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

Authors :
Conomos MP
Laurie CA
Stilp AM
Gogarten SM
McHugh CP
Nelson SC
Sofer T
Fernández-Rhodes L
Justice AE
Graff M
Young KL
Seyerle AA
Avery CL
Taylor KD
Rotter JI
Talavera GA
Daviglus ML
Wassertheil-Smoller S
Schneiderman N
Heiss G
Kaplan RC
Franceschini N
Reiner AP
Shaffer JR
Barr RG
Kerr KF
Browning SR
Browning BL
Weir BS
Avilés-Santa ML
Papanicolaou GJ
Lumley T
Szpiro AA
North KE
Rice K
Thornton TA
Laurie CC
Source :
American journal of human genetics [Am J Hum Genet] 2016 Jan 07; Vol. 98 (1), pp. 165-84.
Publication Year :
2016

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.<br /> (Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1537-6605
Volume :
98
Issue :
1
Database :
MEDLINE
Journal :
American journal of human genetics
Publication Type :
Academic Journal
Accession number :
26748518
Full Text :
https://doi.org/10.1016/j.ajhg.2015.12.001