Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes

Abstract

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m(2) and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk.

Figure 1

Q-Q Plots Showing the p Values from the SKAT and KBAC Association Tests (A) SKAT₁ test (linear kernel, all variants have equal weight). (B) SKAT₂ test (weighted IBS kernel, extra weight to rare variants). (C) KBAC test. Solid lines denote the diagonal.

Figure 2

Power to Detect an Association with SKAT or KBAC for Different Numbers of Causal Genes Different colored curves denote different significance levels. The Bonferroni threshold was 3 × 10⁻⁶. Note that power was low when there were many causal genes (genes containing causal variants) such that the heritability explained by a given gene was very low. (A) SKAT₁ test (linear kernel, all variants have equal weight). (B) SKAT₂ test (weighted IBS kernel, extra weight to rare variants). (C) KBAC test.

Figure 3

Power to Detect at Least One of n Causal Genes at a Bonferroni Significance Threshold for Different Numbers of Causal Genes The power to detect at least one causal gene is calculated as 1 − (1 − power)ⁿ, where n is the number of causal genes and power is estimated from the simulations shown in Figure 2.