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http://ir.nhri.org.tw/handle/3990099045/14604
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| 題名: | Global biobank meta-analysis initiative: Powering genetic discovery across human disease |
| 作者: | Zhou, W;Kanai, M;Wu, KHH;Rasheed, H;Tsuo, K;Hirbo, JB;Wang, Y;Bhattacharya, A;Zhao, H;Namba, S;Surakka, I;Wolford, BN;Lo Faro, V;Lopera-Maya, EA;Läll, K;Favé, MJ;Partanen, JJ;Chapman, SB;Karjalainen, J;Kurki, M;Maasha, M;Brumpton, BM;Chavan, S;Chen, TT;Daya, M;Ding, Y;Feng, YCA;Guare, LA;Gignoux, CR;Graham, SE;Hornsby, WE;Ingold, N;Ismail, SI;Johnson, R;Laisk, T;Lin, K;Lv, J;Millwood, IY;Moreno-Grau, S;Nam, K;Palta, P;Pandit, A;Preuss, MH;Saad, C;Setia-Verma, S;Thorsteinsdottir, U;Uzunovic, J;Verma, A;Zawistowski, M;Zhong, X;Afifi, N;Al-Dabhani, KM;Al Thani, A;Bradford, Y;Campbell, A;Crooks, K;de Bock, GH;Damrauer, SM;Douville, NJ;Finer, S;Fritsche, LG;Fthenou, E;Gonzalez-Arroyo, G;Griffiths, CJ;Guo, Y;Hunt, KA;Ioannidis, A;Jansonius, NM;Konuma, T;Lee, MTM;Lopez-Pineda, A;Matsuda, Y;Marioni, RE;Moatamed, B;Nava-Aguilar, MA;Numakura, K;Patil, S;Rafaels, N;Richmond, A;Rojas-Muñoz, A;Shortt, JA;Straub, P;Tao, R;Vanderwerff, B;Vernekar, M;Veturi, Y;Barnes, KC;Boezen, M;Chen, Z;Chen, CY;Cho, J;Smith, GD;Finucane, HK;Franke, L;Gamazon, ER;Ganna, A;Gaunt, TR;Ge, T;Huang, H;Huffman, J, .;et al. |
| 貢獻者: | Center for Neuropsychiatric Research |
| 摘要: | Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits. |
| 日期: | 2022-10-12 |
| 關聯: | Cell Genomics. 2022 Oct 12;2(10):Article number 100192. |
| Link to: | http://dx.doi.org/10.1016/j.xgen.2022.100192 |
| Cited Times(Scopus): | https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85139994619 |
| 顯示於類別: | [林彥鋒] 期刊論文
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