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Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation
- Mahajan, Anubha;
- Spracklen, Cassandra N;
- Zhang, Weihua;
- Ng, Maggie CY;
- Petty, Lauren E;
- Kitajima, Hidetoshi;
- Yu, Grace Z;
- Rüeger, Sina;
- Speidel, Leo;
- Kim, Young Jin;
- Horikoshi, Momoko;
- Mercader, Josep M;
- Taliun, Daniel;
- Moon, Sanghoon;
- Kwak, Soo-Heon;
- Robertson, Neil R;
- Rayner, Nigel W;
- Loh, Marie;
- Kim, Bong-Jo;
- Chiou, Joshua;
- Miguel-Escalada, Irene;
- della Briotta Parolo, Pietro;
- Lin, Kuang;
- Bragg, Fiona;
- Preuss, Michael H;
- Takeuchi, Fumihiko;
- Nano, Jana;
- Guo, Xiuqing;
- Lamri, Amel;
- Nakatochi, Masahiro;
- Scott, Robert A;
- Lee, Jung-Jin;
- Huerta-Chagoya, Alicia;
- Graff, Mariaelisa;
- Chai, Jin-Fang;
- Parra, Esteban J;
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- Tabara, Yasuharu;
- Hai, Yang;
- Steinthorsdottir, Valgerdur;
- Cook, James P;
- Kals, Mart;
- Grarup, Niels;
- Schmidt, Ellen M;
- Pan, Ian;
- Sofer, Tamar;
- Wuttke, Matthias;
- Sarnowski, Chloe;
- Gieger, Christian;
- Nousome, Darryl;
- Trompet, Stella;
- Long, Jirong;
- Sun, Meng;
- Tong, Lin;
- Chen, Wei-Min;
- Ahmad, Meraj;
- Noordam, Raymond;
- Lim, Victor JY;
- Tam, Claudia HT;
- Joo, Yoonjung Yoonie;
- Chen, Chien-Hsiun;
- Raffield, Laura M;
- Lecoeur, Cécile;
- Prins, Bram Peter;
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- Kabagambe, Edmond K;
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- Engert, James C;
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- Wilson, James G;
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- Wu, Jer-Yuarn;
- Hayes, M Geoffrey;
- Ma, Ronald CW;
- Wong, Tien-Yin;
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- Chandak, Giriraj R;
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- Paré, Guillaume;
- Sale, Michèle M;
- Ahsan, Habibul;
- Motala, Ayesha A;
- Shu, Xiao-Ou;
- Park, Kyong-Soo;
- Jukema, J Wouter;
- Cruz, Miguel;
- McKean-Cowdin, Roberta;
- Grallert, Harald;
- Cheng, Ching-Yu;
- Bottinger, Erwin P;
- Dehghan, Abbas;
- Tai, E-Shyong;
- Dupuis, Josée;
- Kato, Norihiro;
- Laakso, Markku;
- Köttgen, Anna;
- Koh, Woon-Puay;
- Palmer, Colin NA;
- Liu, Simin;
- Abecasis, Goncalo;
- Kooner, Jaspal S;
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- North, Kari E;
- Haiman, Christopher A;
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- Saleheen, Danish;
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- et al.
Published Web Location
https://doi.org/10.1038/s41588-022-01058-3Abstract
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.
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