Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies.

Published

Journal Article

Large-scale multi-ethnic cohorts offer unprecedented opportunities to elucidate the genetic factors influencing complex traits related to health and disease among minority populations. At the same time, the genetic diversity in these cohorts presents new challenges for analysis and interpretation. We consider the utility of race and/or ethnicity categories in genome-wide association studies (GWASs) of multi-ethnic cohorts. We demonstrate that race/ethnicity information enhances the ability to understand population-specific genetic architecture. To address the practical issue that self-identified racial/ethnic information may be incomplete, we propose a machine learning algorithm that produces a surrogate variable, termed HARE. We use height as a model trait to demonstrate the utility of HARE and ethnicity-specific GWASs.

Full Text

Duke Authors

Cited Authors

  • Fang, H; Hui, Q; Lynch, J; Honerlaw, J; Assimes, TL; Huang, J; Vujkovic, M; Damrauer, SM; Pyarajan, S; Gaziano, JM; DuVall, SL; O'Donnell, CJ; Cho, K; Chang, K-M; Wilson, PWF; Tsao, PS; VA Million Veteran Program, ; Sun, YV; Tang, H

Published Date

  • October 3, 2019

Published In

Volume / Issue

  • 105 / 4

Start / End Page

  • 763 - 772

PubMed ID

  • 31564439

Pubmed Central ID

  • 31564439

Electronic International Standard Serial Number (EISSN)

  • 1537-6605

Digital Object Identifier (DOI)

  • 10.1016/j.ajhg.2019.08.012

Language

  • eng

Conference Location

  • United States