Journal
AMERICAN JOURNAL OF HUMAN GENETICS
Volume 105, Issue 4, Pages 763-772Publisher
CELL PRESS
DOI: 10.1016/j.ajhg.2019.08.012
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Funding
- Department of Veterans Affairs Office of Research and Development, Million Veteran Program [I01-BX003362, I01-BX003340, I01-BX002641]
- US National Institutes of Health [R01 GM073059, R35 GM127063, R01 NR013520]
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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.
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