Abstract
Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10−3) and candidate genes from knockout mice (P = 5.2 × 10−3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000–185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.
Highlights
Gene set type 2 diabetes (T2D) drug target effect sizes
We identify gene-level associations of rare variants in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10−3) and candidate genes from knockout mice (P = 5.2 × 10−3)
The strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000–185,000 sequenced cases to achieve exome-wide significance
Summary
As an example of a gene prior that was derived objectively (rather than subjectively), we used a mixture model approach[40] to estimate the proportion of non-null associations across the mouse NIDD gene set (Methods), leading to a prior of approximately 23% for genes of which knockout causes NIDD in mice Our model with this prior (Supplementary Table 18) predicts nonsynonymous variants that achieved P < 0.05 to have PPAs of 9.9% (PPAs of 24.6% for P < 0.005). We predict several nonsynonymous variants in MADD and NOS3 to have PPA ≥ 14% (Supplementary Table 19), suggesting links between variation in these genes and T2D based on combined evidence from human genetic studies and mouse models[41,42] These PPA calculations have limitations (Methods), they present a framework to use suggestive genetic signals to support cost– benefit estimates of ‘go/no-go’ decisions[43] in the language of decision theory[37] (Fig. 4b). We have made our exomesequencing association results publically available through the AMP T2D Knowledge Portal (http://www.type2diabetesgenetics.org/), which supports queries of precomputed associations and further enables dynamic recomputations of associations with custom covariates and sample- and/or variant-filtering criteria
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