant deficiency, impaired detoxification, skin glycation, pigmentation spots, premature aging, photoaging, dermal sensitivity, or sensitivity to sunlight. In some implementations, hair traits include hair thickness, thinning hair, hair loss, baldness, oiliness, dryness, dandruff, or hair volume. In some implementations, nutritional traits include vitamin deficiencies, mineral deficiencies, antioxidant deficiencies, fatty acid deficiencies, metabolic imbalances, impaired metabolism, metabolic sensitivity, allergies, satiety, or effectiveness of a healthy diet. In some embodiments, vitamin deficiency includes deficiencies in vitamins A, B1, B2, B3, B5, B6, B7, B8, B9, B12, C, D, E, and K. In some embodiments, mineral deficiency includes deficiencies in minerals including calcium, iron, magnesium, zinc, or selenium. In some embodiments, antioxidant deficiency includes deficiencies in antioxidants including glutathione or coenzyme Q10 (CoQ10). In some embodiments, fatty acid deficiency includes deficiencies in polyunsaturated or monounsaturated fatty acids. In some embodiments, metabolic imbalance includes glucose imbalance. In some embodiments, metabolic impairment includes metabolic impairment due to caffeine or drug therapy. In some embodiments, metabolic sensitivity includes gluten sensitivity, glycan sensitivity, or lactose sensitivity. In some embodiments, allergy includes allergies to food (food allergy) or allergies to environmental factors (environmental allergy). In some embodiments, the system further includes treatment to effectively improve or prevent a specific trait in an individual, provided that a genetic risk score indicates a high probability that the individual has or will develop the specific trait. In some embodiments, the treatment includes supplements or pharmacological therapy. In some embodiments, supplements include vitamins, minerals, probiotics, antioxidants, anti-inflammatory agents, or combinations thereof. In some embodiments, behavioral changes associated with the specific trait include increasing, decreasing, or avoiding activities including physical exercise, ingestion of drugs, vitamins, or supplements, exposure to products, use of products, dietary changes, sleep changes, alcohol consumption, or caffeine consumption.

In some implementations, this document discloses the use of the system disclosed herein for suggesting behavioral changes or recommending products to individuals based on the GRS calculated in (c).

In some embodiments, this document also discloses a non-transitory computer-readable storage medium comprising computer-executable code configured to cause at least one processor to perform the steps of the methods disclosed herein.

In some embodiments, this document discloses a computer-implemented method for suggesting behavioral changes to an individual based on their lineage and genotype, the method comprising: a) providing the individual's genotype, the genotype including one or more individual-specific genetic variants; b) assigning the individual a lineage at least in part based on the individual's genotype; c) using a database of trait-related variants (which include lineage-specific genetic variants derived from subjects (subject groups) with the same lineage as the individual) to select one or more lineage-specific genetic variants at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants corresponds to: (i) an individual-specific genetic variant of the one or more individual-specific genetic variants. The method includes (i) a heterosexual variant, or (ii) a predetermined variant in linkage disequilibrium (LD) with an individual-specific variant among the one or more individual-specific variants in a group of subjects with the same lineage as the individual, wherein each of the one or more lineage-specific variants and each of the individual-specific variants includes one or more risk units; (d) calculating a genetic risk score for the individual based on the selected one or more lineage-specific variants, wherein the genetic risk score indicates the likelihood that the individual has or will develop the specific trait; and (e) providing the individual with recommendations including behavioral changes related to the specific trait based on the genetic risk score. In some embodiments, the method further includes providing the individual with a questionnaire including one or more questions related to the specific trait. In some embodiments, the method further includes receiving from the individual one or more answers to one or more questions related to the specific trait in a questionnaire provided to the individual. In some embodiments, the method further includes: a) providing the individual with a questionnaire including one or more questions related to the specific trait; and b) receiving one or more answers from the individual to the one or more questions, wherein recommendations for behavioral changes related to the specific trait, including those provided by the individual, are further based on the one or more answers provided by the individual. In some embodiments, the method further includes storing lineage-specific genetic variants associated with the specific trait from the subject group in a trait-related variant database. In some embodiments, the genetic risk score includes percentiles or z-scores. In some embodiments, LD is defined by (i) a D' value of at least about 0.20 or (ii) an ^r² value of at least about 0.70. In some implementations, LD is defined by a D' value, which includes approximately 0.20 to 0.25, 0.25 to 0.30, 0.30 to 0.35, 0.35 to 0.40, 0.40 to 0.45, 0.45 to 0.50, 0.50 to 0.55, 0.55 to 0.60, 0.60 to 0.65, 0.65 to 0.70, 0.70 to 0.75, 0.75 to 0.80, 0.80 to 0.85, 0.85 to 0.90, 0.90 to 0.95, or 0.95 to 1.0. In some embodiments, LD is defined by a D' value, which includes at least about 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.85, 0.90, 0.95, and 1.0. In some embodiments, LD is defined by ^{an r²} value, which includes at least about 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, and 1.0. In some embodiments, LD is defined by ^{an r²} value, which includes at least about 0.70 to 0.75, 0.75 to 0.80, 0.80 to 0.85, 0.85 to 0.90, 0.90 to 0.95, or 0.95 to 1.0. In some embodiments, an individual's genotype is obtained by genotyping or has been performed on genetic material obtained from the individual. In some embodiments, an individual's genotype is obtained by performing deoxyribonucleic acid (DNA) arraying, ribonucleic acid (RNA) arraying, sequencing, or a combination thereof on genetic material obtained from the individual. In some embodiments, sequencing includes next-generation sequencing (NGS). In some embodiments, the method further includes updating the trait-related variant database with the assigned pedigree, specific trait, and genotype of the individual. In some embodiments, principal component analysis (PCA), or maximum likelihood estimation (MLE), or a combination thereof, is used to assign pedigrees to the individuals in (b). In some embodiments, one or more pedigree-specific genetic variants, one or more individual-specific genetic variants, and genetic variants at the LD with one or more individual-specific genetic variants include single nucleotide variants (SNVs). In some embodiments, one or more risk units include risk alleles. In some embodiments, the one or more pedigree-specific genetic variants, the one or more individual-specific genetic variants, and genetic variants at the LD with one or more individual-specific genetic variants include indels characterized by the insertion or deletion of one or more nucleotides. In some embodiments, one or more risk units comprise an insertion (I) or deletion (D) of a nucleotide base. In some embodiments, the one or more lineage-specific genetic variants, or the one or more individual-specific genetic variants, comprise copy number variants (CNVs). In some embodiments, one or more risk units comprise a duplication or deletion of a nucleic acid sequence. In some embodiments, the nucleic acid sequence comprises about two, three, four, five, six, seven, eight, nine, or ten nucleotides. In some embodiments, the nucleic acid sequence comprises more than three nucleotides. In some embodiments, the nucleic acid sequence comprises the entire gene. In some embodiments, the method further includes providing the individual with notification of the risk that the individual has or will develop the specific trait. In some embodiments, the specific trait includes nutritional traits, clinical traits, subclinical traits, physical activity traits, skin traits, hair traits, allergic traits, or mental traits. In some embodiments, a clinical trait includes a disease or condition. In some embodiments, a subclinical trait includes a phenotype of a disease or condition. In some implementations, physical activity traits include aversion to exercise, aerobic capacity, difficulty losing weight, endurance, strength, physical fitness benefits, decreased heart rate response to exercise, lean body mass, muscle soreness, risk of muscle injury, impaired muscle repair, stress fractures, overall injury risk, likelihood of obesity, or impaired resting metabolic rate. In some implementations, skin traits include collagen breakdown, dryness, antioxidant deficiency, impaired detoxification, skin glycation, pigmentation spots, premature aging, photoaging, dermal sensitivity, or sensitivity to sunlight. In some implementations, hair traits include hair thickness, thinning hair, hair loss, baldness, oiliness, dryness, dandruff, or hair volume. In some implementations, nutritional traits include vitamin deficiencies, mineral deficiencies, antioxidant deficiencies, fatty acid deficiencies, metabolic imbalances, impaired metabolism, metabolic sensitivity, allergies, satiety, or effectiveness of a healthy diet. In some embodiments, vitamin deficiency includes deficiencies in vitamins A, B1, B2, B3, B5, B6, B7, B8, B9, B12, C, D, E, and K. In some embodiments, mineral deficiency includes deficiencies in minerals including calcium, iron, magnesium, zinc, or selenium. In some embodiments, antioxidant deficiency includes deficiencies in antioxidants including glutathione or coenzyme Q10 (CoQ10). In some embodiments, fatty acid deficiency includes deficiencies in polyunsaturated or monounsaturated fatty acids. In some embodiments, metabolic imbalance includes glucose imbalance. In some embodiments, metabolic impairment includes metabolic impairment due to caffeine or drug therapy. In some embodiments, metabolic sensitivity includes gluten sensitivity, glycan sensitivity, or lactose sensitivity. In some embodiments, allergy includes allergies to food (food allergy) or allergies to environmental factors (environmental allergy). In some embodiments, the method further includes treatment to effectively improve or prevent a specific trait in an individual, provided that a genetic risk score indicates a high probability that the individual has or will develop the specific trait. In some embodiments, the treatment includes supplements or pharmacological therapy. In some embodiments, supplements include vitamins, minerals, probiotics, antioxidants, anti-inflammatory agents, or combinations thereof. In some embodiments, behavioral changes associated with the specific trait include increasing, decreasing, or avoiding activities including physical exercise, ingestion of drugs, vitamins, or supplements, exposure to products, use of products, dietary changes, sleep changes, alcohol consumption, or caffeine consumption. In some embodiments, recommendations are presented in the report. In some embodiments, the report is presented to the individual via a user interface of an electronic device. In some embodiments, the report also includes the individual's genetic risk score for the specific trait. In some embodiments, a genetic risk score is calculated by: a) calculating a raw score comprising the total number of one or more risk units for each lineage-specific genetic variant for each subject in the subject group, thereby generating a lineage-specific observation range for the raw score; b) calculating the total number of one or more risk units for each individual-specific genetic variant among one or more individual-specific genetic variants, thereby generating an individual raw score; and c) comparing the individual raw score to the lineage-specific observation range to generate the genetic risk score. In some embodiments, a genetic risk score is calculated by: a) determining a concession ratio for each lineage-specific genetic risk variant; and b) if two or more lineage-specific genetic variants are selected, multiplying the concession ratios of each of the two or more lineage-specific genetic variants. In some embodiments, a genetic risk score is calculated by: a) determining a relative risk for each lineage-specific genetic risk variant; and b) if two or more lineage-specific genetic variants are selected, multiplying the relative risks of each of the two or more lineage-specific genetic variants. In some implementations, predetermined genetic variants are determined by: a) providing untyped genotype data from an individual; b) typing the untyped genotype data to generate individual-specific typing haplotypes based on the individual's lineage; c) imputing individual-specific genotypes not present in the typed individual-specific typing haplotypes using typing haplotype data from a reference group with the same lineage as the individual; and d) selecting from the imputed individual-specific genotypes genetic variants that are in linkage disequilibrium (LD) with the individual-specific genetic variants, which are associated with the likelihood that the individual has or will develop the specific trait.

In some embodiments, this document discloses a computer-implemented method for determining the likelihood that an individual has or will develop a specific trait based on the individual's lineage, the method comprising: a) providing the individual's genotype, the genotype comprising one or more individual-specific genetic variants; b) assigning the individual a lineage at least in part based on the individual's genotype; c) using a trait-related variant database (which comprises lineage-specific genetic variants derived from subjects (subject groups) sharing the same lineage as the individual) to select one or more lineage-specific genetic variants at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants corresponds to: ( (i) an individual-specific genetic variant among the one or more individual-specific genetic variants, or (ii) a predetermined genetic variant in linkage disequilibrium (LD) with the individual-specific genetic variant among the one or more individual-specific genetic variants in a subject population with the same lineage as the individual, and wherein each of the one or more lineage-specific genetic variants and each of the individual-specific genetic variants includes one or more risk units; and (d) calculating a genetic risk score for the individual based on the selected one or more lineage-specific genetic variants, wherein the genetic risk score indicates the likelihood that the individual has or will develop the specific trait. In some embodiments, the method further includes providing the individual with notification of the risk that the individual has or will develop the specific trait. In some embodiments, the notification includes recommendations for behavioral changes associated with the specific trait. In some embodiments, behavioral changes associated with the specific trait include increasing, decreasing, or avoiding activities including physical exercise, ingestion of drugs, vitamins, or supplements, exposure to products, use of products, dietary changes, sleep changes, alcohol consumption, or caffeine consumption. In some embodiments, the notification is displayed in a report. In some embodiments, the report is displayed to the individual via a user interface of an electronic device. In some embodiments, the method further includes providing the individual with a questionnaire comprising one or more questions relating to the specific trait. In some embodiments, the method further includes receiving from the individual one or more answers to one or more questions relating to the specific trait in a questionnaire provided to the individual. In some embodiments, the method further includes: a) providing the individual with a questionnaire comprising one or more questions relating to the specific trait; and b) receiving from the individual one or more answers to the one or more questions, wherein recommendations for behavioral changes related to the specific trait for the individual are further based on the one or more answers provided by the individual. In some embodiments, the method further includes storing lineage-specific genetic variants associated with the specific trait from the subject group in a trait-related variant database. In some embodiments, the genetic risk score includes percentiles or z-scores. In some embodiments, LD is defined by (i) a D' value of at least about 0.20 or (ii) an ^r² value of at least about 0.70. In some implementations, LD is defined by a D' value, which includes approximately 0.20 to 0.25, 0.25 to 0.30, 0.30 to 0.35, 0.35 to 0.40, 0.40 to 0.45, 0.45 to 0.50, 0.50 to 0.55, 0.55 to 0.60, 0.60 to 0.65, 0.65 to 0.70, 0.70 to 0.75, 0.75 to 0.80, 0.80 to 0.85, 0.85 to 0.90, 0.90 to 0.95, or 0.95 to 1.0. In some embodiments, LD is defined by ^{an r²} value, which includes approximately 0.70 to 0.75, 0.75 to 0.80, 0.80 to 0.85, 0.85 to 0.90, 0.90 to 0.95, or 0.95 to 1.0. In some embodiments, LD is defined by a D' value, which includes at least approximately 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.85, 0.90, 0.95, and 1.0. In some embodiments, LD is defined by ^{an r²} value, which includes at least approximately 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, and 1.0. In some embodiments, an individual's genotype is obtained by genotyping or has been performed on genetic material obtained from the individual. In some embodiments, an individual's genotype is obtained by performing deoxyribonucleic acid (DNA) arraying, ribonucleic acid (RNA) arraying, sequencing, or a combination thereof on genetic material obtained from the individual. In some embodiments, sequencing includes next-generation sequencing (NGS). In some embodiments, the method further includes updating the trait-related variant database with the assigned pedigree, specific trait, and genotype of the individual. In some embodiments, principal component analysis (PCA), or maximum likelihood estimation (MLE), or a combination thereof, is used to assign pedigrees to the individuals in (b). In some embodiments, one or more pedigree-specific genetic variants, one or more individual-specific genetic variants, and genetic variants at the LD with one or more individual-specific genetic variants include single nucleotide variants (SNVs). In some embodiments, one or more risk units include risk alleles. In some embodiments, the one or more pedigree-specific genetic variants, the one or more individual-specific genetic variants, and genetic variants at the LD with one or more individual-specific genetic variants include indels characterized by the insertion or deletion of one or more nucleotides. In some embodiments, one or more risk units comprise an insertion (I) or deletion (D) of one or more nucleotides. In some embodiments, the one or more lineage-specific genetic variants, or the one or more individual-specific genetic variants, comprise copy number variants (CNVs). In some embodiments, one or more risk units comprise an insertion or deletion of a nucleic acid sequence. In some embodiments, the nucleic acid sequence comprises about two, three, four, five, six, seven, eight, nine, or ten nucleotides. In some embodiments, the nucleic acid sequence comprises more than three nucleotides. In some embodiments, the nucleic acid sequence comprises the entire gene. In some embodiments, the method further includes providing the individual with notification of the risk that the individual has or will develop the specific trait. In some embodiments, the specific trait includes nutritional traits, clinical traits, subclinical traits, physical activity traits, skin traits, hair traits, allergic traits, or mental traits. In some embodiments, a clinical trait includes a disease or condition. In some embodiments, a subclinical trait includes a phenotype of a disease or condition. In some implementations, physical activity traits include aversion to exercise, aerobic capacity, difficulty losing weight, endurance, strength, physical fitness benefits, decreased heart rate response to exercise, lean body mass, muscle soreness, risk of muscle injury, impaired muscle repair, stress fractures, overall injury risk, likelihood of obesity, or impaired resting metabolic rate. In some implementations, skin traits include collagen breakdown, dryness, antioxidant deficiency, impaired detoxification, skin glycation, pigmentation spots, premature aging, photoaging, dermal sensitivity, or sensitivity to sunlight. In some implementations, nutritional traits include vitamin deficiencies, mineral deficiencies, antioxidant deficiencies, fatty acid deficiencies, metabolic imbalances, impaired metabolism, metabolic sensitivity, allergies, satiety, or effectiveness of a healthy diet. In some implementations, hair traits include hair thickness, thinning hair, hair loss, baldness, oiliness, dryness, dandruff, or hair volume. In some embodiments, vitamin deficiency includes deficiencies in vitamins A, B1, B2, B3, B5, B6, B7, B8, B9, B12, C, D, E, and K. In some embodiments, mineral deficiency includes deficiencies in minerals including calcium, iron, magnesium, zinc, or selenium. In some embodiments, antioxidant deficiency includes deficiencies in antioxidants including glutathione or coenzyme Q10 (CoQ10). In some embodiments, fatty acid deficiency includes deficiencies in polyunsaturated or monounsaturated fatty acids. In some embodiments, metabolic imbalance includes glucose imbalance. In some embodiments, metabolic impairment includes metabolic impairment due to caffeine or drug therapy. In some embodiments, metabolic sensitivity includes gluten sensitivity, glycan sensitivity, or lactose sensitivity. In some embodiments, allergy includes allergies to food (food allergy) or allergies to environmental factors (environmental allergy). In some embodiments, the method further includes treatment to effectively improve or prevent a specific trait in an individual, provided that the genetic risk score indicates a high probability that the individual has or will develop the specific trait. In some embodiments, the treatment includes supplements or pharmacological therapy. In some embodiments, supplements include vitamins, minerals, probiotics, antioxidants, anti-inflammatory agents, or combinations thereof. In some embodiments, the genetic risk score is calculated by: a) calculating a raw score comprising the total number of one or more risk units for each lineage-specific genetic variant for each subject in the subject group, thereby generating a lineage-specific observation range for the raw score; b) calculating the total number of one or more risk units for each of one or more individual-specific genetic variants, thereby generating an individual raw score; and c) comparing the individual raw score to the lineage-specific observation range to generate the genetic risk score. In some embodiments, the genetic risk score is calculated by: a) determining a concession ratio for each lineage-specific genetic risk variant; and b) if two or more lineage-specific genetic variants are selected, multiplying the concession ratios of each of the two or more lineage-specific genetic variants. In some embodiments, a genetic risk score is calculated by: a) determining the relative risk of each lineage-specific genetic risk variant; and b) if two or more lineage-specific genetic variants are selected, multiplying the relative risks of each of the two or more lineage-specific genetic variants. In some embodiments, predetermined genetic variants are determined by: a) providing untyped genotype data from an individual; b) typing the untyped genotype data to generate individual-specific typing haplotypes based on the individual's lineage; c) imputing individual-specific genotypes not present in the typed individual-specific typing haplotypes using typing haplotype data from a reference group with the same lineage as the individual; and d) selecting from the imputed individual-specific genotypes genetic variants that are in linkage disequilibrium (LD) with the individual-specific genetic variants, which are associated with the likelihood that the individual has or will develop the specific trait.

In some embodiments, this document discloses a health reporting system comprising: a) a computing device including at least one processor, memory, and software program, the software program including instructions executable by at least one processor to assess the likelihood that an individual has or will develop a particular trait, the instructions comprising the steps of: (i) providing the individual's genotype, the genotype including one or more individual-specific genetic variants; (ii) assigning a lineage to the individual at least in part based on the individual's genotype; and (iii) using a trait-related variant database (which includes lineage-specific genetic variants derived from subjects (subject groups) with the same lineage as the individual) to select one or more lineage-specific genetic variants at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants corresponds to: (i) the... (i) an individual-specific genetic variant among one or more individual-specific genetic variants, or (ii) a predetermined genetic variant in linkage disequilibrium (LD) with the individual-specific genetic variant among one or more individual-specific genetic variants in a subject population of the same lineage as the individual, and wherein each of the one or more lineage-specific genetic variants and each of the individual-specific genetic variants includes one or more risk units; and (iv) calculating a genetic risk score for the individual based on the selected one or more lineage-specific genetic variants, wherein the genetic risk score indicates the likelihood that the individual has or will develop the particular trait; (b) a reporting module that generates a report including the genetic risk score for the particular trait for the individual; and (c) an output module configured to display the report to the individual. In some embodiments, the genetic risk score includes percentiles or z-scores. In some embodiments, LD is defined by (i) a D' value of at least about 0.20 or (ii) an ^r² value of at least about 0.70. In some implementations, LD is defined by a D' value, which includes approximately 0.20 to 0.25, 0.25 to 0.30, 0.30 to 0.35, 0.35 to 0.40, 0.40 to 0.45, 0.45 to 0.50, 0.50 to 0.55, 0.55 to 0.60, 0.60 to 0.65, 0.65 to 0.70, 0.70 to 0.75, 0.75 to 0.80, 0.80 to 0.85, 0.85 to 0.90, 0.90 to 0.95, or 0.95 to 1.0. In some embodiments, LD is defined by ^{an r²} value, which includes approximately 0.70 to 0.75, 0.75 to 0.80, 0.80 to 0.85, 0.85 to 0.90, 0.90 to 0.95, or 0.95 to 1.0. In some embodiments, LD is defined by a D' value, which includes at least approximately 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.85, 0.90, 0.95, and 1.0. In some embodiments, LD is defined by ^{an r²} value, which includes at least approximately 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, and 1.0. In some embodiments, an individual's genotype is obtained by genotyping or has been performed on genetic material obtained from the individual. In some embodiments, an individual's genotype is obtained by performing deoxyribonucleic acid (DNA) arraying, ribonucleic acid (RNA) arraying, sequencing, or a combination thereof on genetic material obtained from the individual. In some embodiments, sequencing includes next-generation sequencing (NGS). In some embodiments, the method further includes updating the trait-related variant database with the assigned pedigree, specific trait, and genotype of the individual. In some embodiments, principal component analysis (PCA), or maximum likelihood estimation (MLE), or a combination thereof, is used to assign pedigrees to the individuals in (b). In some embodiments, one or more pedigree-specific genetic variants, one or more individual-specific genetic variants, and genetic variants at the LD with one or more individual-specific genetic variants include single nucleotide variants (SNVs). In some embodiments, one or more risk units include risk alleles. In some embodiments, the one or more pedigree-specific genetic variants, the one or more individual-specific genetic variants, and genetic variants at the LD with one or more individual-specific genetic variants include indels characterized by the insertion or deletion of one or more nucleotides. In some embodiments, one or more risk units comprise an insertion (I) or deletion (D) of one or more nucleotides. In some embodiments, the one or more lineage-specific genetic variants, or the one or more individual-specific genetic variants, comprise copy number variants (CNVs). In some embodiments, one or more risk units comprise an insertion or deletion of a nucleic acid sequence. In some embodiments, the nucleic acid sequence comprises about two, three, four, five, six, seven, eight, nine, or ten nucleotides. In some embodiments, the nucleic acid sequence comprises more than three nucleotides. In some embodiments, the nucleic acid sequence comprises the entire gene. In some embodiments, the method further includes providing the individual with notification of the risk that the individual has or will develop the specific trait. In some embodiments, the specific trait includes nutritional traits, clinical traits, subclinical traits, physical activity traits, skin traits, hair traits, allergic traits, or mental traits. In some embodiments, a clinical trait includes a disease or condition. In some embodiments, a subclinical trait includes a phenotype of a disease or condition. In some implementations, physical activity traits include aversion to exercise, aerobic capacity, difficulty losing weight, endurance, strength, physical fitness benefits, decreased heart rate response to exercise, lean body mass, muscle soreness, risk of muscle injury, impaired muscle repair, stress fractures, overall injury risk, likelihood of obesity, or impaired resting metabolic rate. In some implementations, skin traits include collagen breakdown, dryness, antioxidant deficiency, impaired detoxification, skin glycation, pigmentation spots, premature aging, photoaging, dermal sensitivity, or sensitivity to sunlight. In some implementations, hair traits include hair thickness, thinning hair, hair loss, baldness, oiliness, dryness, dandruff, or hair volume. In some implementations, nutritional traits include vitamin deficiencies, mineral deficiencies, antioxidant deficiencies, fatty acid deficiencies, metabolic imbalances, impaired metabolism, metabolic sensitivity, allergies, satiety, or effectiveness of a healthy diet. In some embodiments, vitamin deficiency includes deficiencies in vitamins A, B1, B2, B3, B5, B6, B7, B8, B9, B12, C, D, E, and K. In some embodiments, mineral deficiency includes deficiencies in minerals including calcium, iron, magnesium, zinc, or selenium. In some embodiments, antioxidant deficiency includes deficiencies in antioxidants including glutathione or coenzyme Q10 (CoQ10). In some embodiments, fatty acid deficiency includes deficiencies in polyunsaturated or monounsaturated fatty acids. In some embodiments, metabolic imbalance includes glucose imbalance. In some embodiments, metabolic impairment includes metabolic impairment due to caffeine or drug therapy. In some embodiments, metabolic sensitivity includes gluten sensitivity, glycan sensitivity, or lactose sensitivity. In some embodiments, allergy includes allergies to food (food allergy) or allergies to environmental factors (environmental allergy). In some embodiments, the method further includes treatment to effectively improve or prevent a specific trait in an individual, provided that a genetic risk score indicates a high probability that the individual has or will develop the specific trait. In some embodiments, the treatment includes supplements or pharmacological therapy. In some embodiments, supplements include vitamins, minerals, probiotics, antioxidants, anti-inflammatory agents, or combinations thereof. In some embodiments, the instructions further include providing the individual with a questionnaire including one or more questions related to the specific trait. In some embodiments, the instructions further include receiving from the individual one or more answers to one or more questions related to the specific trait in a questionnaire provided to the individual. In some embodiments, the instructions further include: (i) providing the individual with a questionnaire including one or more questions related to the specific trait; and (ii) receiving from the individual one or more answers to the one or more questions. In some embodiments, the instructions further include storing lineage-specific genetic variants associated with the specific trait from the subject group in a trait-related variant database. In some embodiments, the output module is configured to display a report on a user interface of a personal electronic device. In some embodiments, the system also includes a personal electronic device having an application configured to communicate with the output module via a computer network to access the report. In some embodiments, the genetic risk score is calculated by: (1) calculating a raw score comprising the total number of one or more risk units for each lineage-specific genetic variant for each subject in the subject group, thereby generating a lineage-specific observation range for the raw score; (2) calculating the total number of one or more risk units for each individual-specific genetic variant among one or more individual-specific genetic variants, thereby generating an individual raw score; and (3) comparing the individual raw score with the lineage-specific observation range to generate the genetic risk score. In some embodiments, the genetic risk score is calculated by: (1) determining a concession ratio for each lineage-specific genetic risk variant; and (2) if two or more lineage-specific genetic variants are selected, multiplying the concession ratios of each of the two or more lineage-specific genetic variants. In some implementations, the system further includes the steps of determining predetermined genetic variants by: a) providing untyped genotype data from an individual; b) typing the untyped genotype data to generate individual-specific typing haplotypes based on the individual's lineage; c) interpolating individual-specific genotypes not present in the interpolated individual-specific typing haplotypes using typing haplotype data from a reference group with the same lineage as the individual; and d) selecting from the interpolated individual-specific genotypes genetic variants that are in linkage disequilibrium (LD) with the individual-specific genetic variants, which are associated with the likelihood that the individual has or will develop the specific trait.

In some embodiments, this document discloses a non-transitory computer-readable storage medium comprising computer-executable code configured to cause at least one processor to perform the following steps: a) providing a genotype of the individual, the genotype comprising one or more individual-specific genetic variants; b) assigning a lineage to the individual at least in part based on the individual's genotype; c) using a trait-related variant database (which includes lineage-specific genetic variants derived from subjects (subject groups) having the same lineage as the individual) to select one or more lineage-specific genetic variants at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants... A corresponding to: (i) an individual-specific genetic variant among the one or more individual-specific genetic variants, or (ii) a predetermined genetic variant in linkage disequilibrium (LD) with the individual-specific genetic variant among the one or more individual-specific genetic variants in a subject population with the same lineage as the individual, and wherein each of the one or more lineage-specific genetic variants and each of the individual-specific genetic variants includes one or more risk units; and (d) calculating a genetic risk score for the individual based on the selected one or more lineage-specific genetic variants, wherein the genetic risk score indicates the likelihood that the individual has or will develop the specific trait. In some embodiments, the medium further includes providing the individual with a questionnaire comprising one or more questions relating to the specific trait. In some embodiments, the medium further includes receiving from the individual one or more answers to one or more questions relating to the specific trait in a questionnaire provided to the individual. In some embodiments, the medium further includes: a) providing the individual with a questionnaire comprising one or more questions relating to the specific trait; and c) receiving from the individual one or more answers to the one or more questions. In some embodiments, the medium further includes storing lineage-specific genetic variants associated with the specific trait from the subject group in a trait-related variant database. In some embodiments, the genetic risk score includes percentiles or z-scores. In some embodiments, LD is defined by (i) a D' value of at least about 0.20 or (ii) an ^r² value of at least about 0.70. In some embodiments, LD is defined by a D' value, which includes about 0.20 to 0.25, 0.25 to 0.30, 0.30 to 0.35, 0.35 to 0.40, 0.40 to 0.45, 0.45 to 0.50, 0.50 to 0.55, 0.55 to 0.60, 0.60 to 0.65, 0.65 to 0.70, 0.70 to 0.75, 0.75 to 0.80, 0.80 to 0.85, 0.85 to 0.90, 0.90 to 0.95, or 0.95 to 1.0. In some embodiments, LD is defined by ^{an r²} value, which includes approximately 0.70 to 0.75, 0.75 to 0.80, 0.80 to 0.85, 0.85 to 0.90, 0.90 to 0.95, or 0.95 to 1.0. In some embodiments, LD is defined by a D' value, which includes at least approximately 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.85, 0.90, 0.95, and 1.0. In some embodiments, LD is defined by ^{an r²} value, which includes at least approximately 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, and 1.0. In some embodiments, an individual's genotype is obtained by genotyping or has been performed on genetic material obtained from the individual. In some embodiments, an individual's genotype is obtained by performing deoxyribonucleic acid (DNA) arraying, ribonucleic acid (RNA) arraying, sequencing, or a combination thereof on genetic material obtained from the individual. In some embodiments, sequencing includes next-generation sequencing (NGS). In some embodiments, the method further includes updating the trait-related variant database with the assigned pedigree, specific trait, and genotype of the individual. In some embodiments, principal component analysis (PCA), or maximum likelihood estimation (MLE), or a combination thereof, is used to assign pedigrees to the individuals in (b). In some embodiments, one or more pedigree-specific genetic variants, one or more individual-specific genetic variants, and genetic variants at the LD with one or more individual-specific genetic variants include single nucleotide variants (SNVs). In some embodiments, one or more risk units include risk alleles. In some embodiments, the one or more pedigree-specific genetic variants, the one or more individual-specific genetic variants, and genetic variants at the LD with one or more individual-specific genetic variants include indels characterized by the insertion or deletion of one or more nucleotides. In some embodiments, one or more risk units comprise an insertion (I) or deletion (D) of one or more nucleotides. In some embodiments, the one or more lineage-specific genetic variants, or the one or more individual-specific genetic variants, comprise copy number variants (CNVs). In some embodiments, one or more risk units comprise an insertion or deletion of a nucleic acid sequence. In some embodiments, the nucleic acid sequence comprises about two, three, four, five, six, seven, eight, nine, or ten nucleotides. In some embodiments, the nucleic acid sequence comprises more than three nucleotides. In some embodiments, the nucleic acid sequence comprises the entire gene. In some embodiments, the method further includes providing the individual with notification of the risk that the individual has or will develop the specific trait. In some embodiments, the specific trait includes nutritional traits, clinical traits, subclinical traits, physical activity traits, skin traits, hair traits, allergic traits, or mental traits. In some embodiments, a clinical trait includes a disease or condition. In some embodiments, a subclinical trait includes a phenotype of a disease or condition. In some implementations, physical activity traits include aversion to exercise, aerobic capacity, difficulty losing weight, endurance, strength, physical fitness benefits, decreased heart rate response to exercise, lean body mass, muscle soreness, risk of muscle injury, impaired muscle repair, stress fractures, overall injury risk, likelihood of obesity, or impaired resting metabolic rate. In some implementations, skin traits include collagen breakdown, dryness, antioxidant deficiency, impaired detoxification, skin glycation, pigmentation spots, premature aging, photoaging, dermal sensitivity, or sensitivity to sunlight. In some implementations, hair traits include hair thickness, thinning hair, hair loss, baldness, oiliness, dryness, dandruff, or hair volume. In some implementations, nutritional traits include vitamin deficiencies, mineral deficiencies, antioxidant deficiencies, fatty acid deficiencies, metabolic imbalances, impaired metabolism, metabolic sensitivity, allergies, satiety, or effectiveness of a healthy diet. In some embodiments, vitamin deficiency includes deficiencies in vitamins A, B1, B2, B3, B5, B6, B7, B8, B9, B12, C, D, E, and K. In some embodiments, mineral deficiency includes deficiencies in minerals including calcium, iron, magnesium, zinc, or selenium. In some embodiments, antioxidant deficiency includes deficiencies in antioxidants including glutathione or coenzyme Q10 (CoQ10). In some embodiments, fatty acid deficiency includes deficiencies in polyunsaturated or monounsaturated fatty acids. In some embodiments, metabolic imbalance includes glucose imbalance. In some embodiments, metabolic impairment includes metabolic impairment due to caffeine or drug therapy. In some embodiments, metabolic sensitivity includes gluten sensitivity, glycan sensitivity, or lactose sensitivity. In some embodiments, allergy includes allergies to food (food allergy) or allergies to environmental factors (environmental allergy). In some embodiments, the method further includes treatment to effectively improve or prevent a specific trait in an individual, provided that the genetic risk score indicates a high probability that the individual has or will develop the specific trait. In some embodiments, the treatment includes supplements or pharmacological therapy. In some embodiments, supplements include vitamins, minerals, probiotics, antioxidants, anti-inflammatory agents, or combinations thereof. In some embodiments, the genetic risk score is calculated by: (1) calculating a raw score comprising the total number of one or more risk units for each lineage-specific genetic variant for each subject in the subject group, thereby generating a lineage-specific observation range for the raw score; (2) calculating the total number of one or more risk units for each of one or more individual-specific genetic variants, thereby generating an individual raw score; and (3) comparing the individual raw score to the lineage-specific observation range to generate the genetic risk score. In some embodiments, the genetic risk score is calculated by: (1) determining a concession ratio for each lineage-specific genetic risk variant; and (2) if two or more lineage-specific genetic variants are selected, multiplying the concession ratios of each of the two or more lineage-specific genetic variants. In some embodiments, the computer-executable code is further configured to cause at least one processor to perform the steps of determining the predetermined genetic variant by: a) providing untyped genotype data from an individual; b) typing the untyped genotype data to generate individual-specific typing haplotypes based on the individual's lineage; c) imputing individual-specific genotypes not present in the typed individual-specific typing haplotypes using typing haplotype data from a reference group having the same lineage as the individual; and d) selecting from the imputed individual-specific genotypes a genetic variant that is in linkage disequilibrium (LD) with the individual-specific genetic variant, which is associated with the likelihood that the individual has or will develop the specific trait.

Attached Figure Description

Figure 1 is a block diagram illustrating an exemplary system for determining an individual's lineage-specific genetic risk score.

Figure 2 is a flowchart illustrating an exemplary process for determining an individual's genetic risk score.

Figure 3 is a flowchart illustrating an exemplary process for determining an individual’s lineage-specific genetic risk score using one or more reference genetic variants.

Figure 4 is a flowchart illustrating an exemplary process for determining an individual’s lineage-specific genetic risk score using one or more lineage-specific genetic variants from a trait-related database.

Figure 5 is a flowchart illustrating an exemplary process for determining an individual’s lineage-specific genetic risk score using one or more lineage-specific genetic variants from a trait-related database.

Figures 6A-6F illustrate examples of reports according to this implementation plan, in which the GRS of multiple specific phenotypic traits are presented to the subjects. Figure 6A illustrates a summary report of the physical fitness phenotypic trait. Figure 6B illustrates behavioral recommendations related to the physical fitness phenotypic trait: the likelihood of obesity. Figure 6C illustrates a summary report of the skin phenotypic trait. Figure 6D illustrates behavioral recommendations related to the skin phenotypic trait: antioxidant deficiency. Figure 6E illustrates a summary report of the nutritional phenotypic trait. Figure 6F illustrates behavioral recommendations related to the nutritional phenotypic trait: impaired satiety.

Figures 7A-7D illustrate examples of nutrition reports focusing on nutrition and health according to this implementation plan, where GRS for multiple specific phenotypic traits are presented to the subjects. Figure 7A illustrates a summary report of food sensitivity. Figure 7B illustrates a summary report of mineral and nutrient deficiencies. Figure 7C illustrates a summary report of dietary management phenotypes. Figure 7D illustrates a summary report of vitamin deficiencies.

Detailed Implementation

Differences in haplotype heterogeneity and recombination rates are considered important reasons for the variation in linkage disequilibrium (LD) among different lineage populations. Current genetic risk prediction methods fail to consider the lineage of the subject group when selecting surrogate variants, resulting in poor risk indicators selected in a given population. The methods, media, and systems disclosed herein provide a solution to this problem by selecting surrogate variants based on LD within a specific lineage population to which an individual belongs. Furthermore, the methods, media, and systems disclosed herein utilize software programs configured to use a predetermined LD pattern, which can be used when calculating the genetic risk score (GRS) of previously undisclosed individual-specific genetic variants. Therefore, the solution disclosed herein improves the accuracy and efficiency of genetic risk prediction compared to existing methods.

Current risk prediction methods do not utilize lineage-specific risk variant (LD) information. However, whether a genetic variant is in the LD with another genetic variant is largely influenced by the lineage population under study. In a non-limiting example, two genetic variants that are in the LD in a predominantly Caucasian population may not necessarily be in the LD in, for example, a Chinese population. And vice versa. Considering lineage-specific LD patterns when calculating an individual's GRS is superior to the level of existing techniques for many reasons, including but not limited to (i) avoiding errors (e.g., two genetic variants not being in the LD at all in the population) and (ii) avoiding more than one count of genetic variants. Considering lineage-specific LD patterns results in more accurate GRS predictions by ensuring the identification of genetic risk variants in the LD and preventing GRS inflation caused by more than one count of a single genetic variant.

This document discloses, in some embodiments, methods, media, and systems for calculating a genetic risk score (GRS) based on an individual's pedigree, wherein the GRS represents the probability that an individual will develop a particular phenotypic trait. In some embodiments, the GRS is calculated based on the number and type of genetic variants constituting the individual's genotype detected in samples obtained from the individual, compared to a group of subjects with the same pedigree as the individual. In some embodiments, the individual's pedigree is determined by analyzing the individual's genotype. This document also discloses methods, media, and systems for suggesting behavioral changes associated with a specific phenotypic trait to an individual based on the calculated GRS for that phenotypic trait.

Genotypes and genetic variants

Genome-wide association studies (GWAS) consider thousands of genetic variants, including single nucleotide variants (SNVs), insertions/deletions (indels), and copy number variants (CNVs), to identify associations between genetic variants in a population and complex clinical conditions and phenotypic traits. Detection of genetic variants associated with a specific phenotypic trait in a sample obtained from an individual is considered an indication that the individual has or will develop that phenotypic trait. In some embodiments, the individual obtains his or her own sample and provides it to a laboratory for processing and analysis. In some embodiments, genetic material is extracted from the sample obtained from the subject. In some embodiments, genetic variants are detected in the genetic material of the sample obtained from the individual using genotyping assays (e.g., genotyping assays, quantitative polymerase chain reaction (qPCR), and/or fluorescent qPCR). In some embodiments, genetic information is analyzed to determine the individual's pedigree.

Genetic variants (e.g., SNVs, SNPs, indels, CNVs) can be located in the coding region of a gene, the non-coding region of a gene, or the intergenic region between genes. Genetic variants in the coding region of a gene may or may not result in different protein isotypes due to redundancy of the genetic code. Genetic variants in the non-coding region of a gene or the intergenic region can affect the expression and/or activity of the gene or the gene expression product expressed by the gene.

This document discloses methods and systems for determining an individual's genotype in some embodiments. In some embodiments, the individual is suffering from a disease or condition, or symptoms associated with a disease or condition. In some embodiments, the disease or condition includes deficiency diseases, genetic diseases, or mental illnesses. In some embodiments, the disease or condition includes immune diseases and/or metabolic diseases. In some embodiments, immune diseases include autoimmune diseases or disorders. Non-limiting examples of autoimmune diseases or disorders include Graves' disease, Hashimoto's thyroiditis, systemic lupus erythematosus (lupus), multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, Crohn's disease, ulcerative colitis, and cancer. Non-limiting examples of metabolic diseases or conditions include type 1 diabetes, type 2 diabetes, diseases affecting the absorption of macronutrients (e.g., amino acids, carbohydrates, or lipids), diseases affecting the absorption of micronutrients (e.g., vitamins or minerals), diseases affecting mitochondrial function, diseases affecting liver function (e.g., non-alcoholic fatty liver disease), and diseases affecting kidney function.

In some embodiments, this document discloses methods and systems for calculating a genetic risk score (GRS) using the genotypes and/or genetic variants disclosed herein, the GRS representing the probability that an individual has or will develop a particular phenotypic trait. In some embodiments, a single genetic variant is used. In some embodiments, two genetic variants are used. In some embodiments, three genetic variants are used. In some embodiments, four genetic variants are used. In some embodiments, five genetic variants are used. In some embodiments, six genetic variants are used. In some embodiments, seven genetic variants are used. In some embodiments, eight genetic variants are used. In some embodiments, nine genetic variants are used. In some embodiments, ten genetic variants are used. In some embodiments, at least about two genetic variants are used. In some embodiments, at least about three genetic variants are used. In some embodiments, at least about four genetic variants are used. In some embodiments, at least about five genetic variants are used. In some embodiments, at least about six genetic variants are used. In some embodiments, at least about seven genetic variants are used. In some embodiments, at least about eight genetic variants are used. In some embodiments, at least about nine genetic variants are used. In some embodiments, at least about ten genetic variants are used. In some implementations, two genetic variants are used.

In some embodiments, this document discloses genotypes comprising one or more genetic variants (e.g., indel, SNV, SNP), said genotypes being provided in one or more of SEQ ID NO: 1-218 used in the methods, systems, and kits described herein. In some embodiments, the genotypes described herein comprise a single genetic variant. In some embodiments, the genotype comprises two genetic variants. In some embodiments, the genotype comprises three genetic variants. In some embodiments, the genotype comprises four genetic variants. In some embodiments, the genotype comprises five genetic variants. In some embodiments, the genotype comprises six genetic variants. In some embodiments, the genotype comprises seven genetic variants. In some embodiments, the genotype comprises eight genetic variants. In some embodiments, the genotype comprises nine genetic variants. In some embodiments, the genotype comprises ten genetic variants. In some embodiments, the genotype comprises more than ten genetic variants.

In some embodiments, the genotype includes at least about two genetic variants. In some embodiments, the genotype includes at least about three genetic variants. In some embodiments, the genotype includes at least about four genetic variants. In some embodiments, the genotype includes at least about five genetic variants. In some embodiments, the genotype includes at least about six genetic variants. In some embodiments, the genotype includes at least about seven genetic variants. In some embodiments, the genotype includes at least about eight genetic variants. In some embodiments, the genotype includes at least about nine genetic variants. In some embodiments, the genotype includes at least about ten genetic variants.

In some embodiments, at least one genetic variant listed in any one of Tables 1-44 is used. In some embodiments, the genetic variant is used using the detection methods disclosed herein. In some embodiments, the methods and systems described herein use (e.g., to detect, analyze) one or more genetic variants provided in Table 1. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 2. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 3. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 4. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 5. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 6. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 7. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 8. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 9. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 10. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 11. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 12. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 13. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 14. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 15. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 16. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 17. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 18. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 19. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 20. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 21. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 22. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 23. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 24. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 25. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 26. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 27. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 28. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 29. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 30. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 31. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 32. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 33. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 34. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 35. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 36. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 37. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 38. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 39. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 40. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 41. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 42. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 43. In some embodiments, the methods and systems described herein use one or more genetic variants provided in Table 44.

In some embodiments, the methods, systems, and kits utilize a major or minor allele from one or more of the genetic variants. In some embodiments, a minor allele is used. In some embodiments, a major allele is used. In some embodiments, the methods, systems, and kits utilize nucleotides represented by non-nucleic acid letters or codes. In some cases, the non-nucleic acid letters or codes are the International Union of Pure and Applied Chemistry (IUPAC) nucleotide codes provided in any of SEQ ID NOs: 1-218. The genetic variants provided in any of Tables 1-44 have corresponding SEQ ID NOs that provide nucleic acid sequences containing nucleotide or polynucleotide sequences associated with the risk of having or developing a particular phenotypic trait.

The methods and systems disclosed herein are generally applicable to the analysis of samples obtained from individuals. Similarly, the methods disclosed herein include sample processing and/or analysis. In some cases, samples are obtained directly or indirectly from individuals. In some cases, samples are obtained by aspiration, swabs, or fluid collection. In some cases, samples include whole blood, peripheral blood, plasma, serum, saliva, buccal swabs, urine, or other bodily fluids or tissues.

In some embodiments, an individual's genotype is determined by performing nucleic acid-based assays on samples obtained from the individual. In some cases, nucleic acid-based assays include quantitative polymerase chain reaction (qPCR), gel electrophoresis (including, for example, RNA or DNA blotting), immunochemistry, in situ hybridization such as fluorescence in situ hybridization (FISH), cytochemistry, or sequencing. In some embodiments, the sequencing technology includes next-generation sequencing. In some embodiments, the method involves hybridization assays, such as fluorescent qPCR (e.g., TaqMan ^™ or SYBR Green), which involve a nucleic acid amplification reaction with specific primer pairs and hybridization of amplified nucleic acid probes containing a detectable moiety or molecule specific to a target nucleic acid sequence. Other exemplary nucleic acid-based assays include the use of nucleic acid probes conjugated or otherwise immobilized on beads, multiwell plates, arrays, or other substrates, wherein the nucleic acid probes are configured to hybridize with a target nucleic acid sequence. In some cases, the nucleic acid probes are specific to genetic variants (e.g., SNPs, SNVs, CNVs, or indels). In some cases, SNP- or SNV-specific nucleic acid probes comprise nucleic acid probe sequences fully complementary to the risk or protective allele of interest, such that hybridization is specific to the risk or protective allele. In some cases, indel-specific nucleic acid probes comprise nucleic acid probe sequences fully complementary to nucleobase insertions in polynucleotide sequences flanking the insertion, such that hybridization is specific to the indel. In some cases, indel-specific nucleic acid probes comprise probe sequences fully complementary to polynucleotide sequences flanking the nucleobase deletions in polynucleotide sequences, such that hybridization is specific to the indel. In some cases, multiple nucleic acid probes are required to detect CNVs, said nucleic acid probes being specific to various regions within the polynucleotide sequence containing the CNV. In a non-limiting example, multiple nucleic acid probes specific to a single exon CNV within a gene may comprise a high-density nucleic acid array of 2 to 3, 3 to 4, 4 to 5, 5 to 6, and 6 to 7 nucleic acid probes, each nucleic acid probe being fully complementary to the exon region of the gene. In another non-limiting embodiment, multiple nucleic acid probes dispersed throughout an individual's genome can be used to detect long CNVs.

Exemplary nucleic acid probes for detecting the genotypes described herein are risk allele-specific and comprise the oligonucleotide sequences provided in any of SEQ ID NO: 1-218. In some cases, the nucleic acid probe is at least 10 but no more than 50 consecutive nucleotides in length. In some cases, the nucleic acid probe is between about 15 and about 55 nucleotides in length. In some cases, the nucleic acid probe is between about 10 and about 100 nucleotides in length. In some cases, the nucleic acid probe is between about 10 and about 90 nucleotides in length. In some cases, the nucleic acid probe is between about 10 and about 80 nucleotides in length. In some cases, the nucleic acid probe is between about 10 and about 70 nucleotides in length. In some cases, the nucleic acid probe is between about 10 and about 60 nucleotides in length. In some cases, the nucleic acid probe is between about 10 and about 50 nucleotides in length. In some cases, the nucleic acid probe is between about 10 and about 40 nucleotides in length. In some cases, the nucleic acid probe is between about 10 and about 30 nucleotides in length. In some cases, the length of nucleic acid probes is between approximately 20 and approximately 60 nucleotides. In some cases, the length of nucleic acid probes is between approximately 25 and approximately 65 nucleotides. In some cases, the length of nucleic acid probes is between approximately 30 and approximately 70 nucleotides. In some cases, the length of nucleic acid probes is between approximately 35 and approximately 75 nucleotides. In some cases, the length of nucleic acid probes is between approximately 40 and approximately 70 nucleotides.

In some implementations, methods for detecting an individual's genotype include nucleic acid amplification assays on samples obtained from the individual. In some cases, amplification assays include polymerase chain reaction (PCR), qPCR, self-sustaining sequence replication, transcriptional amplification systems, Q-β replicase, rolling cycle replication, or any other suitable nucleic acid amplification technique. Appropriate nucleic acid amplification techniques are configured to amplify regions of nucleic acid sequences containing risk variants (e.g., SNPs, SNVs, CNVs, or indels). In some cases, amplification assays require primers. Known nucleic acid sequences of genes or genetic variants within the genotype are sufficient to enable a person skilled in the art to select primers to amplify any portion of the gene or genetic variant. For example, DNA samples suitable as primers can be obtained by PCR amplification of genomic DNA, genomic DNA fragments, genomic DNA fragments ligated to adaptor sequences, or cloning sequences. Any suitable computer program can be used to design primers with the desired specificity and optimal amplification characteristics, such as Oligo version 7.0 (National Biosciences). Exemplary primers used to amplify the genotypes described herein are at least 10 and no more than 30 nucleotides in length and contain nucleic acid sequences flanking one or more of the provided indels, SNVs, SNPs, or CNVs of interest in SEQ ID NO:1-218.

In some implementations, detecting the presence or absence of a genotype involves sequencing the genetic material in a sample obtained from the subject. Sequencing can be performed using any suitable sequencing technology, including but not limited to single-molecule real-time (SMRT) sequencing, Polony sequencing, ligation sequencing, reversible terminator sequencing, proton detection sequencing, ion semiconductor sequencing, nanopore sequencing, electron sequencing, pyrosequencing, Maxam-Gilbert sequencing, chain termination (e.g., Sanger) sequencing, +S sequencing, or synthetic sequencing. Sequencing methods also include next-generation sequencing, such as modern sequencing technologies like Illumina sequencing (e.g., Solexa), Roche 454 sequencing, ion-fluidic sequencing, and SOLiD sequencing. In some cases, next-generation sequencing involves high-throughput sequencing methods. Other sequencing methods available to those skilled in the art may also be employed.

In some cases, the number of nucleotides sequenced is at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 150, 200, 300, 400, 500, 2000, 4000, 6000, 8000, 10000, 20000, 50000, 100000, or more than 100000 nucleotides. In some cases, the number of nucleotides sequenced falls within the following ranges: approximately 1 to 100,000 nucleotides, approximately 1 to 10,000 nucleotides, approximately 1 to 1,000 nucleotides, approximately 1 to 500 nucleotides, approximately 1 to 300 nucleotides, approximately 1 to 200 nucleotides, approximately 1 to 100 nucleotides, approximately 5 to 100,000 nucleotides, approximately 5 to 10,000 nucleotides, approximately 5 to 1,000 nucleotides, approximately 5 to 500 nucleotides, approximately 5 to 300 nucleotides, approximately 5 to 200 nucleotides, approximately 5 to 100 nucleotides, approximately 10 to 100,000 nucleotides, approximately 10 to 10,000 nucleotides, approximately 10 to 1,000 nucleotides, approximately 10 to 500 nucleotides, approximately 10 to 300 nucleotides, approximately 10 to 200 nucleotides, approximately 10 to 100 nucleotides. Nucleotides, about 20 to about 100,000 nucleotides, about 20 to about 10,000 nucleotides, about 20 to about 1,000 nucleotides, about 20 to about 500 nucleotides, about 20 to about 300 nucleotides, about 20 to about 200 nucleotides, about 20 to about 100 nucleotides, about 30 to about 100,000 nucleotides, about 30 to about 10,000 nucleotides, about 30 to about 1,000 nucleotides, about 30 to about 500 nucleotides, about 30 to about 300 nucleotides, about 30 to about 200 nucleotides, about 30 to about 100 nucleotides, about 50 to about 100,000 nucleotides, about 50 to about 10,000 nucleotides, about 50 to about 1,000 nucleotides, about 50 to about 500 nucleotides, about 50 to about 300 nucleotides, about 50 to about 200 nucleotides, or about 50 to about 100 nucleotides.

In some cases, the nucleic acid sequence of a genotype includes denatured DNA molecules or fragments thereof. In some cases, the nucleic acid sequence includes DNA selected from: genomic DNA, viral DNA, mitochondrial DNA, plasmid DNA, amplified DNA, circular DNA, circulating DNA, cell-free DNA, or exosome DNA. In some cases, the DNA is single-stranded DNA (ssDNA), double-stranded DNA, denatured double-stranded DNA, synthetic DNA, and combinations thereof. Circular DNA may be cleaved or fragmented. In some cases, the nucleic acid sequence includes RNA. In some cases, the nucleic acid sequence includes fragmented RNA. In some cases, the nucleic acid sequence includes partially degraded RNA. In some cases, the nucleic acid sequence includes microRNA or portions thereof. In some cases, the nucleic acid sequence comprises an RNA molecule or a fragmented RNA molecule (RNA fragment), selected from: microRNA (miRNA), pre-miRNA, pri-miRNA, mRNA, pre-mRNA, viral RNA, virusoid RNA, viral RNA, circular RNA (circRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), pre-tRNA, long noncoding RNA (lncRNA), small nuclear RNA (snRNA), circulating RNA, cell-free RNA, exosomal RNA, vector-expressed RNA, RNA transcripts, synthetic RNA, and combinations thereof.

Determine the likelihood that an individual possesses or will develop a specific phenotypic trait.

The aspects disclosed herein provide methods, media, and systems for calculating a genetic risk score (GRS), which represents the likelihood that an individual will develop a particular phenotypic trait. In some embodiments, the particular phenotypic trait includes the phenotypic traits discussed herein, including but not limited to clinical traits, subclinical traits, physical traits, or mental traits.

Figure 2 illustrates an example workflow for determining the likelihood of an individual having or developing a specific trait by calculating a Genetic Risk Score (GRS). An individual's genotype 202 is provided; the genotype includes one or more individual-specific genetic variants. Next, the individual's lineage 204 is assigned, at least in part based on the individual's genotype. Next, one or more reference genetic variants 206 are selected, each of which corresponds to an individual-specific variant among the one or more individual-specific genetic variants or a predetermined variant in linkage disequilibrium (LD) with the individual-specific variant among the one or more individual-specific genetic variants in the subject population. Next, based on the one or more reference genetic variants selected within the subject population, the individual's genetic risk score 208 is calculated, whereby the genetic risk score indicates the likelihood that the individual has or will develop the specific trait. In some cases, any of the methods disclosed herein are used to calculate the GRS.

Figure 3 illustrates an exemplary workflow for determining the likelihood of an individual having or developing a specific trait by calculating the Genetic Risk Score (GRS) as compared to a non-lineage-specific subject population. The individual's genotype 302 is provided; the genotype includes one or more individual-specific genetic variants. Next, the individual's lineage is assigned at least in part based on the individual's genotype 304. Next, one or more reference genetic variants are selected 306, each of which corresponds to an individual-specific variant among the one or more individual-specific genetic variants or a predetermined variant in linkage disequilibrium (LD) with the individual-specific variant among the one or more individual-specific genetic variants in the subject population. Next, an individual-specific raw score 308 is calculated. Numerical values are assigned to risk units within the individual-specific genetic variant, and all numerical values for each individual-specific genetic variant are summed to produce the individual-specific raw score. The same calculation is performed to generate a raw score for each individual within the subject group, thereby generating an observation range (observation range) 310 for the raw scores. Next, the individual-specific raw scores are compared to the observation range to calculate a percentage risk relative to the subject population 312. Next, the Genetic Risk Score (GRS) is assigned to the individual 314. In some cases, the GRS is in the form of percentiles. In other cases, percentiles are in the form of z-scores.

Figure 4 illustrates an exemplary workflow for determining the likelihood that an individual possesses or will develop a specific trait based on their lineage. An individual's genotype 402 is provided; the genotype includes one or more individual-specific genetic variants. Next, the individual's lineage 404 is assigned at least in part based on the individual's genotype. Next, lineage-specific genetic variants 406 are selected from a trait-related variant database from subjects with the same lineage as the individual (lineage-specific subject group), this selection being at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants corresponds to: (i) an individual-specific genetic variant among the one or more individual-specific genetic variants, or (ii) a predetermined genetic variant in linkage disequilibrium (LD) with the individual-specific genetic variant among the one or more individual-specific genetic variants in the subject group with the same lineage as the individual, and wherein each of the one or more lineage-specific genetic variants and each of the individual-specific genetic variants includes one or more risk units. Next, an individual-specific raw score 408 is calculated. Numerical values are assigned to risk units within the individual-specific genetic variants and all numerical values for each individual-specific genetic variant are summed to produce the individual-specific raw score. The same calculations are performed to generate raw scores for each individual within the lineage-specific subject group, thereby generating an observation range (observation range) 410 for the raw scores. Next, the individual-specific raw scores are compared to the lineage-specific observation range to calculate the percentage risk relative to the lineage-specific subject population 412. Next, the genetic risk score (GRS) is assigned to the individuals 414. In some cases, the GRS is in the form of percentiles. In some cases, percentiles are in the form of z-scores.

Figure 5 illustrates an exemplary workflow for determining the likelihood of an individual having or developing a specific trait based on their lineage. An individual's genotype 502 is provided; the genotype includes one or more individual-specific genetic variants. Next, the individual's lineage 504 is assigned at least in part based on the individual's genotype. Next, lineage-specific genetic variants 506 are selected from a trait-related variant database from subjects with the same lineage as the individual (lineage-specific subject group), this selection being at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants corresponds to: (i) an individual-specific genetic variant among the one or more individual-specific genetic variants, or (ii) a predetermined genetic variant in linkage disequilibrium (LD) with the individual-specific genetic variant among the one or more individual-specific genetic variants in the subject group with the same lineage as the individual, and wherein each of the one or more lineage-specific genetic variants and each of the individual-specific genetic variants includes one or more risk units. Next, a genetic risk score (GRS) 508 is calculated for the individual based on the selected one or more lineage-specific genetic variants, wherein the genetic risk score indicates the likelihood of the individual having or developing the specific trait. In some cases, any of the methods disclosed herein may be used to calculate GRS.

Assigning individual lineages

In some cases, pedigrees are assigned to individuals by analyzing their genotypes. In others, methods such as maximum likelihood or principal component analysis (PCA) are used to analyze an individual's genotype. In still others, computer programs including SNPRelate, ADMIXTURE, PLINK, or STRUCTURE are used. For example, after PCA with SNPRelate, the first two principal components (PC1 and PC2) of a population from a known pedigree are each combined into a single data point or centroid. An individual pedigree is classified based on its proximity to the nearest centroid of a known pedigree. This method relies on a nearest centroid classification model.

Trait-related database

In some implementations, a trait-related database is used. In some cases, the trait-related database includes genotype, phenotype, and/or pedigree data of a subject group. In some cases, the subject group is derived from publicly available genome-wide association studies (GWAS). In some cases, publicly available GWAS are documented in peer-reviewed journals. In some cases, the trait-related database enables the selection of genetic variants present in subject groups that are phylogeneticly related to the individual. In some cases, the trait-related database is updated with genotype, phenotype, and/or pedigree data from the individual. Many databases are suitable for storing and retrieving genotype, phenotype, and pedigree data. By way of non-limiting examples, suitable databases include relational databases, non-relational databases, trait-oriented databases, trait databases, entity-relationship model databases, association databases, and XML databases. In some implementations, the database is Internet-based. In some implementations, the database is web-based. In some implementations, the database is cloud-based. In some implementations, the database is connected to a distributed ledger. In some implementations, the distributed ledger includes a blockchain. The database may be based on one or more local computer storage devices.

Select one or more reference genetic variants or lineage-specific genetic variants.

In some implementations, a reference genetic variant or lineage-specific genetic variant is used to calculate an individual's GRS. In some cases, the one or more genetic variants include a reference genetic variant from any group of subjects from any lineage. In some implementations, the group of subjects includes individuals from one or more lineages, including Japanese, German, Irish, African, South African, British, Mexican, Italian, Polish, French, Native American, Scottish, Dutch, Norwegian, Scottish-Irish, Swedish, Puerto Rican, Russian, Spanish, French-Canadian, Filipino, Korean, North Korean, Indonesian, Chinese, Malaysian, African-Caribbean, Caucasian, Native American/Alaskan Native (including those of tribal ancestry from Central and South America), Pacific Islanders (including Hawaiian, Guam, Samoan, etc.), South Asians (including those from Afghanistan, India, Pakistan, Bangladesh, Sri Lanka, and Nepal), Japanese, Thai, and Indigenous Australians (native and Torres Strait Islanders). In some cases, the one or more reference genetic variants include lineage-specific genetic variants derived from a subject group comprising individuals from the same lineage as the individual (lineage-specific genetic variants).

In some cases, reference genetic variants are selected, at least in part, because they come from a group of subjects ancestry-related to the individual (lineage-specific genetic variants). In some cases, an individual's lineage is determined by analyzing the individual's genotype using the methods disclosed herein. In some cases, lineage-specific genetic variants are selected from the trait-related variant database disclosed herein.

In some cases, lineage-specific genetic variants correspond to individual-specific genetic variants within an individual's genotype. In other cases, the corresponding individual-specific genetic variant is unknown; in such cases, another genetic variant is chosen as a proxy for the unknown individual-specific genetic variant.

Select proxy genetic variants

In some embodiments, a surrogate genetic variant is used to calculate GRS when the individual-specific genetic variant is unknown. In some cases, a predetermined genetic variant is selected as the provided surrogate. In some embodiments, a method is disclosed herein for pre-determining a surrogate genetic variant corresponding to an unknown individual-specific genetic variant, the method comprising: (i) providing untyped genotype data from an individual; (ii) typing the untyped genotype data to generate an individual-specific typing haplotype based on the individual's lineage; (iii) imputing an individual-specific genotype absent in the typed individual-specific typing haplotype using typing haplotype data from a reference group having the same lineage as the individual; and (iv) selecting from the imputed individual-specific genotypes a genetic variant in linkage disequilibrium (LD) that is associated with the individual-specific genetic variant and the likelihood that the individual has or will develop the specific trait.

In some cases, the method involves selecting an indel (insertion/deletion) as a proxy for an unknown individual-specific indel. In other cases, the method involves selecting a copy number variant (CNV) as a proxy for an unknown individual-specific CNV.

As used herein, “linkage disequilibrium” or “LD” refers to a non-random association between a risk unit and a genetic risk variant in a given population. LD can be defined by a D' value, which corresponds to the difference between the observed and expected frequencies of risk units in the population (D = Pab - PaPb), scaled by the theoretical maximum value of D. LD can also be defined by ^{an r²} value, which corresponds to the difference between the observed and expected frequencies of risk units in the population (D = Pab - PaPb), scaled by the individual frequencies of different loci. In some embodiments, D' includes at least 0.20. In some embodiments, ^r² includes at least 0.70. In some implementations, LD is defined by a D' value including at least about 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, or 1. In some implementations, LD is defined by an ^r² value including at least about 0.70, 0.75, 0.75, 0.80, 0.85, 0.90, 0.95, or 1.0. LD varies among subject populations belonging to different lineages. In a non-limiting example, an SNV that is at LD with a surrogate SNV in a subject population of Chinese individuals may not necessarily be at LD in a subject population of Caucasian individuals. Therefore, the prior determination of surrogate genetic variants based on lineage-specific haplotype data provides at least a partial improvement in the accuracy of surrogate-based genetic risk prediction.

Calculate genetic risk score

In some embodiments, methods are provided for calculating an individual's genetic risk score (GRS) based on their pedigree. The genetic variants disclosed herein include SNVs, indels, and/or CNVs. Each genetic variant includes risk units for calculating the GRS. In some cases, the risk unit in an SNV includes a risk allele. In some cases, the risk unit in an indel includes an insertion or deletion. In some cases, the risk unit within a CNV includes an increase or decrease in the copy number of a gene or gene segment compared to the wild-type copy number. Those skilled in the art will understand that many methods for calculating GRS can be used to calculate an individual's GRS according to the methods and systems of the present invention.

In some implementations, methods for calculating an individual's GRS are disclosed herein. In some cases, risk units in SNVs (e.g., risk alleles), indels (e.g., insertions or deletions), and/or CNVs (e.g., copy numbers) can be assigned arbitrary values. In a non-limiting example of calculating GRS involving SNVs, the homozygous genotype of a risk allele within an SNV (RR) is assigned the value 2; the heterozygous genotype of a risk allele within an SN (R) is assigned the value 1; and the risk-free (N) genotype is assigned the value 0. Next, each value of the individual SNVs corresponding to the lineage-specific SNV is summed and divided by the total number of genetic variants used in the model to generate the individual's raw score (individual raw score). The same calculation is performed for each individual belonging to the subject group to generate a range of raw scores (observation range). In some cases, the subject group includes individuals of the same lineage as the individual. Next, the individual raw score is compared to the observation range to calculate the percentage of risk relative to the subject population.

In another non-limiting example of calculating GRS involving SNVs, the allele concession ratio (OR) for each selected lineage-specific SNV corresponding to the individual-specific SNV is provided and multiplied. In some cases, the OR is obtained from replicated, publicly available, and/or peer-reviewed GWAS. In some cases, the OR for each selected lineage-specific SNV corresponding to the individual-specific SNV is provided. The genotype ORs for each lineage-specific SNV are then summed; the genotype ORs for the individuals are multiplied. The genotype ORs for individuals and the subject groups are compared, and percentile GRS is calculated.

In another non-restrictive example of calculating GRS involving indels, a value of 2 is assigned to homozygous genotypes of insertions within indel (II); a value of 1 is assigned to heterozygous genotypes of insertions within indel (I); and a value of 0 is assigned to risk-free (N) genotypes. Next, the values of each individual indel corresponding to a lineage-specific indel are summed and divided by the total number of genetic variants used in the model to generate the individual's raw score (individual raw score). The same calculation is performed for each individual belonging to the subject group, resulting in a range of raw scores (observation range). In some cases, the subject group includes individuals of the same lineage as the individual. The individual raw score is then compared to the observation range to calculate the risk percentile relative to the subject population.

In another non-restrictive example of calculating GRS involving indels, the concession ratio (OR) for each choice of pedigree-specific indel corresponding to the individual-specific indel is provided and multiplied. In some cases, the OR is obtained from replicated, publicly available, and/or peer-reviewed GWAS. In some cases, the OR for each choice of pedigree-specific indel corresponding to the individual-specific indel is provided, and the OR for each risk indel allele is multiplied to generate the genotype OR for each subject in the subject group. Next, the same calculation is performed on individuals to generate the individual's genotype OR. The genotype ORs for individuals and subject groups are compared, and percentile GRS is calculated.

In a non-restrictive example of calculating GRS involving CNVs, a value of 0 is assigned to risk-free genotypes (e.g., those with the same copy number as wild-type or normal controls), a value of 1 is assigned to genotypes consisting of 1 CNV, and a value of 2 is assigned to genotypes consisting of 2 CNVs. Next, the values of each individual CNV corresponding to a lineage-specific CNV are summed and divided by the total number of genetic variants used in the model to generate the individual's raw score (individual raw score). The same calculation is performed for each individual belonging to the subject group, resulting in a range of raw scores (observation range). In some cases, the subject group includes individuals of the same lineage as the individual. The individual raw score is then compared to the observation range to calculate the risk percentile relative to the subject population.

In another non-limiting example of calculating GRS involving CNVs, the concession ratio (OR) for each selected lineage-specific SNV corresponding to the individual-specific CNV is provided and multiplied. In some cases, the OR is obtained from replicated, publicly available, and/or peer-reviewed GWAS. In some cases, the OR for each selected lineage-specific CNV corresponding to the individual-specific CNV is provided, and the OR for each CNV is multiplied to generate the genotype OR for each subject in the subject group. Next, the same calculation is performed on individuals to generate the individual's genotype OR. The genotype ORs for individuals and subject groups are compared, and percentile GRS is calculated.

In some embodiments, this document discloses methods, media, and systems for calculating genetic risk scores (GRS) using the methods disclosed above, the methods involving one or more SNVs and one or more CNVs, one or more SNVs and one or more indels, one or more CNVs and one or more indels, or one or more SNVs, one or more CNVs and one or more indels.

Phenotypic traits

Most phenotypic traits and complex diseases are the result of the combined effects of genetic and environmental factors, each of which increases or decreases susceptibility to the development of a phenotypic trait. The ability to predict whether an individual has or will develop a phenotypic trait is useful for a variety of purposes, including but not limited to selecting treatment options for an individual, regulating an individual's diet, and recommending products (e.g., skincare, haircare, cosmetics, supplements, vitamins, exercise, etc.).

The terms "phenotypic trait" and "specific phenotypic trait" are used interchangeably herein to refer to an observable characteristic of an individual caused at least by the individual's genotype. The genetic risk prediction methods, media, and systems disclosed herein quantify the load of genetic variation in an individual's genotype by analyzing the number and type of genetic variants compared to a reference population. The number and type of genetic variants present in a sample obtained from an individual can inform whether the individual's likelihood (or risk) of developing a certain phenotypic trait is increased or decreased. In some cases, a specific phenotypic trait can have an adverse effect on an individual's health or well-being. In some embodiments, methods, systems, and media are disclosed herein for recommending behavioral changes to prevent, mitigate, or improve the adverse effects of a specific phenotypic trait in an individual.

The aspects disclosed herein provide methods and systems for calculating a genetic risk score (GRS), which represents the likelihood that an individual will develop a particular phenotypic trait. The GRS is based on one or more genetic variants present in an individual's genome or genotype. In some embodiments, one or more genetic variants are detected in a sample obtained from an individual using the methods disclosed herein. In some embodiments, the one or more genetic variants include SNVs, indels, and/or CNVs. In some embodiments, the presence of one or more genetic variants in an individual's genotype is associated with an increased likelihood that the individual has or will develop a particular phenotypic trait. In some embodiments, the presence of one or more genetic variants in an individual's genotype is associated with a decreased likelihood that the individual has or will develop a particular phenotypic trait. In some embodiments, the phenotypic trait includes clinical traits, subclinical traits, physical traits, skin traits, hair traits, allergic traits, nutritional traits, or mental traits.

Clinical and subclinical characteristics

In some embodiments, a clinical symptom includes a disease or condition, or a subclinical symptom of said disease or condition. In some embodiments, a clinical symptom includes a diagnosable disease or condition. In some embodiments, a subclinical symptom includes a subdiagnosable disease, condition, or other phenotype associated with a disease or condition. In some embodiments, a disease or condition includes a deficiency disease, a genetic disease, or a mental illness. In some embodiments, a disease or condition includes immune and/or metabolic diseases such as the risk of cataracts, glaucoma, joint inflammation, kidney stones, overall inflammation, pelvic floor dysfunction, inflammatory biomarkers (CRP, ESR, IL18), age-related cognitive decline, age-related hearing loss, vitiligo, and elevated homocysteine levels. Non-limiting examples include the risk of insomnia, kidney stones, and periodontitis. In some embodiments, an immune disease includes an autoimmune disease or disorder. Non-limiting examples of autoimmune diseases or disorders include Graves' disease, Hashimoto's thyroiditis, systemic lupus erythematosus (lupus), multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, Crohn's disease, ulcerative colitis, and cancer. Non-limiting examples of metabolic diseases or conditions include type 1 diabetes, type 2 diabetes, diseases affecting the absorption of macronutrients (e.g., amino acids, carbohydrates, or lipids), diseases affecting the absorption of micronutrients (e.g., vitamins or minerals), diseases affecting mitochondrial function, diseases affecting liver function (e.g., non-alcoholic fatty liver disease), and diseases affecting kidney function. Subclinical characteristics may include subdiagnosable conditions or disorders associated with the diseases or conditions disclosed herein.

Skin characteristics

In some embodiments, phenotypic traits include traits associated with an individual's skin (skin traits). In some embodiments, skin traits include collagen degradation rate. Collagen degradation rate may be influenced by genetic variations within the genes encoding MMP, MMP-3, and MMP-1 collagenases. Non-limiting examples of genetic variations within the genes encoding collagenases include single nucleotide variants (SNVs) disclosed in Table 1.

Table 1

In some implementations, skin traits include dryness. Genetic variations within the gene encoding aquaporin 3 can affect skin hydration, and consequently, skin dryness. Non-limiting examples of genetic variations within the gene encoding aquaporin 3 include the SNVs disclosed in Table 2.

Table 2

In some embodiments, skin traits include a lack of antioxidants in the skin. This lack of antioxidants can be influenced by genetic variations within genes encoding NQO1, SOD2, NFE2L2, GPX1, and/or CAT. Non-limiting examples of genetic variations within genes encoding NQO1, SOD2, NFE2L2, GPX1, and CAT include the SNVs disclosed in Table 3.

Table 3

In some embodiments, skin traits include impaired skin detoxification. Skin detoxification capacity can be affected by genetic variations within genes encoding LOC157273, SGOL1, TBC1D22B, FST, MIR4432, RNASEH2C, and/or TGFB2. Non-limiting examples of genetic variations within genes encoding LOC157273, SGOL1, TBC1D22B, FST, MIR4432, RNASEH2C, and TGFB2 are included in the SNVs disclosed in Table 4.

Table 4

In some embodiments, skin traits include skin glycation. Glycation can be influenced by genetic variations within the genes encoding SLC24A5, SLC45A2, BCN2, MC1R, C16orf55, SPATA33, ASIP, RALY, and/or NAT2. Non-limiting examples of genetic variations within the genes encoding SLC24A5, SLC45A2, BCN2, MC1R, C16orf55, SPATA33, ASIP, RALY, and NAT2 are included in the SNVs disclosed in Table 5.

Table 5

In some embodiments, skin traits include pigmented spots. Skin pigmented spots can be influenced by genetic variations in the genes encoding SEC5L1, IRF4, MC1R, SLC45A2, TYR, NTM, ASIP, and RALY. Non-limiting examples of genetic variations within the genes encoding SEC5L1, IRF4, MC1R, SLC45A2, TYR, NTM, ASIP, and RALY are included in the SNVs disclosed in Table 6.

Table 6

In some embodiments, skin traits include youthfulness. As disclosed herein, "youthfulness" refers to skin quality that includes a slow rate of aging, or skin that appears smaller or younger than it actually is. Youthfulness can be influenced by genetic variations within the gene encoding EDEM1. Non-limiting examples of genetic variations within the gene encoding EDEM1 include the SNVs disclosed in Table 7. In some embodiments, youthfulness refers to skin quality that includes a slower rate of aging compared to the rate of aging of individuals who do not express the SNVs disclosed in Table 7, for 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 1 year, 2 years, 3 years, 4 years, or 5 years.

Table 7

In some embodiments, skin traits include photoaging. "Photoaging" as disclosed herein refers to damage to the skin caused by ultraviolet radiation and is a major cause of premature aging. Photoaging can be affected by genetic variations within genes encoding MC1R, NTM, TYR, FBXO40, STXBP5L, ASIP, RALY, FANCA, and ID4-RPL29P17. Non-limiting examples of genetic variations within genes encoding MC1R, NTM, TYR, FBXO40, STXBP5L, ASIP, RALY, FANCA, and ID4-RPL29P17 include the SNVs disclosed in Table 8.

Table 8

In some implementations, skin traits include dermal sensitivity. As disclosed herein, “dermal sensitivity” refers to genetic variations that can lead to skin barrier defects and promote skin sensitivity and irritation. Dermal sensitivity can be influenced by genetic variations within genes encoding RNASEH2C, DDB2, C11orf49, SELL, TGFB2, SGOL1, ERI1, LOC157273, MFHAS1, MIR597, MIR4660, PPP1R3B, U6, TNKS, BC017578, TBC1D22B, AL833181, BCL11A, JB153659, PAPOLG, MIR4432, and Mir_562. Non-limiting examples of genetic variations within genes encoding RNASEH2C, DDB2, C11orf49, SELL, TGFB2, SGOL1, ERI1, LOC157273, MFHAS1, MIR597, MIR4660, PPP1R3B, U6, TNKS, BC017578, TBC1D22B, AL833181, BCL11A, JB153659, PAPOLG, MIR4432, and Mir_562 are included in the SNVs disclosed in Table 9.

Table 9

In some implementations, skin traits include sensitivity to sunlight. Sensitivity to sunlight refers to the susceptibility of certain skin types to damage from moderate sun exposure. Sensitivity to sunlight can be influenced by genetic variations within genes encoding NTM, TYR, and MC1R. Non-limiting examples of genetic variations within genes encoding NTM, TYR, and MC1R include the SNVs disclosed in Table 10.

Table 10

Physical exercise characteristics

In some embodiments, this document discloses physical fitness traits, including traits related to an individual's physical fitness (physical fitness traits). In some embodiments, physical fitness traits include aversion to exercise. "Aversion to exercise" refers to avoidance and/or dislike of experiencing exercise. Aversion to exercise can be influenced by genetic variations within genes encoding PAPSS2, C18orf2, DNAPTP6, TMEM18, LEP, and MC4R. Non-limiting examples of genetic variations within genes encoding PAPSS2, C18orf2, DNAPTP6, TMEM18, LEP, and MC4R include the single nucleotide variants (SNVs) disclosed in Table 11.

Table 11

In some implementations, physical fitness traits include aerobic capacity. Aerobic capacity can be influenced by genetic variations within genes encoding TSHR, ACSL1, PRDM1, DBX1, GRIN3A, ESRRB, ZIC4, and CDH13. Non-limiting examples of genetic variations within the genes TSHR, ACSL1, PRDM1, DBX1, GRIN3A, ESRRB, ZIC4, and CDH13 include the SNVs disclosed in Table 12.

Table 12

In some implementations, physical traits include difficulty in losing weight. Difficulty in losing weight may be associated with the following codes: FTO, TMEM18, MC4R, KCTD15, CHST8, PPARG, NEGRS1, IRS1, SFRS10, ETV5, DGKG, ATP2A1, SH2B1, BDNF, SEC16B, RASAL2, NOS 1AP, AIF1, NCR3, MSRA, TNKS, SPRY2, SH3PXD2B, NEURL1B, BCDIN3D, FAIM2, CHRNA9, RBM47, RGMA, MCTP2, and MIR42. The effects of genetic variations within the genes of 75, PCDH7, TENM2, PRR16, FTMT, SLC24A5, SDCCAG8, COL25A1, NEURL1B, SH3PXD2B, ERBB4, MIR4776-2, STXBP6, NOVA1, DEFB112, TFAP2D, EEF1A1P11-LOC105378866, MTIF3-RNU6-63P, NRXN3, CEP120 and/or LOC105378866-RN7SL831P. Coding FTO, TMEM18, MC4R, KCTD15, CHST8, PPARG, NEGR1, IRS1, SFRS10, ETV5, DGKG, ATP2A1, SH2B1, BDNF, SEC16B, RASAL2, NOS1 AP, AIF1, NCR3, MSRA, TNKS, SPRY2, SH3PXD2B, NEURL1B, BCDIN3D, FAIM2, CHRNA9, RBM47, RGMA, MCTP2, MIR4275, PCDH7, TEN Non-limiting examples of genetic variations within the genes of M2, PRR16, FTMT, SLC24A5, SDCCAG8, COL25A1, NEURL1B, SH3PXD2B, ERBB4, MIR4776-2, STXBP6, NOVA1, DEFB112, TFAP2D, EEF1A1P11-LOC105378866, MTIF3-RNU6-63P, NRXN3, CEP120 and/or LOC105378866-RN7SL831P include the SNVs disclosed in Table 13.

Table 13

In some implementations, physical traits include endurance. Endurance can be influenced by genetic variations within genes encoding PPARGC1A, PPAR-a, TSHR, ESRRB, and/or CDH13. Non-limiting examples of genetic variations within genes encoding PPARGC1A, PPAR-a, TSHR, ESRRB, and CDH13 include the SNVs disclosed in Table 14.

Table 14

In some implementations, physical traits include strength. Strength can be influenced by genetic variations within genes encoding TSHR, ESRRB, and/or CDH13. Non-limiting examples of genetic variations within genes encoding TSHR, ESRRB, and CDH13 include the SNVs disclosed in Table 15.

Table 15

In some implementations, physical fitness traits include physical fitness benefits. A "physical fitness benefit" refers to an individual possessing certain genetic variations that result in a faster and stronger performance from exercise, while other genetic variations may take longer and have less noticeable effects. Physical fitness benefits can be influenced by genetic variations within genes encoding KLKB1, F12, CETP, APOE, APOC1, EDN1, SORT1, PLA2G7, LPL, LIPC, GALNT2, SCARB1, LIPG, MS4A4E, ABCA1, TMEM49, LOC101928635, MVK, MMAB, FLJ41733, FADS1, RREB1, COL8A1, and/or GCKR. Non-limiting examples of genetic variations within the genes encoding KLKB1, F12, CETP, APOE, APOC1, EDN1, SORT1, PLA2G7, LPL, LIPC, GALNT2, SCARB1, LIPG, MS4A4E, ABCA1, TMEM49, LOC101928635, MVK, MMAB, FLJ41733, FADS1, RREB1, COL8A1, and GCKR are included in the SNVs disclosed in Table 16.

Table 16

In some implementations, physical fitness traits include a reduction in heart rate in response to exercise (e.g., recovery rate). This reduction in heart rate in response to exercise can be influenced by genetic variations within genes encoding RBPMS, PIWIL1, OR6N2, ERBB4, CREB1, MAP2, and/or IKZF2. Non-limiting examples of genetic variations within genes encoding RBPMS, PIWIL1, OR6N2, ERBB4, CREB1, MAP2, and IKZF2 include the SNVs disclosed in Table 17.

Table 17

In some implementations, physical fitness traits include lean body mass. Lean body mass can be influenced by genetic variations within genes encoding TRHR, DARC, GLYAT, FADS1, and/or FADS2. Non-limiting examples of genetic variations within genes encoding TRHR, DARC, GLYAT, FADS1, and FADS2 include the SNVs disclosed in Table 18.

Table 18

In some implementations, physical fitness traits include muscle soreness. Muscle soreness can be affected by genetic variations within genes encoding CD163L1, DARC, CD163, ABO, CRP, CD163, CADM3, CR1, NRNR, NINJ1, CFH, DARC, CPN1, CSF1, HBB, CCL2, and/or IGF2. Non-limiting examples of genetic variations within genes encoding CD163L1, DARC, CD163, ABO, CRP, CD163, CADM3, CR1, NRNR, NINJ1, CFH, DARC, CPN1, CSF1, HBB, CCL2, and IGF2 are included in the SNVs disclosed in Table 19.

Table 19

In some implementations, physical fitness traits include risk of muscle injury. “Muscle injury” refers to a predisposition to an increased risk of muscle injury. Risk of muscle injury can be influenced by genetic variations within genes encoding IGF-II, MLCK, ACTN3, IL-6, and/or COL5A1. Non-limiting examples of genetic variations within genes encoding IGF-II, MLCK, ACTN3, IL-6, and COL5A1 include the SNVs disclosed in Table 20.

Table 20

In some implementations, physical fitness traits include impaired muscle repair. Impaired muscle repair can be affected by genetic variations within genes encoding HCP5, HCG26, MICB, ATP6V1G2, and/or DDX39B. Non-limiting examples of genetic variations within genes encoding HCP5, HCG26, MICB, ATP6V1G2, and DDX39B include the SNVs disclosed in Table 21.

Table 21

In some implementations, physical fitness traits include stress fracture risk. Stress fracture risk can be influenced by genetic variations within genes encoding LOC101060363-LOC105376856, ZBTB40, EN1, FLJ42280, COLEC10, WNT16, ESR1, ATP6V1G1, CLDN14, ESR1FABP3P2, ADAMTS18, SOST, CLDN14, MEF2C, KCNH1, C6orf97, CKAP5, C17orf53, SOST, TNFRSF11A, LOC105373519-LOC728815, PTCH1, SMOC1, LOC646794-LOC101928765, and LOC105377045-MRPS31P1. Non-limiting examples of genetic variations within the genes encoding LOC101060363-LOC105376856, ZBTB40, EN1, FLJ42280, COLEC10, WNT16, ESR1, ATP6V1G1, CLDN14, ESR1FABP3P2, ADAMTS18, SOST, CLDN14, MEF2C, KCNH1, C6orf97, CKAP5, C17orf53, SOST, TNFRSF11A, LOC105373519-LOC728815, PTCH1, SMOC1, LOC646794-LOC101928765, and LOC105377045-MRPS31P1 include the SNVs disclosed in Table 22.

Table 22

In some implementations, physical fitness traits include overall injury risk. Overall injury risk can be influenced by genetic variations within the genes encoding HAO1, RSPO2, EMC2, EIF3E, CCDC91, PTHLH, LOC100506393, LINC00536, EIF3H, CDC5L, SUPT3H, and/or MIR4642. Non-limiting examples of genetic variations within the genes encoding HAO1, RSPO2, EMC2, EIF3E, CCDC91, PTHLH, LOC100506393, LINC00536, EIF3H, CDC5L, SUPT3H, and MIR4642 are included in the SNVs disclosed in Table 23.

Table 23

In some implementations, physical fitness traits include impaired resting metabolic heart rate. Impaired resting metabolic heart rate can be influenced by genetic variations within the gene encoding FTO. Non-limiting examples of genetic variations within the gene encoding FTO include the SNVs disclosed in Table 24.

Table 24

Nutritional traits

In some implementations, this document discloses nutritional traits, including vitamin deficiency, mineral deficiency, antioxidant deficiency, metabolic imbalance, metabolic impairment, metabolic sensitivity, allergies, satiety, and/or the effectiveness of a healthy diet.

In some implementations, nutritional traits include vitamin deficiencies. In some cases, vitamin deficiencies include deficiencies in the following: vitamin A, vitamin B1, vitamin B2, vitamin B3, vitamin B5, vitamin B6, vitamin B7, vitamin B8, vitamin B9, vitamin B12, vitamin C, vitamin D, vitamin E, or vitamin K. Vitamin deficiencies can be caused by the following enzymes: GC, FUT2, HAAO, BCMO1, ALPL, CYP2R1, MS4A3, FFAR4, TTR, CUBN, FUT6, ZNF259, LOC100128347, APOA5, SIK3, BUD13, ZNF259, APOA5, BUD13, KYNU, NBPF3, TCN1, CYP4F2, PDE3B, CYP2R1, CALCA, CALCP, OR7E41P, APOA5, CLYBL, NADSYN1, DHCR7, SCARB1, RNU7-49P, COPB1, RRAS2, PSMA1, PRELID2, CYP2R1, PDE3B, C The effects of genetic variations within the genes ALCA, CALCP, OR7E41P, MUT, ZNF259, CTNAA2, CDO1, SLC23A1, KCNK9, CYP4F2, LOC729645, ZNF259, BUD13, ST6GALNAC3, NKAIN3, VDAC1P12, RASIP1, MYT1L, PAX3, NPY, ADCYAP1R1, HSF5, RNF43, MTMR4, TMEM215-ASS1P12, FAM155A, CD44, BRAF, CD4, LEPREL2, GNB3, MKLN1, SLC6A1, PRICKLE2, SVCT1, and/or SVCT2. Coding GC, FUT2, HAAO, BCMO1, ALPL, CYP2R1, MS4A3, FFAR4, TTR, CUBN, FUT6, ZNF259, LOC100128347, APOA5, SIK3, BUD13, ZNF259, APOA5, BUD13, KYNU, NBPF3, TCN1, C YP4F2, PDE3B, CYP2R1, CALCA, CALCP, OR7E41P, APOA5, CLYBL, NADSYN1, DHCR7, SCARB1, RNU7-49P, COPB1, RRAS2, PSMA1, PRELID2, CYP2R1, PDE3B, CALCA, CALCP, Non-restrictive examples of genetic variations within the genes of OR7E41P, MUT, ZNF259, CTNAA2, CDO1, SLC23A1, KCNK9, CYP4F2, LOC729645, ZNF259, BUD13, ST6GALNAC3, NKAIN3, VDAC1P12, RASIP1, MYT1L, PAX3, NPY, ADCYAP1R1, HSF5, RNF43, MTMR4, TMEM215-ASS1P12, FAM155A, CD44, BRAF, CD4, LEPREL2, GNB3, MKLN1, SLC6A1, PRICKLE2, SVCT1, and SVCT2 include the SNVs listed in Table 25.

Table 25

In some implementations, nutritional traits include mineral deficiencies. In some cases, mineral deficiencies include deficiencies in calcium, iron, magnesium, zinc, and/or selenium. In some cases, mineral deficiencies may be coded by CASR, TF, TFR2, SCAMP5, PPCDC, ARSB, BHMT2, DMGDH, ATP2B1, DCDC5, TRPM6, SHROOM3, CYP24A1, BHMT, BHMT2, JMY, TMPRSS6, GCKR, KIAA0564, DGKH, HFE, GATA3, VKORC1L1, MDS1, MUC1, CSTA, JMY, HOMER1, MAX, FNTB, SLC36A4, CCDC67, MIR379, FGFR2, LUZP2, PAPSS2, HOXD9, LOC1. 02724653-IGLV4-60, HOOK3, FNTA, MEOX2, LOC101928964, PRPF8, MGC14376, SMYD4, SERPINF2, SERPINF1, WDR81, MIR4778, MEIS1-AS3, PRDM9, CALC OCO1, HOXC13, GPR39, SLC22A16, CDK19, TMOD1, TXNRD1, NFYB, MYOM2, CSMD1, KBTBD11, ARHGEF10, DYNC2H1, DCUN1D5, PDGFD, PRMT7, SERPINF2, WDR81 , CRMP1, FLJ46481, KHDRBS2-LOC100132056, CD109, LOC100616530, SLC16A7, FLRT2, KYNU, ARHGAP15, RARB, C3orf58, PLOD2, RPRM, GALNT13, EPHA6 , RGS14, SLC34A1, SLC22A18, PHLDA2, CDKN1C, NAP1L4, LOC101929578, ZNF14, ZNF101, ATP13A1, PYGB, CHD5, SDCCAG8, XDH, SRD5A2, CMYA5, RP11-314 The effects of genetic variations within the genes C16.1, TFAP2A, PTPRN2, CA1, KNOP1P1, RNU7-14P-LOC107987283, FNDC4, IFT172, GCKR, C2orf16, CBLB, LINC00882, LOC107983965, MIR4790, AC069277.1, IRX2, C5orf38, ZNF521, SS18, ATG4C, LPHN2, TTLL7, SAG, DGKD, RN7SKP61-MRPS17P3, GPBP1, STXBP6, NOVA1, TMEM211, and/or MT2A. Coding CASR, TF, TFR2, SCAMP5, PPCDC, ARSB, BHMT2, DMGDH, ATP2B1, DCDC5, TRPM6, SHROOM3, CYP24A1, BHMT, BHMT2, JMY, TMPRSS6, GCKR, KIAA0564, DGKH , HFE, GATA3, VKORC1L1, MDS1, MUC1, CSTA, JMY, HOMER1, MAX, FNTB, SLC36A4, CCDC67, MIR379, FGFR2, LUZP2, PAPSS2, HOXD9, LOC102724653-IGLV4- 60. HOOK3, FNTA, MEOX2, LOC101928964, PRPF8, MGC14376, SMYD4, SERPINF2, SERPINF1, WDR81, MIR4778, MEIS1-AS3, PRDM9, CALCOCO1, HOXC13, GPR 39. SLC22A16, CDK19, TMOD1, TXNRD1, NFYB, MYOM2, CSMD1, KBTBD11, ARHGEF10, DYNC2H1, DCUN1D5, PDGFD, PRMT7, SERPINF2, WDR81, CRMP1, FLJ46481 , KHDRBS2-LOC100132056, CD109, LOC100616530, SLC16A7, FLRT2, KYNU, ARHGAP15, RARB, C3orf58, PLOD2, RPRM, GALNT13, EPHA6, RGS14, SLC34A1, SLC22A18, PHLDA2, CDKN1C, NAP1L4, LOC101929578, ZNF14, ZNF101, ATP13A1, PYGB, CHD5, SDCCAG8, XDH, SRD5A2, CMYA5, RP11-314C16.1, TFAP2A, P Non-restrictive examples of genetic variations within the genes of TPRN2, CA1, KNOP1P1, RNU7-14P-LOC107987283, FNDC4, IFT172, GCKR, C2orf16, CBLB, LINC00882, LOC107983965, MIR4790, AC069277.1, IRX2, C5orf38, ZNF521, SS18, ATG4C, LPHN2, TTLL7, SAG, DGKD, RN7SKP61-MRPS17P3, GPBP1, STXBP6, NOVA1, TMEM211, and MT2A include the SNVs listed in Table 26.

Table 26

In some implementations, nutritional traits include antioxidant deficiency. In some cases, antioxidant deficiency includes a deficiency of glutathione and/or coenzyme Q10 (CoQ10). Antioxidant deficiency can be caused by the following enzymes: GGT1, GGTLC2, MYL2, C12orf27, HNF1A, OAS1, C14orf73, ZNF827, RORA, EPHA2, RSG1, MICAL3, DPM3, EFNA1, PKLR, GCKR, C2orf16, NEDD4L, MYO1B, STAT4, CCBL2, PKN2, SLC2A2, ITGA1, DLG5, FUT2, ATP8B1, EFHD1, CDH6, CD276, FLJ37644, SOX9, DDT, DDTL, GS. The effects of genetic variations within the genes of TT1, GSTT2B, MIF, MLIP, MLXIPL, DYNLRB2, CEPT1, DENND2D, COLEC12, LOC101927479-ARHGEF19, LOC105377979, MMP26, DNM1, LUZP1, ADH5P2-LOC553139, FST, MIR4708-LOC105370537, LOC105373450-KCNS3, LOC107984041-GRIK2, LINC01520, and/or NQO1. Coding GGT1, GGTLC2, MYL2, C12orf27, HNF1A, OAS1, C14orf73, ZNF827, RORA, EPHA2, RSG1, MICAL3, DPM3, EFNA1, PKLR, GCKR, C2orf16, NED D4L, MYO1B, STAT4, CCBL2, PKN2, SLC2A2, ITGA1, DLG5, FUT2, ATP8B1, EFHD1, CDH6, CD276, FLJ37644, SOX9, DDT, DDTL, GSTT1, GSTT2B, Non-restrictive examples of genetic variations within the genes of MIF, MLIP, MLXIPL, DYNLRB2, CEPT1, DENND2D, COLEC12, LOC101927479-ARHGEF19, LOC105377979, MMP26, DNM1, LUZP1, ADH5P2-LOC553139, FST, MIR4708-LOC105370537, LOC105373450-KCNS3, LOC107984041-GRIK2, LINC01520, and NQO1 include the SNVs listed in Table 27.

Table 27

In some implementations, nutritional traits include metabolic imbalances. In some cases, metabolic imbalances include glucose imbalances. Metabolic imbalances can be influenced by genetic variations within genes encoding G6PC2, MTNR1B, GCK, ADCY5, MADD, ADRA2A, GCKR, MRPL33, ABCB11, FADS1, PCSK1, CRY2, ARAP1, SIX2, SIX3, PPP1R3B, SLC2A2, GLIS3, DPYSL5, SLC30A8, PROX1, CDKN2A, CDKN2B, FOXA2, TMEM195, DGKB, PDK1, RAPGEF4, PDX1, CDKAL1, KANK1, IGF1R, C2CD4B, LEPR, GRB10, LMO1, RREB1, FBXL10, and/or FOXN3. Non-restrictive examples of genetic variations within the genes encoding G6PC2, MTNR1B, GCK, ADCY5, MADD, ADRA2A, GCKR, MRPL33, ABCB11, FADS1, PCSK1, CRY2, ARAP1, SIX2, SIX3, PPP1R3B, SLC2A2, GLIS3, DPYSL5, SLC30A8, PROX1, CDKN2A, CDKN2B, FOXA2, TMEM195, DGKB, PDK1, RAPGEF4, PDX1, CDKAL1, KANK1, IGF1R, C2CD4B, LEPR, GRB10, LMO1, RREB1, FBXL10, and FOXN3 include the SNVs listed in Table 28.

Table 28

In some implementations, nutritional traits include impaired metabolism. In some cases, impaired metabolism includes impaired metabolism of caffeine and/or drugs. Impaired metabolism can be affected by genetic variations within the genes encoding MTNR1B, CACNA2D3, NEDD4L, AC105008.1, P2RY2, RP11-479A21.1, MTUS2, PRIMA1, and/or RP11-430J3.1. Non-limiting examples of genetic variations within the genes encoding MTNR1B, CACNA2D3, NEDD4L, AC105008.1, P2RY2, RP11-479A21.1, MTUS2, PRIMA1, and RP11-430J3.1 include the SNVs listed in Table 29.

Table 29

In some implementations, nutritional traits include metabolic sensitivity. In some cases, metabolic sensitivity includes gluten sensitivity, salt sensitivity, polysaccharide sensitivity, and/or lactose sensitivity. Metabolic sensitivity can be affected by genetic variations within the genes encoding PIBF1, IRAK1BP1, PRMT6, CDCA7, NOTCH4, HLA-DRA, BTNL2, ARSJ, CSMD1, ALX4, NSUN3, RAB9BP1, GPR65, C15orf32, TSN, CREB1, and/or ARMC9. Non-limiting examples of genetic variations within the genes encoding PIBF1, IRAK1BP1, PRMT6, CDCA7, NOTCH4, HLA-DRA, BTNL2, ARSJ, CSMD1, ALX4, NSUN3, RAB9BP1, GPR65, C15orf32, TSN, CREB1, and ARMC9 include the SNVs listed in Table 30.

Table 30

In some embodiments, the nutritional trait includes food allergies. In some embodiments, food allergies include peanut allergies. Peanut allergies can be influenced by genetic variations within genes encoding HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DQA2, HCG27, HLA-C, ADGB, RPS15P9, MUM1, RYR1, LINC00992, LOC100129526, FAM118A, SMC1B, MIATNB, ATP2C2, PLAGL1, MRPL42, and/or STAT6. Non-restrictive examples of genetic variations within the genes encoding HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DQA2, HCG27, HLA-C, ADGB, RPS15P9, MUM1, RYR1, LINC00992, LOC100129526, FAM118A, SMC1B, MIATNB, ATP2C2, PLAGL1, MRPL42, and STAT6 include the SNVs listed in Table 31.

Table 31

In some implementations, the nutritional trait includes satiety. Satiety can be influenced by genetic variations within the gene encoding LEPR. Non-limiting examples of genetic variations within the gene encoding LEPR include the SNVs listed in Table 32.

Table 32

The effectiveness of a healthy diet can be assessed by the codes FGF21, ZPR1, TANK, FNBP1, RNU6-229P-LOC105375346, ARGFX, BEND3, SUMO2P6-LOC105377740, LOC101929216-GDF10, LOC105377451-LOC105377622, CPA3, KCNQ3, THBS4, TENM2, HSPA9P2-LOC105372045, LINC00113-LINC00314, and SH3BGRL2. The influence of genetic variations within the genes of NKAIN2, OPRM1, LOC105377795, NCALD, LOC728503, LOC105370491, LOC107985318-MIA3, BECN1P2-LYPLA1P3, LOC105376778-LINC01082, SOX5, LHX5-AS1-LOC105369990, NBAS, ABCG2, PPARγ2, CLOCK, RARB, FTO, IRS1, TCF7L2, HNMT and/or PFKL. Encoding: FGF21, ZPR1, TANK, FNBP1, RNU6-229P-LOC105375346, ARGFX, BEND3, SUMO2P6-LOC105377740, LOC101929216-GDF10, LOC105377451-LOC105377622, CPA3, KCNQ3, THBS4, TENM2, HSPA9P2-LOC105372045, LINC00113-LINC00314, SH3BGRL2, NKAIN2, OPRM1 Non-restrictive examples of genetic variations within the genes of LOC105377795, NCALD, LOC728503, LOC105370491, LOC107985318-MIA3, BECN1P2-LYPLA1P3, LOC105376778-LINC01082, SOX5, LHX5-AS1-LOC105369990, NBAS, ABCG2, PPARγ2, CLOCK, RARB, FTO, IRS1, TCF7L2, HNMT, and PFKL include the SNVs listed in Table 33.

Table 33

Allergic symptoms

In some embodiments, allergic traits are disclosed herein. In some embodiments, allergic traits include skin allergies, dust allergies, insect bite allergies, pet allergies, eye allergies, drug allergies, latex allergies, mold allergies, and/or pest allergies. In some embodiments, allergic traits include allergic inflammation. As used herein, "allergic inflammation" refers to inflammation caused by or associated with an allergic reaction.

In some implementations, nutritional traits include allergic inflammation. In some cases, allergic inflammation may be mediated by substances encoding FCER1A, LRRC32, C11orf30, IL13, OR10J3, HLA-A, STAT6, TSLP, SLC25A46, WDR36, CAMK4, HLA-DQB1, HLA-DQA1, STAT6, NAB2, DARC, IL18R1, IL1RL1, IL18RAP, FAM114A1, MIR574, TLR10, TLR1, TLR6, LPP, BCL6, MYC, PVT1, IL2, ADAD1, KIAA1109, and I. The influence of genetic variations within the genes of L21, HLA region, TMEM232, SLCA25A46, HLA-DQA2, HLA-G, MICA, HLA-C, HLA-B, MICB, HLA-DRB1, IL4R, ID2, LOC730217, OPRK1, WWP2, EPS15, ANAPC1, LPP, LOC101927026, IL4R, IL21R, SUCLG2, TMEM108, DNAH5, OR6X1, DOCK10, ABL2, COL21A1, and/or CDH13. Coding FCER1A, LRRC32, C11orf30, IL13,OR10J3, HLA-A, STAT6, TSLP, SLC25A46, WDR36, CAMK4, HLA-DQB1, HLA-DQA1, STAT6, NAB2, DARC , IL18R1, IL1RL1, IL18RAP, FAM114A1, MIR574, TLR10, TLR1, TLR6, LPP, BCL6, MYC, PVT1, IL2, ADAD1, KIAA1109, IL21, HLA region, Non-restrictive examples of intragenetic variations in TMEM232, SLCA25A46, HLA-DQA2, HLA-G, MICA, HLA-C, HLA-B, MICB, HLA-DRB1, IL4R, ID2, LOC730217, OPRK1, WWP2, EPS15, ANAPC1, LPP, LOC101927026, IL4R, IL21R, SUCLG2, TMEM108, DNAH5, OR6X1, DOCK10, ABL2, COL21A1, and CDH13 include the SNVs listed in Table 34.

Table 34

In some embodiments, the allergic trait includes pest allergy. In some embodiments, pest allergy includes mite allergy. Mite allergy can be affected by genetic variations within genes encoding LOC730217, OPRK1, OR6X1, DOCK10, CDH13, CapS, IL4, ADAM33, IRS2, ABHD13, LINC00299, IL18, CYP2R1, and/or VDR. Non-limiting examples of genetic variations within genes encoding LOC730217, OPRK1, OR6X1, DOCK10, CDH13, CapS, IL4, ADAM33, IRS2, ABHD13, LINC00299, IL18, CYP2R1, and VDR include the SNVs listed in Table 35.

Table 35

Psychological characteristics

In some implementations, this document discloses mental traits, which include traits related to an individual's mental health or mental acuity, mental illness, or mental condition. Non-limiting examples of mental health or mental acuity include a degree of stress, short-term memory retention, long-term memory retention, creativity or artistry (e.g., "right brain"), analytical and orderly behavior (e.g., "left brain"). Non-limiting examples of mental illness include schizophrenia, bipolar disorder, manic-depressive disorder, autism spectrum disorder, and Down syndrome. Non-limiting examples of mental condition include risk of depression, social anxiety, likelihood of introversion, and likelihood of extroversion. Non-limiting examples of mental traits include early riser, empathy, anxious personality, mathematical ability, addictive personality, memory performance, OCD tendency, exploratory behavior, reading ability, experiential learning difficulties, general creativity, general intelligence, impulsivity, symptoms of inattention, mathematical ability, mental reaction time, musical creativity, nail biting, reading and spelling difficulties, verbal and numerical reasoning, and pronunciation errors.

In some implementations, mental traits include memory ability. Memory ability may be coded by APOC1, APOE, FASTKD2, MIR3130-1, MIR3130-2, SPOCK3, ANXA10, ISL1, PARP8, BAIAP2, HS3ST4, C16orf82, AJAP1, C1orf174, ODZ4, NARS2, PRR16, FTMT, PCDH20, TDRD3, LBXCOR1, MAP2K5, PTGER3, ZRANB2, AXUD1, TTC21A, GFRA2, DOK2, SLC39A14, P The influence of genetic variations within the genes of PP3CC, VPS26B, NCAPD3, ZNF236, MBP, RIN2, NAT5, SEMA5A, MTRR, DGKB, ETV1, BHLHB5, CYP7B1, TMEPAI, ZBP1, TBC1D1, KLHL1, DACH1, LLRTM4, C2orf3, B3GAT1, LOC89944, ATP8B4, SLC27A2, CHD6, EMILIN3, RWDD3, TMEM56, SCN1A, KIBRA, and/or NCAN. Coding APOC1, APOE, FASTKD2, MIR3130-1, MIR3130-2, SPOCK3, ANXA10, ISL1, PARP8, BAIAP2, HS3ST4, C16orf82, AJAP1, C1orf174, OD Z4, NARS2, PRR16, FTMT, PCDH20, TDRD3, LBXCOR1, MAP2K5, PTGER3, ZRANB2, AXUD1, TTC21A, GFRA2, DOK2, SLC39A14, PPP3CC, VPS26 Non-restrictive examples of genetic variation within the genes of B, NCAPD3, ZNF236, MBP, RIN2, NAT5, SEMA5A, MTRR, DGKB, ETV1, BHLHB5, CYP7B1, TMEPAI, ZBP1, TBC1D1, KLHL1, DACH1, LLRTM4, C2orf3, B3GAT1, LOC89944, ATP8B4, SLC27A2, CHD6, EMILIN3, RWDD3, TMEM56, SCN1A, KIBRA, and NCAN include the SNVs listed in Table 36.

Table 36

In some implementations, the mental condition includes obsessive-compulsive disorder (OCD) predisposition. OCD predisposition can be influenced by genetic variations within the genes encoding PTPRD, LOC646114, LOC100049717, FAIM2, AQP2, TXNL1, WDR7, CDH10, MSNL1, GRIK2, HACE1, DACH1, MZT1, DLGAP1, EFNA5, and/or GRIN2B. Non-limiting examples of genetic variations within the genes encoding PTPRD, LOC646114, LOC100049717, FAIM2, AQP2, TXNL1, WDR7, CDH10, MSNL1, GRIK2, HACE1, DACH1, MZT1, DLGAP1, EFNA5, and GRIN2B include the SNVs listed in Table 37.

Table 37

Hair characteristics

In some embodiments, hair traits are disclosed herein. In some embodiments, hair traits include hair thickness, thinning hair, hair loss, baldness, oiliness, dryness, dandruff, pseudofolliculitis of the beard (razor razor bumps), moniliform hair, pili trianguli, twisted hair, and/or hair volume. In some embodiments, the term "baldness" as used herein refers to androgenetic alopecia (AGA). In some embodiments, pili trianguli may be affected by genetic variations within genes encoding PADI3, TGM3, and/or TCHH. In some embodiments, pseudofolliculitis of the beard may be affected by genetic variations within genes encoding K6HF. In some embodiments, pili trianguli may be affected by genetic variations within genes encoding KRT81, KRT83, KRT86, and/or DSG4. In some embodiments, twisted hair may be affected by genetic variations within genes encoding BCS1L. In some implementations, baldness can be influenced by genetic variations within genes encoding PAX1, TARDBP, HDAC4, HDAC9, AUTS2, MAPT-AS1, SPPL2C, SETBP1, GRID1, WNT10A, EBF1, SUCNR1, MBNL1, SSPN, ITPR2, AR, EDA2R, EDA2R, ICOS, CTLA4, IL2, IL21, ULBP3, ULBP6, STX17, IL2RA, PRDX5, IKZF4, and/or HLA-DQA2. Non-restrictive genes influencing baldness include, but are not limited to, the SNVs listed in Table 38.

Table 38

behavior change

The aspects disclosed herein provide methods and systems for recommending behavioral changes associated with a specific phenotypic trait to an individual, at least in part, based on a Genetic Risk Score (GRS) for that trait. In some cases, multiple behavioral change recommendations are provided to the individual. In some cases, the individual provides a questionnaire including questions related to the specific phenotypic trait of interest. In some cases, behavioral changes are based on the GRS for the trait and the answers to questions received from the individual. In some cases, behavioral changes include increasing, decreasing, or avoiding activities. Non-limiting examples of activities include, but are not limited to, physical exercise, ingestion of a substance (e.g., supplements or drugs), exposure to products (e.g., smoke, toxins, irritants, etc.), use of products (e.g., skin care products, hair care products, nail care products, etc.), diet, lifestyle, sleep, and consumption (e.g., consumption of alcohol, drugs, caffeine, allergens, food, or a class of foods). In some cases, behavioral changes include activities used to remedy or prevent a specific phenotypic trait (e.g., engaging in or refraining from activities that are a cause of or associated with the occurrence of the specific phenotypic trait).

This disclosure provides, by way of non-limiting examples, various recommendations for behavioral changes related to the specific phenotypic traits described herein. In some embodiments, individuals with GRS indicating an increased likelihood of dry skin, compared to the subject population, are advised to engage in remedial and/or preventative activities for dry skin (e.g., applying moisturizer daily). In some embodiments, individuals with GRS indicating an increased likelihood of collagen breakdown, compared to the subject population, are advised to engage in remedial and/or preventative activities for collagen breakdown (e.g., consuming collagen supplements, using specific products or devices, avoiding the use of specific products or devices). In some implementations, individuals with a GRS indicating an increased likelihood of aversion to exercise, compared to the subject population, are advised to engage in unconventional physical activities (e.g., hobbies such as rock climbing, hiking, backpacking, etc.). In some implementations, individuals with a GRS indicating an increased risk of muscle injury, compared to the subject population, are advised to avoid activities (e.g., fitness, extreme endurance events, etc.) to remedy or prevent muscle injury. In some implementations, individuals with a GRS indicating an increased likelihood of stress fractures, compared to the subject population, are advised to avoid activities (e.g., repetitive and/or high-impact activities such as running) to remedy or prevent stress fractures. In some implementations, individuals with a GRS indicating an increased likelihood of impaired alcohol metabolism, compared to the subject population, are advised to avoid or reduce alcohol consumption. In some implementations, the subject population is lineage-specific for individuals.

Report

In some embodiments, reports such as health reports are disclosed herein. Non-limiting examples of reports in this embodiment are provided in Figures 6A-6F and 7A-7D. Reports are generated using the methods and systems described herein to provide individuals with the results of lineage-specific genetic risk score (GRS) analyses of an individual's genotype against one or more specific phenotypic traits described herein. In some cases, the report includes recommendations for the individual, such as behavioral changes or product recommendations based on the individual's GRS.

In some implementations, the report includes the results of a GRS analysis, expressed as a range of risk (e.g., normal to high) of developing or having a specific phenotypic trait of interest, relative to a reference group. In some cases, the reference group consists of individuals of the same lineage as the individual. In other cases, the reference group is not lineage-specific for the individual. Generally, a "normal" result indicates that the individual is not predisposed to developing or having the stated phenotypic trait. In contrast, a "high" result indicates that the individual is more likely to develop or have the stated phenotypic trait compared to the reference group. A "low" risk indicates that the individual is predisposed to not having or developing the stated phenotypic trait. "Slightly high" or "slightly low" results represent scores between normal and high or low, respectively.

In some cases, the reports described herein provide product recommendations based on an individual's GRS for a specific phenotypic trait. In a non-limiting example, individuals prone to premature collagen breakdown (e.g., those scoring at the 50th percentile or higher) will be recommended a product to restore, halt, or prevent collagen breakdown, such as a collagen supplement. In various embodiments, the report also includes a hyperlink to the recommended product. The hyperlink will direct the individual to online resources related to the product, such as an online marketplace for purchasing the product, or research articles or literature reviews relevant to the specific phenotypic trait.

In some implementations, the reports disclosed herein provide an individual with GRS results for multiple specific phenotypic traits, such as those described herein. For example, in some cases, a single report includes results for one or more specific phenotypic traits related to one or more of skin, physical fitness, nutrition, and others, as provided in and described herein in Figures 6A-6F and 7A-7D.

The report is formatted for delivery to the individual using any suitable method, including electronically or by mail. In some implementations, the report is an electronic report. In some cases, the electronic report is formatted for transmission over a computer network to the individual's personal electronic device (e.g., tablet, laptop, smartphone, fitness tracker). In some cases, the report is integrated into a mobile application on the personal electronic device. In some cases, the application is interactive, allowing the individual to click hyperlinks embedded in the report that automatically redirect the user to online resources. In some cases, the report is encrypted or otherwise protected to safeguard the individual's privacy. In some cases, the report is printed and mailed to the individual.

system

The aspects disclosed herein provide systems configured to implement the methods described herein, including, but not limited to, determining the likelihood that an individual has or will develop a particular phenotypic trait.

Figure 1 illustrates an exemplary health reporting system including a computing device comprising at least one processor 104, 110, memory, and software program 118, the software program 118 including instructions executable by at least one processor to assess the likelihood that an individual has or will develop a particular phenotypic trait. In some cases, the system includes a reporting module configured to generate a report GRS for the individual. In some cases, the report includes recommendations for behavioral changes related to a particular phenotypic trait. In some cases, the system includes an output module configured to display the report to the individual. In some cases, the system includes a central processing unit (CPU), memory (e.g., random access memory, flash memory), electronic storage units, software program, communication interfaces for communicating with one or more other systems, and any combination thereof. In some cases, the system is coupled to a computer network, such as the Internet, an intranet and/or extranet communicating with the Internet, telecommunications or data networks. In some cases, the system is connected to a distributed ledger. In some cases, the distributed ledger includes a blockchain. In some embodiments, the system includes a storage unit for storing data and information regarding any aspect of the methods described in this disclosure. A aspect of the system is a product, article, or artifact.

The exemplary health reporting system of Figure 1 includes a feature of a software program comprising a sequence of instructions executable by at least one processor, the sequence of instructions being written to perform a specified task. In some embodiments, the computer-readable instructions are implemented as program modules that perform a specific task or implement a specific data type, such as functions, features, application programming interfaces (APIs), data structures, etc. Based on the disclosure provided herein, those skilled in the art will recognize that the software program can be written in various versions of various languages. In some embodiments, software program 118 includes instructions executable by at least one processor described herein. In some implementations, the instructions include the following steps: (i) providing the individual's genotype, the genotype including one or more individual-specific genetic variants; (ii) assigning the individual a lineage 106 at least in part based on the individual's genotype; (iii) using a trait-related variant database 108 (which includes lineage-specific genetic variants derived from subjects (subject groups) with the same lineage as the individual) to select one or more lineage-specific genetic variants at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants corresponds to: (1) an individual-specific genetic variant among the one or more individual-specific genetic variants, or (2) a predetermined genetic variant in linkage disequilibrium (LD) with the individual-specific genetic variant among the one or more individual-specific genetic variants in a subject group with the same lineage as the individual, and wherein each of the one or more lineage-specific genetic variants and each of the individual-specific genetic variants includes one or more risk units; and (iv) calculating a genetic risk score 112 for the individual based on the selected one or more lineage-specific genetic variants, wherein the genetic risk score indicates the likelihood that the individual has or will develop the particular trait. In some embodiments, the software program 118 further includes instructions executable by at least one processor described herein, including predetermined genetic variants at linkage disequilibrium (LD) with individual-specific genetic variants. In some cases, the software program includes instructions executable by at least one processor to determine predetermined genetic variants, the instructions including the steps of: (i) providing untyped genotype data from an individual; (ii) typing the untyped genotype data to generate individual-specific typing haplotypes based on the individual's lineage; (iii) interpolating individual-specific genotypes absent in the typed individual-specific typing haplotypes using typing haplotype data from a reference group having the same lineage as the individual; and (iv) selecting from the interpolated individual-specific genotypes genetic variants at linkage disequilibrium (LD) that are associated with the individual's likelihood of having or developing a particular trait. In some embodiments, LD is defined by a D' value of at least about 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, or 1.0. In some embodiments, LD is defined by an ^r² value of at least about 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, or 1.0.

The software program allows for the combination or distribution of computer-readable instructions as needed in various environments. In some cases, the software program comprises one or more sequences of instructions. The software program can be provided from one location. The software program can be provided from multiple locations. In some embodiments, the software program comprises one or more software modules. In some embodiments, the computer program comprises (partially or wholly) one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plugins, extensions, add-ons, or appenders, or combinations thereof.

Figure 1 illustrates an exemplary health reporting system including a reporting module 114. The reporting module 114 described herein includes at least one processor configured to perform the task of generating a report comprising a calculated GRS indicating the individual's likelihood of having or developing a specific phenotypic trait of interest. In some cases, the at least one processor is the same processor 118 described above and is additionally configured to perform the report generation step. In some cases, the at least one processor comprises a single processor, e.g., in a dual-CPU configuration. In some cases, the reporting module 114 is configured to perform the task of retrieving one or more answers to one or more questions related to a specific trait from a questionnaire provided by the individual to the system. In some cases, the report also includes recommendations for trait-related behavioral changes, at least partially based on the GRS. In some cases, the report generated by the reporting module 114 includes recommendations for behavioral changes related to a specific phenotypic trait of interest, based on the GRS for said trait and the retrieval of one or more answers to one or more questions related to said trait.

In some embodiments, the exemplary health reporting system of Figure 1 includes an output module 116. The output module 116 described herein includes hardware or software programs executable on a processor, configured to display a report to an individual. In some embodiments, the output module 116 includes a user interface, including a screen or other output display (e.g., a projector). In some embodiments, the output module 116 includes an email service capable of emailing an electronic version of the report to its associated individual. In some embodiments, the output module 116 includes a user interface on a personal computing device such as a computer, smartphone, or tablet. In some embodiments, the personal computing device is remotely connected to the system described herein via a computer network. In some cases, the personal computing device belongs to an individual. In some embodiments, the personal electronic device is configured to run an application configured to communicate with the reporting module via a computer network to access the report.

Web Applications

In some embodiments, the software programs described herein include web applications. Based on the disclosure provided herein, those skilled in the art will recognize that web applications can utilize one or more software frameworks and one or more database systems. For example, web applications are created on software frameworks such as .NET or Ruby on Rails (RoR). In some cases, web applications utilize one or more database systems, which, by way of non-limiting examples, include relational database systems, non-relational database systems, feature-oriented database systems, relational database systems, and XML database systems. By way of non-limiting examples, suitable relational database systems include SQL Server, MySQL ^™ , and those skilled in the art will also recognize that web applications can be written in one or more languages and one or more versions thereof. In some embodiments, web applications are written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof. In some embodiments, web applications are written to some extent in a markup language, such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or Extensible Markup Language (XML). In some embodiments, web applications are written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some implementations, the web application is written to some extent in a client-side scripting language, such as Asynchronous JavaScript and XML (AJAX), ActionScript, or JavaScript. Alternatively, in some implementations, the web application is written to some extent in a server-side coding language, such as Active Server Pages (ASP), Perl, Java ^™ , JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python ^™ , Ruby, Tcl, Smalltalk, or Groovy. In some implementations, the web application is written to some extent in a database query language such as Structured Query Language (SQL). The web application can integrate with enterprise server products; for example, a Lotus web application may include a media player element. The media player element can utilize one or more of many suitable multimedia technologies, including (by way of non-limiting example) HTML5, Java ^™ , and mobile applications.

In some cases, the software programs described herein include mobile applications provided to mobile digital processing devices. Mobile applications can be provided to mobile digital processing devices during the manufacture of the mobile digital processing device. Mobile applications can be provided to mobile digital processing devices via the computer networks described herein.

Mobile applications are created using hardware, languages, and development environments known in the art, employing techniques known to those skilled in the art. Those skilled in the art will recognize that mobile applications can be written in a variety of languages. By way of non-limiting examples, suitable programming languages include: C, C++, C#, Featured-C, Java ^™ , Javascript, Pascal, Feature Pascal, Python ^™ , Ruby, VB.NET, WML, and XHTML/HTML with or without CSS, or combinations thereof.

Suitable mobile application development environments are available from multiple sources. Commercially available development environments, through non-restricted examples, include AirplaySDK, alcheMo, Celsius, Bedrock, FlashLite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are available free of charge, through non-restricted examples, including Lazarus, MobiFlex, MoSync, and PhoneGap. Additionally, mobile device manufacturers distribute software development kits, through non-restricted examples, including the iPhone and iPad (iOS) SDK, Android ^™ SDK, BREW SDK, OS SDK, Symbian SDK, webOS SDK, and Mobile SDK.

Those skilled in the art will recognize that numerous business forums can be used for the distribution of mobile applications, including, by way of non-limiting examples, app stores, Android ^™ Marketplace, App World, App Store for Palm devices, App Catalog for webOS, Marketplace for mobile, Ovi Store for devices, App Store, and DSi Store.

standalone application

In some implementations, the software programs described herein include standalone applications, which are programs that can run as independent computer processes, rather than appendages to existing processes, e.g., not plugins. Those skilled in the art will recognize that sometimes standalone applications are compiled. In some cases, a compiler is a computer program that translates source code written in a programming language into binary characteristic code, such as assembly language or machine code. By way of non-limiting examples, suitable compilation programming languages include C, C++, Featured-C, COBOL, Delphi, Eiffel, Java ^™ , Lisp, Perl, R, Python ^™ , Visual Basic, and VB.NET, or combinations thereof. Compilation may often be performed, at least partially, to create an executable program. In some cases, a computer program includes one or more executable compiled applications.

Web browser plugin

In some implementations, this document discloses software programs that include web browser plugins in certain aspects. In computing, a plugin is, in some cases, one or more software components that add specific functionality to a larger software application. Software application manufacturers may support plugins to enable third-party developers to create the ability to extend applications, support the easy addition of new features, and reduce the application size. If supported, a plugin can customize the functionality of a software application. For example, plugins are commonly used in web browsers to play videos, generate interactivity, scan for viruses, and display specific file types. Those skilled in the art will be familiar with various web browser plugins, including Player, and toolbars may include one or more web browser extensions, add-ons, or appenders. A toolbar may include one or more browser bars, toolbars, or desktop ribbons. Those skilled in the art will recognize that various plugin frameworks are available for developing plugins in a variety of programming languages, including, by way of non-limiting examples, C++, Delphi, Java ^™ , PHP, Python ^™ , and VB.NET, or combinations thereof.

In some implementations, a web browser (also known as an internet browser) is a software application designed for use with a network-connected digital processing device to retrieve, render, and traverse information resources on the World Wide Web. Suitable web browsers, by way of non-limiting example, include Internet Chrome, Opera, and KDE Konqueror. In some cases, the web browser is a mobile web browser. Mobile web browsers (also known as microbrowsers, mini-browsers, and wireless browsers) can be designed for use on mobile digital processing devices, including, by way of non-limiting example, handheld computers, tablet computers, netbook computers, mini-notebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers, by way of non-limiting example, include: Internet Explorer, RIM Browser, Blazer Browser, Internet Mobile, Basic Web Browser, Opera Mobile, and PSP ^™ Browser.

Software Module

The media, methods, and systems disclosed herein include one or more software, server, and database modules, or their uses. Given the disclosure provided herein, software modules can be created using techniques known to those skilled in the art, utilizing machines, software, and languages known in the art. The software modules disclosed herein can be implemented in a variety of ways. In some embodiments, a software module includes files, code segments, programming features, programming structures, or combinations thereof. A software module may include multiple files, multiple code segments, multiple programming features, multiple programming structures, or combinations thereof. By way of non-limiting example, the one or more software modules include web applications, mobile applications, and/or standalone applications. A software module may be in one computer program or application. A software module may be in more than one computer program or application. A software module may be hosted on one machine. A software module may be hosted on multiple machines. A software module may be hosted on a cloud computing platform. A software module may be hosted on one or more machines located in one location. A software module may be hosted on one or more machines located in more than one location.

database

The media, methods, and systems disclosed herein include one or more databases, such as the trait-related databases described herein, or their uses. Those skilled in the art will recognize that many databases are suitable for storing and retrieving geographic spectra, operator activities, target zones, and/or contact information of concessionaires. By way of non-limiting example, suitable databases include relational databases, non-relational databases, feature-oriented databases, feature databases, entity-relationship model databases, association databases, and XML databases. In some embodiments, the database is Internet-based. In some embodiments, the database is web-based. In some embodiments, the database is cloud-based. The database may be based on one or more local computer storage devices.

Data transmission

The methods, systems, and media described herein are configured to be performed in one or more facilities at one or more locations. Facility locations are not nationally restricted and include any country or region. In some cases, one or more steps of the methods described herein are performed in a country different from another step of the methods. In some cases, one or more steps for obtaining a sample are performed in a country different from one or more steps for analyzing the genotype of the sample. In some embodiments, one or more method steps involving a computer system are performed in a country different from another step of the methods provided herein. In some embodiments, data processing and analysis are performed in a country or location different from one or more steps of the methods described herein. In some embodiments, one or more articles, products, or data are transferred from one or more facilities to one or more different facilities for analysis or further analysis. Articles include, but are not limited to, one or more components obtained from samples from subjects and any articles or products disclosed herein as articles or products. Data includes, but is not limited to, information about genotypes and any data generated by the methods disclosed herein. In some embodiments of the methods and systems described herein, an analysis is performed, and subsequent data transfer steps communicate or transmit the results of the analysis.

In some embodiments, any step of any method described herein is performed by a software program or module on a computer. In other or further embodiments, data from any step of any method described herein is transmitted to and from facilities located in the same or different countries, including analysis performed in a facility at a specific location and data transmitted to another location or directly to individuals in the same or different countries. In other or further embodiments, data from any step of any method described herein is transmitted to and/or received from facilities located in the same or different countries, including analysis of data inputs (e.g., cellular material) performed in a facility at a specific location and corresponding data transmitted to another location or directly to individuals, such as data related to diagnosis, prognosis, response to treatment, etc., in the same or different locations or countries.

Non-transitory computer-readable storage medium

The aspects disclosed herein provide one or more non-transitory computer-readable storage media encoded with software programs, said software programs including instructions executable by an operating system. In some embodiments, the encoded software includes one or more software programs described herein. In other embodiments, the computer-readable storage medium is a tangible component of a computing device. In still other embodiments, the computer-readable storage medium is optionally removable from the computing device. In some embodiments, by way of non-limiting example, the computer-readable storage medium includes CD-ROMs, DVDs, flash memory devices, solid-state storage, disk drives, tape drives, optical disc drives, cloud computing systems and services, etc. In some cases, programs and instructions are permanently, substantially permanently, semi-permanently, or non-transitory encoded on the medium.

reagent kits and products

In some embodiments, the present invention discloses compositions for detecting genotypes or biomarkers in samples obtained from subjects using the methods described herein. The compositions provided by the aspects disclosed herein comprise a polynucleotide sequence comprising at least 10 but less than 50 consecutive nucleotides of one or more of SEQ ID NO:1-218 or their inverse complementary sequences, wherein the consecutive polynucleotide sequence comprises a detectable molecule. In some embodiments, the polynucleotide sequence comprises a nucleobase at position 26 or 31 of one or more of SEQ ID NO:1-218. In various embodiments, the detectable molecule comprises a fluorophore. In other embodiments, the polynucleotide sequence further comprises a quencher.

In some embodiments, kits for detecting the genotypes described herein are also disclosed herein. In some embodiments, the kits disclosed herein can be used to predict whether an individual has or will develop a particular phenotypic trait. In some cases, the kits aid in the diagnosis or prediction of an individual's disease or condition. In some cases, the kits are useful for selecting treatment for a patient. In some cases, the kits include product recommendations, such as supplements or over-the-counter medications. In some cases, the kits include advice to consult a physician or healthcare professional.

In some embodiments, the kit includes the compositions described herein, which can be used to perform the methods described herein for detecting genotypes. The kit includes a composition of materials or components, said materials or components including at least one of the compositions. In other embodiments, the kit includes all components necessary and/or sufficient to perform an assay for detecting genotypes, including all controls, instructions for performing the assay, and any necessary software for analyzing and presenting results. In some cases, the kits disclosed herein are suitable for assays such as PCR and qPCR. In some cases, the kit includes a genotyping chip that can be used when needed. The exact properties of the components configured in the kit depend on their intended use.

Instructions for use may be included in the kit. Optionally, the kit may also include other useful components such as diluents, buffers, pharmaceutically acceptable carriers, syringes, catheters, dressings, pipettes or measuring instruments, bandage materials, or other useful supplies. Materials or components assembled in the kit may be provided to practitioners for storage in any convenient and suitable manner to maintain their operability and usability. For example, components may be in dissolved, dehydrated, or lyophilized form; they may be provided at room temperature, refrigerated, or frozen temperatures. These components are typically contained in suitable packaging materials. As used herein, the phrase “packaging material” refers to one or more physical structures used to contain the contents of the kit, such as compositions, etc. The packaging material is constructed by well-known methods, preferably to provide a sterile, contaminant-free environment. The packaging materials used in the kit are those commonly used in gene expression assays and therapeutic administration. As used herein, the term “packaging” refers to a suitable solid matrix or material, such as glass, plastic, paper, foil, etc., capable of containing the individual kit components. Thus, for example, packaging may be a glass vial or a pre-filled syringe for containing an appropriate amount of the pharmaceutical composition. The packaging material has an external label that indicates the contents and/or uses of the kit and its components.

certain terms

In the following description, certain specific details are set forth in order to provide a thorough understanding of the various embodiments. However, those skilled in the art will understand that the embodiments provided can be practiced without these details. Unless the context otherwise requires, throughout the following specification and claims, the word “comprising” and its variations, such as “including” and “contains”, shall be interpreted in an open, inclusive sense, meaning “including but not limited to”. Unless the context clearly specifies otherwise, the singular forms “a,” “an,” and “the” as used in this specification and the appended claims include a plural of indicators. It should also be noted that the term “or” is generally used in its meaning including “and/or” unless the context expressly specifies otherwise. Furthermore, the headings provided herein are for convenience only and do not explain the scope or meaning of the claimed embodiments.

As used herein, the term “about” means an amount that is approximately 10%, 5%, or 1% of the stated amount.

When used to define compositions and methods, the phrase “consistent essentially of…” as used herein shall mean excluding other elements that are of any significance to the combination for the stated purpose. Therefore, a composition consisting essentially of the elements defined herein does not exclude other materials or steps that do not materially affect the fundamental and novelty of the claimed disclosure (e.g., a composition for treating skin disorders such as acne, eczema, psoriasis, and rosacea).

The term “increased” or “increased” as used herein generally refers to an increase of a statistically significant amount; in some embodiments, the term “increased” or “increased” means an increase of at least 10% compared to a reference level, such as an increase of at least about 10%, an increase of at least about 20%, or an increase of at least about 30%, or an increase of at least about 40%, or an increase of at least about 50%, or an increase of at least about 60%, or an increase of at least about 70%, or an increase of at least about 80%, or an increase of at least about 90%, or an increase of up to and including 100% or any increase between 10 and 100%. Other examples of “increased” include an increase of at least 2 times, at least 5 times, at least 10 times, at least 20 times, at least 50 times, at least 100 times, at least 1000 times or more compared to a reference level.

The terms “reduced” or “reduction” are generally used herein to indicate a statistically significant reduction. In some embodiments, “reduced” or “reduction” means a reduction of at least 10% compared to a reference level, such as at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90%, or a reduction of up to and including 100% (e.g., no level or undetectable level compared to the reference level), or any reduction between 10% and 100%. In the context of a biomarker or symptom, these terms refer to a statistically significant reduction of this level. The reduction can be, for example, at least 10%, at least 20%, at least 30%, at least 40%, or more, and is preferably reduced to an acceptable level within the normal range for an individual without a given disease.

The “lineage” disclosed in this article refers to the genetic lineage of an individual.

As disclosed in this article, the term "genotype" refers to the chemical composition of the polynucleotide sequence within an individual's genome.

As used herein, “treatment” refers to therapeutic treatment and preventive or preventative measures where the aim is to prevent or alleviate (mitigate) a target condition, prevent the condition, pursue or achieve a good overall outcome, or reduce an individual’s chance of developing the condition, even if the treatment is ultimately unsuccessful. In some aspects provided herein, subjects requiring treatment include those who already have a disease or condition, those who are prone to developing said disease or condition, or those in whom the prevention of said disease or condition is sought. In some cases, treatment includes supplements. Non-limiting examples of supplements include vitamins, minerals, antioxidants, probiotics, and anti-inflammatory agents. In some cases, treatment includes pharmaceutical treatment. In some cases, pharmaceutical treatment includes antibiotics, antibodies, or small molecule compounds that target genes disclosed herein or their gene expression products.

The term "genotype" as disclosed herein refers to the chemical composition of a polynucleotide sequence within an individual's genome. In some embodiments, a genotype includes SNVs, single nucleotide polymorphisms (SNPs), indels, and/or CNVs. The terms "single nucleotide variant" or "single nucleotide variation" or SNV as disclosed herein refer to a variation of a single nucleotide in a polynucleotide sequence. Variations of SNVs can take many different forms. A single form of SNV is referred to as an "allele." For example, the reference polynucleotide sequence read from 5' to 3' is TTACG. An SNV at allele position 3 (5'-TTACG-3') includes the substitution of the reference allele "A" for the non-reference allele "C." If the "C" allele of an SNV is associated with an increased probability of developing the phenotypic trait, that allele is considered a "risk" allele. However, the same SNV may also contain the substitution of the "A" allele for the "T" allele. If the T allele of an SNV is associated with a decreased probability of developing the phenotypic trait, that allele is considered a "protective" allele. SNVs can include single nucleotide polymorphisms (SNPs), and in some cases, SNVs observed in at least 1% of a given population. In some embodiments, an SNV is represented by an “rs” number, which refers to a reference cluster of another submitted SNV in the dbSNP bioinformatics database, characterized by a sequence comprising a total number of 5' to 3' nucleotides, including submitted variations. In some embodiments, an SNV can also be defined by the position of the SNV (nucleotide) within the provided sequence, which is always located at 5' length plus 1 of the sequence. In some embodiments, an SNV is defined as a genomic location and allelic variation in a reference genome (e.g., in reference human genome build version 37, a change from the G allele to the A allele at positions 234,123,567 on chromosome 7). In some embodiments, an SNV is defined as a genomic location in the sequence disclosed herein identified by a non-nucleotide letter or code (e.g., IUPAC nucleotide code).

As disclosed herein, an "Indel" refers to an insertion or deletion of a nucleobase within a polynucleotide sequence. In some embodiments, an indel is represented by an "rs" number, which refers to a reference cluster entry for another submitted indel in the dbSNP bioinformatics database, characterized by a sequence comprising a total of 5' to 3' nucleobases, including submitted variations. In some embodiments, an indel may also be defined by the location of the insertion/deletion within the provided sequence, which is always located at 5' length plus 1 of the sequence. In some embodiments, an indel is defined as a genomic location and allelic variation within a reference genome. In some embodiments, an indel is defined as a genomic location within the sequence disclosed herein identified by a non-nucleotide letter or code (e.g., IUPAC nucleotide code).

The term "copy number variant" or "CNV" disclosed herein refers to the phenomenon of partial duplication or deletion of a polynucleotide sequence, with the number of duplications in the genome varying among individuals in a given population. In some embodiments, the portion of the polynucleotide sequence is "short," comprising approximately two nucleotides (dinucleotide CNV) or three nucleotides (trinucleotide CNV). In some embodiments, the portion of the polynucleotide sequence is "long," comprising four nucleotides and many nucleotides between the full length of the gene and the entire length of the gene.

Non-limiting examples of "sample" include any material from which nucleic acids and/or proteins can be obtained. By way of non-limiting examples, this includes whole blood, peripheral blood, plasma, serum, saliva, mucus, urine, semen, lymph, fecal extracts, buccal swabs, cells, or other bodily fluids or tissues, including but not limited to tissues obtained through surgical biopsy or surgical resection. In various embodiments, the sample includes tissue from the large intestine and/or small intestine. In various embodiments, large intestine samples include the cecum, colon (ascending colon, transverse colon, descending colon, and sigmoid colon), rectum, and/or anal canal. In some embodiments, small intestine samples include the duodenum, jejunum, and/or ileum. Alternatively, the sample may be obtained from a primary patient-derived cell line, or from a patient sample archived as a preserved sample, or a freshly frozen sample.

Example

Example 1. Calculate an individual's lineage-specific genetic risk score, which represents the likelihood that the individual will have better aerobic exercise capacity.

First, the individual's genotype is provided. The individual's genotype can be in the format of an Illumina genotyping array. The genotype includes individual-specific genetic risk variants (ANVs). ANVs can include single nucleotide variants (SNVs), single nucleotide polymorphisms (SNPs), indels, and/or copy number variants (CNVs). The Illumina genotyping array includes nucleic acid probes specific to various SNVs, indels, SNPs, and/or CNVs. Principal component analysis (PCA) is used to analyze the genotype to determine the individual's pedigree, identifying the individual as of African descent.

Next, reference genetic variants were selected from genome-wide association studies (GWAS) of subjects who shared the same lineage as the individuals identified by PCA (e.g., Africa) (the lineage-specific subject group). Lineage-specific variants were located at reported aerobic susceptibility loci, including TSHR, ACSL1, PRDM1, DBX1, GRIN3A, ESRRB, ZIC4, and/or CDH13, and selection was based on a strong correlation (P = 1.0 x ^10⁻⁴ or lower) between the lineage-specific variants and the aerobic trait. Variants are provided in Table 39.

Table 39

If an individual-specific genetic risk variant is unknown, meaning the identification number of the genotyping array corresponding to the individual-specific genetic variant is not published in the aforementioned GWAS, a surrogate variant is selected as the basis for genetic risk calculation. If the surrogate variant is in linkage disequilibrium (LD) with the unknown individual-specific genetic risk variant ( ^r² value of at least 0.70 or D' value of at least about 0.20), the surrogate variant is selected, also known as "insertion".

Next, the individual-specific raw score is calculated. Numerical values are assigned to risk units (e.g., risk alleles) within the individual-specific genetic variant, and all numerical values for each individual-specific genetic variant are summed and divided by the total number of individual-specific genetic variants and/or surrogate variants to produce the individual-specific raw score.

Next, the same calculations are performed to generate raw scores for each individual within the lineage-specific subject group, thereby generating the observation range (observation range) for the raw scores. Next, the individual-specific raw scores are compared to the lineage-specific observation range to calculate the percentage risk relative to the lineage-specific subject population. Finally, the Genetic Risk Score (GRS) is assigned to the individuals.

For example, in order to calculate the GRS for an individual's aerobic capacity, which consists of seven genetic variants, in this embodiment, the SNPs (rs7144481 with risk allele C, rs6552828 with risk allele G, rs1049904 with risk allele A, rs10500872 with risk allele A, rs1535628 with risk allele G, rs1289359 with risk allele T, and rs1171582 with risk allele A) require each genotype to be determined by actual genotyping or imputation, and the average of the sum of all risk alleles to be calculated. Therefore, individuals with genotypes rs7144481 (CC), rs6552828 (AA), rs1049904 (GG), rs10500872 (AG), rs1535628 (AA), rs1289359 (CT), and rs1171582 (AA) have 2, 0, 0, 1, 0, 1, and 2 risk alleles, respectively, totaling 6, with an average genetic risk score of 0.86 (＝6/7; risk alleles divided by the total number of variants constituting the model). Table 40 provides exemplary calculations based on the provided embodiments.

Table 40

The GRS score is calculated similarly for lineage-specific populations. When an individual's GRS score is compared to the distribution of GRS scores from the same lineage-specific population, the individual's GRS score is at the 50th percentile. This individual is predicted to have average aerobic capacity.

Example 2. Calculate an individual's lineage-specific genetic risk score, which represents the likelihood that the individual will experience collagen breakdown.

First, the individual's genotype is provided. The individual's genotype can be in the format of an Illumina genotyping array. The genotype includes individual-specific genetic risk variants (INVs). These genetic risk variants can include single nucleotide variants (SNVs), single nucleotide polymorphisms (SNPs), indels, and/or copy number variants (CNVs). The Illumina genotyping array includes nucleic acid probes specific to various SNVs, SNPs, and/or CNVs. Principal component analysis (PCA) is used to analyze the genotype to determine the individual's pedigree, identifying the individual as Chinese.

Next, reference genetic variants were selected from the GWAS. These variants were located at the reported collagen breakdown susceptibility loci MMP1, MMP3, and MMP9, and were selected based on a strong correlation between the genetic variants and physical traits (P = 1.0 x ^10⁻⁴ or lower). The variants are provided in Table 41.

Table 41

If an individual-specific genetic risk variant is unknown, meaning the array identification number corresponding to the individual-specific genetic variant is not published in the aforementioned GWAS, then a surrogate genetic variant is selected as the basis for genetic risk calculation. If the surrogate genetic variant is in linkage disequilibrium (LD) with the unknown individual-specific genetic risk variant ( ^r² value of at least 0.70 or D' value of at least approximately 0.20, based on subjects of the same lineage as the individual), then the surrogate genetic variant is selected.

Next, the individual-specific raw score is calculated. Numerical values are assigned to risk units (e.g., risk alleles) within the individual-specific genetic variant, and all numerical values for each individual-specific genetic variant are summed and divided by the total number of individual-specific genetic variants or surrogate variants to produce the individual-specific raw score.

Next, the same calculations are performed to generate raw scores for each individual within the lineage-specific subject group, thereby generating the observation range (observation range) for the raw scores. Next, the individual-specific raw scores are compared to the lineage-specific observation range to calculate the percentage risk relative to the lineage-specific subject population. Finally, the Genetic Risk Score (GRS) is assigned to the individuals.

For example, to calculate the GRS for the collagen breakdown trait in an individual, which consists of two genetic variants, in this embodiment, the SNPs (rs495366 with risk allele G and rs11226373 with risk allele G) require determining each genotype through actual genotyping or imputation and calculating the average of the sums of all risk alleles. Therefore, individuals with genotypes rs495366 (GG) and rs11226373 (GA) have 2 and 1 risk alleles respectively, totaling 3, with an average genetic risk score of 1.5 (=3/2; risk alleles divided by the total number of variants constituting the model). Table 42 provides exemplary calculations according to this embodiment.

Table 42

The GRS score was calculated similarly for lineage-specific populations. An individual's GRS score was ranked at the 90th percentile when compared to the distribution of GRS scores from the same lineage-specific population. This individual was predicted to have a high risk of collagen breakdown and was advised to moisturize their skin and apply collagen cream.

Example 3. Calculate an individual's lineage-specific genetic risk score, which represents the likelihood that the individual will experience vitamin A deficiency.

First, the individual's genotype is provided. The individual's genotype can be in the format of an Illumina genotyping array. The genotype includes individual-specific genetic risk variants (INVs). These genetic risk variants can include single nucleotide variants (SNVs), single nucleotide polymorphisms (SNPs), indels, and/or copy number variants (CNVs). The Illumina genotyping array includes nucleic acid probes specific to various SNVs, SNPs, indels, and/or CNVs. Principal component analysis (PCA) is used to analyze the genotype to determine the individual's pedigree, identifying the individual as Chinese.

Next, reference genetic variants were selected from GWAS published in high-impact journals. These variants were located at the reported vitamin A deficiency susceptibility loci BCMO1, FFAR4, and TTR, and were selected based on a strong correlation between genetic variation and nutritional traits (P = 1.0 x ^10⁻⁴ or lower). Lineage-specific variants are provided in Table 43.

Table 43

If an individual-specific genetic risk variant is unknown, meaning the array identification number corresponding to the individual-specific genetic variant is not published in the aforementioned GWAS, then a surrogate genetic variant is selected as the basis for genetic risk calculation. If the surrogate genetic variant is in linkage disequilibrium (LD) with the unknown individual-specific genetic risk variant ( ^r² value of at least 0.70 or D' value of at least approximately 0.20, based on subjects of the same lineage as the individual), then the surrogate genetic variant is selected.

Next, the individual-specific raw score is calculated. Numerical values are assigned to risk units (e.g., risk alleles) within the individual-specific genetic variant, and all numerical values for each individual-specific genetic variant are summed and divided by the total number of individual-specific genetic variants or surrogate variants to produce the individual-specific raw score.

Next, the same calculations are performed to generate raw scores for each individual within the lineage-specific subject group, thereby generating the observation range (observation range) for the raw scores. Next, the individual-specific raw scores are compared to the lineage-specific observation range to calculate the percentage risk relative to the lineage-specific subject population. Finally, the Genetic Risk Score (GRS) is assigned to the individuals.

For example, to calculate the GRS for a vitamin A deficiency trait consisting of three genetic variants in an individual, in this embodiment, the SNPs (rs6564851 with risk allele T, rs1082272 with risk allele C, and rs1667255 with risk allele A) require determining each genotype through actual genotyping or imputation and calculating the average of the sums of all risk alleles. Therefore, individuals with genotypes rs6564851 (TG), rs1082272 (TT), and rs1667255 (AC) have 1, 0, and 1 risk alleles, respectively, totaling 2, with an average genetic risk score of 1.67 (＝2/3; risk alleles divided by the total number of variants constituting the model). Table 44 provides an exemplary calculation according to this embodiment.

Table 44

GRS scores were calculated similarly in ancestry-specific populations. When an individual's GRS score was compared to the distribution of GRS scores from the same ancestry-specific population, the individual's GRS score was one standard deviation above the mean. The individual was predicted to be at risk of vitamin A deficiency and was advised to take vitamin A supplements.

Example 4. Lineage-specific genetic risk score for alcohol flushing

Alcohol flushing is a condition in which an individual experiences flushing or rashes on the face, neck, shoulders, and in some cases, the entire body after consuming an alcoholic beverage. Approximately one-third of East Asians experience facial flushing due to alcohol consumption. A single nucleotide polymorphism (SNP) in the rs671 allele A of the gene encoding alcohol dehydrogenase 1B (ADH1B) is associated with flushing. This SNP is present in the trait-related variant database disclosed herein. As an example, and not a limitation, Tables 45-46 show the likelihood of an individual developing or being developed alcohol flushing by calculating a genetic risk score (GRS) using a major European reference population, compared to calculating a GRS using a reference population consisting of individuals of the same ancestry as the subject. Individuals with a score greater than or equal to 1 are predicted to have moderate alcohol flushing because rs671 acts in a dominant-negative manner.

The cohort analysis included a dataset comprising genotypes from 1669 individuals. Of the 1669 individuals, 193 had European lineage (EUR) and 1476 had East Asian lineage (EAS). The lineage-specific variant (rs671) was located at the previously reported susceptibility locus for the alcohol dehydrogenase ADH1B and was selected based on a strong correlation with the alcohol flushing trait in the reference population.

Genotyping. Saliva samples were obtained from each subject. Samples were genotyped on an Illumina Core-24 BeadChip platform (Illumina, Inc., San Diego, Calif. 92121). Quality control measures were performed according to the Illumina Core-24 platform's manufacturing instructions. SNPs were excluded from the analysis if: genotyping accuracy <93%; SNP loss rate >10%; and small allele frequency <0.01.

Patriarchal assignment was performed on the subjects. Principal component analysis (PCA) of genotypes was used for population grouping analysis. For each subject, the closest group to the sample was calculated using a distance-based method (K-means), and pedigrees were assigned.

Imputation of rs671. The rs671 genotype was imputed for alcohol response trait scoring. Imputation was performed using a reference population of 1000 genomes, which were either pedigree-specific or non-pedigree-specific, for comparison.

Lineage-specific reference populations . The genotypes of subjects were imputed based on their assigned lineage. Lineage-specific reference populations were selected from the 1000 Genomes project described in “A global reference for human genetic variations,” Nature 526, 68-74 (October 1, 2015). If a subject was assigned an East Asian lineage in the previous step, their genotype was imputed using an East Asian population from the 1000 Genomes project as the reference population. If a subject was assigned a European lineage in the previous step, their genotype was imputed using a European population from the 1000 Genomes project as the reference population.

Non-lineage-specific reference population . To compare the accuracy of GRS, a non-lineage-specific reference population was used, and a reference genetic variant was also selected. The genotypes of the subjects were imputed using a European population from 1000 genomes as the reference population.

GRS calculation. Analysis was performed on subjects with alcohol flushing scores based on rs671-interpolated genotypes, comparing them with pedigree-specific and non-pedigree-specific reference populations. Subjects' alcohol flushing response scores were calculated based on the number of non-reference alleles.

Results. Table 45 illustrates a pipeline using a single reference population, without considering lineage, while Table 46 illustrates a lineage-specific pipeline, where the pipeline is specific to an individual's lineage. It is understood that 36%–45% of East Asians experience facial flushing due to alcohol consumption. This is accurately reflected in Table 46, where the lineage-specific pipeline predicted that 42% of individuals with East Asian lineages had already developed or would develop an alcohol flushing response. In contrast, Table 45 shows that no East Asian individuals were predicted to have an alcohol flushing response when Asian lineages were not considered, which is a known mismatch. "EUR" refers to Europeans, and "EAS" refers to East Asians.

Table 45. Production lines using a single reference population (EUR). Lineage is not considered. EUR: European

Table 46. Lineage-Specific Pipelines (Lineage-specific pipelines for individuals, where EUR is used for EUR individuals and EAS for EAS individuals). EUR: European; EAS: East Asian

Example 5. Lineage-specific genetic risk score for lactose tolerance

Lactose-tolerant individuals are adults who can consume animal milk and animal milk products without experiencing lactose intolerance symptoms such as bloating, pain, cramps, diarrhea, flatulence, or nausea. The heritable trait of lactose tolerance is associated with functional single nucleotide variants (SNVs) in the regulatory region approximately 14 kb upstream of the lactase gene (LCT), including -13910*T (rs4988235), -13915*g (rs41380347), and -14010*c (rs145946881). These three SNVs are present in the trait-related variant database published herein. As an example, and not a limitation, Tables 47-48 show whether an individual is lactose-tolerant or lactose-intolerant by calculating a genetic risk score (GRS) using a major European reference population, compared to calculating a GRS using a reference population consisting of individuals of the same ancestry as the subject. Individuals with a score of zero are predicted to be lactose intolerant, while individuals with a score greater than zero are predicted to be lactose tolerant, because one allele is sufficient to metabolize lactose.

The cohort analysis included a dataset of 1669 individuals. Of these 1669 individuals, 193 had a European lineage (EUR) and 1476 had an East Asian lineage (EAS). Lineage-specific variants 13910*T (rs4988235), -13915*G (rs41380347), and -14010*C (rs145946881), located upstream of the LCT gene at previously reported susceptibility loci, were selected based on their strong correlation with the alcohol flushing trait in the reference population.

Genotyping. Saliva samples were obtained from the subjects. Samples were genotyped on an Illumina Core BeadChip platform (Illumina, Inc., San Diego, Calif. 92121). Quality control measures were performed according to the Illumina Core-24 platform's manufacturing instructions. SNVs were excluded from the analysis if: genotyping accuracy <93%; SNP loss rate >10%; and small allele frequency <0.01.

Patriarchal assignment was performed to the subjects. Principal component analysis (PCA) of genotypes was used for population grouping analysis. For each subject, the nearest population sample was calculated and pedigrees were assigned using a distance-based method (K-means).

Imputation of rs4988235, rs41380347, and rs145946881. Genotypes of rs4988235, rs41380347, and rs145946881 were imputed for lactose tolerance scoring. Imputation was performed using a reference population of 1000 genomes, which were either pedigree-specific or non-pedigree-specific, for comparison.

Lineage-specific reference population . The subject's genotype is imputed based on their assigned lineage. If the subject was designated as an East Asian lineage in the previous step, their genotype will be imputed using an East Asian population from 1000 genomes as the reference population. If the subject was designated as a European lineage in the previous step, their genotype will be imputed using a European population from 1000 genomes as the reference population.

Non-lineage-specific reference population . To compare the accuracy of GRS, a non-lineage-specific reference population was used, with the genotypes of the subjects being imputed using a European population of 1000 genomes as the reference population.

GRS calculation. Comparative analyses were performed on lactose tolerance of the rs4988235, rs41380347, and rs145946881 intercalated genotypes, using pedigree-specific and non-pedigree-specific reference populations for comparison. Subjects' lactose tolerance scores were calculated based on scores from non-reference alleles.

Results. Table 47 illustrates the pipeline using a single reference population, without considering lineage, while Table 48 illustrates a lineage-specific pipeline, where the pipeline is specific to the individual's lineage. It is understood that in equatorial Asian countries, 98% of the population is lactose intolerant. This is accurately reflected in Table 48, where the lineage-specific pipeline predicts that 100% of individuals with East Asian lineages residing in Singapore are lactose intolerant. In contrast, Table 47 shows that 57% of East Asian individuals are predicted to be lactose tolerant when Asian lineages are not considered, which is a known mismatch. "EUR" refers to Europeans, and "EAS" refers to East Asians.

Table 47. Production lines using a single reference group without considering lineage

Table 48. Lineage-specific pipelines (lineage-specific pipelines for individuals, where EUR is used for EUR individuals and EAS is used for EAS individuals).

Example 7. Gene Symbols and Gene Names

This article discloses several gene symbols representing target human genes. Table 49 provides a list of genes and their corresponding names.

Table 49

While preferred embodiments of the methods, media, and systems disclosed herein have been shown and described, these embodiments are provided by way of example only. Many variations, modifications, and substitutions can be made without departing from the methods, media, and systems disclosed herein. It should be understood that various alternatives to the embodiments of the methods, media, and systems disclosed herein can be used in practicing the inventive concept disclosed herein. The appended claims are intended to define the scope of the methods, media, and systems, and thereby cover the methods and structures within the scope of these claims and their equivalents.

Claims (20)

1. A computer-implemented method for determining the likelihood that an individual possesses or will develop a specific phenotypic trait based on its pedigree, the method comprising: a. Assigning pedigrees to the individuals using distance-based or model-based computer programs to analyze the individuals' genotypes, which include one or more individual-specific genetic variants; b. Selecting one or more lineage-specific genetic variants from a database of trait-related variants, including those derived from lineage-specific genetic variants of subjects with the same lineage as the individual, at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants corresponds to: i. The individual-specific genetic variant among the one or more individual-specific genetic variants, or ii. A predetermined genetic variant in linkage disequilibrium (LD) with an individual-specific genetic variant among the one or more individual-specific genetic variants in a subject population of the same lineage as the individual, wherein the predetermined genetic variant is determined by: Process 1: Type the untyped genotype data from the individual to generate individual-specific haplotypes based on the individual's lineage; Process 2: Using haplotype data from a reference group with the same pedigree as the individual, imput individual-specific genotypes not present in the individual-specific haplotypes of the individual being genotyped; and Process 3: Select a genetic variant from the imputed individual-specific genotype that matches the individual-specific genetic variant, which is associated with the individual's likelihood of having or developing a specific phenotypic trait and corresponds to one or more lineage-specific genetic variants. Each of the one or more lineage-specific genetic variants and each of the one or more individual-specific genetic variants includes one or more risk units; and c. Calculate a genetic risk score for the individual based on one or more selected lineage-specific genetic variants. The genetic risk score indicates the likelihood that the individual has or will develop the specific phenotypic trait. 2. The method of claim 1, wherein the one or more lineage-specific genetic variants, the one or more individual-specific genetic variants, and the predetermined genetic variants at the LD with the one or more individual-specific genetic variants include single nucleotide variants (SNVs), indels, and/or copy number variants (CNVs). 3. The method of claim 2, wherein one or more risk units of the SNV include risk alleles; one or more risk units of the indel include nucleotide insertions or deletions; and one or more risk units of the CNV include nucleic acid sequence insertions or deletions. 4. The method of claim 1, further comprising providing the individual with a notification that includes a risk that the individual has or will develop the particular phenotypic trait. 5. The method of claim 1, wherein the specific phenotypic trait includes nutritional traits, clinical traits, subclinical traits, physical activity traits, skin traits, hair traits, allergic traits, or psychological traits. 6. The method of claim 4, wherein the notification further includes a suggestion of behavioral change related to the particular phenotypic trait. 7. The method of claim 6, wherein the behavioral change associated with the particular phenotypic trait includes increasing, decreasing, or avoiding an activity, said activity including: engaging in physical exercise; ingesting drugs, vitamins, or supplements; contact with products; use of products; dietary changes; sleep changes; alcohol consumption or caffeine consumption. 8. The method of claim 1, wherein the distance-based computer program is principal component analysis, and wherein the model-based computer program is a maximum likelihood or Bayesian method. 9. A health reporting system, comprising: A computing device, comprising at least one processor, memory, and software program, the software program including instructions executable by at least one processor to assess the likelihood that an individual has or will develop a particular phenotypic trait, the instructions comprising the following steps: a. Assigning the lineage of the individuals using distance-based or model-based computer programs to analyze the genotypes of the individuals, the genotypes including one or more individual-specific genetic variants; b. Selecting one or more lineage-specific genetic variants from a database of trait-related variants, including those derived from lineage-specific genetic variants of subjects with the same lineage as the individual, at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants corresponds to: i. The individual-specific genetic variant among the one or more individual-specific genetic variants, or ii. A predetermined genetic variant in linkage disequilibrium (LD) with an individual-specific genetic variant among the one or more individual-specific genetic variants in a subject population of the same lineage as the individual, wherein the predetermined genetic variant is determined by: Process 1: Type the untyped genotype data from the individual to generate individual-specific haplotypes based on the individual's lineage; Process 2: Using haplotype data from a reference group with the same pedigree as the individual, imput individual-specific genotypes not present in the individual-specific haplotypes of the individual being genotyped; and Process 3: Select a genetic variant from the interpolated individual-specific genotype that matches the individual-specific genetic variant, which is associated with the individual's likelihood of having or developing a specific phenotypic trait and corresponds to one or more lineage-specific genetic variants. Each of the one or more lineage-specific genetic variants and each of the one or more individual-specific genetic variants includes one or more risk units; and c. Calculate a genetic risk score for the individual based on one or more selected lineage-specific genetic variants. The genetic risk score indicates the likelihood that the individual has or will develop the specific phenotypic trait. A reporting module configured to generate a report including a genetic risk score for the individual for the specific phenotypic trait; and An output module configured to display the report to the individual. 10. The system of claim 9, wherein the one or more lineage-specific genetic variants, the one or more individual-specific genetic variants, and the predetermined genetic variants at the LD with the one or more individual-specific genetic variants comprise single nucleotide variants (SNVs), indels, and/or copy number variants (CNVs). 11. The system of claim 10, wherein one or more risk units of the SNV include risk alleles; one or more risk units of the indel include nucleotide insertions or deletions; and one or more risk units of the CNV include nucleic acid sequence insertions or deletions. 12. The system of claim 9, wherein the report further includes recommendations for behavioral changes related to the particular phenotypic trait. 13. The system of claim 9, wherein the specific phenotypic trait includes nutritional traits, clinical traits, subclinical traits, physical activity traits, skin traits, hair traits, allergic traits, or psychological traits. 14. The system of claim 9, further comprising a personal electronic device having an application configured to communicate with the output module via a computer network to access the report. 15. The system of claim 9, wherein the distance-based computer program is principal component analysis, and wherein the model-based computer program is a maximum likelihood or Bayesian method. 16. A non-transitory computer-readable storage medium comprising computer-executable code configured to cause at least one processor to perform the following steps: a. Assigning lineages to individuals using distance-based or model-based computer programs to analyze the genotypes of the individuals, which include one or more individual-specific genetic variants; b. Selecting one or more lineage-specific genetic variants from a database of trait-related variants, including those derived from lineage-specific genetic variants of subjects with the same lineage as the individual, at least in part based on the individual's lineage, wherein each of the one or more lineage-specific genetic variants corresponds to: i. The individual-specific genetic variant among the one or more individual-specific genetic variants, or ii. A predetermined genetic variant in linkage disequilibrium (LD) with an individual-specific genetic variant among the one or more individual-specific genetic variants in a subject population of the same lineage as the individual, wherein the predetermined genetic variant is determined by: Process 1: Provide untyped genotype data from the individuals; Process 2: The untyped genotype data is genotyped to generate individual-specific haplotypes based on the individual's lineage; Process 3: Using haplotype data from a reference group with the same lineage as the individual, imput individual-specific genotypes not present in the individual-specific haplotypes of the individual being genotyped; and Process 4: Selecting a genetic variant from the imputed individual-specific genotype that matches the individual-specific genetic variant, the individual-specific genetic variant being associated with the likelihood that the individual has or will develop a particular phenotypic trait; and c. Calculate a genetic risk score for the individual based on one or more selected lineage-specific genetic variants. The genetic risk score indicates the likelihood that the individual has or will develop the specific phenotypic trait. 17. The medium of claim 16, wherein the one or more lineage-specific genetic variants, the one or more individual-specific genetic variants, and the predetermined genetic variants at the LD with the one or more individual-specific genetic variants comprise single nucleotide variants (SNVs), indels, and/or copy number variants (CNVs). 18. The medium of claim 17, wherein each of the one or more lineage-specific genetic variants and each of the individual-specific genetic variants comprises one or more risk units, and wherein the one or more risk units of the SNV comprise risk alleles; the one or more risk units of the indel comprise nucleotide insertions or deletions; and the one or more risk units of the CNV comprise nucleic acid sequence insertions or deletions. 19. The medium of claim 16, wherein the step further includes providing a notification to the individual, the notification including the possibility that the individual has or will develop the particular phenotypic trait. 20. The medium of claim 16, wherein the specific phenotypic trait includes nutritional traits, clinical traits, subclinical traits, physical activity traits, skin traits, hair traits, allergic traits, or psychogenic traits.