Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. - Michael N Weedon; Mark I McCarthy; Graham Hitman; Mark Walker; Christopher J Groves; Eleftheria Zeggini; N William Rayner; Beverley Shields; Katharine R Owen; Andrew T Hattersley; Timothy M Frayling (PLoS Medicine)
Background A limited number of studies have assessed the risk of common diseases when combining information from several predisposing polymorphisms. In most cases, individual polymorphisms only moderately increase risk (approximately 20%), and they are thought to be unhelpful in assessing individuals' risk clinically. The value of analyzing multiple alleles simultaneously is not well studied. This is often because, for any given disease, very few common risk alleles have been confirmed. Methods and findings Three common variants (Lys23 of KCNJ11, Pro12 of PPARG, and the T allele at rs7903146 of TCF7L2) have been shown to predispose to type 2 diabetes mellitus across many large studies. Risk allele frequencies ranged from 0.30 to 0.88 in controls. To assess the combined effect of multiple susceptibility alleles, we genotyped these variants in a large case-control study (3,668 controls versus 2,409 cases). Individual allele odds ratios (ORs) ranged from 1.14 (95% confidence interval [CI], 1.05 to 1.23) to 1.48 (95% CI, 1.36 to 1.60). We found no evidence of gene-gene interaction, and the risks of multiple alleles were consistent with a multiplicative model. Each additional risk allele increased the odds of type 2 diabetes by 1.28 (95% CI, 1.21 to 1.35) times. Participants with all six risk alleles had an OR of 5.71 (95% CI, 1.15 to 28.3) compared to those with no risk alleles. The 8.1% of participants that were double-homozygous for the risk alleles at TCF7L2 and Pro12Ala had an OR of 3.16 (95% CI, 2.22 to 4.50), compared to 4.3% with no TCF7L2 risk alleles and either no or one Glu23Lys or Pro12Ala risk alleles. Conclusions Combining information from several known common risk polymorphisms allows the identification of population subgroups with markedly differing risks of developing type 2 diabetes compared to those obtained using single polymorphisms. This approach may have a role in future preventative measures for common, polygenic diseases.