CN1882604A - Genetic markers for obesity - Google Patents

Abstract

The present invention relates to novel genetic variants or polymorphisms at the perilipin locus (PLIN), including PLIN1: 6209T (allele 1) > C (allele 2); PLIN310171 (allele 1) A > T (allele 2); PLIN4: 11482G (allele 1) > A (allele 2); PLIN5: 13041A (allele 1) > G (allele 2) and PLIN6: 14995A (allele 1) > T (allele 2), and their use in the diagnosis and prognosis of obesity and obesity-related diseases, such as metabolic syndrome and cardiovascular disease.

Description

genetic markers of obesity

cross reference

This application claims U.S. Provisional Application No. 60/504,830, filed September 22, 2003, U.S. Provisional Application No. 60/519,109, filed November 12, 2003, and U.S. Provisional Application No. 60/544,524, filed February 13, 2004 Interests.

governmental support

This invention was supported by NIH/NHLBI Grant No. HL 54776 and contracts 53-K06-5-10 and 58-1950-9-001 from the U.S. Department of Agriculture. The US government has certain rights.

background

Over the course of evolution, the human body has developed ingenious ways of dealing with insufficient caloric intake, and it is only recently that we have begun to appreciate the complexity of these metabolic networks. During the current period of high caloric intake in the developed world, this delicate and complex system begins to affect us negatively, leading to a severe epidemic of obesity and related metabolic diseases.

Adipose tissue is an essential component in the human body. However, too much body fat leads to obesity, a serious medical condition that currently affects about 1/3 of adults in the United States, and about 14% of children and adolescents. Energy-rich and sedentary lifestyles in developed countries have made obesity a worldwide phenomenon. Obesity is arguably currently the second leading cause of preventable death after smoking in the United States (www.obesity.org).

Obesity is a typical multifactorial disease caused by a combination of environmental and genetic factors. For example, strong evidence for a genetic component of human obesity can be seen in familial clustering and high concordance in body composition among monozygotic twins. However, the role of genetic factors is complex and may be determined by the interaction of several genes, each of which may have relatively small effects. Such genes are called "susceptibility" genes and their phenotypic effects are seen in their combination with each other and with environmental factors such as nutritional intake, physical activity and smoking.

To date, at least about 80 genes have been reported to be associated with obesity (see, eg, the Obesity Gene Map Database at http://obesitygene.pbrc.edu). Many of these genes play regulatory roles in the formation and maintenance of adipose tissue.

Obesity is often associated with other diseases. For example, the "metabolic cluster" associated with abdominal obesity and including glucose intolerance, dyslipidemia, and hypertension is sometimes referred to as metabolic syndrome X (Reaven, 1988) or abdominal obesity-metabolic syndrome (Bjorntorp, 1991). The underlying cause of this combination of symptoms appears to be an intimate interplay of abdominal fat patterns, general obesity, and insulin resistance. Obesity is also often a pre-existing condition of adult-onset non-insulin-dependent diabetes mellitus (type II diabetes) and a variety of other diseases. Despite advances in the knowledge of adipose tissue metabolism, current treatment options for diseases of adipose tissue metabolism remain deficient and the development of new therapies is desired.

Summary of the invention

The present invention relates to novel genetic variants or polymorphisms at the perilipin locus and their use in diagnostic and prognostic applications of obesity and related metabolic diseases.

The present invention provides a method for determining the increased risk of obesity and obesity-related diseases in an individual, comprising the steps of: a) determining PLIN16209T/C, PLIN3 10171A/T, PLIN4 11482G/A, PLIN4 11482G/A, genotypes of the PLIN5 13041A/G, PLIN614995A/T loci; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) correlating said haplotypes with the ethnic background of the individual, wherein the The ethnic background of the individual is selected from PLIN5-G/PLIN6T, PLIN5-A/PLIN6-T, PLIN1-T/PLIN4-G/PLIN5-G/PLIN6-T, PLIN1-T/PLIN4-G, PLIN1-T /PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-T/PLIN3-A/PLIN4-APLIN5-A/PLIN6-T, PLIN1-T/PLIN3-A/PLIN/4-A/PLIN5-G/PLIN6 -T, PLIN4-A/PLIN5-A/PLIN6-T, PLIN4-A/PLIN5-G/PLIN6-T, PLIN4-G/PLIN5-G/PLIN6-A, PLIN1-T/PLIN3-A haplotypes indicated An increased risk of obesity and obesity-related diseases in the individual.

In one embodiment, there is provided a method of determining the increased risk of obesity and obesity-related diseases in an individual of Caucasian ancestry, comprising the step of: a) determining PLIN1 6209T/C, PLIN4 11482G in a biological sample obtained from the individual genotypes of the /A, PLIN5 13041A/G, PLIN6 14995A/T loci; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) correlating said haplotypes with the ethnic background of the individual , wherein a haplotype selected from PLIN5-G/PLIN6T, PLIN5-A/PLIN6-T, and PLIN1-T/PLIN4-G/PLIN5-G/PLIN6-T indicates obesity and obesity-related diseases in the individual of Caucasian ancestry of increased risk.

In one embodiment, there is provided a method of determining an increased risk of obesity and obesity-related diseases in an individual of Mediterranean ancestry comprising the steps of: a) determining PLIN1 6209T/C, PLIN4 11482G in a biological sample obtained from the individual genotypes of the /A, PLIN5 13041A/G, PLIN6 14995A/T loci; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) correlating said haplotypes with the ethnic background of the individual , wherein the haplotype selected from PLIN1-T/PLIN4-G, PLIN1-T/PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-T/PLIN4-G/PLIN5-G/PLIN6-T indicates that the Mediterranean Increased risk of obesity and obesity-related diseases among ancestry individuals.

In one embodiment, there is provided a method of determining the increased risk of obesity and obesity-related diseases in an individual of Malay ancestry, comprising the steps of: a) determining PLIN1 6209T/C, PLIN3 in a biological sample obtained from the individual Genotypes for the 10171A/T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T loci; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) comparing said haplotypes with the Ethnic background association of the individual selected from PLIN1-T/PLIN3-A/PLIN4-A/PLIN5-A/PLIN6-T, PLIN1-T/PLIN3-A/PLIN/4-A/PLIN5-G/PLIN6- The haplotypes of T, PLIN4-A/PLIN5-A/PLIN6-T, PLIN4-A/PLIN5-G/PLIN6-T, PLIN4-G/PLIN5-G/PLIN6-A, PLIN1-T/PLIN3-A indicate that the Increased risk of obesity and obesity-related diseases in individuals of Malay ancestry.

In one embodiment, there is provided a method of determining the increased risk of obesity and obesity-related diseases in an individual of Indian ancestry, comprising the steps of: a) determining PLIN1 6209T/C, PLIN3 10171A in a biological sample obtained from the individual /T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T loci genotypes; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) comparing said haplotypes to the individual The ethnic background association of PLIN1-T/PLIN3-A/PLIN4-A/PLIN5-A/PLIN6-T, PLIN4-A/PLIN5-A/PLIN6-T, PLIN4-G/PLIN5-G/PLIN6 The haplotypes of -T and PLIN1-T/PLIN3-A indicate an increased risk of obesity and obesity-related diseases in individuals of Indian ancestry.

In one embodiment, there is provided a method of determining the increased risk of obesity and obesity-related diseases in an individual of Caucasian ancestry comprising determining the PLIN5 13041A/G and PLIN6 14995A/T loci in a biological sample obtained from the individual A genotype in which homozygosity for the allele G in the PLIN5 locus or homozygosity for the allele T in the PLIN6 locus indicates an increased risk of obesity and obesity-related diseases in individuals of Caucasian ancestry.

In one embodiment, there is provided a method of determining the increased risk of obesity and obesity-related diseases in an individual of Malay or Indian ancestry comprising determining the PLIN6 14995A/T locus in a biological sample obtained from the individual A genotype wherein homozygosity for the allele T in the PLIN6 locus indicates an increased risk of obesity and obesity-related diseases in the individual of Malay or Indian ancestry.

In one embodiment, there is provided a method of determining the increased risk of obesity and obesity-related diseases in an individual of Malay or Indian ancestry comprising determining the PLIN4 11482G/A locus in a biological sample obtained from the individual A genotype wherein homozygosity for allele A in the PLIN4 locus indicates an increased risk of obesity and obesity-related diseases in individuals of Malay or Indian ancestry.

In one embodiment, there is provided a method of determining the increased risk of obesity and obesity-related diseases in an individual of Malay or Indian ancestry comprising determining the PLIN5 13041A/G locus in a biological sample obtained from the individual A genotype wherein homozygosity for the allele G in the PLIN5 locus indicates an increased risk of obesity and obesity-related diseases in the individual of Malay or Indian ancestry.

In one embodiment, the individual assessed for increased risk of obesity and obesity-related diseases is a woman.

In one embodiment, the individual who is assessed for increased risk of obesity and obesity-related diseases has received a weight loss diet.

In one embodiment, the obesity-related disease is cardiovascular disease.

In one embodiment, the obesity-related disease is metabolic syndrome.

In another embodiment, there is provided a method of determining a reduced risk of obesity and obesity-related diseases in an individual comprising the steps of: a) determining PLIN1 6209T/C, PLIN3 10171A/ Genotypes of T, PLIN4 11482G/A, PLIN513041A/G, PLIN6 14995A/T loci; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) comparing said haplotypes to the individual's human background association, wherein the individual's ethnic background is selected from PLIN5-A/PLIN6-A, PLIN1-C/PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-C/PLIN4-A, PLIN1- C/PLIN4-A/PLIN5-A/PLIN6-A, PLIN1-T/PLIN3-T/PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-C/PLIN3-A/PLIN/4-G/PLIN5- Haplotypes of A/PLIN6-A and PLIN1-C/PLIN3-T indicate reduced risk of obesity and obesity-related diseases.

In one embodiment, there is provided a method of determining the reduced risk of obesity and obesity-related diseases in an individual of Caucasian ancestry comprising the steps of: a) determining PLIN1 6209T/C, PLIN4 in a biological sample obtained from the individual genotypes of the 11482G/A, PLIN4 13041A/G, PLIN6 14995A/T loci; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) comparing said haplotypes to the ethnic background of the individual Association wherein a haplotype selected from PLIN5-A/PLIN6-A and PLIN1-C/PLIN4-G/PLIN5-A/PLIN6-A indicates a reduced risk of obesity and obesity-related diseases in individuals of Caucasian ancestry .

In one embodiment there is provided a method of determining the reduced risk of obesity and obesity-related diseases in an individual of Mediterranean ancestry comprising the steps of: a) determining PLIN1 6209T/C, PLIN4 in a biological sample obtained from the individual genotypes of the 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T loci; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) comparing said haplotypes to the ethnic background of the individual Association wherein a haplotype selected from PLIN1-C/PLIN4-A and PLIN1-C/PLIN4-A/PLIN5-A/PLIN6-A indicates a reduced risk of obesity and obesity-related diseases in individuals of Mediterranean ancestry .

In one embodiment, there is provided a method of determining the reduced risk of obesity and obesity-related diseases in an individual of Malay ancestry comprising the steps of: a) determining PLIN1 6209T/C, Genotypes of PLIN3 10171A/T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T loci; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) comparing said haplotypes with Ethnic background association of the individual selected from PLIN1-T/PLIN3-T/PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-C/PLIN3-A/PLIN4-G/PLIN5-A/PLIN6-A and PLIN1-C/PLIN3-T haplotypes indicated a reduced risk of obesity and obesity-related diseases in individuals of Malay ancestry.

In one embodiment, there is provided a method of determining the reduced risk of obesity and obesity-related diseases in an individual of Indian ancestry comprising the steps of: a) determining PLIN1 6209T/C, PLIN3 in a biological sample obtained from the individual Genotypes for the 10171A/T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T loci; b) generating haplotypes based on the PLIN genotypes determined in step (a); and c) comparing said haplotypes with the Ethnic background association of the individual selected from PLIN1-C/PLIN3-A/PLIN4-G/PLIN5-A/PLIN6A, PLIN1-C/PLIN3-A/PLIN4-G/PLIN5-A/PLIN6-A and PLIN1- The haplotype of C/PLIN3-C indicates a reduced risk of obesity and obesity-related diseases in individuals of Indian ancestry.

In one embodiment, the individual for whom the reduced risk of obesity and obesity-related diseases is assessed is a woman.

The present invention also provides a kit comprising primer pairs for amplifying nucleic acid regions covering PLIN1 6209T/C, PLIN3 10171A/T, PLIN4 11482G/A, PLIN5 13041A/G and PLIN6 14995A/T polymorphisms, and Instructions comprising haplotypes associated with increased or decreased risk of obesity and their association with human populations.

In one embodiment, the kit comprises a primer pair of SEQ ID NO: 1 and SEQ ID NO: 2 for amplifying the nucleic acid region covering the PLIN1 polymorphism; for amplifying the nucleic acid region covering the PLIN3 polymorphism Primer pair of SEQ ID NO: 7 and SEQ ID NO: 8; primer pair of SEQ ID NO: 10 and SEQ ID NO: 11 for amplifying the nucleic acid region covering the PLIN4 polymorphism; used for amplifying the polymorphism covering PLIN5 SEQ ID NO: 13 and SEQ ID NO: 14 primer pairs for the nucleic acid region of polymorphism; and the SEQ ID NO: 16 and SEQ ID NO: 17 primer pair for amplifying the nucleic acid region covering the PLIN6 polymorphism; and instructions, It includes haplotypes associated with increased or decreased risk of obesity and their association with human populations.

Brief description of the drawings

Figure 1 shows the nomenclature of PLIN polymorphisms. The positions of the polymorphisms examined in this study are indicated as short vertical lines with names below them. The box above the gene map shows the sequence including the nucleotide denoted "+1" in our nomenclature. The A of the initiation methionine codon ATG is indicated as a bold italic letter above which is indicated its genomic position on the reference sequence (GenBank accession number GI21431190). The corresponding amino acids are also set forth. Boxes with slash lines indicate regions where alternative splicing may occur.

Figure 2 shows the BMI for the combined genotype of the PLIN1 and PLIN4 SNPs adjusted for PLIN5 and PLIN6 in women from sample 1. Age-adjusted means; error bars: SEM.

Figure 3 shows the BMI for the combined genotype of the PLIN5 and PLIN6 SNPs after adjustment for PLIN1 and PLIN4 in women from sample 1. Age-adjusted means; error bars: SEM.

Figures 4A and 4B show graphs of weight gain or loss after dieting in women with PLIN4 wild-type allele 1 and carriers of PLIN4 allele 2. This figure clearly shows that a woman with heterozygous PLIN4 allele 2 will be more likely to gain weight if she does not continue to diet.

Figure 5 shows a diagram of the LD matrix in the study population. Pairwise LD measures (D') between the four genotype-analyzed PLINSNPs (6209C>T, 11482G>A, 13041A>G, and 14995A>T) are shown above the diagonal, while the corresponding P-values are on the diagonal given below.

Figure 6 illustrates differences and standard errors in body fat measures (BMI, percent body fat, and waist) between genotypes at PLIN 13041A>G and 14995A>T SNPs in women. For PLIN 13041A > G SNP, 11 = AA, 12 = AG and 22 = GG. For the 14995A>T SNP, 11=AA, 12=AT and 22=TT.

Figure 7 shows the LD matrix diagram of ethnicity in Singapore, the pairwise LD measure (D ') are shown above the diagonal, while the corresponding P-values are given below the diagonal.

Figure 8 shows odds ratio (OR) plots for various PLINS. Multivariate OR and 95% CI of obesity (BMI ≥ 30 kg/m2) for PLIN 11482G>A, 13041A>G, and 14995A>T in Malays and Indians. For each SNP, the genotype panel with wild-type homozygotes and heterozygotes was used as a reference. The OR is obtained by comparing the homozygous variant with the reference.

Detailed description of the invention

The present invention relates to novel genetic variants or polymorphisms on the perilipid droplet protein locus (PLIN), including PLIN1: 6209T (allele 1) > C (allele 2); PLIN310171 (allele 1) A > T (allele 2); PLIN4: 11482G (allele 1) > A (allele 2); PLIN5: 13041A (allele 1) > G (allele 2) and PLIN6: 14995A (etc. Allele 1)>T(Allele 2), and their use in diagnostic and prognostic applications of obesity and related metabolic diseases and their use in the treatment of obesity and related metabolic diseases. The sequence numbers mentioned are according to GenBank Sequence ID No. gi21431190.

The present invention relates to novel PLIN haplotypes, which are associated with lower body mass index (BMI) and thus protect against obesity and related metabolic diseases, such as cardiovascular diseases, and PLIN haplotypes, which are associated with elevated BMI and It is therefore a risk factor for obesity and related metabolic diseases such as cardiovascular disease and metabolic syndrome.

As used herein, an "individual of Mediterranean ancestry" refers to a person with ancestry from the Mediterranean geographic region including, but not limited to, Spain, France, Italy, and Portugal. Preferably, at least one ancestor is from the geographical region of the Mediterranean.

As used herein, an "individual of Caucasian ancestry" refers to a person with ancestry from a geographical region of Northern, Eastern or Central Europe. Typically, these individuals have light skin color and are from regions including, but not limited to, North America, Great Britain, Russia, and Germany. Preferably, at least one ancestor is from Northern, Eastern or Central European.

As used herein, an "individual of Malay ancestry" refers to a person with ancestry from the geographic region of the Malay Archipelago and surrounding areas including, but not limited to, Malaysia, Indonesia, Brunei and Singapore. Preferably, at least one ancestor is from the Malay Archipelago or surrounding areas.

As used herein, an "individual of Indian ancestry" refers to a person having ancestry from geographic regions in and around India, including, but not limited to, India, Pakistan, Nepal, and Bangladesh. Preferably, at least one ancestor is from India or surrounding areas.

Cardiovascular disease (CVD) or diseases of the circulatory system represents a variety of clinical conditions resulting from atherosclerotic damage to coronary, cerebral or peripheral arteries. CVD is considered today the leading cause of death in both men and women in developed countries. Detailed epidemiological data on CVD are available from the American Heart Association's "2002 Heart and Statistical Update," which outlines risk factors. 61,800,000 Americans have one or more types of CVD (Rational diagnosis of cardiovascular disease, Müller M M, Griesmacher A, eJIFCC Vol14no2: http://www.ifcc.org/ejifcc/vol14no2/1402062003012n.htm). Several markers are currently available to diagnose acute cardiovascular disease, including the use of so-called "early" and "late" markers released from cardiomyocytes under ischemic conditions, such as myotropin and cardiac troponin ( Id.).

Metabolic syndrome is characterized by a group of metabolic risk factors in one person. These factors include a) central obesity (excess fatty tissue in and around the abdomen), b) atherogenic dyslipidemia (blood lipids, which promote plaque buildup in artery walls), c) elevated Blood pressure (130/85 mmHg or higher), d) insulin resistance or glucose intolerance, e) prothrombotic state (eg, high blood fibrinogen or plasminogen activator inhibitor and f) A proinflammatory state (eg, elevated high-sensitivity C-reactive protein in the blood). The underlying causes of the syndrome are overweight/obesity, physical inactivity, and genetic factors. People with metabolic syndrome have an increased risk of coronary heart disease, other diseases related to the buildup of plaque in artery walls (eg, stroke and peripheral vascular disease), and type 2 diabetes.

In one embodiment, the present invention provides novel methods for assessing susceptibility to cardiovascular disease and metabolic syndrome in individuals by determining PLIN haplotypes.

Perilipid droplet protein (PLIN) is a hormone-regulated phosphoprotein that surrounds lipid storage droplets in adipocytes (Greenberg, A.S.; Egan, J.J.; Wek, S.A.; Takeda, T.; Londos, C.; Kimmel, A.K. (Abstract) Clin, Res. 39: 287A only, 1991). It is the major cellular A-kinase substrate in adipocytes, coats intracellular lipid droplets and regulates adipocyte lipolytic activity. Nishiu et al. cloned the cDNA encoding human perilipid droplet protein from the adipose tissue cDNA library (Genomics 48: 254-257, 1998; GenBank Nucleic Acid ID No.gi: 3041770). This human gene encodes a 522 amino acid polypeptide that has 79% identity to the rat homologue isolated by Greenberg et al. (Proc. Nat. Acad. Sci. 90: 12035-12039, 1993).

The present invention is based on the identification and evaluation of several novel genetic variants at the perilipidin locus (PLIN), including PLIN1:6209T>C; PLIN3, in relation to obesity and related metabolic and cardiovascular diseases. PLIN4: 11482G>A; PLIN5: 13041A>G and PLIN6: 14995A>T.

We determined associations of PLIN polymorphisms and haplotypes in 788 men and 801 women (sample 1) and 157 hospitalized obese subjects (sample 2) randomly selected from a Mediterranean population. Surprisingly, the less common alleles of the perilipid protein, PLIN1 allele 2 and PLIN4 allele 2, were significantly associated with reduced risk of obesity in women in the entire population (respectively OR=0.65, 95% CI: 0.48-0.88 and OR=0.60, 95% CI: 0.44-0.83). We also surprisingly found that in women from sample 1, the less common alleles of PLIN1 and PLIN4 were significantly associated with lower BMI compared with wild type, allele 1. In these women, PLIN4 was also associated with lower waist-to-hip ratio, fasting glucose, and plasma triglyceride concentrations. Haplotype analysis confirmed these results and revealed a synergistic effect of PLIN1 and PLIN4 on BMI in all women. No statistically significant association was found among men from sample 1. However, among obese men, carriers of the less common allele 2 of PLIN4 had significantly lower BMI than noncarriers. Less common alleles of PLIN1 and PLIN4 were associated with higher plasma glucose in both obese men and women and were different from sample 1 (P<0.05 for interaction). Thus, our data suggest that the PLIN-2/PLIN4-2 haplotype is a protective obesity-susceptibility haplotype and is associated with the development of metabolic syndrome and cardiovascular disease.

Accordingly, in one embodiment, the present invention provides methods of assessing individual predispositions for obesity and obesity-related diseases in an individual. The method comprises identifying and analyzing a PLIN polymorphism in an isolated nucleic acid sample obtained from the individual, wherein PLIN1 allele 1 and PLIN4 allele 1 are present together on the same chromatid in the nucleic acid sample (e.g. , PLIN1-1/PLIN4-1 haplotype) indicates a genetic predisposition for obesity and related metabolic diseases in this individual. Preferably, said individual is of Mediterranean or Caucasian descent.

In one embodiment, the invention provides a method of assessing an individual's predisposition to cardiovascular disease, wherein the method comprises identifying and analyzing a PLIN polymorphism in a nucleic acid sample isolated from the individual, wherein PLIN1 allele 1 and The presence of PLIN4 allele 1 together on the same chromatid in the nucleic acid sample (eg, PLIN1-1/PLIN4-1 haplotype) indicates predisposition to cardiovascular disease. Preferably, said individual is of Mediterranean or Caucasian descent.

Alternatively, in one embodiment, the invention provides a method of identifying individuals who are less likely to gain weight and who are expected to better maintain lost weight after dieting. The method comprises analyzing isolated nucleic acid from an individual for PLIN alleles, wherein the presence of allele 2 of PLIN1 and PLIN4 indicates the presence of an obesity-protective genotype in the individual. Preferably, said individual is of Mediterranean or Caucasian descent.

The present invention also provides haplotypes for diagnosing individuals at risk of developing obesity and/or obesity-related diseases, including, but not limited to, cardiovascular disease. One of these haplotypes consists of polymorphisms including PLIN1, PLIN4, PLIN5 and PLIN6. Thus, haplotype 1111 consists of allele 1 of all the loci identified above and haplotype 2222 consists of allele 2 of all the loci identified above. Haplotype 2211 in a nucleic acid sample from an individual, preferably a woman, indicates that the individual is at reduced risk of obesity and/or cardiovascular disease. Conversely, individuals with haplotype 1122 or 1111 have an increased risk of obesity and/or cardiovascular disease. Preferably, when these haplotypes are used for prognosis and/or diagnosis, said individual is of Caucasian or Mediterranean ancestry.

In yet another embodiment, the invention provides a method of identifying individuals at risk of gaining weight again after dieting. The method comprises analyzing a nucleic acid sample from the individual for the PLIN4 locus, wherein the presence of allele 2 in one or both alleles of the PLIN4 locus indicates an increased risk of gaining weight again.

We also identified associations of individual polymorphisms in multiple PLIN loci and PLIN haplotypes in multiethnic Asian populations. We examined five common single nucleotide polymorphisms (SNPs) on the perilipid droplet protein (PLIN) loci PLIN1, PLIN3, PLIN4, PLIN5, and PLIN6, wherein the polymorphisms were: PLIN 6209C>T, 10171A>T, 11482G>A, 13041A>G and 14995A>T. We investigated their association with obesity risk and other variables associated with metabolic syndrome. The study population included 4,131 subjects from three ethnic groups (Chinese, Malay and Indian) in Singapore. The analysis showed that haplotype 11212 was shared by Malays and Indians and was significantly associated with increased risk of obesity compared with the most common haplotype 21111 (OR=1.65, 95%CI 1.11-2.46 for Malays, 1.11-2.46 for Indians Human OR=1.94, 95% CI 1.06-3.53). Reciprocal haplotype analysis using subgroups of SNPs (11482G>A, 13041A>G, and 14995A>T) in positive LD showed that after adjusting for covariates, haplotype 212 (OR=2.04, 95% CI 1.28-3.25) and 222 (OR=2.05, 95%CI 1.35-3.12) were associated with increased risk of obesity in Malays, and haplotype 212 (OR=2.16, 95%CI 1.10-4.26) was associated with increased obesity in Indians Risk was significantly associated with covariates including age, sex, smoking, alcohol consumption, exercise, and diabetes status. Individual SNP analyzes demonstrated that after adjusting for covariates, PLIN14995A > T SNP was associated with increased obesity in Malays (OR=2.28, 95% CI 1.45-3.57) and Indians (OR=2.04, 95% CI 1.08-3.84) Risks are significantly related. Whereas PLIN 11482G>A ((OR=1.94, 95%CI 1.22-3.08) and PLIN 13041A>G (OR=1.87, 95%CI 1.08-3.25) were only associated with increased risk of obesity in Malays.

Thus, in one embodiment, the present invention provides a method of assessing the increased risk of obesity-related disease in an individual of Malay or Indian ancestry. The method comprises identifying and analyzing a PLIN polymorphism in an isolated nucleic acid sample obtained from the individual, wherein the haplotype PLIN4-2/PLIN6-2, i.e. PLIN4 allele 2 and PLIN6 allele 2, are present together in the nucleic acid sample On the same chromatid in , indicates the risk of obesity and related diseases in that individual.

In one embodiment, the present invention provides a method of assessing the predisposition to cardiovascular disease in an individual of Malay or Indian ancestry, wherein the method comprises identifying and analyzing PLIN polymorphisms and units in an isolated nucleic acid sample obtained from the individual type, wherein the haplotype PLIN4-2/PLIN6-2, ie, PLIN4 allele 2 and PLIN6 allele 2, present together on the same chromatid in a nucleic acid sample indicates a predisposition to cardiovascular disease.

In another embodiment, the present invention provides a method of assessing the predisposition to obesity and obesity-related diseases in any individual of Malay or Indian ancestry, wherein the method comprises identifying and determining the isolated Genotype of the PLIN6 locus in a nucleic acid sample, wherein homozygosity for the presence of the T allele (allele 2) at PLIN6 indicates an increased risk of obesity and related diseases in the individual of Malay or Indian ancestry.

In another embodiment, the present invention provides a method of assessing the predisposition to obesity and obesity-related diseases in any individual of Malay or Indian ancestry, wherein the method comprises identifying and determining the isolated The genotype of the PLIN4 locus in a nucleic acid sample, wherein homozygosity for the presence of the A allele (rare allele) at PLIN4 indicates an increased risk of obesity and related diseases in the individual of Malay or Indian ancestry.

In another embodiment, the present invention provides a method of assessing the predisposition to obesity and obesity-related diseases in any individual of Malay or Indian ancestry, wherein the method comprises identifying and determining the isolated The genotype of the PLIN5 locus in a nucleic acid sample, wherein homozygosity for the G allele (rare allele) at PLIN5 is indicative of an increased risk of obesity and related diseases in the individual of Malay or Indian ancestry.

The present invention also provides haplotypes for use in the diagnosis of Malay or Indian individuals at increased risk of obesity and/or obesity-related diseases. A haplotype consists of polymorphisms including PLIN1, PLIN3, PLIN4, PLIN5 and PLIN6. Thus, haplotype 11111 consists of allele 1 of all the loci identified above, and haplotype 22222 consists of allele 2 of all the loci identified above. Haplotype 11212 or 11222 in a nucleic acid sample from an individual of Malay ancestry indicates that the individual is at increased risk of obesity and/or cardiovascular disease. Haplotype 11212 in a nucleic acid sample from an individual of Indian ancestry indicates that the individual is at increased risk of obesity and/or cardiovascular disease. Haplotypes 12111 or 21111 in nucleotide samples from individuals of Malay ancestry are associated with reduced risk of obesity. Furthermore, haplotype 21111 in nucleotide samples from Indians was associated with reduced risk of obesity.

Another haplotype used to diagnose individuals of Malay and Indian ancestry consists of polymorphisms including PLIN4, PLIN5 and PLIN6. Thus, haplotype 111 consists of allele 1 of all the loci identified above, and haplotype 222 consists of allele 2 of all the loci identified above, with haplotypes 212, 222 from individuals of Malay ancestry or 121 indicates that the individual is at increased risk of obesity and/or cardiovascular disease. The presence of haplotype 212 or 122 in a nucleic acid sample from an individual of Indian ancestry indicates that the individual is at increased risk for obesity and/or cardiovascular disease.

In another embodiment, the present invention provides a method of assessing the predisposition to obesity and obesity-related diseases in individuals of Malay or Indian ancestry, wherein the method comprises determining the PLIN1 and PLIN3 loci in nucleic acid isolated from the individual genotype and produce a phenotype that encompasses both loci, where the haplotype PLIN1-1/PLIN-3/1, that is, the presence of PLIN1 allele 1 and PLIN3 allele 1 together in the same chromatid indicates a diseased Increased risk of obesity and/or cardiovascular disease.

In another embodiment, the present invention also provides a method of identifying individuals of Malay or Indian descent who are less likely to gain weight and who are expected to better maintain lost weight after dieting. The method comprises determining the genotypes of the PLIN1 and PLIN3 loci in an isolated nucleic acid obtained from an individual and generating haplotypes of the PLIN alleles, wherein the haplotypes PLIN1-1/PLIN3-2, i.e. PLIN1 allele 1 and PLIN3 etc. The presence of alleles 2 together in the same chromatid indicates the presence of an obesity-protective genotype in this individual.

We also performed a study to determine the association of PLIN polymorphisms and haplotypes in individuals of Caucasian ancestry from the United States. Four PLIN SNPs (PLIN 6209T>C, 11482G>A, 13041A>G, and 14995A>T) were genotyped in 734 white subjects (373 men and 361 women) participating in a residential lifestyle intervention program . Multivariate analysis demonstrated that in women, two SNPs (13041A>G and 14995A>T) were significantly associated with percent body fat (P=0.016 for 13041A>G, P=0.010 for 14995A>T) and waist circumference (for 13041A >G, P=0.020, for 14995A>T, P=0.045). Moreover, haplotype analysis using these two SNPs showed that both haplotypes PLIN5-A/PLIN6-T and PLIN5-G/PLIN6-T were associated with significantly increased obesity when compared with haplotype PLIN5-A/PLIN6-A associated with risk of disease (for haplotype PLIN5-A/PLIN6-T, OR=1.76, 95%CI 1.07-2.90, and, for haplotype PLIN5-G/PLIN6-T, OR=1.73, 95%CI 1.06-2.82) . No significant association was found between PLIN variants and obesity in men. Thus, PLIN is an important genetic determinant of obesity risk in Caucasians, and women are more sensitive than men to the genetic effects of perilipid droplet proteins.

Accordingly, in one embodiment, the present invention provides methods of assessing individual predispositions to obesity and obesity-related diseases in individuals of Caucasian ancestry. The method comprises determining the genotype and haplotype of a PLIN polymorphism in an isolated nucleic acid sample obtained from an individual of Caucasian ancestry, wherein the haplotype PLIN5-2/PLIN6-2 or PLIN5-1/PLIN6-2 is present in said nucleic acid sample The presence of in indicates an increased risk of obesity and related diseases in that individual. Preferably, said individual is a woman.

In one embodiment, the invention provides a method of assessing the predisposition to cardiovascular disease in an individual of Caucasian ancestry, wherein the method comprises determining the genotype and haplotype of the PLIN polymorphism in an isolated nucleic acid sample obtained from the individual of Caucasian ancestry , wherein the presence of haplotype PLIN5-2/PLIN6-2 or PLIN5-1/PLIN6-2 in said nucleic acid sample indicates an increased risk of cardiovascular disease. Preferably, said individual is a woman.

Alternatively, in one embodiment, the present invention provides a method of identifying individuals of Caucasian ancestry who are less likely to gain weight and who are expected to better maintain lost weight after dieting. The method comprises isolating nucleic acid from an individual, determining the genotype of the PLIN locus, wherein the presence of allele 1 of PLIN5 and PLIN6 indicates the presence of an obesity-protective genotype in the individual and indicates that the individual is more likely not to gain weight after dieting . Preferably, said individual is a woman.

The present invention also provides haplotypes for diagnosing individuals of Caucasian ancestry at risk of developing obesity and/or obesity-related diseases, including, but not limited to, cardiovascular disease. One of these haplotypes consists of alleles in the loci PLIN1, PLIN4, PLIN5 and PLIN6. Thus, haplotype 1111 consists of allele 1 of all the loci identified above, and haplotype 22222 consists of allele 2 of all the loci identified above, where haplotype 1122 in nucleic acid samples from individuals of Caucasian ancestry It shows that the individual is more susceptible to obesity and/or cardiovascular disease, and among them Caucasians with haplotype 2111 are less susceptible to obesity and/or cardiovascular disease (see Table 15).

The present invention also provides novel PLIN polymorphisms and oligonucleotides for analyzing the novel PLIN polymorphisms by amplification across the single nucleotide polymorphic sites of the present invention. The present invention also provides oligonucleotides for sequencing said amplified sequence.

In one embodiment, the primers used to amplify PLIN1, PLIN2, PLIN3, PLIN4, PLIN5, and PLIN6 are SEQ ID NO: 1 and 2, SEQ ID NO: 4 and 5, SEQ ID NO: 7 and 8, respectively. The nucleic acid sequences described in SEQ ID NO: 10 and 11, SEQ ID NO: 13 and 14 and SEQ ID NO: 16 and 17.

The present invention also provides the following new polymorphisms: PLIN1: 6209 T (allele 1) > 6209 C (allele 2); PLIN 310171 (allele 1) A > T (allele 2) ; PLIN4: 11482 G (allele 1) > 11482 A (allele 2); PLIN5: 13041 A > 13041 G (allele 2) and PLIN6: 14995 A (allele 1) > 14995 T (etc. bit gene 2). See table below.

loci Allele 1 Allele 2 PLIN1 T C PLIN3 A T PLIN4 G A PLIN5 A G PLIN6 A T

Accordingly, in one embodiment, the present invention provides polymorphisms that are risk factor propensities for weight gain and/or cardiovascular disease in Mediterranean individuals. In one embodiment, the polymorphism is allele 1 (6209 T) of PLIN1. In another embodiment, the polymorphism is allele 1 (11482G) of PLIN4.

In another embodiment, the present invention provides polymorphisms that are risk factor propensities for weight gain and/or cardiovascular disease in individuals of Caucasian ancestry. When identified as homozygous in the PLIN locus, they were associated with an increased risk of weight gain. In one embodiment, the polymorphism is allele G (13041 G) of PLIN5. In another embodiment, the polymorphism is allele T (14995 T) of PLIN6.

In another embodiment, the present invention provides polymorphisms which, when present as homozygous alleles, are risk factor propensities for weight gain and/or cardiovascular disease in individuals of Malay or Indian ancestry. The polymorphism is allele 2 of the PLIN6 (14995 T) locus, that is, T/T is a risk factor in PLIN6.

In another embodiment, the present invention provides polymorphisms that are risk factor propensities for weight gain and/or cardiovascular disease in individuals of Malay ancestry. In one embodiment, the polymorphism is allele 2 (13041G) of PLIN5. In another embodiment, the polymorphism is allele 2 (11482 A) of PLIN4.

The present invention also provides a diagnostic method for identifying individuals less prone to obesity and obesity-related diseases, comprising the steps of obtaining a nucleic acid sample from the individual, analyzing the isolated nucleic acid, and determining said allelic variant in the sample. The genotype of an individual and the generation of a haplotype from that genotype. Table 15 sets forth haplotypes which, when present in individuals of the indicated human populations, indicate that the individual is less prone to obesity and obesity-related diseases. The haplotype vertical reading in Table 15, for example, haplotype (a) is PLIN5-A/PLIN6-A, while haplotype (h) is PLIN1-C/PLIN3-A/PLIN4-G/PLIN5A/PLIN6A.

The invention also provides diagnostic methods for identifying individuals at increased risk of obesity and obesity-related diseases, such as cardiovascular disease. Said method comprises the steps of obtaining a nucleic acid sample from an individual, analyzing the isolated nucleic acid, determining the genotype of said allelic variant in the sample and generating a haplotype from the genotype. Table 16 sets forth haplotypes which, when present in individuals of the indicated human population groups, indicate an increased risk of obesity and obesity-related diseases in that individual. The haplotype vertical reading in Table 16, for example, haplotype (k) is PLIN5-G/PLIN6-T and haplotype (w) is PLIN1-T/PLIN3-A/PLIN4-A/PLIN5A/PLIN6T.

In another embodiment, the present invention provides a diagnostic method for identifying women at risk of obesity and obesity-related diseases, such as cardiovascular disease, comprising the steps of: obtaining a nucleic acid sample from a female individual, using Appropriate PLIN-PCR primers for amplification of morphological loci amplify sequences, detect allelic variants in the sample, and analyze the results.

Biological samples used as source material for isolating nucleic acids in the present invention include solid materials (eg, tissues, cell pellets, biopsies) and biological fluids (eg, blood, saliva, amniotic fluid, mouthwash, urine). Nucleic acid molecules of the invention include DNA and RNA and can be isolated from the particular biological sample for which the particular isolation method is chosen using any of a number of methods known in the art. Methods for isolating and analyzing nucleic acid variants as described above are well known to those skilled in the art and can be found, for example, in Molecular Cloning: A Laboratory Manual, 3rd Ed., Sambrook and Russel, Cold Spring Harbor Laboratory Press, 2001.

The PLIN polymorphisms of the present invention can be detected from isolated nucleic acids using techniques that include direct analysis of the isolated nucleic acids, such as Southern hybridization (DNA) or direct nucleic acid sequencing (Molecular Cloning: A Laboratory Manual, 3rd Ed. , Sambrook and Russel, Cold Spring Harbor Laboratory Press, 2001).

An alternative method for direct analysis of PLIN polymorphisms according to the present invention is the INVADER ^(R) assay (Third Wave Technologies, Inc (Madison, WI)). The assay is typically based on the structure-specific nuclease activity of enzymes that cleave target-dependent cleavage structures, thereby indicating the presence of a specific nucleic acid sequence, or a specific variation thereof, in a sample (see, eg, US Pat. No. 6,458,535).

Preferably, PCR-based techniques are used. After PCR, polymorphic nucleic acids can be identified using, for example, direct sequencing with labeled primers, such as radioactively or fluorescently labeled primers; single-strand conformational polymorphism analysis (SSCP), denaturing gradient gel electrophoresis (DGGE); and chemical cleavage assays, all of which are explained in detail, for example, in Molecular Cloning: A Laboratory Manual, 3rd Ed., Sambrook and Russel, Cold Spring Harbor Laboratory Press, 2001.

Polymorphisms are preferably analyzed using methods that are easily automated, such as the different methods used for primer extension analysis. Primer extension analysis can be performed using any method known to those skilled in the art, including PYROSEQUENCING ^™ (Uppsala, Sweden); mass spectrometry, including MALDI-TOF, or matrix-assisted laser desorption ionization-time-of-flight; genomic nucleic acid arrays (Shalon et al., Genome Research 6(7):639-45, 1996; Bernard et al., Nucleic Acids Research 24(8):1435-42, 1996); solid-phase mini-sequencing technology (US Pat. No. 6,013,431, Suomalainen et al. People, Mol.Biotechnol.Jun; 15(2):123-31, 2000); ion-pair high performance liquid chromatography (Doris et al., J.Chromatogr.A May 8; 806(1):47-60, 1998 ); and 5' nuclease assay or real-time RT-PCR (Holland et al., Proc Natl Acad Sci USA 88:7276-7280, 1991), or the primer extension method described in US Patent No. 6,355,433. Nucleic acid sequencing, eg, using any automated sequencing system and labeled primers or labeled terminating dideoxynucleotides can be used to detect polymorphisms. Systems for automated sequence analysis include, for example, Hitachi FMBIO ^(R) and Hitachi FMBIO( ^R ) II Fluorescent Scanners (Hitachi GeneticSystems, Alameda, CA); Spectrumedix ^(R) SCE 9610 Fully Automated 96-Capillary Electrophoresis Genetic Analysis System (Fully Automated 96 - Capillary Electrophoresis Genetic Analysis System) (SpectruMedix LLC, State College, PA); ABI PRISM ⁽ R) 377 DNA Sequencer; ABI ^(R) 373 DNA Sequencer; ABI PRISM ⁽ R) 310 Genetic Analyzer; ABI PRISM ⁽ R ⁾ 3100 Genetic Analyzer; Analyzer (Applied Biosystems, Headquarters, Foster City, CA); Molecular Dynamics FluorImager ^TM 575 and SI fluorescence scanner and Molecular Dynamics FluorImager ^TM 595 fluorescence scanner (Amersham Biosciences UK Limited, Little Chalfont, Buckinghamshire, UK); GenomyxSC ^TM DNA sequencing system ( Genomyx Corporation (Foster City, Calif.); Pharmacia ALF ^™ DNA Sequencer and Pharmacia ALFexpress ^™ (Amersham Biosciences UK Limited, Little Chalfont, Buckinghamshire, UK).

PCR, nucleic acid sequencing, and primer extension reactions of a nucleic acid sample can be performed in the same or separate reactions using primers designed to amplify and detect polymorphic PLIN nucleotides.

In one embodiment, the present invention provides a nucleic acid chip comprising polymorphisms PLIN1, PLIN3, PLIN4, PLIN5 and PLIN6 alleles, the nucleic acid chip is used to screen obesity associated with PLIN and/or obesity-related Individuals at risk of disease, including cardiovascular disease, or individuals with PLIN-related protection from obesity and/or obesity-related diseases, such as cardiovascular disease. Such chips may include any number of other obesity-associated mutations and polymorphisms including, but not limited to, leptin, leptin receptor, MC4R, and the like. A list of obesity-associated genes and polymorphisms can be found, for example, in Chagnon, Y.C., Pérusse, L., Weisnagel, S.J., Rankinen, T. and Bouchard, C. The Human Obesity Gene Map: The 1999 Update. Obesity Research8(1) : 89-117, 2000 and website http://www.obesity.chair.ulaval.ca/genemap.html.

可用于阵列分析的方法和技术已描述于U.S.S.N 09/536,841、WO00/58516、美国专利号412,087、6,147,205、6,262,216、6,310,189、5,889,165和5,959,098、5,143,854、5,242,974、5,252,743、5,324,633、5,384,261、5,405,783、5,424,186、5,451,683 、5,482,867、5,491,074、5,527,681、5,550,215、5,571,639、5,578,832、5,593,839、5,599,695、5,624,711、5,631,734、5,795,716、5,831,070、5,837,832、5,856,101、5,858,659、5,936,324、5,968,740、5,974,164、5,981,185、5,981,956、6,025,601、6,033,860、6,040,193、6,090,555、6,136,269 , 6,269,846 and 6,428,752, PCT Application Nos. PCT/US99/00730 (International Publication No. W099/36760) and PCT/US01/04285, which are incorporated herein by reference in their entirety for all purposes. Other methods of sample preparation and techniques for reducing the complexity of nucleic acid samples are described, for example, in Dong et al., Genome Research 11, 1418 (2001), U.S. Patent Nos. 6,361,947, 6,391,592 and U.S. Patent Application Nos. /920,491, 09/910,292 and 10/013,598.

Methods for performing polynucleotide hybridization assays on a chip are well established in the art. Hybridization assay steps and conditions will depend on the application and be selected according to general binding methods known to include those addressed in: Maniatis et al. Molecular Cloning: A Laboratory Manual (2nd Ed. Cold Spring Harbor, N.Y., 1989); Berger and Kimmel Methods in Enzymology, Vol. 152, Guide to Molecular Cloning Techniques (Academic Press, Inc., San Diego, CA, 1987); Young and Davism, P.N.A.S, 80:1194 (1983). Methods and apparatus for performing iterative and controlled hybridization reactions have been described, for example, in US Pat.

用于信号检测和强度数据处理的方法和装置的实例描述于例如，美国专利号5,143,854、5,547,839、5,578,832、5,631,734、5,800,992、5,834,758、5,856,092、5,902,723、5,936,324、5,981,956、6,025,601、6,090,555、6,141,096、6,185,030、6,201,639 , 6,218,803 and 6,225,625, US Patent Application 60/364,731 and PCT Application PCT/US99/06097 (published as WO99/47964), which are incorporated herein by reference in their entirety for all purposes.

The practice of the present invention also employs conventional biological methods, software and systems. A computer software product of the invention typically includes a computer-readable medium having computer-executable instructions for performing the logical steps of the method of the invention. Suitable computer readable media include floppy disks, CD-ROM/DVD/DVD-ROM, hard drives, flash memory, ROM/RAM, magnetic tape, and the like. The computer-executable instructions may be written in a suitable computer language or a combination of several languages. Basic computational biology methods are described, for example, in Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: APractical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2nd ed., 2001) .

The present invention also utilizes various computer program products and software for various purposes such as probe design, data management, analysis, and instrument operation. See, eg, U.S. Patent Nos. 5,593,839, 5,795,716, 5,733,729, 5,974,164, 6,066,454, 6,090,555, 6,185,561, 6,188,783, 6,223,127, 6,229,911, and 6,308,170.

Furthermore, the present invention may have preferred embodiments including a method for providing genetic information on a network such as the Internet.

The invention also provides diagnostic kits. In one embodiment, the invention provides a kit comprising one or more primer pairs capable of amplifying the PL IN nucleic acid region comprising the obesity-associated polymorphic nucleotide of the present invention; for PCR reactions buffer and nucleotide mixtures; suitable enzymes for performing PCR reactions in the same or separate containers, and manually defined PCR conditions, for example, as described in the Examples below, and listed in the specification Description of obesity-associated alleles and haplotypes. The kit may further comprise nucleic acid probes, preferably the probes listed in Table 1, in dry or buffered form in tubes or vials. In preferred embodiments, these primers are those listed in Table 1. Primers may also be provided in the kit in dry form in tubes or vials, or alternatively, dissolved in a suitable aqueous buffer. The kit may also contain primers for use in primer extension methods for detecting specific PLIN polymorphisms as described above.

The kit also preferably includes a table listing obesity risk haplotypes in various human populations, such as Tables 15 and 16 shown herein.

In one embodiment, the components of the kit are part of a kit that provides a variety of obesity-associated genes, polymorphisms and mutations known to those skilled in the art.

DNA haplotypes, the phase-determined associations of several polymorphic markers (eg, SNPs), are a statistically more powerful method of determining disease association than using only one marker. Methods of determining or identifying haplotypes according to the invention include physical separation of homologous chromosomes by, for example, mouse cell line hybrids, cloning into plasmids and allele-specific PCR and computational determination of haplotypes.

According to the present invention, methods that can be used to determine the haplotype SNP in the PLIN locus include, but are not limited to, single-strand conformation polymorphism (SSCP) analysis (Orita et al. (1989) Proc.Natl.Acad.Sci.USA 86 : 2766-2770), heteroduplex analysis (Prior et al. (1995) Hum.Mutat.5: 263-268), oligonucleotide ligation (Nickerson et al. (1990) Proc.Natl.Acad.Sci.USA 87:8923-8927) and hybridization assays (Conner et al. (1983) Proc. Natl. Acad. Sci. USA 80:278-282). Strategies based on conventional Taq polymerase PCR, such as PCR-RFLP, allele-specific amplification (ASA) (Ruano and Kidd (1989) Nucleic Acids Res. 17:8392), single molecule dilution (SMD) (Ruano et al. (1990) Proc.Natl.Acad.Sci.USA 87:6296-6300), and coupled amplification and sequencing (CAS) (Ruano and Kidd (1991) Nucleic Acids Res.19:6877-6882) are easy to carry out (1996) Nucleic Acids Res.24:4841-4843; Barnes (1994) Proc.Natl.Acad.Sci.USA 91:5695- 5699; Ruano and Kidd (1991) Nucleic Acids Res. 19:6877-6882).

In one embodiment, large-scale PCR (LR-PCR) is used to determine the haplotype of a SNP of the invention. SNP genotypes of LR-PCR products were determined using genotyping methods known to those skilled in the art, and haplotypes were derived mathematically (e.g., the Clark algorithm (Clark (1990) Mol. Biol. Evol. 7:111-122) ).

In one embodiment, the method for haplotyping according to the invention is to physically separate alleles by cloning followed by sequencing. Other methods for haplotype analysis according to the present invention include, but are not limited to, monoallelic mutation analysis (MAMA) (Papadopoulos et al. (1995) Nature Genet. 11:99-102) and carbon nanotube (nanotube) probing. Needles (Woolley et al. (2000) Nature Biotech. 18:760-763). US Patent Application No. US 2002/0081598 also discloses useful methods of haplotype analysis involving the use of PCR amplification.

Computational algorithms such as expectation-maximization (EM), subtraction, and PHASE are useful methods for statistical estimation of haplotypes (see, e.g., Clark, A.G. Inference of haplotypes from PCR-amplified samples of diploid populations. Mol Biol Evol 7, 111- 22. (1990); Stephens, M., Smith, N.J. & Donnelly, P.A new statistical method for haplotype reconstruction frompopulation data. Am J Hum Genet 68, 978-89. (2001); Templeton, A.R., Sing, C.F., Kessling , A. & Humphries, S.A cladistic analysis of phenotype associations with haplotypes inferred from restriction endonuclease mapping. II. The analysis of natural populations. Genetics 120, 1145-54. (1988)).

All of the methods discussed above are useful methods that can be used to determine haplotypes according to the methods of the invention.

Example

Example 1: Sex-specific effects of PLIN polymorphisms on obesity-related variables in individuals from the eastern Mediterranean coast of Spain

Materials and methods

Subjects and Study Design

A total of 1746 unrelated Caucasian subjects were included in this report. The study population consisted of 1589 individuals randomly selected from the Valencia region of the eastern Mediterranean coast of Spain (sample 1) and 157 obese subjects (sample 2) from the University General Hospital located in the same geographical region. Briefly, sample 1 consisted of 788 men and 801 women, aged 18-85 years, who were selected to participate in a study aimed at determining the prevalence of genetic and environmental cardiovascular risk factors in a Mediterranean Hispanic population individuals (14, 15). The sample consisted of randomly selected workers using continuously updated computerized population registries, as well as randomly selected subjects from the general population (15, 16). All these subjects were examined between 1999 and 2002. Sample 2 consisted of 29 men and 128 women, aged 18-78 years, randomly selected from the Endocrinology Unit of the University General Hospital of Valencia, from those who were on continuous weight loss therapy between 2001 and 2002 individual. The study used baseline data. The study protocol was approved by the ethics committees of Valencia University and University General Hospital. Informed consent for participation was provided for all included subjects and available data on PLIN genotype and other variables were examined. The mean age of subjects in sample 1 was 41.5±13.4 years and in sample 2 was 47.0±13.7 years. A cross-sectional approach as well as a case-control approach were used in the statistical analysis. In a case-control approach, 438 subjects (157 from hospitals and 281 from the general population) were classified as obese if their body mass index (BMI) > 30 Kg/ ^m2 . The remaining 1308 subjects from the general population were classified as not obese.

Anthropometric and Blood Pressure Measurements

Anthropometric measurements were performed using standard techniques: weight on a digital scale with light clothing on; height without shoes on a stationary stadiometer. BMI was calculated as weight (kg)/height (m ² ). Waist circumference is measured midway between the lower border of the ribs and the iliac crest in a horizontal plane. Measure your hips at the point where your hips create the greatest circumference. Blood pressure was measured with a calibrated mercury sphygmomanometer according to the WHO MONICA protocol, and the average of two consecutive readings of the first and fifth Korotkoff sounds was used for systolic and diastolic blood pressure (SBP and DBP), respectively.

Biochemical, clinical and lifestyle data

Participants were instructed to fast for at least 12 hours prior to the morning examination. Venous blood was collected into glass tubes containing EDTA. Plasma total cholesterol and TAG were determined by the Technicon Chem 1 assay (Technicon Instruments, Tarrytown, NY), and high-density lipoprotein cholesterol (HDL- C). For samples with serum TAG concentrations below 400 mg/dL, low-density lipoprotein cholesterol (LDL-C) was calculated according to the equation of Friedewald et al. (17). Measure fasting glucose in fresh samples with a hexokinase kit.

Data on gender, date of birth, ethnicity, marital status, education, medical care, health problems, history of type 2 diabetes, smoking, alcohol consumption, and physical activity were assessed by self-administered questionnaires as previously reported. (14) Current smokers were defined as individuals who smoked at least one cigarette per day. Alcohol consumption was carefully assessed by a set of 22 questions on the use of alcoholic beverages on weekdays and weekends. Physical activity was estimated from questions about regular free-time physical activity, and the average number of hours per week spent on each activity. Subjects were classified according to type and time as sedentary (no physical activity), moderate (less than 3 hours/week of one activity) and highly (more than 3 hours/week of one activity or more than 2 week). This variable was then dichotomized into sedentary (no physical activity) and active (moderate plus high). Divide education into three categories: primary, secondary and university [including cycle I (3 years) and cycle II (5 or more years)] (14, 15).

DNA extraction and genotype analysis

Genomic DNA was isolated from leukocytes by phenol-chloroform extraction and ethanol precipitation. The descriptions and names of the six single nucleotide polymorphisms (SNPs) examined in this study are given in Figure 1 and Table 1. Naming polymorphisms according to a recent recommendation (18). The reference sequence is GI21431190 (GenBank). Genotype analysis was performed with single nucleotide extensions. First, a DNA fragment including 4 polymorphisms was amplified by multiplex polymerase chain reaction (PCR). The primers used are given in Table 1. For PLIN1, PLIN2, PLIN3, PLIN4, PLIN5 and PLIN6, the PCR products were 422bp, 391bp, 318bp, 350bp, 190bp and 469bp, respectively. PCR amplification was performed in a 10 μl reaction volume containing 0.2 mmol/l of each dNTP, 0.2 μmol/l of each primer, 3.0 mmol/l magnesium chloride and 0.8 U Qiagen Hotstar Taq polymerase. PCR cycling conditions were 95°C for 10 minutes, followed by 7 cycles of 95°C for 30 seconds, 70°C for 30 seconds, and 72°C for 1 minute, followed by 41 cycles of 95°C for 30 seconds, 65°C for 30 seconds, and 72°C for 1 minute . A final extension phase of 2 min at 72°C was included at the end of the protocol. The PCR product was incubated with 2.5U of each exonuclease I (New England Biolabs, Inc. Beverly, MA) and calf intestinal phosphatase (New England Biolabs, Inc. Beverly, MA) at 37°C for 60 minutes to remove Unincorporated dNTPs and primers. The enzyme was then inactivated by incubation at 75°C for 15 minutes.

Subsequently, single nucleotide extensions were performed with the ABI Prism SnaPshot multiplex system (Applied Biosystems, Foster City, CA). Probes used for single nucleotide extension are listed in Table 1. Use a PCR thermocycler in a reaction chamber containing 1.5 μl Snapshot Ready ReactionMastermix (Applied Biosystems, Foster City, CA), 1.0 μl water and 1.5 μl multiplex PCR products and 1.0 μl probe mix (1.5 μmol/m for PLIN1, PLIN2, PLIN3 and PLIN4 1, and 2.0 μmol/l for PLIN5 and PLIN6) in 5 μl of the reaction mixture for extension reactions. The reaction conditions were 35 cycles of 96°C for 30 seconds, 50°C for 30 seconds and 60°C for 30 seconds. The reaction product was incubated with 3U calf intestinal phosphatase for 60 minutes at 37°C to remove unincorporated dNTPs, followed by incubation at 75°C for 15 minutes to inactivate the enzyme. The final products were genotyped on an ABI Prism 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA) using Genotyper version 3.7 (Applied Biosystems, Foster City, CA).

Statistical analysis

Allele frequencies were estimated from gene counts and 95% confidence intervals (CI) were calculated. Differences between observed and expected frequencies were tested with the χ2 test (Pearson, Fisher's exact test, or Monte Carlo method) to assume Hardy-Weinberg equilibrium, to test for linkage disequilibrium, and to test for differences in percentages. Pairwise linkage disequilibrium coefficients were estimated by the LINKAGE program. Calculate the D and D'(D/Dmax) coefficients. Haplotypes were estimated by the EH program, which uses an expectation-maximization algorithm to obtain maximum likelihood estimates of haplotype frequencies. Checks for normal distribution of all continuous variables. Log-transformed triglycerides to improve normality. Apply parametric tests to compare means. In addition, nonparametric tests (Mann-Whitney or Kruskal-Wallis) were applied when the number of cases in each subgroup was very small. The null hypothesis of no association between genetic variants and obesity-associated phenotypes was tested with multivariate linear regression analysis with dummy variables for categorical items. These statistical models allowed us to estimate the association of genetic polymorphisms with each dependent variable (obesity-associated phenotype) after adjusting for covariates. The main covariates were sex, age, BMI, or lifestyle factors (smoking, alcohol consumption, physical activity, and education). Regression coefficients and adjusted means for each predicted value were estimated from the model. Homogeneity of allelic effects according to sex or genetic or environmental factors was tested by introducing counterparts for interactions in more parsimonious linear regression models. The adequacy of these models was ensured using standard regression diagnostics. In categorical analyses, obesity was defined dichotomously as a BMI > 30 kg/m ² . Logarithmic regression models were fitted to estimate risk: odds ratios (OR) and 95% confidence intervals (CI) for obesity associated with the presence of each genetic variant compared with wild type. Multiple logarithmic regression models with and without interaction terms were also fitted to adjust for the effects of covariates and effect modifiers. Association analysis was performed using SPSS (version 10.0) for windows.

result

Identify novel polymorphisms, frequencies, and linkage disequilibrium

We searched for polymorphisms at the PLIN locus using two different strategies (Fig. 1). First, we sequenced the 5' region of the PLIN gene in 40 unrelated subjects to look for common mutations that might be involved in the regulation of the PLIN gene. We focused on those regions that are significantly conserved between human and murine sequences (21). These analyzes did not reveal any common mutations in the regions examined. Our second approach is based on finding common polymorphisms in a public SNP database (http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId=5346). We selected initial targets based on the following criteria: 1) SNPs in exons were preferred over SNPs in introns; 2) If several SNPs clustered in a narrow region, only one of them was selected. Six reported SNPs were initially selected (Table 1), two of which (PLIN2 and PLIN3) were not polymorphic, and our analysis was based on the other four SNPs (PLIN1, PLIN4, PLIN5 and PLIN6).

Table 2 shows the demographic, biochemical and lifestyle characteristics of the 1746 unrelated subjects examined in this study: 1589 from the general population (sample 1), 157 hospitalized morbidly obese patients (sample 2) . In Sample 1, BMI ranged from 16.2 to 52.5 Kg/m ² , with only 4% of subjects having a BMI > 35 Kg/m ² . In sample 2, BMI ranged from 30.1 to 79.1 Kg/m ² , with 88% of subjects having a BMI > 35 Kg/m ² . The PLIN genotypes, allele frequencies and linkage disequilibrium coefficients of population sample 1 are given in Table 3. The genotype distribution does not deviate from Hardy-Weinberg expectations. Since sex differences in genotype distributions were not significant for either polymorphism, data for men and women were combined and allele frequencies and pairwise linkage disequilibrium parameters were estimated for the entire sample. Allele 2 (G) of the PLIN5 locus was the most prevalent genetic variant in sample 1 (allele frequency: 0.385; 95% CI 0.368 to 0.402); while allele 2 (A) of the PLIN4 locus is less prevalent (allele frequency: 0.262; 95% CI 0.247 to 0.278). The strongest pairwise linkage disequilibrium was found between the PLIN1 and PLIN4 polymorphisms (D': 0.958; p<0.001). The positive linkage disequilibrium observed among the other polymorphisms, although statistically significant, was much lower with D' coefficients ranging from 0.453 to 0.149 (Table 3). The prevalence and linkage disequilibrium among PLIN polymorphisms in sample 2 were not different from those in sample 1. Likewise, the distribution of genotypes in sample 2 did not differ between men and women. The less common allele frequencies of the PLIN1, PLIN4, PLIN5, and PLIN6 polymorphisms in sample 2 were 0.37 (0.32-0.43); 0.24 (0.19-0.29); 0.40 (0.35-0.46) and 0.38 (0.33-0.46 ). However, the small sample size of this group largely influenced the random error of these estimates. Thus, haplotypes were estimated from all genotyped individuals in sample 1 only (Table 4). All 16 possible four polymorphic haplotypes were estimated to be present in this Mediterranean population. The haplotype consisting of the most frequent allele of each polymorphism (("6209T/11482G/13041A/14995A", also known as "1111") was the most prevalent with a relative frequency of 0.388. Among the 15 Of the remaining haplotypes, only four had allele frequencies above 0.08, including the haplotype consisting of the least frequent allele for each polymorphism (“6209C/11482A/13041G/14995T”, also called as "2222").

Association between PLIN polymorphisms and obesity-associated phenotypes. Single polymorphism genotype analysis

We next examined associations between PLIN polymorphisms and obesity-related variables. Taking into account clinical differences and lifestyle differences between samples 1 and 2, association analyzes were performed separately for subjects from the general population and obese patients. To increase statistical detection power, and after demonstrating the presence of an allelic effect compatible with a dominant or at least codominant genetic model, classify individuals as homozygous for the most common allele for each polymorphism or Carrier of the less common allele (1/2+2/2).

Associations in Sample 1

First, we assessed the homogeneity of genetic effects produced by sex and demonstrated several important interactions. Therefore, we analyzed each gender separately. Table 5 shows age-adjusted means for BMI and other obesity-related variables among men from sample 1 according to carrier status for allele 2 variants within each of the four PLIN polymorphisms. We found no significant differences between the genotype groups regarding BMI, body weight, waist-to-hip ratio, glucose, total cholesterol, HDL-C, LDL-C, TAGs, and blood pressure. However, we found significant differences in BMI between genotypes for the PLIN1 and PLIN4 polymorphisms in women from sample 1, with allele 2 being associated with lower BMI (Table 6). For the PLIN1 polymorphism, the mean BMI was 26.3±0.3 Kg/m ² in 1/1 homozygotes versus 25.3±0.2 Kg/m ² in women carrying allele 2 (p=0.004); for For the PLIN4 polymorphism, the mean BMI was 26.1±0.2 Kg/m ² in 1/1 homozygotes versus 25.2±0.3 Kg/m ² in allele 2 carriers (p=0.004). Likewise, carriers of allele 2 at the PLIN1 locus weighed significantly less (p=0.007) than women homozygous for the wild-type genotype. The same was true for carriers of the less frequent allele at the PLIN4 locus (p=0.01). Furthermore, compared with 1/1 homozygotes, female carriers of allele 2 of the PLIN4 polymorphism showed lower waist-to-hip ratio (p=0.032), lower fasting glucose (p=0.008) and Lower plasma TAG concentrations (p=0.005). Similar differences were found for the PLIN1 polymorphism with borderline P values of 0.090 for fasting glucose and 0.099 for TAG. Two SNPs (PLIN1 and PLIN4) exhibited significant gene-sex interactions in determining BMI and body weight. Furthermore, for the PLIN4 polymorphism, we found a significant gene*sex interaction determining waist-to-hip ratio (p=0.023) and TAGs (p=0.009). No significant gene*sex interaction was detected for either the PLIN5 polymorphism or the PLIN6 polymorphism.

Carriers and non-carriers of allele 2 of each polymorphism did not differ significantly for smoking, alcohol consumption, education, physical activity, and diabetes among men and women (results not shown). Thus, the differences found for PLIN1 and PLIN4 polymorphisms remained statistically significant even after adjustment for these potential confounders (p=0.012 and p=0.020 for BMI and body weight for the PLIN1 polymorphism; For the PLIN4 polymorphism, p=0.014, p=0.029, p=0.046, p=0.003 and p=0.042 for BMI, body weight, waist-to-hip ratio, glucose and TAG, respectively). Additional adjustment for BMI and medication did not alter the significance of the association between fasting glucose and plasma lipids and PLIN4 genotype [116.4 ± 1.3 mg/dL in noncarriers vs. 113.7±1.7 mg/dL (p=0.010)]. However, the difference in TAG concentrations was not statistically significant (p=0.327).

Association in Sample 2

When we performed a similar association analysis in a group of morbidly obese subjects (sample 2), a reduction in BMI associated with allele 2 in the PLIN1 and PLIN4 polymorphisms was detected in both men and women. This reduction was higher and statistically significant in men carrying allele 2 of the PLIN4 polymorphism. In contrast to results observed in men from the general population, PLIN SNPs were associated with significant differences in BMI in this group of predominantly morbidly obese men. Thus, for PLIN4, the age-adjusted mean of BMI was 45.9±1.9 Kg/ ^m2 in non-carriers compared to 35.6±1.3 in male carriers of the 2 allele Kg/m ² (p=0.001). Likewise, the adjusted mean for body weight was 141.3±6.0 Kg among non-carriers versus 107.9±6.3 Kg in carriers of 2 alleles (p=0.001). Despite the small number of cases, these results in obese men were consistent and statistically significant in parametric as well as nonparametric tests. Among obese women from sample 2, the BMI and weight loss observed in carriers of allele 2 of the PLIN4 polymorphism was similar to that observed in women from the general population, however, because women in this group The number was low, so the difference did not reach statistical significance [age-adjusted mean: 43.1±0.9Kg/m2 versus 41.1±0.9Kg/ ^m2 versus 41.1± 6.3 Kg/m ² (p=0.199) and 108.2±2.1 Kg versus 102.4±2.9 Kg (p=0.112)]. Further multivariate adjustment for smoking, alcohol consumption, education, physical activity, and diabetes did not affect the statistical significance of these results. Although a reduction in BMI was associated with allele 2 in these obese subjects, TAG concentrations did not differ significantly by genotype. Furthermore, among these subjects, carriers of allele 2 of the PLIN4 polymorphism showed higher plasma glucose concentrations than noncarriers. This effect was noted in both men and women and was different from that observed for the same allele in subjects from the general population. Thus, in men from sample 2, plasma fasting glucose concentrations were 94.5±7.9 mg/dL versus 117.1±7.7 mg/dL in non-carriers versus carriers of the PLIN4 2 allele (P : PLIN4*obesity=0.028), while in men from sample 1 no difference was noted. Conversely, in women from the general population, a decrease in plasma glucose associated with allele 2 was found, while in women from sample 2, an increase in plasma glucose concentration was observed (non-carriers of the PLIN4 2 allele 102.4±3.5 mg/dL versus 108.2±3.9 mg/dL in carriers, respectively). Statistically significant interaction terms were also obtained for the PLIN1, PLIN5, and PLIN6 polymorphisms associated with obesity in measuring fasting glucose concentrations.

Association of PLIN haplotypes with metabolic syndrome-related variables

We also evaluated the effect of PLIN haplotypes on several variables (BMI, TAG, and fasting glucose) associated with metabolic syndrome risk. Eleven of the 16 possible haplotypes occurred at very low relative frequencies (less than 5%). We therefore used a pseudo-haplotype approach by comparing the effect of homozygosity for the most common haplotypes with that of selected combinations of genotypes, depending on their frequency and the specific association analysis performed. Results from Tables 5 and 6 were first adjusted for the corresponding confounding effects of the other polymorphisms by including these variables as control factors in the multiple regression model. Given the higher association between PLIN1 and PLIN4, these variables were not adjusted for each other simultaneously in order to avoid multicollinearity bias. Thus, polymorphisms modulate PLIN1 and PLIN4 associations for PLIN5 and PLIN6, PLIN5 for PLIN4 and PLIN6, and PLIN6 for PLIN4 and PLIN5. The association between PLIN1 polymorphism and BMI in women remained statistically significant after these adjustments (p=0.002). Furthermore, the borderline statistically significant association of the PLIN1 polymorphism with fasting glucose in women reached statistical significance after adjustment for the PLIN6 polymorphism (p=0.032), and the P value for triglycerides was significantly higher than that for PLIN5 (p=0.032). 0.056) and PLIN6 (p=0.085) decreased slightly after adjustment. Likewise, an independent effect of PLIN4 polymorphism in women was confirmed after adjustment for PLIN5 and PLIN6 polymorphisms, and the association previously reported in Table 6 remained statistically significant after these adjustments (for both PLIN5 and PLIN6 After adjustment, BMI, fasting glucose and TAG were p=0.023; p=0.015; p=0.035, respectively). In men, no significant changes were detected when the results in Table 5 were adjusted for additional genetic variants.

We also investigated potential synergistic associations between PLIN1 and PLIN4 and related variables. Subjects from sample 1 were divided into three categories: 1) homozygous for allele 1 at both PLIN1 and PLIN4 SNPs; 2) carriers of 2 alleles of PLIN1 or PLIN4, and 3) PLIN1 and PLIN4 allele Carriers of gene 2. Figure 2 shows the age-adjusted means of BMI according to the combined genotypes among the women from sample 1. Furthermore, for PLIN5 and PLIN6 SNP regulation models. The two SNP variables combined were significantly associated with BMI (p=0.007), with women homozygous from the most common haplotype "11" being more likely than women carrying at least one allele 2 of the PLIN1 and PLIN4 SNPs (25.1 ± 0.3 Kg /m ² ) showed a higher BMI (26.3±0.3 Kg/m ² ; p=0.002). Carriers of at least one 2-allele of PLIN1 or PLIN4 SNPs had an intermediate BMI phenotype. We also found a statistically significant association between the combined SNP variable and TAG (p=0.020) and glucose (p=0.040), homozygous for the most common haplotype with the highest concentration of.

When this combined genotype analysis was performed for the PLIN5 and PLIN6 polymorphisms, after additional adjustment for PLIN1 and PLIN4, no relationship between this haplotype variable and any obesity-related parameters was detected in men or women from sample 1 association. Figure 3 shows the age-adjusted means of BMI according to the combined genotypes in the women (sample 1). Although not significant, homozygous carriers of the most frequent haplotypes had the lowest BMI values compared to the other haplotypes.

We performed a similar analysis using all four polymorphisms. For this, we considered four groups: 1) subjects homozygous for the most common allele - haplotype "1111"; 2) homozygous for the most common allele of PLIN1 and PLIN4 and at PLIN5 and PLIN6 etc. Carriers of allele 2; 3) carriers of allele 2 at PLIN1 and PLIN4 and homozygotes for the most common allele at PLIN5 and PLIN6; 4) carriers of allele 2 at PLIN1, PLIN4, and PLIN5 and PLIN6 By. Subjects carrying any other combination of genotypes were not included in these analyses. To increase statistical detection power, individuals from sample 1 and sample 2 were pooled and analyzed together. Figure 7 shows age-adjusted means for weight and BMI in men and women according to pooled genotypes. In women, a highly statistically significant association between the pooled genotype variable and weight and BMI was found among carriers of allele 2 at the PLIN1 and PLIN4 loci and homozygotes for the most common allele at PLIN5 and PLIN6 shows the lowest value. In men, we did not find any significant association between genetic group and BMI or weight.

Obesity risk associated with PLIN gene variants

Finally, to estimate the risk of obesity associated with PLIN variants, subjects from samples 1 and 2 were pooled and subdivided according to BMI category: non-obese subjects (BMI<30 kg/m ² ), and Obese (BMI ≥ 30 kg/m ² ). In men, no significant differences in the prevalence of any PLIN polymorphisms between obese and non-obese were detected. However, in women, for the PLIN1 polymorphism, a lower prevalence was detected for subjects carrying allele 2 in obese subjects compared to non-obese subjects (50.2% vs 60.4% ; p=0.004). Because obese and non-obese subjects differed in age, hazard estimates (OR) were adjusted for age in the logarithmic regression model. After this adjustment, women carrying allele 2 at the PLIN1 polymorphism had a lower risk of obesity than noncarriers: OR: 0.65; 95% CI, 0.48 to 0.88. Likewise, the prevalence of women carrying allele 2 at the PLIN4 polymorphism was lower in the obese group than in the non-obese group (32.5% vs. 45.2%; p<0.001). After adjusting for age, allele 2 at the PLIN4 locus was consistently associated with a lower risk of obesity in women, OR: 0.60; 95% CI, 0.44 to 0.83. Furthermore, these estimates remained statistically significant after adjusting for smoking, alcohol consumption, physical activity, diabetes, and education. In genotype analysis of the combination of the two polymorphisms and after adjustment for age, women carrying allele 2 at the PLIN1 and PLIN4 SNPs had a lower risk of obesity (compared with homozygotes for the most common allele , (OR: 0.56; 95% CI 0.39 to 0.79; p=0.001), while carriers of only one allele 2 at the PLIN1 or PLIN4 locus showed no obesity compared with homozygotes for the most common allele There was a statistically significant difference in risk (OR: 0.95; 95% CI: 0.63 to 1.43). These results did not change after further adjustment for PLIN5 and PLIN6 polymorphisms. For the PLIN5 and PLIN6 loci, the single polymorphism analysis Neither the combined genotype analysis nor the combined genotype analysis found a statistically significant association with the risk of obesity.

discuss

Studies using experimental models have demonstrated that perilipid droplet proteins play an important role in TAG storage in adipocytes by regulating both basal and hormone-stimulated lipolysis (7; 11, 12). We have studied the association of four common novel PLIN polymorphisms with measures of obesity, lipid metabolism, and insulin sensitivity in a sample of Caucasian individuals, and we have demonstrated for the first time that variation at the human PLIN locus Always associated with obesity-related variables, thus suggesting that perilipid droplet proteins may play a relevant role in human obesity, hypertriglyceridemia and possibly the development of metabolic syndrome. In addition, we have found that in the general population, most associations are sex-specific, affecting mainly women.

Association between PLIN polymorphisms and obesity-associated phenotypes. Single polymorphism genotype analysis.

In our analysis, we applied case-control and cross-sectional approaches to investigate the association between PLIN polymorphisms and obesity-related measures. In a case-control design including obese subjects from the general population and hospitalized obese patients, and after adjusting for age and other potential confounding factors, we have found that among women carrying allele 2 of the PLIN1 polymorphism Consistent and statistically significant lower risk of obesity. The association was found at the PLIN4 SNP but not at allele 2 of the PLIN5 or PLIN6 polymorphisms. Strong linkage disequilibrium (D'>0.9) between PLIN1 and PLIN4 and their lower linkage to the other two SNPs support these results. Furthermore, the lower risk of obesity seen in women associated with less common alleles of the PLIN1 and PLIN4 SNPs parallels the findings for perilipiplin null mice, thus linking perilipiplin ablation to Lean phenotypes are associated (11, 13). Furthermore, inactivation of the PLIN gene also protected Lepr(db/db) mice from obesity, a genetic model of obesity caused by leptin resistance (13). The lack of a significant association among men from the general population underscores the importance of sex hormone factors in regulating body weight and fat distribution in humans.

In samples from the general population, female carriers of the less common alleles of the PLIN1 and PLIN4 SNPs had statistically significantly lower BMI than women homozygous for the most common alleles. Furthermore, we found that female carriers of the less common allele of the PLIN4 SNP had significantly lower plasma glucose and TAG concentrations. Furthermore, PLIN4 polymorphisms were also associated with reduced waist-to-hip ratio in women, suggesting a greater effect on abdominal (visceral) fat depots. This finding is particularly important because abdominal (visceral) fat is highly associated with the metabolic syndrome: glucose intolerance, dyslipidemia, insulin resistance, hypertension, as well as cardiovascular disease and type 2 diabetes (19). Furthermore, this same allele was also associated with lower fasting glucose levels. An interesting finding of our study in these lines was the consistent and statistically significant interaction between PLIN1 polymorphisms and obesity in determining plasma glucose concentrations. In contrast, no significant association was observed among men from the general population.

In obese women from sample 2, despite the consistent association between allele 2 of the PLIN4 SNP and lower BMI, this allele was associated with higher plasma glucose concentrations. However, these results are consistent with the observations of Tansey et al. (11 ) in perilipidin knockout mice and with the findings of Martinez-Botas et al. (13). Fatty acids released from adipose tissue are involved in the development of type 2 diabetes, and one would expect Peri-null mice to be sensitive to insulin resistance. Martinez-Botas et al. (13) could not detect glucose intolerance in their Peri-null animals, and a more detailed study by Tansey et al. (11) repeated Martinez-Botas et al. ( 13) Discovery. However, when animals exceeded 30 g, significant glucose intolerance developed in Peri null mice compared to wild type. This is consistent with the idea that perilipid droplet proteins that protect from obesity may lead to a more deleterious phenotype once an individual becomes obese. Furthermore, although no effect of the PLIN allele on plasma fasting glucose was found in men from the general population, allele 2 was also associated with higher glucose concentrations in obese men, further evidence of an obesity-reciprocal Effects of the role hypothesis. Another interesting finding involving the interaction between obesity and PLIN SNPs concerns the association of allele 2 at the PLIN4 locus with lower BMI in men from sample 2. These findings are consistent with a role for this allele in women and have led to the hypothesis that either a specific higher adiposity in obese men or some undetected environmental factor is required to elicit the effect of the PLIN allele.

The biological basis for these associations is unclear. None of the polymorphisms examined in our study were functional. Both PLIN1 and PLIN4 are intronic mutations. PLIN5 is a silent mutation in exon 8, and PLIN6 is located in the untranslated region of exon 9. None of these mutations modify protein structure and, generally, they have not been considered to have a regulatory function. However, some evidence suggests that intronic polymorphisms can also regulate gene expression by affecting nuclear factor binding (20). Perilipid droplet proteins are the most abundant proteins in adipocytes coating the surface of lipid droplets (4-6). Their physiological relevance has become apparent following recent reports showing that PLIN-null mice had significantly reduced fat stores and increased basal lipolysis in their isolated adipocytes compared to wild-type mice (11, 13). Based on these data, a possible explanation for our findings is that PLIN1 and PLIN4 polymorphisms may be associated with lower expression of PLIN genes or impaired perilipid droplet protein activity. An alternative hypothesis is that these polymorphisms are directly involved or involved in LD with mutations that alter mRNA splicing. PLIN4, PLIN5 and PLIN6 are all close to regions subject to alternative splicing (see Figure 1). All perilipidoproteins share the same 22-kDa amino acid terminus, and they have different carboxy-terminal sequences of varying lengths (21). The two major splice variants of the PLIN gene, perilipoprotein A and perilipoprotein B, show differential responses to PKA activation and may exert differential protection against lipolysis. Structural differences between these splice variants, particularly affecting the length of the C-terminal tail that wraps around the droplet surface, could determine their function.

The sex-specific role of PLIN genotype is consistent with sex-specific differences in the development and distribution of adipose tissue, as well as in the risk of obesity-related diseases. Lipolytic capacity, one of the most important determinants of adipose tissue accumulation, has also been shown to be sex-dependent (22, 23). Current data do not determine whether sex hormones can modify the actions of PLIN genes, and no data are currently available to explain the interplay between sex hormones and perilipid protein function. We hypothesized that estrogen could amplify the protective effect of PLIN variants by an unknown mechanism that needs to be elucidated, whereas testosterone had no effect or minimized the protective effect of PLIN variants.

Association between PLIN polymorphisms and obesity-associated phenotypes

Haplotype analysis

Our data indicate a lower risk of obesity among women carrying allele 2 on the PLIN1 and PLIN4 SNPs, suggesting that these SNPs may act in an additive or synergistic manner. Complex trait susceptibility is often controlled by the combined effects of several different variants within a gene. Therefore, we propose that the biological effects of these markers are related but they are not associated with the same function mutations.

Neither PLIN5 nor PLIN6 SNPs were associated with BMI and other obesity-related measures, respectively. However, haplotype analysis revealed a more interesting picture. We found that women carrying variant alleles of PLIN1 and PLIN4 but not PLIN5 and PLIN6 showed the lowest body weight and BMI (62.9 Kg and 24.8 kg/m ² ). Conversely, the presence of variant alleles of PLIN5 and PLIN6 in the absence of the less common alleles of PLIN1 and PLIN4 was associated with the highest body weight and BMI (72.2.Kg and 28.7Kg/m ² ), which was inversely About 15% biologically significant difference between haplotypes.

In conclusion, our study is the first to report an association between PLIN genotypes and obesity-related measures in humans. This is consistent with recent findings from linkage analyzes as well as emerging data from animal models. A related question left to be explored concerns the potential correlation between these SNPs and dietary factors. This is relevant given the relationship between perilipoprotein expression and fatty acid metabolism (24).

Example 1 References

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Example II: Sex-specific association of the perilipidin (PLIN) gene haplotype with risk of obesity in a white population from the United States

Materials and methods

Subjects and Study Design

A total of 734 white subjects, 373 men (mean age, 58.6 years) and 361 women (mean age, 56.1 years ). In this population, reported current smokers were 10.2% of subjects and alcohol consumers (>1 drink per week) were 46.8% of subjects. Medication use was as follows: 10.1% were on hypoglycemic drugs, 16.1% were on cholesterol-lowering drugs, 14.9% were on thyroid medication, and 35.7% of the female subjects were on hormone replacement therapy. Due to limitations in DNA availability, the PLIN 6209T>C and 13041A>G genotypes were successfully obtained from 706 subjects, and the PLIN 11482G>A and 14995A>T genotypes were obtained from 705 subjects. Obesity was defined as BMI ≥ 30 kg/m2. There were no significant differences in anthropometric and biochemical measures between individuals with or without genotype information.

biochemical measurement

Fasting blood samples (baseline) were drawn from all subjects entering the program. Blood samples were placed in tubes containing SST clot activating gel (Becton-Dickinson vacutainer system) for lipid and glucose measurements, or 0.1% EDTA for apolipoprotein measurements. Samples for lipid and glucose measurements were allowed to clot and serum was separated by centrifugation at 2500 rpm for 15 minutes. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and glucose levels were measured by standard automated enzymatic methods (Smith-Kline Beecham Laboratories), while low Density lipoprotein cholesterol (LDL-C).

DNA isolation and genotype analysis

Genomic DNA was isolated from whole blood using the QIA amp Blood Kit (Qiagen). First, a DNA fragment containing the target SNP is amplified by multiplex polymerase chain reaction (PCR). The primers used are shown in Table 1. PCR reactions were performed in a 10 μl reaction volume containing 0.2 mmol/l of each dNTP, 0.2 μmol/l of each primer, 3.0 mmol/l magnesium chloride and 0.8 U Qiagen Hotstar Taq polymerase. PCR cycling conditions were 95°C for 10 minutes, followed by 7 cycles of 95°C for 30 seconds, 70°C for 30 seconds, and 72°C for 1 minute, followed by 41 cycles of 95°C for 30 seconds, 65°C for 30 seconds, and 72°C for 1 minute . A final extension phase of 5 minutes at 72°C was included at the end of the protocol. The PCR product was incubated with 2.5U of each exonuclease I (New England Biolabs., Inc. Beverly, MA) and calf intestinal phosphatase (New England Biolabs., Inc. Beverly, MA) at 37°C for 60 minutes to remove Unincorporated dNTPs and primers were then incubated at 75°C for 15 minutes to inactivate the enzyme. Subsequent single nucleotide extensions were performed using the ABIPrism SnaPshot system (Applied Biosystems, Foster City, CA). The probes used are given in Table 1.

The reaction mixture for the extension reaction contained 1.5 μl Snapshot ReadyReaction Mastermix (Applied Biosystems, Foster City, CA), 1.0 μl water and 1.5 μl multiplex PCR product and 1.0 μl probe mix (2 μmol/l for each probe). The reaction conditions were 35 cycles of 96°C for 30 seconds, 50°C for 30 seconds and 60°C for 30 seconds. The product was incubated with 3U calf intestinal phosphatase for 60 minutes at 37°C to remove unincorporated dNTPs, followed by incubation at 75°C for 15 minutes to inactivate the enzyme. Finally, genotyping was performed with an ABI Prism 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA) using Genotyper version 3.7 (Applied Biosystems, Foster City, CA).

Statistical analysis

The null hypothesis of no association between genetic variants and phenotypic outcomes adjusted for covariates (age, BMI, smoking, alcohol consumption, and medication status) was tested with multivariate linear regression analysis. Phenotypic outcomes between genotype groups were compared using ANCOVA (Tukey's test) with multiple adjustment for covariates. Finally an additive genetic model (grouping based on the number of variant alleles at each polymorphic site) was used based on the observed allelic effects. The interaction between sex and PLIN genotype was tested by introducing the corresponding product term into the model. Hypothesis testing was performed using the SAS 8.0 statistical package. A statistical P value of less than 0.05 was considered to be a boundary of significance. Statistical tests were performed after logarithmically transforming fasting glucose and triglycerides into a normal distribution. Allele frequencies were calculated with the THESIAS program to test for pairwise linkage disequilibrium (LD), and to derive haplotypes. The computer program is based on the maximum likelihood model described by Tregouet et al. (21). The association of haplotypes with obesity risk was examined after multiple adjustment for the above covariates.

result

By resequencing the region conserved between human and mouse in 40 unrelated subjects and by searching one of the public SNP databases (http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi? locusId=5346) for the identification of common polymorphisms at the PLIN locus. Four common polymorphisms PLIN 6209T>C, 11482G>A, 13041A>G and 14995A>T were identified and selected for this study. The numbering of these SNPs reflects their relative position to the A of the initiation methionine codon ATG of PLIN, which is numbered "+1" (position 157157 on the reference sequence, accession number GI21431190). The genotype distribution did not deviate from Hardy-Weinberg expectations. The minor allele frequencies of the SNPs examined were 0.453 for 6209T, 0.299 for 11482A, 0.336 for 13041G, and 0.360 for 14995T. Pairwise linkage disequilibrium (LD) examination indicated that both PLIN 6209T>C and 11482G>A were present in strong LD (D'=0.92, P<0.001). No significant LD was detected between these SNPs and the 13041A>G SNP (D′=0.04, P=0.224 for the 6209T>C/13041A>G pair, D′=0.05 for the 11482G>A/13041A>G pair, P=0.110). Finally, as shown in Figure 5, PLIN 14995A>T showed different levels of LD.

For outcome variables, we found a significant interaction between PLIN genotype and sex. Therefore, we conduct separate analyzes for men and women. First, we examined the association of alleles for each SNP with body fat measures including BMI, percent body fat, and waist circumference. In women, we found significant allelic differences in percent body fat and waist circumference. For PLIN 13041A>G, the mean percent body fat values were 30.6%, 32.7%, and 33.3% for the AA, AG, and GG groups, respectively (P=0.0166). Similar associations were observed for mean waist circumference: 95.1; 96.9; and 105.1 cm for AA, AG, and GG subjects, respectively (P=0.020). We observed a similar association for PLIN 14995A > TSNP. Mean percent body fat in AA, AT, and TT subjects was 30.5%, 32.5%, and 33.7% (P=0.0104); mean waist circumference was 95.7, 98.9, and 102.6 cm, respectively (P=0.0453). Subjects carrying G/A and G/G genotypes in PLIN 13041A>G had higher BMI values of 1.25 kg/m2 and 1.60 kg/m2 than AA subjects. Similarly, for the PLIN 14995A>T SNP, AT and TT subjects had 0.87 kg/m2 and 2.32 kg/m2 higher BMI than AA subjects (Figure 6). No significant associations were found between PLIN 6209T>C and PLIN 11482G>A genotypes and body fat measurements in females. In men, there were no significant genotype-related differences for any of the variables examined (data not shown).

We also examined the association between PLIN variants and obesity risk. We inferred haplotypes from the 4 SNPs and used these panels for further hazard analysis. Haplotypes containing minor alleles at SNP 13041 and/or 14995 tend to have increased risk of obesity, whereas haplotypes containing minor alleles at 6209 and/or 11482 tend to have reduced obesity in women Danger. Among them, haplotype T/G/G/T was associated with the highest risk of obesity (OR=2.09, 95% CI 0.83-5.23), and haplotype C/G/ A/A was associated with the highest protection against obesity (OR=0.58, 95% CI 0.25-1.34) (Table 2). However, none of these associations reached statistical significance due to sample size limitations. To improve the detection power of the study, we also analyzed haplotype associations based on the 6209T>C/11482G>A or 13041A>G/14995A>T haplotypes. We did not find any significant association between the haplotypes inferred from 6209T>C/11482G>A in men and women. When examining the haplotypes inferred from 13041A>G/14995A>T, compared with haplotype A/A in women, haplotype A/T (OR=1.76, 95% CI 1.07-2.90) and haplotype G/ Both T (OR=1.73, 95% CI 1.06-2.82) were significantly associated with an increased risk of obesity (Table 8). We did not find a significant association between the 13041A>G/14995A>T haplotype and obesity risk in men.

Because of the tight relationship between body fat and energy homeostasis, we analyzed the association between PLIN genotype and some metabolic measures related to energy homeostasis. In female subjects, PLIN 13041A>G and 14995A>T were not significantly associated with the metabolic measures examined, although associated with increased body fat (Table 9). In contrast, PLIN 6209T>C and 11482G>A were associated with LDL-C levels (P=0.007 for PLIN 6209T>C and P=0.028 for PLIN 11482G>A, Table 9). In addition, PLIN 11482G>A was also associated with TC levels with slight significance (P=0.068). Unlike the additive allelic effect on body fat shown by PLIN 13041A>G/14995A>T, only carriers of the homozygous variant PLIN6209T>C/11482G>A tended to have higher LDL-C and/or TC, whereas carriers of the other genotypes had comparable levels in these measures. In males, we found that study subjects carrying PLIN 13041G had lower TC and LDL-C levels compared with those carrying wild-type homozygotes. Note that such associations are all slight (P=0.051 for TC and P=0.049 for LDL-C). In addition, a small association between PLIN 13041A>G and HDL-C levels was also observed (P=0.047). However, it appeared that the main difference in LDL-C levels was between the GA and AA groups. The PLIN 6209T>C, 11482G>A and 14995A>T genotypes were not associated with any of the metabolic measures examined in men (Table 10).

discuss

Since first reported in the early 1990s, perilipid droplet proteins are emerging as key regulators of lipolysis in adipocytes and body fat accumulation (14-17, 22-24). Recently, genetic variation at the PLIN locus was associated with reduced perilipid droplet protein content and increased lipolytic activity in human adipocytes (18), supporting the role of PLIN as a candidate gene for obesity in the general population. In the present study, we examined the association between variability at the PLIN locus and anthropometric and metabolic variables in a white population with elevated mean BMI. Of the 4 common SNPs identified and genotyped in this population, we found that two SNPs located in the 3' untranslated region (PLIN 13041A>G and 14995A>T) were significantly associated with increased percent body fat and waist circumference, and Minor association with increased BMI in female subjects. Furthermore, analysis of the inferred haplotypes using PLIN 13041A>G and 14995A>T SNPs demonstrated an increased risk of obesity for the A/T and G/T haplotypes. In contrast, in males, PLIN polymorphisms were not significantly associated with any measured parameter of body fat.

Perilipid droplet proteins are mainly expressed in adipocytes and sterogenic cells. Because of their physical localization in fat depots, the role of perilipid droplet proteins in regulating the mobilization of fat stores and body fat accumulation has been examined, and several in vitro studies support this notion (13, 23, 25). Additional in vivo evidence for these effects comes from knockout mouse models (15, 16). Our current findings on human PLIN gene variants are also consistent with results from experimental models, suggesting a conserved role for perilipid droplet proteins in lipolysis across species.

Several perilipidin isoforms resulting from alternative splicing have been identified (9, 26) and these isoforms may be functionally distinct (24). PLIN 13041A>G and 14995A>T are both located in the 3' untranslated region where alternative splicing occurs in transcription. It is possible that these polymorphisms could alter transcripts by affecting splicing. PLIN 13041A>G and 14995A>T were in significant linkage disequilibrium with each other. We therefore speculate that the observed associations between these two polymorphisms and body fat measures could point to the same causative mutation, and given that the 14995T allele is always present in the haplotype associated with increased risk of obesity , we hypothesized that this allele might be more closely associated with the causative mutation.

In our study, we examined several anthropometric measures (BMI, percent body fat, and waist circumference). Although they were significantly correlated, these measures were not identical in representative body fat. Thus, BMI does not differentiate between fat and lean mass. Moreover, these associations are age-dependent (27, 28). On the other hand, waist circumference has been proposed to be a more precise measure for identifying those individuals at higher risk of metabolic syndrome (29). Despite those differences, it is reassuring that we have found consistent associations between PLIN polymorphisms and several obesity indices.

Measures of obesity often correlate with abnormalities in glucose and lipid metabolism. However, in our study, we did not find significant associations between PLIN 13041A>G and 14995A>T SNPs and glucose or lipid-related measures. Similar findings have been observed in experimental models. Thus, PLIN knockout mice appear to adapt to the constitutively activated lipolysis resulting from PLIN gene ablation by activating mechanisms to remove these lipolysis products through upregulation of oxidative catabolic pathways and downregulation of lipid/sterol synthesis pathways (30) . We propose that such compensatory mechanisms may also occur when lipolysis is inhibited.

The other two SNPs examined (PLIN 6209T>C and 11482G>A) were not associated with body obesity in this study. Mottagui-Tabar et al. previously reported that PLIN 11482G>A was associated with decreased perilipid droplet protein content and increased lipolysis in obese women (18). Therefore, we expected that PLIN 11482A would be associated with a lean phenotype. Several reasons might explain the zero correlation between this polymorphism and body fat measures in our study: First, our study population had more obese subjects than the general population (mean BMI = 29.6 kg/m2). It is possible that these subjects were genetically predisposed to obesity due to the influence of other loci, and the expression of the protective effects of PLIN 11482A could be suppressed under these conditions. In addition, the PLIN 11482G>A polymorphism reported by Mottagui-Tabar is an intronic SNP, which may be in LD with a functional mutation. Likewise, associations between PLIN 11482G > A and phenotypic variables can be achieved through population-specific genetic architecture, in which the magnitude of pairwise LD between PLIN 11482G > A and functional variation in our population can be reduced.

The finding that women carrying the PLIN 11482AA genotype appear to have higher TC and LDL-C is consistent with the Mottagui-Tabar study, in which the AA genotype was associated with an increased rate of fat lipolysis (18). Elevated fatty acids in the circulation will increase their flux to the liver, leading to altered lipid metabolism and increased cholesterol production (31). Because PLIN 6209T>C and 11482G>A were almost entirely in LD, we speculated that the observed association between PLIN 6209 and LDL-C concentrations might have the same genetic basis as the PLIN 11482G>A SNP.

The PLIN locus was not associated with obesity-related measures in male subjects. It has been suggested that men and women may have different sets of obesity susceptibility genes (7). Furthermore, twin studies suggest that obesity may be more heritable in women than men (32). However, larger studies are needed before we can conclude that PLIN is not a candidate gene for obesity-associated phenotypes in men. Differential expression levels of perilipid proteins in men and women (33) may account for their differential susceptibility to genetic effects of PLIN.

In conclusion, we found a significant association between two SNPs in the 3' untranslated region of the PLIN gene (PLIN 13041A>G and 14995A>T) and obesity risk in Caucasian women. Carriers of variant alleles of these two SNPs had increased mean body fat mass, waist circumference, and BMI compared to carriers of the wild-type genotype. In contrast, no significant association was found between PLIN polymorphisms and body fat measurements in men. Our findings support an important role for PLIN as a candidate gene for obesity risk in women.

Example II References

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9. Greenberg AS, Egan JJ, Wek SA et al. Isolation of cDNAs for perilipins A and B: sequence and expression of lipid droplet-associated proteins of adipocytes. Proc Natl Acad Sci. U S A 1993; 90: 12035-12039.

10. Nishiu J, Tanaka T, Nakamura Y. Isolation and chromosomal mapping of the human homolog of perilipin (PLIN), a rat adipose tissue-specific gene, by differential display method. Genomics 1998; 48: 254-257.

11.Servetnick DA, Brasaemle DL, Gruia-Gray J et al.Perilipins are associated with cholesteryl ester droplets in steroidogenic adrenal cortical and Leydigcells.J Biol Chem.1995;270:16970-16973.

12. Brasaemle DL, Barber T, Wolins NE et al. Adipose differentiation-related protein is an ubiquitously expressed lipid storage droplet-associated protein. J Lipid Res. 1997; 38: 2249-2263.

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14. Brasaemle DL, Rubin B, Harten IA et al. Perilipin A increases triacylglycerol storage by decreasing the rate of triacylglycerol hydrolysis. J Biol Chem. 2000; 275: 38486 38493.

15. Martinez-Botas J, Anderson JB, Tessier D et al. Absence of perilipin results leanness and reverses obesity in Lepr(db/db) mice. Nat Genet. 2000; 26: 474-479.

16. Tansey JT, Sztalryd C, Gruia-Gray J et al. Perilipin ablation results in a lean mouse with aberrant adipocyte lipolysis, enhanced leptin production, and resistance to diet-induced obesity. Proc Natl Acad Sci U 4 1; 4-8 A 20 6499.

17. Kern PA, Di Gregorio G, Lu T et al. Perilipin expression in human adipose tissue issue is elevated with obesity. J Clin Endocrinol Metab. 2004; 89: 1352-1358.

18. Mottagui-Tabar S, Ryden M, Lofgren P et al. Evidence for an important role of perilipin in the regulation of human adipocyte lipolysis. Diabetologia2003; 46: 789-797.

19.Barnard RJ.Effects of life-style modification on serum lipids.Arch InternMed.1991;151:1389-1394.

20. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972; 18: 499-502.

21. Tregouet DA, Barbaux S, Escolano S et al. Specific haplotypes of the P-selectin gene are associated with myocardial infarction. Hum Mol Genet2002; 11: 2015-2023.

22. Londos C, Gruia-Gray J, Brasaemle DL et al.Perilipin: possible roles institution and metabolism of intracellular neutral lipids in adipocytes and steroidogenic cells. Int J Obes Relat Metab Disord.1996; 21 Suppl 3:

23. Sztalryd C, Xu G, Dorward H et al. Perilipin A is essential for the translocation of hormone-sensitive lipase during lipolytic activation. J Cell Biol. 2003: 161: 1093-1103.

24. Tansey JJ, Huml AM, Vogt R et al.Functional studies on native and mutated forms of perilipins: A role in protein kinase A-mediated lipolysis oftriacylglycerols in CHO cells.J Biol Chem.2002.

25.Souza SC, Muliro KV, Liscum L et al.Modulation of hormone-sensitivelipase and protein kinase A-mediated lipolysis by perilipin A in an adenoviralreconstituted system.J Biol Chem.2002;277:8267-8272.

26. Lu X, Gruia-Gray J, Copeland NG et al. The murine perilipin gene: the lipiddroplet-associated perilipins derive from tissue-specific, mRNA splice variants and define a gene family of ancient origin. Mamm Genome 2001; 12: 741- 749.

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28. Allison DB, Saunders SE. Obesity in North America. An overview. Med ClinNorth Am. 2000; 84: 305-32, v.

29 Visscher TL, Seidell JC, Molarius A et al. A comparison of body mass index, waist-hip ratio and waist circumference as predictors of all-cause mortality among the elderly: the Rotterdam study. Int J Obes Relat Metab Disord; 2.5: 200 1730-1735.

30. Castro-Chavez F, Yechoor VK, Saha PK et al. Coordinated Upregulation of Oxidative Pathways and Downregulation of Lipid Biosynthesis Underlie Obesity Resistance in Perilipin Knockout Mice: A Microarray GeneExpression Profile. Diabetes 20036-6266-52:52

31. Arner P. Insulin resistance in type 2 diabetes: role of fatty acids. Diabetes Metab Res Rev. 2002; 18 Suppl 2: S5-S9.

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Example III: Intragenic linkage disequilibrium structure of the human perilipidin gene (PLIN) and haplotypes associated with increased risk of obesity in a multiethnic Asian population

Materials and methods

Subjects and Study Design

A total of 4,131 subjects participating in the NHS 98 were included in the study. NHS 98 is the first step in determining risk factors for major noncommunicable diseases in Singapore. The detailed method has been described previously (11). The method used for NHS98 is based on the WHO-recommended protocol and procedures for field surveys of diabetes and other non-communicable diseases and the WHOMONICA (Multi-national Monitoring of Trends and Determinants in Cardiovascular Disease) protocol for population surveys. Briefly, 11,200 individuals were selected from the National Database on Dwellings from addresses representing housing types (a proxy for socio-economic status) of the entire Singapore resident population. Random samples were selected from these individuals by disproportionate stratification and systematic sampling. Malays and Indians were oversampled to ensure that prevalence estimates for these minority groups were reliable. A total of 4,723 subjects participated in the study, and the ethnic composition was 64% Chinese, 21% Malay and 15% Asian Indian. Informed consent was obtained from all participants in this survey. The study was approved by the Ministry of Health in Singapore and the Ethics committee of the Singapore General Hospital.

Data on lifestyle factors were collected using a questionnaire applied to the interviewees. Body fat is assessed using anthropometric measures commonly used in large-scale epidemiological studies, including body weight, body mass index (BMI), waist circumference, hip circumference, and waist/hip ratio (WHR). Briefly, body weight was measured without shoes and in light indoor clothing using a calibrated digital scale (SECA, Hamburg, Germany) with an accuracy of 0.1 kg. Using a wall-mounted stadiometer, height was measured to the nearest 0.1 cm without shoes at the Frankfurt level. BMI was calculated by dividing body weight by the square of height (weight (kg)/height (m2)). Lumbar measurements are made to the nearest 0.1 cm and are measured midway between the lower border of the rib cage and the iliac crest at the end of a gentle exhalation. Measure directly on the skin. Measure the hip circumference directly on the largest rotor on the underwear to the nearest 0.1 cm (12). Obesity was defined dichotomously as BMI > 30 kg/m2 and overweight as 30 kg/m2 > BMI > 25 kg/m2. Using the above criteria, there were a total of 300 obesity cases, of which 1,333 subjects were classified as overweight.

No differences were found in the main anthropometric and biochemical measures of subjects who were and were not genotyped for the PLIN gene.

DNA isolation and genotype analysis

Genotype analysis with single nucleotide extensions. First, a DNA fragment containing five newly identified SNPs at the PLIN locus was amplified by multiplex polymerase chain reaction (PCR). SNPs were numbered according to their A position relative to the ATG of the initiation methionine codon of PLIN (6209 T>C, 10171 A>T, 11482 G>A, 13041 A>G, 14995A>T), so The A number of the ATG is "+1" (position 157157 on the reference sequence, accession number GI21431190). The primers used are given in Table 1. PCR amplification was performed in a 10 μl reaction volume containing 0.2 mmol/l of each dNTP, 0.2 μmol/l of each primer, 3.0 mmol/l magnesium chloride and 0.8 U QiagenHotstar Taq polymerase. PCR cycling conditions were 95°C for 10 minutes, followed by 7 cycles of 95°C for 30 seconds, 70°C for 30 seconds, and 72°C for 1 minute, followed by 41 cycles of 95°C for 30 seconds, 65°C for 30 seconds, and 72°C for 1 minute . A final extension phase of 2 minutes at 72°C was included at the end of the protocol. The PCR products were incubated with 2.5 U each of exonuclease I (New England Biolabs., Inc. Beverly, MA) and calf intestinal phosphatase (New England Biolabs., Inc. Beverly, MA) at 37°C for 60 minutes to remove unincorporated dNTPs and primers. The enzyme was then inactivated by incubation at 75°C for 15 minutes.

Subsequent single nucleotide extensions were performed using the ABI Prism SnaPshot Multiplex System (Applied Biosystems, Foster City, CA). Probes used for single nucleotide extension are listed in Table 1. The extension reaction was performed using a PCR thermal cycler in a 5 μl reaction mix containing 1.5 μl Snapshot Ready Reaction Mastermix (Applied Biosystems, Foster City, CA), 1.0 μl water and 1.5 μl multiplex PCR products and 1.0 μl probe mix (for 6209C >T, 10171A>T and 11482G>A 1.5 μmol/l; for 13041A>G and 14995A>T 2.0 μmol/l). The reaction conditions were 35 cycles of 96°C for 30 seconds, 50°C for 30 seconds and 60°C for 30 seconds. The reaction product was incubated with 3U calf intestinal phosphatase for 60 minutes at 37°C to remove unincorporated dNTPs, followed by incubation at 75°C for 15 minutes to inactivate the enzyme. Genotype analysis was performed with an ABI Prism 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA) using Genotyper version 3.7 (Applied Biosystems, Foster City, CA). Quality control of genotype analysis was established and results were interpreted independently by two researchers.

Statistical analysis

Allele frequencies were estimated using Arlequin (available at http://lgb.unige.ch/arlequin/), genotype frequencies at each SNP locus were checked for agreement with Hardy-Weinberg equilibrium, and the SNPs examined were estimated Pairwise LD between. Statistical significance of LD between each pair of SNPs was tested using the likelihood ratio test. Haplotypes were inferred using the THESIAS program (available at http://ecgene.net/genecanvas/modules/mydownloads/singlefile.php?cid=l&lid=1 ), which was designed to test haplotypes in unrelated subjects , while adjusting for covariates. The computer program is based on the maximum likelihood model described by Tregouet et al. (13). Individual associations were analyzed with SAS (Windows version 8.0) and statistical significance was defined at the 5% level. Differences in the prevalence of PLIN genotypes between obese cases and non-obese controls were analyzed by x2 analysis. Relative risks of obesity were estimated using odds ratios (OR) with 95% confidence intervals (CI). Multivariate logistic regression analyzes were used to adjust for possible covariates of obesity (age, sex, smoking, alcohol consumption, exercise, and diabetic status). Interactions between genetic effects and sex were tested by introducing corresponding product terms into the model. A general genetic model (grouping subjects according to the genotype of each SNP) was first used to examine allelic effects and, finally, an appropriate genetic model (dominant, recessive, , or additive).

result

Five common pairs of allelic polymorphisms (6209T>C, 10171A>T, 11482G>A, 13041A>G and 14995A>T) were selected and genotyped in Singapore NHS98 population. These SNPs were located in intron 2 (6209), intron 5 (10171), intron 6 (11482), exon 8 (13041) and exon 9 (14995). Genotype information of 5 PLIN polymorphisms was obtained from 4,131 research subjects. The characteristics of the participants genotyped are shown in Table 11. Chinese accounted for 67.28%, Malays accounted for 18.16%, and Indians accounted for 14.56%. In general, Indians are older and Chinese are younger. Among men, Malays and Indians have comparable mean BMIs, which are -1.0 kg/m2 higher than among Chinese. Among women, Malays had the highest BMI (26.3±5.6kg/m2), followed by Indians (25.6±5.0kg/m2) and Chinese (22.1±3.6kg/m2). For both men and women, obesity (BMI ≥ 30kg/m2) and overweight (BMI ≥ 25kg/m2) were most prevalent among Malays, followed by Indians. The prevalence of obesity and overweight was much higher in these two population groups than among the Chinese. Indian men and women have the highest rates of diabetes (18.2% for men and 17.4% for women), higher than those observed in Malays (10.9% for men and 14.8% for women), while among Chinese, these figures Much lower: 7.2 percent for men and 6.6 percent for women. Malays have the highest proportion of current smokers, while alcohol is consumed most frequently among Chinese.

In the three populations, the minor allele frequencies were: 0.320 to 0.462 for PLIN 6209C>T, 0.135 to 0.255 for PLIN 10171A>T, 0.326 to 0.439 for PLIN 11482G>A, and 0.326 to 0.439 for PLIN 13041A>G 0.296 to 0.471, and PLIN 14995A>T is 0.361 to 0.444. The observed and expected genotype frequencies were consistent with Hardy-Weinberg equilibrium for all polymorphisms in the three populations. Chi-square tests for uniformity indicated no significant differences in genotype/allele distribution between men and women for any of the 5 SNPs examined. Instead, we observed significant inter-ethnic differences in the distribution of genotypes at each polymorphic locus. Significant non-random allelic associations were found between each pair of SNPs, as indicated by D' for pairwise LD in Figure 7. It seems that the LD structure within PLIN is not uniform. The PLIN 6209C>T and 10171A>T SNPs were in negative LD with all other SNPs, while the PLIN 11482G>A, 13041A>G and 14995A>T SNPs were in positive LD with each other in the three population groups. In the positive association, the strongest LD was found between PLIN11482G>A and 14995A>T, where D' ranged from 0.76 to 0.83 in the three populations.

We examined potential associations between inferred PLIN haplotypes and the risk of obesity (defined as BMI ≥ 30 kg/m2) in three population groups. We use THESIAS, its maximum likelihood algorithm based on haplotype reconstruction (13). We detected no significant gene-sex interactions. So analyze men and women together. Using 5 SNPs, we deduced 24, 18 and 18 haplotypes for Chinese, Malay and Indian, respectively. We then examined the association between common haplotypes (frequency greater than -5%) and obesity risk (Table 12). In Malays, we found that haplotypes 11222 (OR=1.64, 95% CI 1.08-2.48) and 11212 (OR=1.65, 95% CI 1.11-2.46) were associated with obesity compared with the most prevalent haplotype 21111 significantly associated with an increased risk of the disease. It was also found that haplotype 11212 was also significantly associated with obesity risk in Indians (OR=1.94, 95% CI 1.06-3.53). Conversely, haplotype 12111 was associated with a reduced risk of obesity compared with haplotype 21111, reaching marginal significance in Indians (OR=0.30, 95% CI 0.09-1.06). Likewise, this haplotype was also associated with a slightly lower risk of obesity in Malays. Adjustment for relevant covariates (age, sex, smoking, alcohol consumption, exercise, and diabetes status) did not change the significance of the associations observed in Malays, but were slightly less significant in Indians. We did not find a significant association between PLIN haplotypes and obesity risk in Chinese.

We also examined haplotype association using a subset of SNPs (PLIN 11482, 13041 and 14995) that were in positive LD with each other. Among these three SNPs, we inferred 8 haplotypes within each human population. Examination of the association between individual haplotypes (frequency greater than ~5%) and risk of obesity showed that, in Malays, haplotypes 212, 222 and 121 were associated with increased obesity compared with the most common haplotype 111 Significantly associated with the superiority of 212 (OR=2.12, 95% CI 1.36-3.32 for 212, OR=2.02, 95% CI 1.36-3.01 for 222, and OR=1.89, 95% CI 1.05-3.41 for 121). Among Indians, haplotype 212 was significantly associated with increased odds of obesity compared with haplotype 111 (OR=2.39, 95% CI 1.26-4.50). Haplotype 122 was also associated with increased risk of obesity with small significance. Adjustment for major obesity risk factors (age, sex, smoking, alcohol consumption, exercise, and diabetes status) did not alter the observed association, except that the association with haplotype 121 became marginally significant in Malays (Table 13 and Table 14).

In addition, we examined the association of each individual SNP with obesity risk. No significant association was found for PLIN6209C>T and 11482G>A. In Malays and Indians, homozygosity for the T allele at PLIN 14995A>T was significantly associated with increased odds of obesity compared with other genotypes (for Malays, multivariate OR=2.28, 95 % CI 1.45-3.57, for Indians, multivariate OR = 2.04, 95% CI 1.08-3.84). Homozygosity for the rare alleles of PLIN 11482G>A or 13041A>G was also found to be associated with an increased predominance of obesity in Indians and Malays, but statistical significance was only reached in the latter group (for For PLIN 11482G > A, multivariate OR = 1.94, 95% CI 1.22-3.08, for PLIN 13041A > G, multivariate OR = 1.87, 95% CI 1.08-3.25) (see Figure 8). No significant association between these polymorphisms and obesity risk was found in Chinese.

discuss

In this study, we have investigated the association between PLIN gene variants and obesity risk in 4,131 subjects with different ethnic backgrounds using SNP- and haplotype-based analyses. We genotyped five double-allelic polymorphisms at the PLIN locus, a candidate gene for obesity, in Asian populations including three populations (Chinese, Malay and Indian) (PLIN 6209C>T , 10171A>T, 11482G>A, 13041A>G and 14995A>T). By examining the association of the inferred haplotypes with obesity risk, we demonstrate that the PLIN 11212 haplotype is significantly associated with increased risk of obesity in Malays and Indians. Additional haplotype analysis using three SNPs in positive linkage disequilibrium (11482G>A, 13041A>G, and 14995A>T) showed that haplotypes 212 and 222 were associated with increased adiposity in Malays after covariate adjustment haplotype 212 was significantly associated with increased obesity risk in Indians. Finally, individual SNP analysis indicated that PLIN 14995A>T was significantly associated with obesity risk in Malays and Indians.

Our findings provide strong support for considering PLIN as a candidate gene for obesity risk in humans (reference http://obesitygene.pbrc.edu/). Perilipid droplet proteins are the major lipid droplet-associated proteins in adipocytes (2, 3, 14). It has been found that perilipid droplet proteins may play an important role in regulating PKA-mediated intracellular lipolysis in adipocytes and affecting the turnover of stored TAGs (4, 5, 15). In vivo experimental models have demonstrated that the product of the PLIN gene plays a key role in determining body fat composition (6,7). In humans, the abundance of perilipid proteins in adipose tissue also correlates with the rate of lipolysis, and one of its genetic variants can affect both perilipid protein content and the rate of lipolysis (8).

Our data showed consistent associations between PLIN haplotypes and obesity risk in two of the three populations examined. In Malays and Indians, haplotype 11212 was consistently associated with increased risk of obesity, suggesting that this haplotype may contain functional mutations. Furthermore, haplotype analysis using SNPs at loci 11482, 13041 and 14995 increased the magnitude and statistical significance of the association. After adjustment for relevant covariates, haplotype 212 (11482, 13041, and 14995) was associated with increased risk of obesity compared with wild-type haplotype (111) between Malays and Indians. Given the consistent association with increased obesity risk in both populations, we hypothesized that haplotype 212 from the 11482G>A, 13041A>G, and 14995A>T SNPs was more likely to contain or co-segregate with a functional mutation.

The results of analysis of individual SNPs suggested that PLIN 14995A>T was the most important single genetic contributor to the observed association of the haplotype with obesity. This polymorphism was consistently associated with obesity risk and had the highest odds ratios in Malays and Indians. Although two other SNPs: PLIN 11482G>A and 13041A>G were also found to be associated with an increased risk of obesity, the low magnitude of this finding and the fact that it was limited to one population suggest that their association may be due to They are LD with PLIN 14995A>T SNP.

We did not find a significant association between PLIN variants and obesity risk in Chinese. Some researchers have suggested that lower cut-off values should be used to define obesity in Asians (16, 17). However, using lower cut-off values (27 kg/m2 and 25 kg/m2) in our analysis did not change the magnitude of this finding (data not shown). Alternatively, we hypothesized that differential penetrance of genetic effects may underlie the observed bias between Chinese and the other two populations regarding the relationship between PLIN and obesity. In Singapore, despite living in similar environments, Malays and Indians have comparable mean BMIs, which are significantly higher than the mean BMI among Chinese, suggesting that Chinese may have a lower genetic predisposition to obesity.

The PLIN 13041A>G and PLIN 14995A>T SNPs are located in regions where alternative splicing occurs during PLIN transcription, resulting in several perilipid droplet protein isoforms (18). Recent data suggest that perilipid droplet protein isoforms may function with different efficiencies in protecting fat storage from PKA-mediated lipolysis (19). Thus, without wishing to be bound by theory, the underlying genetic effect that may be associated with PLIN 13041A>G and PLIN 14995A>T is through effects on splicing and expression of different perilipid droplet protein isoforms. It is also possible that PLIN11482G>A simply represents genetic markers in these associations, rather than functional mutations. We have noted important differences in the LD structure for the PLIN gene between Asian and Caucasian populations (data not shown), and we argue that different intragenic LD structures between different populations may drive LD in multiple populations. different associations. Such differences in LD structures may explain the discrepancy between our findings and those of earlier studies. Mottagui-Tabar et al recently reported that the A allele at the PLIN 11482G>A SNP was associated with enhanced basal and norepinephrine-induced lipolysis. Furthermore, the same allele is associated with lower perilipid protein content in obese women (8). From this finding, and contrary to our observations, a negative association between PLIN 11482AA genotype and body fat would be expected. However, in the study by Mottagui-Tabar et al., the subjects were Caucasian females. Ethnic differences in LD structure may also explain the lack of association between genetic variants at this locus and obesity in Chinese.

In conclusion, we found a consistent association between PLIN haplotypes and increased risk of obesity in Singaporean Malays and Indians. Malays and Indians share common risk haplotypes that make these populations predisposed to obesity. SNP analysis alone suggested that PLIN 14995A>T may be the more relevant genetic marker for the observed haplotype associations.

Example III References

1. Greenberg AS, Egan JJ, Wek SA et al. Perilipin, a major hormonally regulated adipocyte-specific phosphoprotein associated with the periphery of lipidstorage droplets. J Biol Chem 1991; 266: 11341-11346.

2. Greenberg AS, Egan JJ, Wek SA et al. Isolation of cDNAs for perilipins A and B: sequence and expression of lipid droplet-associated proteins of adipocytes Proc Natl Acad Sci U S A 1993; 90: 12035-12039.

3.Servetnick DA, Brasaemle DL, Gruia-Gray J et al. Perilipins are associated with cholesteryl ester droplets in steroidogenie adrenal cortical and Leydigcells. J Biol Chem 1995; 270: 16970-16973.

4. Brasaemle DL, Rubin B, Harten IA et al. Perilipin A increases triacylglycerol storage by decreasing the rate of triacylglycerol hydrolysis. J Biol Chem 2000; 275: 38486-38493.

5.Souza SC, de Vargas LM, Yamamoto MT et al. Overexpression of perilipin A and B blocks the ability of tumor necrosis factor alpha to increase lipolysis in 3T3-L1 adipocytes. J Biol Chem 1998; 273: 46665-2

6. Martinez-Botas J, Anderson JB, Tessier D et al. Absence of perilipin results leanness and reverses obesity in Lepr(db/db) mice. Nat Genet 2000; 26: 474-479.

7. Tansey JT, Sztalryd C, Gruia-Gray J et al. Perilipin ablation results in a lean mouse with aberrant adipocyte lipolysis, enhanced leptin production, and resistance to diet-induced obesity. Proc Natl Acad Sci U 4 S ; 49-8: 6 200 6499.

8. Mottagui-Tabar S, Ryden M, Lofgren P et al. Evidence for an important role of perilipin in the regulation of human adipocyte lipolysis. Diabetologia 2003; 46: 789-797.

9. Poulsen P, Vaag A, Kyvik K, Beck-Nielsen H. Genetic versus environment alaetiology of the metabolic syndrome among male and female twins. Diabetologia 2001; 44: 537-43.

10. Hughes K, Aw TC, Kuperan P et al. Central obesity, insulin resistance, syndrome X, lipoprotein (a), and cardiovascular risk in Indians, Malaysia, and Chinese in Singapore. J Epidemiol Community Health 1997; 51: 394-399 .

11. Cutter J, Tan BY, Chew SK. Levels of cardiovascular disease risk factors in Singapore following a national intervention programme. Bull World Health Organ 2001; 79: 908-915.

12. Deurenberg-Yap M, Li T, Tan WL et al. Can dietary factors explain differences in serum cholesterol profiles among different ethnic groups (Chinese, Malays and Indians) in Singapore? Asia Pac J Clin Nutr 2001;10:39-45.

13. Tregouet DA, Barbaux S, Escolano S et al.Specific haplotypes of the P-selectin gene are associated with myocardial infarction. Hum Mol Genet 2002;11:2015-2023.

14. Nishiu J, Tanaka T, Nakamura Y. Isolation and chromosomal mapping of the human homolog of perilipin (PLIN), a rat adipose tissue-specific gene, by differential display method. Genomics 1998; 48: 254-257.

15. Sztalryd C, Xu G, Dorward H et al. Perilipin A is essential for the translocation of hormone-sensitive lipase during lipolytic activation. J Cell Biol 2003; 161: 1093-1103.

16. Chang CJ, Wu CH, Chang CS et al. Low body mass index but high percent body fat in Taiwanese subjects: implications of obesity cutoffs. Int J ObesRelat Metab Disord 2003; 27: 253 259.

17. WHO expert consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies, The Lancet 2004; 363: 157-163.

18. Lu X, Gruia-Gray J, Copeland NG et al. The murine perilipin gene: the lipiddroplet-associated perilipins derive from tissue-specific, mRNA splice variants and define a gene family of ancient origin. Mamm Genome 2001; 12: 741- 749.

19. Tansey JJ, Huml AM, vogt R et al. Functional studies on native and mutated forms of perilipins: A role in protein kinase A-mediated lipolysis oftriacylglycerols in CHO cells. J Biol Chem 2002.

References cited herein and throughout this specification are hereby incorporated by reference in their entirety.

Table 1 SNPs Primers and Probes PLIN1(6209 ¹ T>C) dbSNP rs#2289487 ² intron 2 contig position: 150949 ³ Forward: CTCTGTTTCCAGGGACCCAAGTCAGAT (SEQ ID NO.: 1) Reverse: CCTACACTCTGGGGATGCGGAGAT (SEQ ID NO.: 2) Probe: GACTGACTGACTGACTGACTGACCCCACTGCCTAGAA (SEQ ID NO.: 3) PLIN2(ND) ⁴ intron 3dbSNP rs#1561726 contig position: 149309 Forward: GAGGGAGAAGAGAGGTGTGAGAGGGA (SEQ ID NO.: 4) Reverse: CATCTGGGCTCTCTGCTGCTTGAG (SEQ ID NO.: 5) Probe: GACTGACTGACTGACTGACTGACTGACTGTGTCCCCCGGAGAG (SEQ ID NO.: 6) PLIN3 (10171A>T ⁵ ) dbSNP rs#2304794 intron 5 contig position: 146987 Forward: TTGGCCTTGGGAGACTTCTGGG (SEQ ID NO.: 7) Reverse: TTGTCACACACACTGCCTGGGAAT (SEQ ID NO.: 8) Probe: GACTGACTGACTGACTGACTGACTGACTGACTGACTGCAGGAGGTGGCTCA (SEQ ID NO.: 9) PLIN4(11482G>A) dbSNP rs#894160 intron 6 Forward: AAGTGTTGCCCCTGCAGGAAT (SEQ ID NO.: 10) Reverse: GAGTGGAACTGCTGGGCCATA (SEQ ID NO.: 11) Probe: GACTGACTGACTGACTGACTGACTGACTGACTGA Contig position: 145676 CTTGTGGGGCTCCCTAGA (SEQ ID NO.: 12) PLIN5 (13041A>G) dbSNP rs#2304795 exon 8 (synonymous) contig position: 144116 Forward: CTCACCGGCACGTAATGCAC (SEQ ID NO.: 13) Reverse: CCCTCCAGACCACCATCTCG (SEQ ID NO.: 14) Probe: GACTGACTGACTGACTGACTGACTGACTGACTGACTGACCTTGGTTGAGGAGACAGC (SEQ ID NO.: 15) PLIN6(14995A>T) dbSNP rs#1052700 exon 9 (untranslated region) contig Location: 142163 Forward: AAGCAGCTGGCTCTACAAAGCA (SEQ ID NO.: 16) Reverse: AGCATCCTTTGGGGCTTCA (SEQ ID NO.: 17) Probe: GACTGACTGACTGACTGACTGACTGACTGACTGACTGACTGACTGACTGCCTGCTGGGAGCCT (SEQ ID NO.: 18)

¹ : The numbering is the base number of A (expressed as nucleotide+1) from the variant to the ATG of the initial methionine codon.

2: Refer to "http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId=5346".

³ : Genomic position in the reference sequence (GI21431190). ⁴ : not detected

⁵ : The observed less common allele frequency is less than 2%.

Table 2. Demographic, biochemical, and lifestyle characteristics of study subjects, depending on sample selection: sample 1 (population-based) and sample 2 (hospital-based) sample 1 sample 2 Men (n=788) Women (n=801) Men (n=29) Women (n=128) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Age (years) weight (kg) height (m) ² body mass index (kg/m ² ) waist (cm) hip (cm) waist-hip ratio fasting glucose (mg/dL) triglycerides (mg/dL) total- C (mg/dL) LDL-C (mg/dL) HDL-C (mg/dL) systolic blood pressure (mmHg) diastolic blood pressure (mmHg) obesity (BMI>=30kg/m ² ) (%) overweight (BMI> = 25kg/m ² ) (%) BMI > 35kg/m ² (%) current smoker (%) alcohol drinker (%) sports (%) sedentary activity education (%) elementary school (I and II ) type 2 diabetes (%) taking lipid-lowering drugs (%) ( 16.1)75.6(10.5)15.06171.639.590.636.363.743.732.324.03.85.7 42.4(14.8) ^{* 64.4} (12.7) ^* 1.59(0.06) ^* 25.7(5.4) ^* 88.3(15.4) ^* 102.0(13.0) 0.86(0.07) ^{* 96.1(20.3) *} 94.5(56.6) ^* 201.4(38.4) ^* 128.1 (33.2) ^* 54.9(11.5) ^{* 123.2} (21.6)74.6(12.5)20.3 * 46.6 ^* 6.933.2 ^* 56.8 ^* 58.4 ^* 41.647.1 ^* 22.330.54.38.1 47.5(14.1)125.2(29.5)1.74(0.07)40.9(8.9)128.2(18.1)126.0(21.3)1.02(0.12)126.2(54.2)147.7(72.8)187.1(30.4)112.7(30.3)44.1(13.0(13.0) 15.0)83.7(11.6)100.0100.079.335.766.796.04066.718.514814.314.3 47.4(13.6)106.8(19.1) ^* 1.58(0.05) ^* 42.7(8.2)120.0(16.7)132.4(11.6)0.91(0.08) ^* 120.4(16.7)148.2(83.8)204.0(41.9)125.2(13.7)50.5 ) ^* 136.7(15.6)84.9(11.1)100.0100.089.126.7 ^* 30.8 ^* 74.8 ^* 25.275.216.58.321.521.5

SD: standard deviation; total-C: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HLD-C: high-density lipoprotein cholesterol.

^* : P-value < 0.05 in comparison between men and women. Student's t-test was used for comparison of means, and chi-square test was used for percentages.

University I: 3 years. College II: 5 years or more.

Table 3. Genotype distribution, allele frequency and linkage disequilibrium of polymorphic gene variants at the PLIN locus in a Mediterranean Hispanic population (sample 1). genotype PLIN1 PLIN4 PLIN5 PLIN6 man woman man woman man woman man woman n(%) n(%) n(%) n (%) n(%) n(%) n(%) n(%) 111222 309(40.8)334(44.1)114(15.1) 331(42.4)342(43.8)108(13.8) 405(52.5)307(39.8)60(7.8) 451(57.7)271(34.7)59(7.6) 282(36.2)380(48.7)118(15.1) 318(40.5)345(43.9)122(15.5) 328(44.6)321(43.7)86(11.7) 346(44.7)333(43.0)95(12.3) Allele frequency and 95% CI allele 2 0.364 (0.347-0.381) 0.262 (0.247-0.278) 0.385(0.368-0.402) 0.337(0.320-0.353) Linkage disequilibrium between variants: D; D' and (p) PLIN1PLIN4PLIN5PLIN6 0.159; 0.958 (p<0.001) 0.033; 0.149 (p<0.001) 0.031; 0.191 (p<0.001) 0.085; 0.394 (p<0.001) 0.078; 0.453 (p<0.001) 0.066; 0.320 (p<0.001)

CI: confidence interval

Differences by sex between genotypes were not significant for PLIN1 (p=0.727), PLIN4 (p=0.097), PLIN5 (p=0.142) or PLIN6 (p=0.932) polymorphisms.

Thus, allele frequencies and linkage disequilibrium between polymorphisms have been estimated for men and women.

D: linkage disequilibrium coefficient

D': linkage disequilibrium coefficient D(D/Dmax) normalized by the maximum value that can be taken

Table 4: Frequencies of the 16 detected haplotypes for the four PLIN loci in sample 1 (men + women) haplotype frequency PLIN1 PLIN4 PLIN5 PLIN6 1111111122222222 1111222211112222 1122112211221122 1212121212121212 0.38850.03680.12500.08790.00460.00070.00010.00260.04010.01970.01840.02470.04350.08090.04590.0807

Table 5: Body mass index (BMI) and obesity-associated phenotypes according to carrier status of allele 2 variants at each PLIN polymorphism in a Mediterranean Hispanic population (sample 1). Age-adjusted mean among men. man PLIN1 PLIN4 PLIN5 PLIN6 11 (n=309) mean (SE) 12+22 (n=448) mean (SE) P 11 (n=405) mean (SE) 12+22 (n=367) mean (SE) P 11 (n=282) mean (SE) 12+22 (n=498) mean (SE) P 11 (n=328) mean (SE) 12+22 (n=407) mean (SE) P BMI (kg/m2) body weight (Kg) waist-to-hip ratio glucose (mg/dL) total-C (mg/dL) LDL-C (mg/dL) HDL-C (mg/dL) TG (mg/dL) SBP(mmHg)DBP(mmHg) 26.4(0.2)78.8(0.6)0.95(0.01)94.0(1.3)207.9(2.0)136.9(2.0)45.7(0.6)130.0(4.8)124.8(0.8)75.5(0.6) 26.4(0.2)78.8(0.5)0.95(0.01)94.3(1.1)206.5(1.7)134.4(1.7)46.8(0.5)133.7(4.5)124.7(0.7)75.9(0.5) 0.9260.9590.6530.7640.6040.3500.1210.4590.9230.509 26.3(0.2)78.6(0.5)0.95(0.01)94.3(1.2)208.8(1.8)137.1(1.8)46.0(0.5)130.1(4.1)124.5(0.7)75.1(0.5) 26.5(0.2)78.9(0.5)0.96(0.01)92.8(1.2)204.5(1.9)133.0(1.9)46.8(0.5)134.8(4.4)124.7(0.8)76.2(0.5) 0.7760.6430.1810.4120.1020.1220.2640.3320.8670.142 26.2(0.2)78.3(0.6)0.95(0.01)94.3(1.4)205.0(2.1)133.4(2.2)45.9(0.6)129.2(4.9)123.6(0.9)74.9(0.6) 26.4(0.1)78.9(0.4)0.95(0.01)93.6(1.1)207.0(1.7)135.5(1.7)46.8(0.5)133.6(4.8)125.4(0.7)76.0(0.5) 0.3960.4660.6820.6590.4260.4340.4870.3300.1080.123 26.4(0.2)78.9(0.6)0.95(0.01)94.4(1.3)207.6(1.9)135.2(2.0)45.7(0.6)133.1(4.7)125.3(0.8)75.5(0.6) 26.4(0.2)78.8(0.5)0.95(0.01)94.9(1.2)205.7(1.8)134.6(1.8)46.7(0.5)133.9(4.3)124.7(0.7)76.0(0.5) 0.7560.8030.9610.8170.4910.8370.1920.8980.6050.498

SE: standard error

Total-C: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HLD-C: high-density lipoprotein cholesterol. TG: triglycerides, SBP: systolic blood pressure, DBP: diastolic blood pressure. Weight is additionally adjusted for height.

Table 6: Body mass index (BMI) and obesity-associated phenotypes according to carrier status of allele 2 variants at each PLIN polymorphism in a Mediterranean Hispanic population (sample 1). Age-adjusted mean among women. woman PLIN1 PLIN4 PLIN5 PL 11 (n=331) mean (SE) 12+22 (n=450) mean (SE) P 11 (n=451) mean (SE) 12+22 (n=330) mean (SE) P 11 (n=318) mean (SE) 12+22 (n=467) mean (SE) P 11 (n=346) mean (SE) BM (kg/m2) body weight (Kg) waist-to-hip ratio glucose (mg/dL) total-C (mg/dL) LDL-C (mg/dL) HDL-C (mg/dL) TG (mg/dL) SBP(mmHg)DBP(mmHg) 26.3(0.3)65.7(0.6)0.86(0.01)97.8(0.9)202.1(1.8)127.9(1.8)54.3(0.6)99.5(3.0)124.2(0.9)75.5(0.6) 25.3(0.2)63.5(0.5)0.86(0.01)95.5(0.9)201.1(1.6)128.6(1.5)54.8(0.5)95.1(2.6)122.0(0.8)74.1(0.5) 0.0040.0070.5190.0900.6520.7610.4980.0990.0970.105 26.1(0.2)65.4(0.6)0.87(0.01)97.9(0.8)201.3(1.6)127.1(1.5)54.2(0.5)102.5(2.6)123.5(0.8)74.8(0.5) 25.2(0.3)63.2(0.6)0.85(0.01)94.5(1.0)201.4(1.8)129.9(1.7)55.0(0.6)89.4(2.9)121.9(0.9)74.6(0.6) 0.0040.0110.0320.0080.9620.2220.3610.0050.1980.841 25.8(0.3)64.5(0.6)0.86(0.01)96.8(0.9)201.1(1.7)127.8(1.8)54.1(0.6)102.0(3.0)122.7(0.9)74.4(0.6) 25.7(0.2)64.4(0.5)0.87(0.01)96.6(0.8)202.3(1.6)128.9(1.5)54.9(0.5)95.4(2.6)123.7(0.8)75.0(0.5) 0.9650.8440.1720.8620.6450.6530.2450.2070.4330.410 25.9(0.4)64.9(0.6)0.87(0.01)96.9(0.9)200.8(1.8)127.7(1.7)53.8(0.6)100.1(2.9)123.2(0.9)74.4(0.6)

SE: standard error

Table 7: Combined effect of PLIN polymorphisms on body weight and BMI in men and women from sample 1 and sample 2 Group PLIN polymorphism woman man weight BMI weight BMI PLIN1 PLIN4 PLIN5 PLIN6 no Average (SE) P Average (SE) P no Average (SE) P Average (SE) P 1234 111112 or 2212 or 22 111112 or 2212 or 22 1112 or 221112 or 22 1112 or 221112 or 22 12910829184 69.5(1.5)72.2(1.6)62.9(3.1)66.1(1.3) 0.0070.047 ³ 0.009 ³ <0.05 ^{1, 2} 0.003 ² 27.7(0.6)28.7(0.6)24.8(1.2)26.3(0.5) 0.0050.043 ³ 0.006 ³ <0.05 ^{1, 2} 0.003 ² 1077824178 81.3(1.4)81.9(1.7)80.9(3.0)81.5(1.1) 0.991 27.0(0.5)27.2(0.5)26.9(0.9)27.0(0.4) 0.995

Table 8: Estimated PLIN haplotype frequencies according to obese/non-obese status and haplotype OR in women PLIN SNP Non-obese (n=237) Obesity (n=122) OR ^* 95%CI 6209 11482 13041 14995 lower limit upper limit 4SNP ^haplotype TTTTCCCC GGGGGAAA AGAGAAGA AATTAATT 0.3060.1330.0450.0390.0820.0890.0670.109 0.2670.1120.0630.0720.0470.0650.1030.120 1 ^§ 0.811.362.090.580.771.791.21 0.400.470.830.250.310.820.58 1.633.915.231.341.923.922.52 2SNP haplotypes (13041 and 14995) ^ AAGG ATAT 0.4850.2010.1650.149 0.3710.2500.1920.187 1 ^§ 1.761.441.73 1.070.811.06 2.902.552.82

^* : Multiple adjustments for age, smoking, alcohol consumption, and medication status

^ : Likelihood ratio test for global haplotype effect: LRT statistic value = 11.82, degree of freedom (df) is 7, P = 0.107

^ : Likelihood ratio test for global haplotype effect: LRT statistic = 8.60, df = 3, P = 0.035

^§ : Haplotype treated as reference.

Table 9: Plasma Lipid and Glucose Measures by PLIN Genotype in Women* genotype P ^ genotype P ^ PLIN 6209T＞C PLIN 11482G＞A FG(mg/dL)TG(mg/dL)TC(mg/dL)LDIC(mg/dL)HDL-C(mg/dL)TC/HDL-C TT (n=103) 94.2 (2.7) 153.3 (7.5) 215.3 (3.9) 123.7 (3.3) 61.0 (1.4) 3.73 (0.10) TC(n=168)97.0(2.1)155.5(5.8)210.8(3.0)115.8(2.6)63.5(1.1)3.48(0.08) CC(n=80)95.8(3.1)147.6(8.5)219.8(4.4)129.8(3.7)60.7(1.6)3.71(0.11) 0.8310.9140.2230.0060.1900.078 GG (n=163) 96.3 (2.2) 147.9 (5.9) 211.5 (3.1) 121.1 (2.6) 61.0 (1.1) 3.63 (0.08) GA (n=154) 95.4 (2.2) 159.8 (6.0) 214.3 (3.1) 118.6 (2.7) 63.5 (1.1) 3.56 (0.08) AA (n=34) 96.5 (4.7) 150.6 (12.9) 227.7 (6.7) 136.2 (5.7) 61.8 (2.3) 3.69 (0.17) 0.9980.1860.0900.0210.2650.705 PLIN 13041A＞G) PLIN 14995A＞T FG(mg/dL)TG(mg/dL)TC(mg/dL)LDL-C(mg/dL)HDL-C(mg/dL)TC/HDL-C AA (n=151) 93.9 (2.2) 147.7 (6.1) 210.7 (3.2) 120.4 (2.7) 61.0 (1.1) 3.60 (0.08) AG (n=164) 96.7 (2.2) 154.5 (5.9) 214.5 (3.0) 120.4 (2.6) 62.6 (1.1) 3.57 (0.08) GG (n=36) 101.2 (4.7) 170.2 (12.8) 227.2 (6.6) 1294 (5.8) 648 (2.4) 3.84 (0.17) 0.4100.1720.0810.3420.2980.333 AA (n=138) 93.5 (2.3) 145.4 (6.4) 212.4 (3.3) 120.2 (2.9) 63.1 (1.2) 3.56 (0.08) AT(n=159)98.2(2.2)156.3(6.0)214.7(3.1)121.3(2.7)61.8(1.1)3.65(0.08) TT (n=55) 95.2 (3.7) 164.0 (10.1) 216.7 (5.3) 123.7 (4.6) 60.7 (1.9) 3.61 (0.13) 0.4870.1550.7560.8140.5040.742

TC: total cholesterol. LDL-C: low-density lipoprotein cholesterol; HLD-C: high-density lipoprotein cholesterol. TG: triglyceride; FG: fasting glucose.

^* : Expressed as mean value (standard error).

^ : test for homogeneity, with multiple adjustments for age, BMI, smoking, alcohol consumption, and medication status.

Table 10: Plasma Lipid and Glucose Measures by PLIN Genotype in Men* genotype P ^ genotype PLIN 6209T＞C PLIN 11482G＞A FG(mg/dL)TG(mg/dL)TC(mg/dL)LDL-C(mg/dL)HDL-C(mg/dL)TC/HDL-C TT(n=118)107.4(3.2)186.6(10.1)211.1(4.1)124.6(3.3)48.0(1.1)4.65(0.12) TC (n=162) 1068 (2.7) 189.9 (8.6) 206.4 (3.5) 123.5 (2.8) 47.3 (0.9) 4.57 (0.11) CC(n=75)107.9(4.1)192.5(12.9)208.7(5.2)125.1(4.2)46.2(1.4)4.77(0.16) 0.9920.7910.6750.9380.5850.582 GG (n=189) 109.4 (2.6) 190.3 (8.0) 206.7 (3.2) 121.0 (2.6) 47.9 (0.9) 4.56 (0.10) GA (n=131) 105.3 (3.1) 189.2 (9.6) 210.3 (3.9) 128.1 (3.2) 46.0 (1.1) 4.74 (0.12) AA(n=34)103.2(6.0)182.0(19.0)-212.1(7.6)127.2(6.1)49.2(2.1)4.61(0.23) PLIN 13041A＞G PLIN 14995A＞T FG(mg/dL)TG(mg/dL)TC(mg/dL)LDL-C(mg/dL)HDL-C(mg/dL)TC/HDL-C AA (n=160) 110.0 (2.8) 191.3 (8.6) 214.7 (3.5) 129.3 (2.8) 47.6 (0.9) 4.75 (0.11) AG (n=151) 104.6 (2.8) 196.4 (8.9) 203.1 (3.6) 119.7 (2.9) 45.9 (1.0) 4.62 (0.11) GG (n=44) 105.8 (5.4) 157.8 (16.7) 203.8 (6.7) 120.6 (5.4) 50.9 (1.8) 4.30 (0.21) 0.4760.1530.0510.0490.0470.158 AA (n=158) 109.9 (2.8) 197.6 (8.8) 208.7 (3.5) 122.6 (2.9) 46.7 (1.0) 4.71 (0.11) AT(n=151)104.8(2.9)183.4(9.0)207.0(3.6)124.7(2.9)47.5(1.0)4.60(0.11) TT (n=44) 106.3 (5.4) 180.8 (16.7) 213.8 (6.7) 128.7 (5.4) 49.3 (1.8) 4.52 (0.21)

TC: total cholesterol. LDL-C: low-density lipoprotein cholesterol; HLD-C: high-density lipoprotein cholesterol. TG: triglyceride; FG: fasting glucose.

^* : Expressed as mean value (standard error).

^ : test for homogeneity, with multiple adjustments for age, BMI, smoking, alcohol consumption, and medication status.

Table ¹¹ : Descriptive Characteristics of the Singapore Population by Sex and Ethnicity1 Singapore Chinese Malay Indian Men (n=1263) Women (n=1500) Men (n=360) Women (n=386) Men (n=286) Women (n=312) Age (years) BMI (kg/m ² ) Total-C (mmol/l) LDL-C (mmol/l) HDL-C (mmol/l) Fasting TG (mmol//l) Obesity (%) ² Overweight (%) ^2Current Smoker (%)Drinker (%)Diabetes (%) 38.2±12.323.5±3.75.52±1.043.54±0.951.27±0.321.69±1.5554(4.28)401(15.36)298(23.36)749(59.30)40(3.17) 37.8±12.222.1±3.65.33±1.053.24±0.931.56±0.371.16±0.7546(3.07)140(9.33)45(3.00)494(32.93)24(1.60) 39.6±12.724.7±4.05.88±1.133.95±1.021.15±0.282.00±1.5929(8.06)93(25.83)162(45.00)44(12.22)16(4.44) 38.4±12.726.3±5.65.73±1.173.75±1.131.44±0.331.39±0.8894(24.35)152(39.38)15(3.89)12(3.11)21(5.44) 41.3±12.124.6±4.05.72±1.173.88±1.081.06±0.292.08±1.7822(7.69)70(24.48)87(30.42)149(52.10)27(9.44) 40.0±12.125.6±5.05.33±1.033.53±0.961.23±0.311.33±0.6855(17.63)117(37.50)1(0.32)55(17.63)24(7.69)

¹ : Continuous variables are presented as mean ± SD, while categorical variables are presented as number of cases and percent prevalence.

² : Obesity: BMI>=30kg/m2; Overweight: BMI>=25kg/ ^m2

Total-C: Total cholesterol. LDL-C: low-density lipoprotein cholesterol; HLD-C: high-density lipoprotein cholesterol. TG: Triglycerides.

Table 12: PLIN haplotype frequencies and OR estimates in obese subjects and non-obese controls in Chinese inferred haplotype Non-obese (n=2663) Obesity (n=100) OR (95%CI) P Adj-OR (95% CI) ¹ P code ³ 6209 10171 11482 13041 14995 5SNP haplotype 211111122211212121112112112121 TCCCTC AAATAT GAAGGG AGAAGG ATTAAA 0.2200.1730.1910.1890.0520.041 0.2560.1780.2150.1830.0440.034 1 ² 1.05(0.67-1.63)1.14(0.76-1.70)0.99(0.62-1.58)0.85(0.32-2.22)0.83(0.30-2.28) 0.83870.51900.96250.73750.7204 1 ² 1.02(0.65-1.60)1.20(0.79-1.82)1.04(0.65-1.69)1.17(0.59-2.29)0.83(0.31-2.24) 0.92980.38920.85860.65440.7100 3SNP haplotype 111212222121211112 GAAGAG AAGGGA ATTATT 0.4140.1920.1740.1080.0360.035 0.2700.2490.2270.1290.0430.048 1 ² 1.07(0.74-1.56)0.98(0.65-1.49)0.76(0.37-1.58)0.88(0.33-2.37)0.76(0.37-1.56) 0.71540.93510.46190.80640.4562 1 ² 1.10(0.75-1.63)0.95(0.62-1.46)0.75(0.34-1.62)0.83(0.31-2.22)0.82(0.38-1.74) 0.62300.82510.45880.70970.6006

¹ : Controls for age, gender, smoking, alcohol consumption, exercise, and diabetes status

² : Used as a reference haplotype

³ :1 represents the common allele and 2 represents the minor allele

Table 13: PLIN haplotype frequencies and OR estimates in obese subjects and non-obese controls in Malay inferred haplotype Non-obese (n=623) Obesity (n=123) OR (95%CI) P Adj-OR (95% CI) ¹ P the code 6209 10171 11482 13041 14995 5SNP haplotype 211111122211212121112112111211 TCCCTC AAATAA GAAGGA AGAAGG ATTAAT 0.2410.1730.1890.1530.0740.034 0.1590.2290.2480.1020.0810.037 1 ² 1.64(1.08-2.48)1.65(1.11-2.46)0.80(0.44-1.44)1.33(0.71-2.50)1.30(0.54-3.11) 0.01970.01410.45360.37280.5561 1 ² 1.67(1.07-2.60)1.55(1.01-2.38)0.82(0.45-1.50)1.17(0.59-2.29)1.23(0.52-2.93) 0.02270.04370.53160.65440.6389 3SNP haplotype 111212222121211112 GAAGAG AAGGGA ATTATT 0.4140.1920.1740.1080.0360.035 0.2700.2490.2270.1290.0430.048 1 ² 2.12(1.36-3.32)2.02(1.36-3.01)1.89(1.05-3.41)1.84(0.71-4.78)2.30(0.97-5.30) 0.00100.00050.03320.21200.0599 1 ² 2.04(1.28-3.25)2.05(1.35-3.12)1.59(0.87-2.90)1.81(0.70-4.67)2.25(0.96-5.25) 0.00290.00070.13310.22130.0610

¹ : Moderation for age, gender, smoking, alcohol consumption, exercise and diabetes status

² : Used as a reference haplotype

³ :1 represents the common allele and 2 represents the minor allele

Table 14: PLIN haplotype frequencies and OR estimates in obese subjects and non-obese controls in Indians inferred haplotype Non-obese (n=521) Obesity (n=77) OR (95%CI) P Adj-OR (95% CI) ¹ P the code 6209 10171 11482 13041 14995 5SNP haplotype 211111122211212121112112112121 TCCCTC AAATAT GAAGGG AGAAGG ATTAAA 0.2470.1790.0780.0820.1570.051 0.2370.1540.1540.0250.1600.052 1 ² 0.87(0.53-1.42)1.94(1.06-3.53)0.30(0.09-1.06)0.97(0.54-1.76)1.04(0.44-2.46) 0.57080.03050.06060.91760.9221 1 ² 0.80(0.46-1.37)1.67(0.87-3.22)0.29(0.08-1.07)0.91(0.49-1.70)0.965(0.39-2.40) 0.41860.12340.06240.77220.9387 3SNP haplotype 111212222121211122 GAAGAG AAGGGG ATTATT 0.3630.0870.1810.2320.0430.049 0.3040.1610.1520.2240.0340.075 1 ² 2.39(1.26-4.50)0.98(0.58-1.66)1.16(0.70-1.95)0.77(0.19-3.17)2.03(0.94-4.39) 0.00730.93680.55770.71410.0714 1 ² 2.16(1.10-4.26)0.90(0.51-1.59)1.11(0.63-1.95)0.71(0.15-3.40)2.08(0.93-4.67) 0.02610.71580.71470.66560.0751

¹ : Moderation for age, gender, smoking, alcohol consumption, exercise and diabetes status

² : Used as a reference haplotype

³ :1 represents the common allele and 2 represents the minor allele

Table 15: Obesity Protection loci Haplotype with reduced risk of obesity (Caucasian) (Mediterranean) (Malay) (Indian) a b c d E. f g h i j PLIN1 C C C T C C C C C PLIN3 T A T A A T PLIN4 G A A G G G G PLIN5 A A A A A A A PLIN6 A A A A A A A

Table 16: Diagnosis of increased risk of obesity loci Haplotype with increased risk of obesity (Caucasian) (Mediterranean) (Malay) (Indian) k l m no o p Q r the s t u v w x the y z PLIN1 T T T T T T T T T PLIN3 A A A A A PLIN4 G G G G A A A A G A A G PLIN5 G A G A G A G A G G A A G PLIN6 T T T A T T T T T A T T T

Claims (21)

1. the method for the danger of the increase of obesity and obesity relative disease in definite individuality, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN310171A/T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, wherein relevant with this individual ethnic background PLIN5-G/PLIN6T that is selected from, PLIN5-A/PLIN6-T, PLIN1-T/PLIN4-G/PLIN5-G/PLIN6-T, PLIN1-T/PLIN4-G, PLIN1-T/PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-T/PLIN3-A/PLIN4-APLIN5-A/PLIN6-T, PLIN1-T/PLIN3-A/PLIN/4-A/PLIN5-G/PLIN6-T, PLIN4-A/PLIN5-A/PLIN6-T, PLIN4-A/PLIN5-G/PLIN6-T, PLIN4-G/PLIN5-G/PLIN6-A, the haplotype of PLIN1-T/PLIN3-A shows the danger of the increase of obesity and obesity relative disease in this individuality.

2. the method for the danger of the increase of obesity and obesity relative disease in definite Caucasian blood lineage individuality, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN411482G/A, PLIN5 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, and the haplotype that wherein is selected from PLIN5-G/PLIN6T, PLIN5-A/PLIN6-T and PLIN1-T/PLIN4-G/PLIN5-G/PLIN6-T shows the danger of the increase of obesity and obesity relative disease in this Caucasian blood lineage individuality.

3. the method for the danger of the increase of obesity and obesity relative disease in the blood lineage individuality of definite people from Mediterranean Sea, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN411482G/A, PLIN5 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, and the haplotype that wherein is selected from PLIN1-T/PLIN4-G, PLIN1-T/PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-T/PLIN4-G/PLIN5-G/PLIN6-T shows the danger of the increase of obesity and obesity relative disease in the blood lineage individuality of this people from Mediterranean Sea.

4. the method for the danger of the increase of obesity and obesity relative disease in definite Malaysian blood lineage individuality, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN310171A/T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, and the haplotype that wherein is selected from PLIN1-T/PLIN3-A/PLIN4-A/PLIN5-A/PLIN6-T, PLIN1-T/PLIN3-A/PLIN/4-A/PLIN5-G/PLIN6-T, PLIN4-A/PLIN5-A/PLIN6-T, PLIN4-A/PLIN5-G/PLIN6-T, PLIN4-G/PLIN5-G/PLIN6-A, PLIN1-T/PLIN3-A shows the danger of the increase of obesity and obesity relative disease in this Malaysian blood lineage individuality.

5. the method for the danger of the increase of obesity and obesity relative disease in definite Indian blood lineage individuality, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN310171A/T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, and the haplotype that wherein is selected from PLIN1-T/PLIN3-A/PLIN4-A/PLIN5-A/PLIN6-T, PLIN4-A/PLIN5-A/PLIN6-T, PLIN4-G/PLIN5-G/PLIN6-T and PLIN1-T/PLIN3-A shows the danger of the increase of obesity and obesity relative disease in this Indian blood lineage individuality.

6. the method for the danger of the increase of obesity and obesity relative disease in definite Caucasian blood lineage individuality, it comprises the genotype of determining PLIN513041A/G and PLIN6 14995A/T locus from the biological sample that this individuality obtains, wherein in the PLIN5 locus in the homozygosity of allelotrope G or the PLIN6 locus homozygosity of allelotrope T show the danger of the increase of obesity and obesity relative disease in this Caucasian blood lineage individuality.

7. the method for the danger of the increase of obesity and obesity relative disease in definite Malaysian or the Indian blood lineage individuality, it comprises the genotype of determining PLIN6 14995A/T locus from the biological sample that this individuality obtains, and wherein the homozygosity of allelotrope T shows the danger of the increase of obesity and obesity relative disease in this Malaysian or the Indian blood lineage individuality in the PLIN6 locus.

8. the method for the danger of the increase of obesity and obesity relative disease in definite Malaysian or the Indian blood lineage individuality, it comprises the genotype of determining PLIN4 11482G/A locus from the biological sample that this individuality obtains, and wherein the homozygosity of equipotential gene A shows the danger of the increase of obesity and obesity relative disease in this Malaysian or the Indian blood lineage individuality in the PLIN4 locus.

9. the method for the danger of the increase of obesity and obesity relative disease in definite Malaysian or the Indian blood lineage individuality, it comprises the genotype of determining PLIN5 13041A/G locus from the biological sample that this individuality obtains, and wherein the homozygosity of allelotrope G shows the danger of the increase of obesity and obesity relative disease in this Malaysian or the Indian blood lineage individuality in the PLIN5 locus.

10. each method of claim 1-9, wherein said individuality is the woman.

11. each method of claim 1-9, wherein said individuality has been accepted the diet that loses weight.

12. each method of claim 1-9, wherein said obesity relative disease is a cardiovascular disorder.

13. each method of claim 1-9, wherein said obesity relative disease is a metabolism syndrome.

14. the method for the danger that reduces of obesity and obesity relative disease in definite individuality, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN310171A/T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, and wherein relevant with this individual ethnic background haplotype that is selected from PI.IN5-A/PLIN6-A, PLIN1-C/PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-C/PLIN4-A, PLIN1-C/PLIN4-A/PLIN5-A/PLIN6-A, PLIN1-T/PLIN3-T/PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-C/PLIN3-A/PLIN/4-G/PLIN5-A/PLIN6-A and PLIN1-C/PLIN3-T shows the danger that reduces of obesity and obesity relative disease.

15. the method for the danger that reduces of obesity and obesity relative disease in definite Caucasian blood lineage individuality, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN411482G/A, PLIN4 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, and the haplotype that wherein is selected from PLIN5-A/PLIN6-A and PLIN1-C/PLIN4-G/PLIN5-A/PLIN6-A shows the danger that reduces of obesity and obesity relative disease in this Caucasian blood lineage individuality.

Go into the method for the danger that reduces of obesity and obesity relative disease in blood lineage's individuality 16. determine Mediterranean Sea, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN411482G/A, PLIN5 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, and the haplotype that wherein is selected from PLIN1-C/PLIN4-A and PLIN1-C/PLIN4-A/PLIN5-A/PLIN6-A shows the danger that reduces of obesity and obesity relative disease in the blood lineage individuality of this people from Mediterranean Sea.

17. the method for the danger that reduces of obesity and obesity relative disease in definite Malaysian blood lineage individuality, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN310171A/T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, and the haplotype that wherein is selected from PLIN1-T/PLIN3-T/PLIN4-G/PLIN5-A/PLIN6-A, PLIN1-C/PLIN3-A/PLIN4-G/PLIN5-A/PLIN6-A and PLIN1-C/PLIN3-T shows the danger that reduces of obesity and obesity relative disease in this Malaysian blood lineage individuality.

18. the method for the danger that reduces of obesity and obesity relative disease in definite Indian blood lineage individuality, it comprises step:

A) determine the genotype of PLIN1 6209T/C, PLIN310171A/T, PLIN4 11482G/A, PLIN5 13041A/G, PLIN6 14995A/T locus from the biological sample that this individuality obtains;

B) based on the definite PLIN genotype generation unit type of step (a); With

C) described haplotype and this is individual ethnic background is related, and the haplotype that wherein is selected from PLIN1-C/PLIN3-A/PLIN4-G/PLlN5-A/PLIN6A, PLIN1-C/PLIN3-A/PLIN4-G/PLIN5-A/PLIN6-A and PLIN1-C/PLIN3-C shows the danger that reduces of obesity and obesity relative disease in this Indian blood lineage individuality.

19. each method of claim 14-18, wherein said individuality is the woman.

20. test kit, its primer that comprises the nucleic acid region that covers PLIN1 6209T/C, PLIN310171A/T, PLIN4 11482G/A, PLIN5 13041A/G and PLIN6 14995A/T polymorphism of being used to increase is right, and specification sheets, it comprises the related of the haplotype relevant with obesity danger that increase or that reduce and they and ethnic group.

21. test kit, its SEQ ID NO:1 and SEQ ID NO:2 primer that comprises the nucleic acid region that covers the PLIN1 polymorphism of being used to increase is right; The SEQ ID NO:7 and the SEQ ID NO:8 primer of the nucleic acid region that covers the PLIN3 polymorphism of being used to increase is right; The SEQ ID NO:10 and the SEQ ID NO:11 primer of the nucleic acid region that covers the PLIN4 polymorphism of being used to increase is right; The SEQ ID NO:13 and the SEQ IDNO:14 primer of the nucleic acid region that covers the PLIN5 polymorphism of being used to increase is right; Right with the SEQ ID NO:16 and the SEQ ID NO:17 primer of the nucleic acid region that covers the PLIN6 polymorphism of being used to increase; And specification sheets, it comprises the related of the haplotype relevant with obesity danger that increase or that reduce and they and ethnic group.

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