Variation at Diabetes- and Obesity-Associated Loci May Mirror Neutral Patterns of Human Population Diversity and Diabetes Prevalence in India

Authors


Corresponding authors: Srilakshmi M Raj, 101 Biotechnology Building, Cornell University, Ithaca, NY 14853. Tel: +1 607 255 2556; Fax: +1 607 255 6249; E-mail: smr46@cornell.edu. Toomas Kivisild, Leverhulme Centre for Human Evolutionary Studies, University of Cambridge, The Henry Wellcome Building, Fitzwilliam Street, Cambridge CB2 1QH, UK. Tel: +44 (0)1223 764703; Fax: +44 (0) 1223 764710; E-mail: tk331@cam.ac.uk. Kumarasamy Thangaraj, CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500 007, India. Tel: +91 40 27192828; Fax: +91 40 27160591; E-mail: thangs@ccmb.res.in

Summary

South Asian populations harbor a high degree of genetic diversity, due in part to demographic history. Two studies on genome-wide variation in Indian populations have shown that most Indian populations show varying degrees of admixture between ancestral north Indian and ancestral south Indian components. As a result of this structure, genetic variation in India appears to follow a geographic cline. Similarly, Indian populations seem to show detectable differences in diabetes and obesity prevalence between different geographic regions of the country. We tested the hypothesis that genetic variation at diabetes- and obesity-associated loci may be potentially related to different genetic ancestries. We genotyped 2977 individuals from 61 populations across India for 18 SNPs in genes implicated in T2D and obesity. We examined patterns of variation in allele frequency across different geographical gradients and considered state of origin and language affiliation. Our results show that most of the 18 SNPs show no significant correlation with latitude, the geographic cline reported in previous studies, or by language family. Exceptions include KCNQ1 with latitude and THADA and JAK1 with language, which suggests that genetic variation at previously ascertained diabetes-associated loci may only partly mirror geographic patterns of genome-wide diversity in Indian populations.

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