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Am J Hum Genet. 2016 Jan 7;98(1):165-84. doi: 10.1016/j.ajhg.2015.12.001.

Genetic Diversity and Association Studies in US Hispanic/Latino Populations: Applications in the Hispanic Community Health Study/Study of Latinos.

Author information

1
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. Electronic address: mconomos@uw.edu.
2
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
3
Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27514, USA.
4
Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA 90502, USA.
5
Graduate School of Public Health, San Diego State University, San Diego, CA 92182 USA.
6
Institute for Minority Health, University of Illinois at Chicago, Chicago, IL 60612, USA.
7
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
8
Department of Psychology and Behavioral Medicine, University of Miami, Miami, FL 33124, USA.
9
Department of Epidemiology, University of Washington, Seattle, WA 98195, USA.
10
Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
11
Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA.
12
Department of Medicine, University of Washington, Seattle, WA 98077, USA.
13
Division of Cardiovascular Sciences, NHLBI, NIH, Bethesda, MD 20892, USA.
14
Department of Statistics, University of Auckland, Auckland 1010, New Zealand.
15
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. Electronic address: cclaurie@uw.edu.

Abstract

US Hispanic/Latino individuals are diverse in genetic ancestry, culture, and environmental exposures. Here, we characterized and controlled for this diversity in genome-wide association studies (GWASs) for the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We simultaneously estimated population-structure principal components (PCs) robust to familial relatedness and pairwise kinship coefficients (KCs) robust to population structure, admixture, and Hardy-Weinberg departures. The PCs revealed substantial genetic differentiation within and among six self-identified background groups (Cuban, Dominican, Puerto Rican, Mexican, and Central and South American). To control for variation among groups, we developed a multi-dimensional clustering method to define a "genetic-analysis group" variable that retains many properties of self-identified background while achieving substantially greater genetic homogeneity within groups and including participants with non-specific self-identification. In GWASs of 22 biomedical traits, we used a linear mixed model (LMM) including pairwise empirical KCs to account for familial relatedness, PCs for ancestry, and genetic-analysis groups for additional group-associated effects. Including the genetic-analysis group as a covariate accounted for significant trait variation in 8 of 22 traits, even after we fit 20 PCs. Additionally, genetic-analysis groups had significant heterogeneity of residual variance for 20 of 22 traits, and modeling this heteroscedasticity within the LMM reduced genomic inflation for 19 traits. Furthermore, fitting an LMM that utilized a genetic-analysis group rather than a self-identified background group achieved higher power to detect previously reported associations. We expect that the methods applied here will be useful in other studies with multiple ethnic groups, admixture, and relatedness.

PMID:
26748518
PMCID:
PMC4716704
DOI:
10.1016/j.ajhg.2015.12.001
[Indexed for MEDLINE]
Free PMC Article

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