| Literature DB >> 31217584 |
Genevieve L Wojcik1, Mariaelisa Graff2, Katherine K Nishimura3, Ran Tao4,5, Jeffrey Haessler3, Christopher R Gignoux1,6, Heather M Highland2, Yesha M Patel7, Elena P Sorokin1, Christy L Avery2, Gillian M Belbin8,9, Stephanie A Bien3, Iona Cheng10, Sinead Cullina8,9, Chani J Hodonsky2, Yao Hu3, Laura M Huckins11, Janina Jeff8,9, Anne E Justice2, Jonathan M Kocarnik3, Unhee Lim12, Bridget M Lin2, Yingchang Lu9, Sarah C Nelson13, Sung-Shim L Park7, Hannah Poisner8,9, Michael H Preuss9, Melissa A Richard14, Claudia Schurmann9,15,16, Veronica W Setiawan7, Alexandra Sockell1, Karan Vahi17, Marie Verbanck9, Abhishek Vishnu9, Ryan W Walker9, Kristin L Young2, Niha Zubair3, Victor Acuña-Alonso18, Jose Luis Ambite17, Kathleen C Barnes6, Eric Boerwinkle19, Erwin P Bottinger9,15,16, Carlos D Bustamante1, Christian Caberto12, Samuel Canizales-Quinteros20, Matthew P Conomos13, Ewa Deelman17, Ron Do9,11, Kimberly Doheny21, Lindsay Fernández-Rhodes2,22, Myriam Fornage14, Benyam Hailu23, Gerardo Heiss2, Brenna M Henn24, Lucia A Hindorff25, Rebecca D Jackson26, Cecelia A Laurie13, Cathy C Laurie13, Yuqing Li10,27, Dan-Yu Lin2, Andres Moreno-Estrada28, Girish Nadkarni9, Paul J Norman6, Loreall C Pooler7, Alexander P Reiner13, Jane Romm21, Chiara Sabatti1, Karla Sandoval28, Xin Sheng7, Eli A Stahl11, Daniel O Stram7, Timothy A Thornton13, Christina L Wassel29, Lynne R Wilkens12, Cheryl A Winkler30, Sachi Yoneyama2, Steven Buyske31, Christopher A Haiman32, Charles Kooperberg3, Loic Le Marchand12, Ruth J F Loos9,11, Tara C Matise33, Kari E North2, Ulrike Peters3, Eimear E Kenny34,35,36,37, Christopher S Carlson38.
Abstract
Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.Entities:
Mesh：
Year: 2019 PMID： 31217584 PMCID： PMC6785182 DOI： 10.1038/s41586-019-1310-4
Source DB: PubMed Journal: Nature ISSN： 0028-0836 Impact factor: 69.504