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A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations
Duan, Qing1,2,3; Xu, Zheng1,4,5,7,8; Raffield, Laura M.1; Chang, Suhua9; Wu, Di4,6; Lange, Ethan M.10,11; Reiner, Alex P.12,13; Li, Yun1,2,4,5
2018-04-01
发表期刊GENETIC EPIDEMIOLOGY
通讯作者邮箱yunli@med.unc.edu
ISSN0741-0395
文章类型Article
卷号42期号:3页码:288-302
产权排序9
摘要

Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two-step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry-specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups' previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at . https://yunliweb.its.unc.edu/LAAA.

关键词Admixed Populations Association Analysis Effect Heterogeneity Genome-wide Association Studies Gwas Local Ancestry Population Stratification Testing Procedure
DOI10.1002/gepi.22104
收录类别SCI
语种英语
项目资助者National Human Genome Research Institute(R01HG006292 ; National Heart, Lung, and Blood Institute(R01HL129132 ; R01HG006703) ; R21HL126045)
WOS研究方向Genetics & Heredity ; Mathematical & Computational Biology
WOS类目Genetics & Heredity ; Mathematical & Computational Biology
WOS记录号WOS:000427473900005
WOS标题词Science & Technology ; Life Sciences & Biomedicine
关键词[WOS]GENOME-WIDE ASSOCIATION ; BLOOD-CELL TRAITS ; GENETIC ASSOCIATION ; AFRICAN-AMERICANS ; VARIANTS ; ADMIXTURE ; INFERENCE ; DESIGN ; STRATIFICATION ; HETEROGENEITY
引用统计
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/26055
专题中国科学院心理健康重点实验室
作者单位1.Univ N Carolina, Dept Genet, Campus Box 7264, Chapel Hill, NC 27599 USA
2.Univ N Carolina, Curriculum Bioinformat & Computat Biol, Chapel Hill, NC USA
3.Univ N Carolina, Dept Stat, Chapel Hill, NC USA
4.Univ N Carolina, Dept Biostat, Chapel Hill, NC USA
5.Univ N Carolina, Dept Comp Sci, Chapel Hill, NC USA
6.Univ N Carolina, Dept Periodontol, Chapel Hill, NC USA
7.Univ Nebraska, Dept Stat, Lincoln, NE USA
8.Univ Nebraska, Initiat Quantitat Life Sci, Lincoln, NE USA
9.Chinese Acad Sci, Inst Psychol, CAS Key Lab Mental Hlth, Beijing, Peoples R China
10.Univ Colorado, Dept Med, Anschutz Med Campus, Denver, CO USA
11.Univ Colorado, Dept Biostat & Informat, Denver, CO 80202 USA
12.Fred Hutchinson Canc Res Ctr, 1124 Columbia St, Seattle, WA 98104 USA
13.Univ Washington, Dept Epidemiol, Seattle, WA 98195 USA
推荐引用方式
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Duan, Qing,Xu, Zheng,Raffield, Laura M.,et al. A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations[J]. GENETIC EPIDEMIOLOGY,2018,42(3):288-302.
APA Duan, Qing.,Xu, Zheng.,Raffield, Laura M..,Chang, Suhua.,Wu, Di.,...&Li, Yun.(2018).A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations.GENETIC EPIDEMIOLOGY,42(3),288-302.
MLA Duan, Qing,et al."A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations".GENETIC EPIDEMIOLOGY 42.3(2018):288-302.
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