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Chen, J; Spracklen, CN; Marenne, G; Varshney, A; Corbin, LJ; Luan, JA; Willems, SM; Wu, Y; Zhang, XS; Horikoshi, M; Boutin, TS; Magi, R; Waage, J; Li-Gao, RF; Chan, KHK; Yao, J; Anasanti, MD; Chu, AY; Claringbould, A; Heikkinen, J; Hong, J; Hottenga, JJ; Huo, SF; Kaakinen, MA; Louie, T; Marz, W; Moreno-Macias, H; Ndungu, A; Nelson, SC; Nolte, IM; North, KE; Raulerson, CK; Ray, D; Rohde, R; Rybin, D; Schurmann, C; Sim, XL; Southam, L; Stewart, ID; Wang, CA; Wang, YJ; Wu, PT; Zhang, WH; Ahluwalia, TS; Appel, EVR; Bielak, LF; Brody, JA; Burtt, NP; Cabrera, CP; Cade, BE; Chai, JF; Chai, XR; Chang, LC; Chen, CH; Chen, BH; Chitrala, KN; Chiu, YF; de Haan, HG; Delgado, GE; Demirkan, A; Duan, Q; Engmann, J; Fatumo, SA; Gayan, J; Giulianini, F; Gong, JH; Gustafsson, S; Hai, Y; Hartwig, FP; He, J; Heianza, Y; Huang, T; Huerta-Chagoya, A; Hwang, MY; Jensen, RA; Kawaguchi, T; Kentistou, KA; Kim, YJ; Kleber, ME; Kooner, IK; Lai, SQ; Lange, LA; Langefeld, CD; Lauzon, M; Li, M; Ligthart, S; Liu, J; Loh, M; Long, JR; Lyssenko, V; Mangino, M; Marzi, C; Montasser, ME; Nag, A; Nakatochi, M; Noce, D; Noordam, R; Pistis, G; Preuss, M; Raffield, L; Rasmussen-Torvik, LJ; Rich, SS; Robertson, NR; Rueedi, R; Ryan, K; Sanna, S; Saxena, R; Schraut, KE; Sennblad, B; Setoh, K; Smith, AV; Sparso, T; Strawbridge, RJ; Takeuchi, F; Tan, JY; Trompet, S; van den Akker, E; van der Most, PJ; Verweij, N; Vogel, M; Wang, HM; Wang, CL; Wang, N; Warren, HR; Wen, WQ; Wilsgaard, T; Wong, A; Wood, AR; Xie, T; Zafarmand, MH; Zhao, JH; Zhao, W; Amin, N; Arzumanyan, Z; Astrup, A; Bakker, SJL; Baldassarre, D; Beekman, M; Bergman, RN; Bertoni, A; Bluher, M; Bonnycastle, LL; Bornstein, SR; Bowden, DW; Cai, QY; Campbell, A; Campbell, H; Chang, YC; de Geus, EJC; Dehghan, A; Du, SF; Eiriksdottir, G; Farmaki, AE; Franberg, M; Fuchsberger, C; Gao, YT; Gjesing, AP; Goel, A; Han, S; Hartman, CA; Herder, C; Hicks, AA; Hsieh, CH; Hsueh, WA; Ichihara, S; Igase, M; Ikram, MA; Johnson, WC; Jorgensen, ME; Joshi, PK; Kalyani, RR; Kandeel, FR; Katsuya, T; Khor, CC; Kiess, W; Kolcic, I; Kuulasmaa, T; Kuusisto, J; Lall, K; Lam, K; Lawlor, DA; Lee, NR; Lemaitre, RN; Li, HL; Lin, SY; Lindstrom, J; Linneberg, A; Liu, JJ; Lorenzo, C; Matsubara, T; Matsuda, F; Mingrone, G; Mooijaart, S; Moon, S; Nabika, T; Nadkarni, GN; Nadler, JL; Nelis, M; Neville, MJ; Norris, JM; Ohyagi, Y; Peters, A; Peyser, PA; Polasek, O; Qi, QB; Raven, D; Reilly, DF; Reiner, A; Rivideneira, F; Roll, K; Rudan, I; Sabanayagam, C; Sandow, K; Sattar, N; Schurmann, A; Shi, JX; Stringham, HM; Taylor, KD; Teslovich, TM; Thuesen, B; Timmers, PRHJ; Tremoli, E; Tsai, MY; Uitterlinden, A; van Dam, RM; van Heemst, D; Vlieg, AV; van Vliet-Ostaptchouk, JV; Vangipurapu, J; Vestergaard, H; Wang, T; van Dijk, KW; Zemunik, T; Abecasis, GR; Adair, LS; Aguilar-Salinas, CA; Alarcon-Riquelme, ME; An, P; Aviles-Santa, L; Becker, DM; Beilin, LJ; Bergmann, S; Bisgaard, H; Black, C; Boehnke, M; Boerwinkle, E; Bohm, BO; Bonnelykke, K; Boomsma, DI; Bottinger, EP; Buchanan, TA; Canouil, M; Caulfield, MJ; Chambers, JC; Chasman, DI; Chen, YDI; Cheng, CY; Collins, FS; Correa, A; Cucca, F; de Silva, HJ; Dedoussis, G; Elmstahl, S; Evans, MK; Ferrannini, E; Ferrucci, L; Florez, JC; Franks, PW; Frayling, TM; Froguel, P; Gigante, B; Goodarzi, MO; Gordon-Larsen, P; Grallert, H; Grarup, N; Grimsgaard, S; Groop, L; Gudnason, V; Guo, XQ; Hamsten, A; Hansen, T; Hayward, C; Heckbert, SR; Horta, BL; Huang, W; Ingelsson, E; James, PS; Jarvelin, MR; Jonas, JB; Jukema, JW; Kaleeb.
The trans-ancestral genomic architecture of glycemic traits
NAT GENET. 2021; Doi: 10.1038/s41588-021-00852-9 [OPEN ACCESS]
Web of Science PubMed FullText FullText_MUG

 

Co-authors Med Uni Graz

März Winfried

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Abstract:

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

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