Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies.
Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies.
复制标题
混合种群在全基因组关联研究中提高了变异发现和可移植性的功能。
DOI：
10.3389/fgene.2021.673167
复制
发表时间：
2021
影响因子：
3.7
通讯作者：
Gignoux CR
中科院分区：
文献类型：
--
作者：
Lin M;Park DS;Zaitlen NA;Henn BM;Gignoux CR
Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.
登录
查看更多内容
影响因子：
4.5
作者：
Homburger JR;Moreno-Estrada A;Gignoux CR;Nelson D;Sanchez E;Ortiz-Tello P;Pons-Estel BA;Acevedo-Vasquez E;Miranda P;Langefeld CD;Gravel S;Alarcón-Riquelme ME;Bustamante CD
通讯作者：
Bustamante CD
影响因子：
30.8
作者：
Atkinson EG;Maihofer AX;Kanai M;Martin AR;Karczewski KJ;Santoro ML;Ulirsch JC;Kamatani Y;Okada Y;Finucane HK;Koenen KC;Nievergelt CM;Daly MJ;Neale BM
通讯作者：
Neale BM
前列腺癌的种族差异：分子视角。
DOI：
10.2741/4515
发表时间：
2017-01-01
期刊：
Frontiers in bioscience (Landmark edition)
影响因子：
--
作者：
Bhardwaj A;Srivastava SK;Khan MA;Prajapati VK;Singh S;Carter JE;Singh AP
通讯作者：
Singh AP
影响因子：
3.3
作者：
Novembre J;Barton NH
通讯作者：
Barton NH
影响因子：
9.8
作者：
Dahl, Andy;Khiem Nguyen;Zaitlen, Noah
通讯作者：
Zaitlen, Noah