Combining information from common type 2 diabetes risk polymorphisms improves disease prediction

Abstract

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.

Figure 1. Power to Detect Interaction ORs for Combinations of SNPs

The power calculations assume a multiplicative mode of inheritance. Estimates of allele frequencies and expected effect size were determined from the literature; our study frequencies and effects sizes are consistent with these. α was set at 0.05. The power calculations were performed using Quanto [22].

Figure 2. The Distribution of Risk Alleles in the Controls and Cases

Controls are indicated by black bars, cases by white bars.

Figure 3. ORs and 95% CIs for Participants Carrying Increasing Numbers of Risk Alleles

The reference group is individuals with zero or one risk allele.

Figure 4. ROC for the Information Provided by the Glu23Lys, Pro12Ala, and rs7903146 Variants after Fitting a Logistic Regression Model

AUC = 0.58.