Rubina Tabassum

Impact of Common Variants of PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 on the Risk of Type 2 Diabetes in 5,164 Indians

OBJECTIVE Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies. We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case–control studies. RESEARCH DESIGN AND METHODS We genotyped eight single nucleotide polymorphisms (PPARG-rs1801282, KCNJ11-rs5219, TCF7L2-rs7903146, SLC30A8-rs13266634, HHEX-rs1111875, CDKN2A-rs10811661, IGF2BP2-rs4402960, and CDKAL1-rs10946398) in 5,164 unrelated Indians of Indo-European ethnicity, including 2,486 type 2 diabetic patients and 2,678 ethnically matched control subjects. RESULTS We confirmed the association of all eight loci with type 2 diabetes with odds ratio (OR) ranging from 1.18 to 1.89 (P = 1.6 × 10−3 to 4.6 × 10−34). The strongest association with the highest effect size was observed for TCF7L2 (OR 1.89 [95% CI 1.71–2.09], P = 4.6 × 10−34). We also found significant association of PPARG and TCF7L2 with homeostasis model assessment of β-cell function (P = 6.9 × 10−8 and 3 × 10−4, respectively), which looked consistent with recessive and under-dominant models, respectively. CONCLUSIONS Our study replicates the association of well-established common variants with type 2 diabetes in Indians and shows larger effect size for most of them than those reported in Europeans.

Genome-wide association study for type 2 diabetes in Indians identifies a new susceptibility locus at 2q21.

Indians undergoing socioeconomic and lifestyle transitions will be maximally affected by epidemic of type 2 diabetes (T2D). We conducted a two-stage genome-wide association study of T2D in 12,535 Indians, a less explored but high-risk group. We identified a new type 2 diabetes–associated locus at 2q21, with the lead signal being rs6723108 (odds ratio 1.31; P = 3.32 × 10−9). Imputation analysis refined the signal to rs998451 (odds ratio 1.56; P = 6.3 × 10−12) within TMEM163 that encodes a probable vesicular transporter in nerve terminals. TMEM163 variants also showed association with decreased fasting plasma insulin and homeostatic model assessment of insulin resistance, indicating a plausible effect through impaired insulin secretion. The 2q21 region also harbors RAB3GAP1 and ACMSD; those are involved in neurologic disorders. Forty-nine of 56 previously reported signals showed consistency in direction with similar effect sizes in Indians and previous studies, and 25 of them were also associated (P < 0.05). Known loci and the newly identified 2q21 locus altogether explained 7.65% variance in the risk of T2D in Indians. Our study suggests that common susceptibility variants for T2D are largely the same across populations, but also reveals a population-specific locus and provides further insights into genetic architecture and etiology of T2D.

The Indian Genome Variation database (IGVdb): A project overview

Indian population, comprising of more than a billion people, consists of 4693 communities with several thousands of endogamous groups, 325 functioning languages and 25 scripts. To address the questions related to ethnic diversity, migrations, founder populations, predisposition to complex disorders or pharmacogenomics, one needs to understand the diversity and relatedness at the genetic level in such a diverse population. In this backdrop, six constituent laboratories of the Council of Scientific and Industrial Research (CSIR), with funding from the Government of India, initiated a network program on predictive medicine using repeats and single nucleotide polymorphisms. The Indian Genome Variation (IGV) consortium aims to provide data on validated SNPs and repeats, both novel and reported, along with gene duplications, in over a thousand genes, in 15,000 individuals drawn from Indian subpopulations. These genes have been selected on the basis of their relevance as functional and positional candidates in many common diseases including genes relevant to pharmacogenomics. This is the first large-scale comprehensive study of the structure of the Indian population with wide-reaching implications. A comprehensive platform for Indian Genome Variation (IGV) data management, analysis and creation of IGVdb portal has also been developed. The samples are being collected following ethical guidelines of Indian Council of Medical Research (ICMR) and Department of Biotechnology (DBT), India. This paper reveals the structure of the IGV project highlighting its various aspects like genesis, objectives, strategies for selection of genes, identification of the Indian subpopulations, collection of samples and discovery and validation of genetic markers, data analysis and monitoring as well as the project’s data release policy.Indian population, comprising of more than a billion people, consists of 4693 communities with several thousands of endogamous groups, 325 functioning languages and 25 scripts. To address the questions related to ethnic diversity, migrations, founder populations, predisposition to complex disorders or pharmacogenomics, one needs to understand the diversity and relatedness at the genetic level in such a diverse population. In this backdrop, six constituent laboratories of the Council of Scientific and Industrial Research (CSIR), with funding from the Government of India, initiated a network program on predictive medicine using repeats and single nucleotide polymorphisms. The Indian Genome Variation (IGV) consortium aims to provide data on validated SNPs and repeats, both novel and reported, along with gene duplications, in over a thousand genes, in 15,000 individuals drawn from Indian subpopulations. These genes have been selected on the basis of their relevance as functional and positional candidates in many common diseases including genes relevant to pharmacogenomics. This is the first large-scale comprehensive study of the structure of the Indian population with wide-reaching implications. A comprehensive platform for Indian Genome Variation (IGV) data management, analysis and creation of IGVdb portal has also been developed. The samples are being collected following ethical guidelines of Indian Council of Medical Research (ICMR) and Department of Biotechnology (DBT), India. This paper reveals the structure of the IGV project highlighting its various aspects like genesis, objectives, strategies for selection of genes, identification of the Indian subpopulations, collection of samples and discovery and validation of genetic markers, data analysis and monitoring as well as the project’s data release policy.

Common variants of FTO and the risk of obesity and type 2 diabetes in Indians

Common variants of fat mass and obesity-associated gene (FTO, fat mass- and obesity-associated gene) have been shown to be associated with obesity and type 2 diabetes in population of European and non-European ethnicity. However, studies in Indian population have provided inconsistent results. Here, we examined association of eight FTO variants (rs1421085, rs8050136, rs9939609, rs9930506, rs1861867, rs9926180, rs2540769 and rs708277) with obesity and type 2 diabetes in 5364 North Indians (2474 type 2 diabetes patients and 2890 non-diabetic controls) in two stages. None of the variants including previously reported intron 1 variants (rs1421085, rs8050136, rs9939609 and rs9930506) showed body mass index (BMI)-dependent/independent association with type 2 diabetes. However, rs1421085, rs8050136 and rs9939609 were associated with obesity status and measures of obesity (BMI, waist circumference and waist-to-hip ratio) in stage 2 and combined study population. Meta-analysis of the two study population results also revealed that rs1421085, rs8050136 and rs9939609 were significantly associated with BMI both under the random- and fixed-effect models (P (random/fixed)=0.02/0.0001, 0.004/0.0006 and 0.01/0.01, respectively). In conclusion, common variants of FTO were associated with obesity, but not with type 2 diabetes in North Indian population.

High-sensitivity C-reactive protein levels and type 2 diabetes in urban North Indians.

CONTEXT Elevated high-sensitivity C-reactive protein (hsCRP) levels have frequently been shown to be associated with type 2 diabetes (T2D); however, very little is known about this in Asian Indians, a high-risk group. OBJECTIVE The aim of the study was to assess the association of hsCRP with T2D and to determine its correlates in North Indians of Indo-European origin. DESIGN AND PATIENTS A cross-sectional population-based study of 2520 urban subjects, comprising 1410 T2D patients and 1110 nondiabetic subjects, was carried out and 18 metabolic traits were assessed. RESULTS Median hsCRP levels were significantly higher in both diabetic men and women as compared to their nondiabetic counterparts (P < 0.0001). Elevated hsCRP was positively associated with T2D (odds ratio, 1.66; 95% confidence interval, 1.21-2.28; P = 0.002) even after adjusting for markers of obesity. After adjustments for age, sex, and BMI, HbA1c was the major correlate of hsCRP in nondiabetic subjects (beta = 0.28; P = 0.03). We observed that T2D patients were at higher risk for cardiovascular disease compared to nondiabetic subjects when classified into low-, intermediate-, and high-risk groups based on hsCRP levels (p(trend) = 3.8 x 10(-15)). CONCLUSIONS We demonstrate the association of low-grade systemic inflammation, as indicated by elevated hsCRP levels, with T2D in North Indian population. This association was independent of obesity. Obesity and glycemic control were the major correlates of hsCRP levels. Future studies are required to evaluate the influence of modulators including genetic variations on the elevation of hsCRP levels in this population.

Common Variants of IL6, LEPR, and PBEF1 Are Associated With Obesity in Indian Children

The increasing prevalence of obesity in urban Indian children is indicative of an impending crisis of metabolic disorders. Although perturbations in the secretion of adipokines and inflammatory molecules in childhood obesity are well documented, the contribution of common variants of genes encoding them is not well investigated. We assessed the association of 125 common variants from 21 genes, encoding adipocytokines and inflammatory markers in 1,325 urban Indian children (862 normal weight [NW group] and 463 overweight/obese [OW/OB group]) and replicated top loci in 1,843 Indian children (1,399 NW children and 444 OW/OB children). Variants of four genes (PBEF1 [rs3801266] [P = 4.5 × 10−4], IL6 [rs2069845] [P = 8.7 × 10−4], LEPR [rs1137100] [P = 1.8 × 10−3], and IL6R [rs7514452] [P = 2.1 × 10−3]) were top signals in the discovery sample. Associations of rs2069845, rs1137100, and rs3801266 were replicated (P = 7.9 × 10−4, 8.3 × 10−3, and 0.036, respectively) and corroborated in meta-analysis (P = 2.3 × 10−6, 3.9 × 10−5, and 4.3 × 10−4, respectively) that remained significant after multiple testing corrections. These variants also were associated with quantitative measures of adiposity (weight, BMI, and waist and hip circumferences). Allele dosage analysis of rs2069845, rs1137100, and rs3801266 revealed that children with five to six risk alleles had an approximately four times increased risk of obesity than children with less than two risk alleles (P = 1.2 × 10−7). In conclusion, our results demonstrate the association of the common variants of IL6, LEPR, and PBEF1 with obesity in Indian children.

Genetic variants of FOXA2: risk of type 2 diabetes and effect on metabolic traits in North Indians

AbstractHere, we examined the association of genetic variants of FOXA2, an upstream activator of the β-cell transcription factor network, with type 2 diabetes and related phenotypes in North India. We genotyped three SNPs (rs1212275, rs1055080, rs6048205) and the (TCC)n repeat polymorphism in 1,656 participants comprising 1,031 patients with type 2 diabetes and 625 controls. SNPs rs1212275 and rs6048205 were uncommon (MAF < 5%) with similar distribution among patients and controls. We found a strong association of (TCC)n common allele A5 with type 2 diabetes [OR = 1.66 (95% CI 1.36–2.04, p = 5.9 × 10−7) for A5 homozygotes]. Obese individuals with A5A5 genotype had enhanced risk when segregated from normal-weight subjects [OR = 1.92 (95% CI 1.47–2.51), p = 1.6 × 10−6]. A5 was also nominally associated with higher fasting glucose (p = 0.02) and lower fasting insulin (p = 0.0028) and C-peptide (p = 0.036) levels among controls. At the rs1055080 locus, GG was found to provide reduced risk among normal-weight subjects [OR = 0.59 (95% CI 0.40–0.88), p = 0.011]. Combination of protective GG and non-risk genotypes of (TCC)n showed reduced risk of type 2 diabetes both among normal-weight [OR = 0.43 (95% CI 0.29–0.65), p = 1.2 × 10−6] and obese individuals [0.47 (95% CI 0.34–0.64), p = 4.3 × 10−5]. For the first time we demonstrated that FOXA2 variants may affect risk of type 2 diabetes and metabolic traits in North India, however replication analyses in other cohorts are required to confirm the findings.

Obesity-dependent association of TNF-LTA locus with type 2 diabetes in North Indians.

Six common genetic variants (rs2229094, rs1041981, rs1800630, rs1800629, rs361525, and rs1800610) in the TNF-LTA locus encoding the pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α) and lymphotoxin-α have been shown to be associated with various metabolic traits including susceptibility to type 2 diabetes, metabolic syndrome, insulin resistance, and increased body mass index (BMI) in Caucasians from different geographic locations and have yielded mixed results. We tested for the association of these variants with type 2 diabetes in North Indians by studying 2,115 participants comprising of 1,073 type 2 diabetes patients and 1,042 controls. We report the association of a promoter region variant of TNF: rs1800630 and non-synonymous LTA variant: rs2229094 with type 2 diabetes [OR = 0.83 (95% CI 0.72–0.95), P = 0.005 and OR = 0.86 (95% CI 0.75–0.98), P = 0.02, respectively]. Although these associations were BMI-dependent, no interactive effect of BMI and variants on type 2 diabetes was detectable. Further, the haplotype carrying all the six major alleles conferred susceptibility to type 2 diabetes [OR = 1.23 (95% CI 1.06–1.42), P = 0.005; Ppermuted = 0.02], with the effect much enhanced in non-obese subjects [OR = 1.45 (95% CI 1.19–1.78), P = 2 × 10−4: Ppermuted = 3 × 10−4]. The minor allele of rs2229094 was associated with lower hsCRP, BMI, and waist circumference (WC), while the minor allele of rs1800630 showed association with lower BMI and WC (all P < 0.01). This is the first report demonstrating association of rs1800630 and rs2229094 with type 2 diabetes in any population, suggesting an important role of the TNF-LTA locus in type 2 diabetes in North Indians.

Gene prioritization in Type 2 Diabetes using domain interactions and network analysis

BackgroundIdentification of disease genes for Type 2 Diabetes (T2D) by traditional methods has yielded limited success. Based on our previous observation that T2D may result from disturbed protein-protein interactions affected through disrupting modular domain interactions, here we have designed an approach to rank the candidates in the T2D linked genomic regions as plausible disease genes.ResultsOur approach integrates Weight value (Wv) method followed by prioritization using clustering coefficients derived from domain interaction network. Wv for each candidate is calculated based on the assumption that disease genes might be functionally related, mainly facilitated by interactions among domains of the interacting proteins. The benchmarking using a test dataset comprising of both known T2D genes and non-T2D genes revealed that Wv method had a sensitivity and specificity of 0.74 and 0.96 respectively with 9 fold enrichment. The candidate genes having a Wv > 0.5 were called High Weight Elements (HWEs). Further, we ranked HWEs by using the network property-the clustering coefficient (Ci). Each HWE with a Ci < 0.015 was prioritized as plausible disease candidates (HWEc) as previous studies indicate that disease genes tend to avoid dense clustering (with an average Ci of 0.015). This method further prioritized the identified disease genes with a sensitivity of 0.32 and a specificity of 0.98 and enriched the candidate list by 6.8 fold. Thus, from the dataset of 4052 positional candidates the method ranked 435 to be most likely disease candidates. The gene ontology sharing for the candidates showed higher representation of metabolic and signaling processes. The approach also captured genes with unknown functions which were characterized by network motif analysis.ConclusionsPrioritization of positional candidates is essential for cost-effective and an expedited discovery of disease genes. Here, we demonstrate a novel approach for disease candidate prioritization from numerous loci linked to T2D.

Elevated levels of C-reactive protein as a risk factor for Metabolic Syndrome in Indians

OBJECTIVE Relationship of high sensitivity C-reactive protein (hsCRP) with metabolic syndrome (MetS) is well documented in many populations, but comprehensive data is lacking in Indian population. Thus, we set out to investigate the association of hsCRP levels with MetS and its features and the effect of obesity and insulin resistance on this association in urban Indians. METHODS This is a cross-sectional study that included 9517 subjects comprising 4066 subjects with MetS. MetS was defined according to the modified National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP III) criteria for Asians. RESULTS Median levels of hsCRP were considerably higher in individuals with MetS with higher levels in women compared to men. Among the features of MetS, waist circumference was most strongly correlated with hsCRP levels (r=0.28) and contributed maximally (β=0.025mg/l lnhsCRP, P=7.4×10(-147)). Subjects with high risk hsCRP levels (>3mg/l) were at high risk of MetS (OR (95% CI)=1.65(1.41-1.92), P=1.7×10(-10)). Risk of MetS increased in a dose dependent manner from low risk to high risk hsCRP category with increase in BMI and HOMA-IR. CONCLUSIONS Our findings suggest that hsCRP predicts the risk of MetS, independent of obesity and insulin resistance, and therefore, can be a valuable tool to aid the identification of individuals at risk of MetS. The study provides a lead for future investigation for effects of hsCRP, obesity, and insulin resistance on MetS in this population.