Impact of nine common type 2 diabetes risk polymorphisms in Asian Indian Sikhs: PPARG2 (Pro12Ala), IGF2BP2, TCF7L2 and FTOvariants confer a significant risk (original) (raw)

BMC Medical Genetics volume 9, Article number: 59 (2008)Cite this article

Abstract

Background

Recent genome-wide association (GWA) studies have identified several unsuspected genes associated with type 2 diabetes (T2D) with previously unknown functions. In this investigation, we have examined the role of 9 most significant SNPs reported in GWA studies: [peroxisome proliferator-activated receptor gamma 2 (PPARG2; rs 1801282); insulin-like growth factor two binding protein 2 (IGF2BP2; rs 4402960); cyclin-dependent kinase 5, a regulatory subunit-associated protein1-like 1 (CDK5; rs7754840); a zinc transporter and member of solute carrier family 30 (SLC30A8; rs13266634); a variant found near cyclin-dependent kinase inhibitor 2A (CDKN2A; rs10811661); hematopoietically expressed homeobox (HHEX; rs 1111875); transcription factor-7-like 2 (TCF7L2; rs 10885409); potassium inwardly rectifying channel subfamily J member 11(KCNJ11; rs 5219); and fat mass obesity-associated gene (FTO; rs 9939609)].

Methods

We genotyped these SNPs in a case-control sample of 918 individuals consisting of 532 T2D cases and 386 normal glucose tolerant (NGT) subjects of an Asian Sikh community from North India. We tested the association between T2D and each SNP using unconditional logistic regression before and after adjusting for age, gender, and other covariates. We also examined the impact of these variants on body mass index (BMI), waist to hip ratio (WHR), fasting insulin, and glucose and lipid levels using multiple linear regression analysis.

Results

Four of the nine SNPs revealed a significant association with T2D; PPARG2 (Pro12Ala) [odds ratio (OR) 0.12; 95% confidence interval (CI) (0.03–0.52); p = 0.005], IGF2BP2 [OR 1.37; 95% CI (1.04–1.82); p = 0.027], TCF7L2 [OR 1.64; 95% CI (1.20–2.24); p = 0.001] and FTO [OR 1.46; 95% CI (1.11–1.93); p = 0.007] after adjusting for age, sex and BMI. Multiple linear regression analysis revealed significant association of two of nine investigated loci with diabetes-related quantitative traits. The 'C' (risk) allele of CDK5 (rs 7754840) was significantly associated with decreased HDL-cholesterol levels in both NGT (p = 0.005) and combined (NGT and T2D) (0.005) groups. The less common 'C' (risk) allele of TCF7L2 (rs 10885409) was associated with increased LDL-cholesterol (p = 0.010) in NGT and total and LDL-cholesterol levels (p = 0.008; p = 0.003, respectively) in combined cohort.

Conclusion

To our knowledge, this is first study reporting the role of some recently emerged loci with T2D in a high risk population of Asian Indian origin. Further investigations are warranted to understand the pathway-based functional implications of these important loci in T2D pathophysiology in different ethnicities.

Peer Review reports

Background

Type 2 diabetes (T2D) is a common disease characterized by insulin resistance and reduced insulin secretion. It has become a health problem world-wide and the underlying molecular mechanisms involved in the development of diabetes remain poorly understood. Linkage and candidate gene studies have been highly successful in identifying genes for monogenic and syndromic forms of diabetes [1]. However, this approach has yielded limited results in mapping the genes for T2D [2–4]. The latest success of the genome-wide association (GWA) studies using high density SNPs across the genome and complex genetic analyses have identified several genes with previously unknown functions [5–9].

In this investigation, we have examined the role of nine most significant loci previously reported to be associated with T2D in Caucasian populations [5, 6, 10–12] in a diabetic case-control cohort of Khatri Sikhs obtained from North India. Our study is focused on the peroxisome proliferator-activated receptor gamma 2 (PPARG2; Pro12 Ala; rs1801282), insulin-like growth factor two binding protein 2 (IGF2BP2; rs 4402960), cyclin-dependent kinase 5, a regulatory subunit-associated protein 1-like 1 (CDK5; rs 7754840), a zinc transporter and member of solute carrier family 30 (SLC30A8; rs 13266634), a variant found near cyclin-dependent kinase inhibitor 2A (CDKN2A; rs 10811661), hematopoietically expressed homeobox (HHEX; rs 1111875), transcription factor-7-like 2 (TCF7L2; rs 10885409), potassium inwardly rectifying channel subfamily J member 11 (KCNJ11; rs 5219), and a recently discovered fat mass obesity-associated gene (FTO; rs 9939609). TCF7L2 gene is the most frequently replicated adult-onset T2D susceptibility gene [8]. Some of the TCF7L2 variants have been found to strongly associated with T2D in Asian Indian populations from South [13, 14] and South West India [14], and also our North Indian cohort [15]. In this study, we have used the most significant TCF7L2 SNP associated with T2D in Sikhs for comparisons. Since the replication of positive association in multiple independently ascertained datasets from different ethnic groups is crucial for identifying the true population-specific susceptibility variants, we have examined the role of polymorphisms of these nine genes in our diabetes case-control cohort of Asian Indian Sikhs.

Methods

Human Subjects

The study subjects are part of the ongoing Sikh Diabetes Study (SDS) [16]. The focus of this study is on an endogamous community of Khatri Sikhs living in the Northern states of India, including Punjab, Haryana, Himachal Pradesh, Delhi, and Jammu & Kashmir. The DNA and serum samples of 532 T2D cases (299 male, 233 female) and 386 normal glucose tolerant (NGT) (184 male, 202 female) subjects were used in this investigation. Of the 532 cases, 324 are from family material (one index case from each family) and the remaining 208 are unrelated T2D cases from the same Sikh community. The cases were 25 or older with mean age at the time of recruitment (mean ± SD) of 54.2 ± 11.0 yrs. The diagnosis of T2D was confirmed by scrutinizing medical records for symptoms, use of medication, and measuring fasting glucose levels following the guidelines of American Diabetes Association [17]. A medical record indicating either (1) a fasting plasma glucose level ≥126 mg/dl or ≥7.0 mmol/l after a minimum12-h fast or (2) a 2-h post glucose level [2-h oral glucose tolerance test (OGTT)] ≥200 mg/dl or ≥11.1 mmol/l on more than one occasion with symptoms of diabetes. IGT was defined as a fasting plasma glucose level ≥100 mg/dl (5.6 mmol/l) but ≤126 mg/dl (7.0 mmol/l) or a 2-h OGTT ≥140 mg/dl (7.8 mmol/l) but ≤200 mg/dl (11.1 mmol/l). In the absence of medical record information, we confirmed self-reported T2D cases by performing a 2-h OGTT. The 2-h OGTTs were performed following the criteria of the World Health Organization (WHO) (75 g oral load of glucose). Body mass index (BMI) was calculated as [weight (kg)/height (meter)2], and waist-hip ratio (WHR) was calculated as the ratio of abdomen or waist circumference to hip circumference.

The NGT subjects participated in this study were from the same Khatri Sikh community from which the T2D patients were recruited [16]. Majority of the subjects were recruited from the state of Punjab from Northern India. Individuals of South, East and Central Indian origin, or with type -1 diabetes or family member with type -1 diabetes, rare forms of T2D called maturity-onset diabetes of young (MODYs), and secondary diabetes (e.g., hemochromatosis, pancreatitis) were excluded from the study. Of the 386 controls, 180 were normal spouses of diabetic patients, and the remaining 206 were unrelated controls who had no first degree relative affected with T2D, but had other chronic illnesses such as hypertension, coronary heart disease or arthritis. The selection of controls was based on a fasting glycemia < 110 mg/dl or < 6.0 mmol/l or a 2-h glucose <140 mg/dl (< 7.8 mmol/l). Control subjects with HbA1c levels >6.5% were excluded. The average age of NGT controls (mean ± SD) was 50.5 ± 13.8 yrs. Additionally, 22 subjects with IGT were excluded from analyses. In general, Sikhs do not smoke for religious and cultural reasons and about 50% of them were life long vegetarians. Clinical characteristics of the SDS subjects used for this investigation are summarized in Table 1. All blood samples were obtained at the baseline visit and all participants provided a written informed consent for investigations. All SDS protocols and consent documents were reviewed and approved by the University of Oklahoma and the University of Pittsburgh Institutional Review Boards as well as the Human Subject Protection Committees at the participating hospitals and institutes in India. Memorandum of understanding and material transfer agreements for sample sharing and intellectual property rights were signed between collaborating Indian and the US Institutes. The study was approved by the Indian Council of Medical Research, New Delhi and Health Ministry Screening Committee, Union Ministry of Health and Family Welfare, Government of India.

Table 1 Clinical characteristics of study population stratified by gender (Mean ± SD)

Full size table

Metabolic Assays

Quantitation of lipids [total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, very low-density lipoprotein (VLDL) cholesterol, triglycerides (TG)], insulin, and creatinine levels were estimated in serum. HbA1c was quantified in whole blood. Lipids were quantified by using standard enzymatic methods (Roche, Basel, Switzerland). HbA1c levels were determined by turbidometric inhibition immunoassay (Tina Quant; Roche, Basel, Switzerland). Creatinine was quantified by using kinetic colorimetric assay (Roche, Basel, Switzerland). Insulin was measured by radio-immuno assay (Diagnostic Products, Cypress, USA). All quantitative parameters were determined by following manufacturer's instructions using Hitachi 902 auto-analyzer (Roche, Basel, Switzerland).

SNP Genotyping

DNA was extracted from buffy coats using QiaAmp blood kits (Qiagen, Chatworth, CA) or by the salting out procedure [18]. Genotyping for all investigated SNPs was performed using TaqMan Pre-Designed or TaqMan Made-to Order SNP genotyping assays from Applied Biosystems (ABI, Foster City, USA). TaqMan genotyping reactions were performed on ABI 7900 genetic analyzer using 2 ul of (10 ng/ul) of genomic DNA following manufacturer's instructions. Florescence was detected on an ABI prism 7900HT sequence detection system (ABI, Foster City, USA). Genotypes were scored by analyzing data on both real-time as well as allele discrimination assay platforms using SDS software provided by the ABI. Data quality for SNP genotyping was checked by establishing reproducibility of control DNA samples. For quality control, 30 replicate positive controls and 8 negative controls were included in each run to match the concordance, and the discrepancy in the concordance was < 0.2%. Genotyping success rate ranged from 96%–99.8% for all the investigated SNPs except for CDKN2A (rs 10811661) for which the success rate was 88.5% due to technical difficulties.

Statistical Analysis

We evaluated Hardy-Weinberg equilibrium using a one-degree of freedom goodness-of-fit test separately among cases and controls using Pearson chi-square test. The allele frequencies in T2D cases were compared to those in controls using chi-square test or Fisher's exact probability test, where appropriate. Statistical evaluations for testing genetic effects of association between the case-control status and each individual SNP, measured by the odds ratio (OR) and its corresponding 95% confidence limits were estimated using unconditional logistic regression before and after adjusting for age, gender, and other covariates. Association analyses were performed assuming a dominant, recessive and co-dominant effect for each polymorphism using SNPassoc [19]. In all analyses, the common homozygote genotype in the control population was defined as the reference category. The likelihood ratio test was used to test the effect of each SNP at the nominal 5% significance level. Akaïke's information criterion [20] was also used to select the best genetic model for each SNP. We also performed multiple linear regression analysis to examine the impact of these variants on quantitative risk variables of T2D, including fasting insulin, glucose, and lipid levels. Skewed variables for the continuous traits (e.g., triglycerides, cholesterol, LDL, glucose) were log-transformed before statistical comparisons. Significant covariates for each dependent trait were identified by using Spearman's correlation and step-wise multiple linear regression with an overall 5% level of significance.

Results

We genotyped nine SNPs, including the six SNPs from newly emerged gene regions recently identified by multiple GWA studies [5–7, 9, 21], in a case-control cohort of Asian Indian Sikhs consisting 918 subjects including, 532 T2D cases and 386 NGT controls matched for ethnicity and geographic origin. Table 1 shows the clinical and physical characteristics of the study subjects separated by gender. The information on chromosomal position and genomic locations of the investigated SNP, base change, genotype, allele frequencies, and ORs is summarized in Tables 2. Genotype distribution for all investigated SNPs was in Hardy-Weinberg equilibrium in both cases and controls. The genes containing SNPs that showed strong association with T2D in our sample were PPARG2 (rs18081282), IGF2BP2 (rs 4402960), TCF7L2 (rs10885409), and FTO (rs9939609). The frequencies of major alleles (at risk allele) of PPARG2 and minor alleles (at risk allele) of IGF2BP2, TCF7L2 and FTO were significantly higher among diabetics than controls (Tables 2). The adjusted ORs under a recessive model provided a strongest evidence of association for PPARG2 [OR 0.12; 95% CI (0.03–0.52); p = 0.005] showing protective association with T2D for the less common Ala (G) allele. On the other hand, dominant models suggested strongest association for the other three genes: OR for IGF2BP2 [1.37; 95% CI (1.04–1.82); p = 0.027], OR for TCF7L2 [1.64; 95% CI (1.20–2.24); p = 0.001] and OR for FTO [1.46; 95% CI (1.11–1.93); p = 0.007] when T2D subjects were compared with NGT. However, our study could not replicate the significant association of the remaining five SNPs reported in earlier studies by analyzing each SNP individually.

Table 2 Candidate gene polymorphisms and type 2 diabetes risk in Khatri Sikhs; comparing NGT vs. T2D

Full size table

To explore the possible mechanisms of action of these variants for affecting T2D susceptibility, we tested the association of these SNPs with the T2D-related quantitative phenotypes (height, weight, BMI, WHR, insulin, creatinine, total cholesterol, LDL-cholesterol, VLDL-cholesterol, HDL-cholesterol, and triglycerides) using multiple linear regression analysis in NGT after adjusting for the effects of age, sex, BMI, disease status, and medication. As shown in Table 3, the 'C' (risk) allele of CDK5 variant was significantly associated with decreased HDL-cholesterol levels in NGT (p = 0.005) as well as combined (NGT+T2D) sample (p = 0.005). The less common 'C' (risk) allele of TCF7L2 polymorphism also showed significant association with increased levels of LDL-cholesterol in NGT (p = 0. 010) and total and LDL-cholesterol in combined sample (p = 0.008; p = 0.003, respectively).

Table 3 SNPs with significant association to quantitative phenotypes of T2D

Full size table

Discussion

In this investigation, we have carried out an unbiased replication of SNPs associated with T2D susceptibility in recently published multiple GWA studies [5–8] in our independent dataset of Asian Indian origin. Two of these SNPs belong to highly replicated biological candidate genes PPARG2 (Pro12Ala; rs 1801282) and KCNJ11 (Glu23Lys; rs 5219) that produced unequivocal evidence of their involvement in T2D in several independent studies performed in different data sets [22, 23]. Moreover, these two genes are also targets for anti-diabetic therapies. PPARG2 encodes a transcription factor involved in adipocyte differentiation and is the target of thizolodinedione class of drugs used for T2D [24] and KCNJ11 is the target of sulphonylurea class of drugs extensively used for treating neonatal diabetes [25, 26]. The remaining seven variants (IGF2BP2, CDK5, SLC30A8, CDKN2A, HHEX, TCF7L2 and FTO) evaluated in this study are from the recently identified gene regions that provide convincing evidence of their involvement in T2D in fine mapping and multiple GWA studies.

We have identified a significant association of PPARG2 (rs1801282; p = 0.005), IGF2BP2 (rs 4402960; p = 0.027) and FTO (rs 9939609; p = 0.007) variants with T2D along with the TCF7L2 variant (rs10885409; p = 0.001) in our Asian Indian population. The protective association of the less common 'Ala' allele at codon 12 of PPARG2 gene has been consistently reproduced in multiple independent studies conducted in different populations [22, 27–31] with few exceptions of small studies e.g. Oji-Cree [32] and Czech [33], where the Ala allele was shown to be associated with T2D risk. The protective association of Ala allele was also not confirmed in a South Indian population [34]. The observed difference of association within Asian Indian populations is important in view of the extensive diversity present between Indian populations [35–37]. IGF2BP2 belongs to a family of three mRNA binding proteins; the human growth hormone gene and insulin like growth factor 1 and 2 genes. It is involved in pancreas development, growth and stimulation of insulin action [38, 39]. We have replicated a significant association of IGF2BP2 (rs 4402960) SNP reported earlier in three different GWA studies [5, 11, 12], meta analysis of three GWA studies [7], and also in Japanese population [40]. However, no association of this variant was noticed with any quantitative trait related to glucose homeostasis or lipid metabolism.

The FTO gene is of unknown function that was originally cloned as a result of identification of fused toe mutant mouse [41]. Recently, the recombinant murine fto has been shown to be involved in nucleic acid methylation by catalyzing Fe (II) – and 2-oxoglutarate (2OG)-dependent dioxygenases [42, 43]. This locus is associated with childhood and adult obesity in several European and US populations [9, 44, 45]. The FTO variant (TA A) (rs 9939609) has also been shown to be strongly associated with T2D risk (OR 1.27; p = 5 × 10-8) in GWA scan performed in UK population [9]. Our study also confirmed significant association of FTO (rs 9939609) variant with T2D in this Asian Indian sample. But the mechanism by which it relates to obesity and T2D in humans is still unclear. Studies have shown that T2D risk associated with 'A' allele of this variant (rs 9939609) is strongly mediated by BMI, and has been shown to potentially affect mass rather than height [9]. In contrast, in this Asian Indian sample, the T2D risk does not seem to be mediated by BMI or weight, as by excluding BMI, the risk associated with this variant did not disappear, as reported in other studies [9, 40]. Perhaps the ethnic difference could be responsible for this difference. Moreover, BMI is not considered an accurate measure for obesity, especially in the populations from South Asia where at a given BMI, their muscle mass is low and visceral and subcutaneous fat is increased [46–48]. Therefore, measures of BMI or waist and hip may not reveal accurate estimates of total fat.

The importance of TCF7L2 as diabetes gene is now very well established across ethnicities but many more genes for diabetes especially in non-white ethnicities still remain to be identified. It is interesting to observe that the risk alleles found to be associated with T2D in this Asian Indian population in four significant SNPs (PPARG2, IGF2BP2, TCF7L2 and FTO) were consistent with those reported in GWA studies in Caucasians and Japanese [5, 7, 11, 40]. However, it is still unclear how these loci contribute to T2D risk as none of these variant was found to influence insulin or fasting glucose levels in this study. More functional studies would be needed to clearly define the role of these variants in glucose homeostasis. Perhaps the causal variants in these genes controlling insulin secretion and insulin resistance are yet to be identified. With regard to remaining loci, our study did not confirm the previous reported association of variants (KCNJ11, CDK5, SLC30A8, CDKN2A and HHEX) when each SNP was analyzed separately. With the exception of CDK5 and TCF7L2, no other gene variant showed significant association with any diabetes-related quantitative traits. The 'C' (risk) allele of CDK5 variant was associated with significantly reduced HDL levels (p = 0.005) in this sample. The possibility of false positive results in our sample cannot be ruled out because of relatively small size of our cohort. Also, while our manuscript was being reviewed, publication of two large meta-analysis studies on lipid traits in subjects with coronary artery disease and T2D did not report this locus affecting lipid traits in Caucasians [49, 50]. However, as we previously reported [15], five SNPs in the TCF7L2 gene (rs 7901695, rs7903146, rs11196205, rs10885409, and rs 12255372) revealed significantly elevated gene-dosage effects on total cholesterol, LDL cholesterol and VLDL cholesterol levels in this Khatri Sikh sample. It is therefore possible that the observed association with quantitative lipid levels could be population-specific and may not be present in other ethnicities. Replication of these findings in a larger sample of Asian Indian origin would be required confirm these findings.

On the other hand, the absence of positive association with T2D in remaining loci may suggest that the genetic variation in some loci could be restricted to a particular genetic background and environment and the contribution of these loci in Indian population may be minor. Alternatively, different SNPs within the same gene could be contributing to the risk and could be population specific. Therefore, further SNP screening and deep sequencing of these genes would be required to identify putative ethnicity-specific functional variants in these novel candidates.

Conclusion

Our study in Asian Indian Sikhs has successfully replicated the association of four of nine loci recently reported to be associated with T2D in Caucasians. To our knowledge, this is the first report describing the role of some recently emerged loci with T2D in a population of Asian Indian origin from North. Further investigations are warranted to cross-validate these findings in other larger datasets. Functional studies would help understand the pathway-based implications of these important loci in T2D pathophysiology in different ethnicities.

References

  1. Botstein D, Risch N: Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nature genetics. 2003, 33 (Suppl): 228-237. 10.1038/ng1090.
    Article CAS PubMed Google Scholar
  2. O'Rahilly S, Barroso I, Wareham NJ: Genetic factors in type 2 diabetes: the end of the beginning?. Science. 2005, 307 (5708): 370-373. 10.1126/science.1104346.
    Article PubMed Google Scholar
  3. Goring HH, Terwilliger JD, Blangero J: Large upward bias in estimation of locus-specific effects from genomewide scans. Am J Hum Genet. 2001, 69 (6): 1357-1369. 10.1086/324471.
    Article CAS PubMed PubMed Central Google Scholar
  4. Ioannidis JP, Trikalinos TA, Ntzani EE, Contopoulos-Ioannidis DG: Genetic associations in large versus small studies: an empirical assessment. Lancet. 2003, 361 (9357): 567-571. 10.1016/S0140-6736(03)12516-0.
    Article PubMed Google Scholar
  5. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, et al: A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007, 316 (5829): 1341-1345. 10.1126/science.1142382.
    Article CAS PubMed PubMed Central Google Scholar
  6. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D, Polychronakos C, Froguel P: A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007, 445 (7130): 881-885. 10.1038/nature05616.
    Article CAS PubMed Google Scholar
  7. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS, Burton PR, Clayton DG, Craddock N, Deloukas P, Duncanson A, Kwiatkowski DP, et al: Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007, 316 (5829): 1336-1341. 10.1126/science.1142364.
    Article CAS PubMed PubMed Central Google Scholar
  8. Frayling TM: Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nature reviews. 2007, 8 (9): 657-662.
    Article CAS PubMed Google Scholar
  9. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham NJ, et al: A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007, 316 (5826): 889-894. 10.1126/science.1141634.
    Article CAS PubMed PubMed Central Google Scholar
  10. Zeggini E: A new era for Type 2 diabetes genetics. Diabet Med. 2007, 24 (11): 1181-1186. 10.1111/j.1464-5491.2007.02274.x.
    Article CAS PubMed PubMed Central Google Scholar
  11. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson Bostrom K, Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR, Tuomi T, et al: Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007, 316 (5829): 1331-1336. 10.1126/science.1142358.
    Article CAS PubMed Google Scholar
  12. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007, 447 (7145): 661-678. 10.1038/nature05911.
  13. Humphries SE, Gable D, Cooper JA, Ireland H, Stephens JW, Hurel SJ, Li KW, Palmen J, Miller MA, Cappuccio FP, Elkeles R, Godsland I, Miller GJ, Talmud PJ: Common variants in the TCF7L2 gene and predisposition to type 2 diabetes in UK European Whites, Indian Asians and Afro-Caribbean men and women. Journal of molecular medicine (Berlin, Germany). 2006, 84 (12): 1005-1014.
    Article CAS Google Scholar
  14. Bodhini D, Radha V, Dhar M, Narayani N, Mohan V: The rs12255372(G/T) and rs7903146(C/T) polymorphisms of the TCF7L2 gene are associated with type 2 diabetes mellitus in Asian Indians. Metabolism: clinical and experimental. 2007, 56 (9): 1174-1178.
    Article CAS Google Scholar
  15. Sanghera DK, Nath SK, Ortega L, Gambarelli M, Kim-Howard X, Singh JR, Ralhan SK, Wander GS, Mehra NK, Mulvihill JJ, Kamboh MI: TCF7L2 Polymorphisms are Associated with Type 2 Diabetes in Khatri Sikhs from North India: Genetic Variation Affects Lipid Levels. Ann Hum Genet. 2008, 72 (4): 499-509. 10.1111/j.1469-1809.2008.00443.x.
    Article CAS PubMed Google Scholar
  16. Sanghera DK, Bhatti JS, Bhatti GK, Ralhan SK, Wander GS, Singh JR, Bunker CH, Weeks DE, Kamboh MI, Ferrell RE: The Khatri Sikh Diabetes Study (SDS): study design, methodology, sample collection, and initial results. Human biology; an international record of research. 2006, 78 (1): 43-63.
    Google Scholar
  17. Diagnosis and classification of diabetes mellitus. Diabetes care. 2004, 27 (Suppl 1): S5-S10.
  18. Miller SA, Dykes DD, Polesky HF: A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic acids research. 1988, 16 (3): 1215-10.1093/nar/16.3.1215.
    Article CAS PubMed PubMed Central Google Scholar
  19. Gonzalez JR, Armengol L, Sole X, Guino E, Mercader JM, Estivill X, Moreno V: SNPassoc: an R package to perform whole genome association studies. Bioinformatics (Oxford, England). 2007, 23 (5): 644-645. 10.1093/bioinformatics/btm025.
    Article Google Scholar
  20. Akaike H: Information theory as an extension of the maximum likelihood principle. 1973, Budapest: Akademiai Kiado
    Google Scholar
  21. Scott LJ, Bonnycastle LL, Willer CJ, Sprau AG, Jackson AU, Narisu N, Duren WL, Chines PS, Stringham HM, Erdos MR, Valle TT, Tuomilehto J, Bergman RN, Mohlke KL, Collins FS, Boehnke M: Association of transcription factor 7-like 2 (TCF7L2) variants with type 2 diabetes in a Finnish sample. Diabetes. 2006, 55 (9): 2649-2653. 10.2337/db06-0341.
    Article CAS PubMed Google Scholar
  22. Altshuler D, Hirschhorn JN, Klannemark M, Lindgren CM, Vohl MC, Nemesh J, Lane CR, Schaffner SF, Bolk S, Brewer C, Tuomi T, Gaudet D, Hudson TJ, Daly M, Groop L, Lander ES: The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nature genetics. 2000, 26 (1): 76-80. 10.1038/79216.
    Article CAS PubMed Google Scholar
  23. Gloyn AL, Weedon MN, Owen KR, Turner MJ, Knight BA, Hitman G, Walker M, Levy JC, Sampson M, Halford S, McCarthy MI, Hattersley AT, Frayling TM: Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes. 2003, 52 (2): 568-572. 10.2337/diabetes.52.2.568.
    Article CAS PubMed Google Scholar
  24. Lehmann JM, Moore LB, Smith-Oliver TA, Wilkison WO, Willson TM, Kliewer SA: An antidiabetic thiazolidinedione is a high affinity ligand for peroxisome proliferator-activated receptor gamma (PPAR gamma). The Journal of biological chemistry. 1995, 270 (22): 12953-12956. 10.1074/jbc.270.22.12953.
    Article CAS PubMed Google Scholar
  25. Jahangir A, Terzic A: K(ATP) channel therapeutics at the bedside. Journal of molecular and cellular cardiology. 2005, 39 (1): 99-112. 10.1016/j.yjmcc.2005.04.006.
    Article CAS PubMed PubMed Central Google Scholar
  26. Pearson ER, Flechtner I, Njolstad PR, Malecki MT, Flanagan SE, Larkin B, Ashcroft FM, Klimes I, Codner E, Iotova V, Slingerland AS, Shield J, Robert JJ, Holst JJ, Clark PM, Ellard S, Sovik O, Polak M, Hattersley AT: Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations. The New England journal of medicine. 2006, 355 (5): 467-477. 10.1056/NEJMoa061759.
    Article CAS PubMed Google Scholar
  27. Deeb SS, Fajas L, Nemoto M, Pihlajamaki J, Mykkanen L, Kuusisto J, Laakso M, Fujimoto W, Auwerx J: A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nature genetics. 1998, 20 (3): 284-287. 10.1038/3099.
    Article CAS PubMed Google Scholar
  28. Hara K, Okada T, Tobe K, Yasuda K, Mori Y, Kadowaki H, Hagura R, Akanuma Y, Kimura S, Ito C, Kadowaki T: The Pro12Ala polymorphism in PPAR gamma2 may confer resistance to type 2 diabetes. Biochemical and biophysical research communications. 2000, 271 (1): 212-216. 10.1006/bbrc.2000.2605.
    Article CAS PubMed Google Scholar
  29. Mori H, Ikegami H, Kawaguchi Y, Seino S, Yokoi N, Takeda J, Inoue I, Seino Y, Yasuda K, Hanafusa T, Yamagata K, Awata T, Kadowaki T, Hara K, Yamada N, Gotoda T, Iwasaki N, Iwamoto Y, Sanke T, Nanjo K, Oka Y, Matsutani A, Maeda E, Kasuga M: The Pro12 – >Ala substitution in PPAR-gamma is associated with resistance to development of diabetes in the general population: possible involvement in impairment of insulin secretion in individuals with type 2 diabetes. Diabetes. 2001, 50 (4): 891-894. 10.2337/diabetes.50.4.891.
    Article CAS PubMed Google Scholar
  30. Douglas JA, Erdos MR, Watanabe RM, Braun A, Johnston CL, Oeth P, Mohlke KL, Valle TT, Ehnholm C, Buchanan TA, Bergman RN, Collins FS, Boehnke M, Tuomilehto J: The peroxisome proliferator-activated receptor-gamma2 Pro12A1a variant: association with type 2 diabetes and trait differences. Diabetes. 2001, 50 (4): 886-890. 10.2337/diabetes.50.4.886.
    Article CAS PubMed Google Scholar
  31. Kawasaki I, Tahara H, Emoto M, Shoji T, Shioji A, Okuno Y, Inaba M, Nishizawa Y: Impact of Prol2Ala variant in the peroxisome proliferator-activated receptor (PPAR) gamma2 on obesity and insulin resistance in Japanese Type 2 diabetic and healthy subjects. Osaka city medical journal. 2002, 48 (1): 23-28.
    CAS PubMed Google Scholar
  32. Hegele RA, Cao H, Harris SB, Zinman B, Hanley AJ, Anderson CM: Peroxisome proliferator-activated receptor-gamma2 P12A and type 2 diabetes in Canadian Oji-Cree. The Journal of clinical endocrinology and metabolism. 2000, 85 (5): 2014-2019. 10.1210/jc.85.5.2014.
    CAS PubMed Google Scholar
  33. Ristow M, Muller-Wieland D, Pfeiffer A, Krone W, Kahn CR: Obesity associated with a mutation in a genetic regulator of adipocyte differentiation. The New England journal of medicine. 1998, 339 (14): 953-959. 10.1056/NEJM199810013391403.
    Article CAS PubMed Google Scholar
  34. Radha V, Vimaleswaran KS, Babu HN, Abate N, Chandalia M, Satija P, Grundy SM, Ghosh S, Majumder PP, Deepa R, Rao SM, Mohan V: Role of genetic polymorphism peroxisome proliferator-activated receptor-gamma2 Pro12Ala on ethnic susceptibility to diabetes in South-Asian and Caucasian subjects: Evidence for heterogeneity. Diabetes care. 2006, 29 (5): 1046-1051. 10.2337/dc05-1473.
    Article CAS PubMed Google Scholar
  35. Papiha SS: Genetic variation in India. Human biology; an international record of research. 1996, 68 (5): 607-628.
    CAS Google Scholar
  36. Papiha SS: The first 25 years of human genetics in Newcastle. Human biology; an international record of research. 1996, 68 (5): 603-606.
    CAS Google Scholar
  37. Papiha SS, Chahal SM, Mastana SS: Variability of genetic markers in Himachal Pradesh, India: variation among the subpopulations. Human biology; an international record of research. 1996, 68 (5): 629-654.
    CAS Google Scholar
  38. Spagnoli FM, Brivanlou AH: The RNA-binding protein, Vg1RBP, is required for pancreatic fate specification. Developmental biology. 2006, 292 (2): 442-456. 10.1016/j.ydbio.2006.01.022.
    Article CAS PubMed Google Scholar
  39. Nielsen J, Christiansen J, Lykke-Andersen J, Johnsen AH, Wewer UM, Nielsen FC: A family of insulin-like growth factor II mRNA-binding proteins represses translation in late development. Mol Cell Biol. 1999, 19 (2): 1262-1270.
    Article CAS PubMed PubMed Central Google Scholar
  40. Omori S, Tanaka Y, Takahashi A, Hirose H, Kashiwagi A, Kaku K, Kawamori R, Nakamura Y, Maeda S: Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes. 2008, 57 (3): 791-795. 10.2337/db07-0979.
    Article CAS PubMed Google Scholar
  41. Hoeven van der F, Schimmang T, Volkmann A, Mattei MG, Kyewski B, Ruther U: Programmed cell death is affected in the novel mouse mutant Fused toes (Ft). Development (Cambridge, England). 1994, 120 (9): 2601-2607.
    Google Scholar
  42. Gerken T, Girard CA, Tung YC, Webby CJ, Saudek V, Hewitson KS, Yeo GS, McDonough MA, Cunliffe S, McNeill LA, Galvanovskis J, Rorsman P, Robins P, Prieur X, Coll AP, Ma M, Jovanovic Z, Farooqi IS, Sedgwick B, Barroso I, Lindahl T, Ponting CP, Ashcroft FM, O'Rahilly S, Schofield CJ: The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science. 2007, 318 (5855): 1469-1472. 10.1126/science.1151710.
    Article CAS PubMed PubMed Central Google Scholar
  43. Sanchez-Pulido L, Andrade-Navarro MA: The FTO (fat mass and obesity associated) gene codes for a novel member of the non-heme dioxygenase superfamily. BMC biochemistry. 2007, 8: 23-10.1186/1471-2091-8-23.
    Article PubMed PubMed Central Google Scholar
  44. Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J, Najjar S, Nagaraja R, Orru M, Usala G, Dei M, Lai S, Maschio A, Busonero F, Mulas A, Ehret GB, Fink AA, Weder AB, Cooper RS, Galan P, Chakravarti A, Schlessinger D, Cao A, Lakatta E, Abecasis GR: Genome-Wide Association Scan Shows Genetic Variants in the FTO Gene Are Associated with Obesity-Related Traits. PLoS Genet. 2007, 3 (7): e115-10.1371/journal.pgen.0030115.
    Article PubMed PubMed Central Google Scholar
  45. Dina C, Meyre D, Gallina S, Durand E, Korner A, Jacobson P, Carlsson LM, Kiess W, Vatin V, Lecoeur C, Delplanque J, Vaillant E, Pattou F, Ruiz J, Weill J, Levy-Marchal C, Horber F, Potoczna N, Hercberg S, Le Stunff C, Bougneres P, Kovacs P, Marre M, Balkau B, Cauchi S, Chevre JC, Froguel P: Variation in FTO contributes to childhood obesity and severe adult obesity. Nature genetics. 2007, 39 (6): 724-726. 10.1038/ng2048.
    Article CAS PubMed Google Scholar
  46. McKeigue PM, Shah B, Marmot MG: Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians. Lancet. 1991, 337 (8738): 382-386. 10.1016/0140-6736(91)91164-P.
    Article CAS PubMed Google Scholar
  47. Chandalia M, Abate N, Garg A, Stray-Gundersen J, Grundy SM: Relationship between generalized and upper body obesity to insulin resistance in Asian Indian men. The Journal of clinical endocrinology and metabolism. 1999, 84 (7): 2329-2335. 10.1210/jc.84.7.2329.
    CAS PubMed Google Scholar
  48. Chambers JC, Eda S, Bassett P, Karim Y, Thompson SG, Gallimore JR, Pepys MB, Kooner JS: C-reactive protein, insulin resistance, central obesity, and coronary heart disease risk in Indian Asians from the United Kingdom compared with European whites. Circulation. 2001, 104 (2): 145-150.
    Article CAS PubMed Google Scholar
  49. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, Heath SC, Timpson NJ, Najjar SS, Stringham HM, Strait J, Duren WL, Maschio A, Busonero F, Mulas A, Albai G, Swift AJ, Morken MA, Narisu N, Bennett D, Parish S, Shen H, Galan P, Meneton P, Hercberg S, Zelenika D, Chen WM, Li Y, Scott LJ, Scheet PA, et al: Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nature genetics. 2008, 40 (2): 161-169. 10.1038/ng.76.
    Article CAS PubMed Google Scholar
  50. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS, Wahlstrand B, Hedner T, Corella D, Tai ES, Ordovas JM, Berglund G, Vartiainen E, Jousilahti P, Hedblad B, Taskinen MR, Newton-Cheh C, Salomaa V, Peltonen L, Groop L, Altshuler DM, Orho-Melander M: Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nature genetics. 2008, 40 (2): 189-197. 10.1038/ng.75.
    Article CAS PubMed PubMed Central Google Scholar

Pre-publication history

Download references

Acknowledgements

This work was supported by NIH grant number KO1 TW006087 from Fogarty International Center, National Institute of Health, USA. We thank all the participants of SDS and are grateful for their contribution in this study. We are also thankful to Mr. Jagtar S. Sanghera for his repeated help in subject recruitment and providing guidance and support in logistical and ethical issues. Technical help provided by Latonya Been and Karamjit Kaur is also duly acknowledged.

Author information

Authors and Affiliations

  1. Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
    Dharambir K Sanghera, Lyda Ortega & John J Mulvihill
  2. Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
    Shizhong Han & Swapan K Nath
  3. Guru Nanak Dev University, Amritsar, Punjab, India
    Jairup Singh
  4. Hero DMC Heart Institute, Ludhiana, Punjab, India
    Sarju K Ralhan & Gurpreet S Wander
  5. All India Institute of Medical Sciences, New Delhi, India
    Narinder K Mehra
  6. Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
    Robert E Ferrell & Mohammed I Kamboh
  7. Department of Pediatrics, Section of Genetics University of Oklahoma Health Sciences Center, 975 NE 10th, BRC-254A, Oklahoma City, OK, 73104, USA
    Dharambir K Sanghera

Authors

  1. Dharambir K Sanghera
    You can also search for this author inPubMed Google Scholar
  2. Lyda Ortega
    You can also search for this author inPubMed Google Scholar
  3. Shizhong Han
    You can also search for this author inPubMed Google Scholar
  4. Jairup Singh
    You can also search for this author inPubMed Google Scholar
  5. Sarju K Ralhan
    You can also search for this author inPubMed Google Scholar
  6. Gurpreet S Wander
    You can also search for this author inPubMed Google Scholar
  7. Narinder K Mehra
    You can also search for this author inPubMed Google Scholar
  8. John J Mulvihill
    You can also search for this author inPubMed Google Scholar
  9. Robert E Ferrell
    You can also search for this author inPubMed Google Scholar
  10. Swapan K Nath
    You can also search for this author inPubMed Google Scholar
  11. Mohammed I Kamboh
    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toDharambir K Sanghera.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

All authors have read and approved the final manuscript. LO carried out the molecular genetic studies, acquisition of data, SH participated in statistical analysis, JS participated in recruitment of study subjects, study design and manuscript editing, SKR participated in recruitment of study subjects, study design and manuscript editing, GSW participated in recruitment of study subjects, study design and manuscript editing, NKM participated in study design, sample collection and manuscript drafting, JJM helped in quality control, and manuscript drafting, REF and MIK helped in conceptualizing the project, data interpretation, intellectual input and manuscript drafting, SKN carried out statistical analysis, data interpretation, DKS is principal investigator and coordinator of the project, involved in conceptualizing the project, study design, genotyping data quality control, analysis, interpretation, manuscript drafting and finalizing the manuscript.

Authors’ original submitted files for images

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Sanghera, D.K., Ortega, L., Han, S. et al. Impact of nine common type 2 diabetes risk polymorphisms in Asian Indian Sikhs: PPARG2 (Pro12Ala), IGF2BP2, TCF7L2 and _FTO_variants confer a significant risk.BMC Med Genet 9, 59 (2008). https://doi.org/10.1186/1471-2350-9-59

Download citation

Keywords