Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.
Mahajan A., Wessel J., Willems SM., Zhao W., Robertson NR., Chu AY., Gan W., Kitajima H., Taliun D., Rayner NW., Guo X., Lu Y., Li M., Jensen RA., Hu Y., Huo S., Lohman KK., Zhang W., Cook JP., Prins BP., Flannick J., Grarup N., Trubetskoy VV., Kravic J., Kim YJ., Rybin DV., Yaghootkar H., Müller-Nurasyid M., Meidtner K., Li-Gao R., Varga TV., Marten J., Li J., Smith AV., An P., Ligthart S., Gustafsson S., Malerba G., Demirkan A., Tajes JF., Steinthorsdottir V., Wuttke M., Lecoeur C., Preuss M., Bielak LF., Graff M., Highland HM., Justice AE., Liu DJ., Marouli E., Peloso GM., Warren HR., ExomeBP Consortium None., MAGIC Consortium None., GIANT Consortium None., Afaq S., Afzal S., Ahlqvist E., Almgren P., Amin N., Bang LB., Bertoni AG., Bombieri C., Bork-Jensen J., Brandslund I., Brody JA., Burtt NP., Canouil M., Chen Y-DI., Cho YS., Christensen C., Eastwood SV., Eckardt K-U., Fischer K., Gambaro G., Giedraitis V., Grove ML., de Haan HG., Hackinger S., Hai Y., Han S., Tybjærg-Hansen A., Hivert M-F., Isomaa B., Jäger S., Jørgensen ME., Jørgensen T., Käräjämäki A., Kim B-J., Kim SS., Koistinen HA., Kovacs P., Kriebel J., Kronenberg F., Läll K., Lange LA., Lee J-J., Lehne B., Li H., Lin K-H., Linneberg A., Liu C-T., Liu J., Loh M., Mägi R., Mamakou V., McKean-Cowdin R., Nadkarni G., Neville M., Nielsen SF., Ntalla I., Peyser PA., Rathmann W., Rice K., Rich SS., Rode L., Rolandsson O., Schönherr S., Selvin E., Small KS., Stančáková A., Surendran P., Taylor KD., Teslovich TM., Thorand B., Thorleifsson G., Tin A., Tönjes A., Varbo A., Witte DR., Wood AR., Yajnik P., Yao J., Yengo L., Young R., Amouyel P., Boeing H., Boerwinkle E., Bottinger EP., Chowdhury R., Collins FS., Dedoussis G., Dehghan A., Deloukas P., Ferrario MM., Ferrières J., Florez JC., Frossard P., Gudnason V., Harris TB., Heckbert SR., Howson JMM., Ingelsson M., Kathiresan S., Kee F., Kuusisto J., Langenberg C., Launer LJ., Lindgren CM., Männistö S., Meitinger T., Melander O., Mohlke KL., Moitry M., Morris AD., Murray AD., de Mutsert R., Orho-Melander M., Owen KR., Perola M., Peters A., Province MA., Rasheed A., Ridker PM., Rivadineira F., Rosendaal FR., Rosengren AH., Salomaa V., Sheu WH-H., Sladek R., Smith BH., Strauch K., Uitterlinden AG., Varma R., Willer CJ., Blüher M., Butterworth AS., Chambers JC., Chasman DI., Danesh J., van Duijn C., Dupuis J., Franco OH., Franks PW., Froguel P., Grallert H., Groop L., Han B-G., Hansen T., Hattersley AT., Hayward C., Ingelsson E., Kardia SLR., Karpe F., Kooner JS., Köttgen A., Kuulasmaa K., Laakso M., Lin X., Lind L., Liu Y., Loos RJF., Marchini J., Metspalu A., Mook-Kanamori D., Nordestgaard BG., Palmer CNA., Pankow JS., Pedersen O., Psaty BM., Rauramaa R., Sattar N., Schulze MB., Soranzo N., Spector TD., Stefansson K., Stumvoll M., Thorsteinsdottir U., Tuomi T., Tuomilehto J., Wareham NJ., Wilson JG., Zeggini E., Scott RA., Barroso I., Frayling TM., Goodarzi MO., Meigs JB., Boehnke M., Saleheen D., Morris AP., Rotter JI., McCarthy MI.
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.