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A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations.

Authors
  • Duan, Qing1, 2, 3
  • Xu, Zheng1, 4, 5, 6, 7
  • Raffield, Laura M1
  • Chang, Suhua8
  • Wu, Di4, 9
  • Lange, Ethan M10, 11
  • Reiner, Alex P12, 13
  • Li, Yun1, 2, 4, 5
  • 1 Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America. , (United States)
  • 2 Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America. , (United States)
  • 3 Department of Statistics, University of North Carolina, Chapel Hill, North Carolina, United States of America. , (United States)
  • 4 Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America. , (United States)
  • 5 Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States of America. , (United States)
  • 6 Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America. , (United States)
  • 7 Initiative of Quantitative Life Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America. , (United States)
  • 8 CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China. , (China)
  • 9 Department of Periodontology, University of North Carolina, Chapel Hill, North Carolina, United States of America. , (United States)
  • 10 Department of Medicine, University of Colorado at Denver, Anschutz Medical Campus, Aurora, Colorado, United States of America. , (United States)
  • 11 Department of Biostatistics and Informatics, University of Colorado at Denver, Anschutz Medical Campus, Aurora, Colorado, United States of America. , (United States)
  • 12 Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America. , (United States)
  • 13 Department of Epidemiology, University of Washington, Seattle, Washington, United States of America. , (United States)
Type
Published Article
Journal
Genetic Epidemiology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Dec 10, 2017
Identifiers
DOI: 10.1002/gepi.22104
PMID: 29226381
Source
Medline
Keywords
License
Unknown

Abstract

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.

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