Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes
Research output: Contribution to journal › Journal article › Research › peer-review
Anubha Mahajan, Jennifer Wessel, Sara M Willems, Wei Zhao, Neil R Robertson, Audrey Y Chu, Wei Gan, Hidetoshi Kitajima, Daniel Taliun, N William Rayner, Xiuqing Guo, Yingchang Lu, Man Li, Richard A Jensen, Yao Hu, Shaofeng Huo, Kurt K Lohman, Weihua Zhang, James P Cook, Bram Peter Prins & 31 more
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.
|Number of pages||13|
|Publication status||Published - Apr 2018|