Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

Abstract

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10^-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.

Figure 1. Posterior probabilities for coding variants across loci with annotation-informed priors

Fine-mapping of 37 distinct association signals was performed using European ancestry GWAS meta-analysis including 50,160 T2D cases and 465,272 controls. For each signal, we constructed a credible set of variants accounting for 99% of the posterior probability of driving the association, incorporating an “annotation informed” prior model of causality which “boosts” the posterior probability of driving the association signal that is attributed to coding variants. Each bar represents a signal with the total probability attributed to the coding variants within the 99% credible set plotted on the y-axis. When the probability (bar) is split across multiple coding variants (at least 0.05 probability attributed to a variant) at a particular locus, these are indicated by blue, pink, yellow, and green colours. The combined probability of the remaining coding variants is highlighted in grey. RREB1(a): RREB1 p. Asp1171Asn; RREB1(b): RREB1 p.Ser1499Tyr; HNF1A(a): HNF1A p.Ala146Val; HNF1A(b): HNF1A p.Ile75Leu; PPIP5K2† : PPIP5K2 p.Ser1207Gly; MTMR3†: MTMR3 p.Asn960Ser; IL17REL†: IL17REL p.Gly70Arg; NBEAL2†: NBEAL2 p.Arg511Gly, KIF9†: KIF9 p.Arg638Trp.

Figure 2. Plot of measures of variant-specific and gene-specific features of distinct coding signals to access the functional impact of coding alleles

Each point represents a coding variant with the minor allele frequency plotted on the x-axis and the Combined Annotation Dependent Depletion score (CADD-score) plotted on the y-axis. Size of each point varies with the measure of intolerance of the gene to loss of function variants (pLI) and the colour represents the fine-mapping group each variant is assigned to. Group 1: signal is driven by coding variant. Group 2: signal attributable to non-coding variants. Group 3: consistent with partial role for coding variants. Group 4: Unclassified category; includes PAX4, ZHX3, and signal at TCF19 within the MHC region where we did not perform fine-mapping. Inset: plot shows the distribution of CADD-score between different groups. The plot is a combination of violin plots and box plots; width of each violin indicates frequency at the corresponding CADD-score and box plots show the median and the 25% and 75% quantiles. P value indicates significance from two-sample Kolmogorov-Smirnov test.