TY - JOUR
T1 - Control of confounding of genetic associations in stratified populations
AU - Hoggart, Clive J.
AU - Parra, Esteban J.
AU - Shriver, Mark D.
AU - Bonilla, Carolina
AU - Kittles, Rick A.
AU - Clayton, David G.
AU - McKeigue, Paul M.
N1 - Funding Information:
We thank Sonia Dios, Richard Hamman, Robert E. Ferrell, and Julie A. Marshall for allowing us to use their data. This work was supported by National Institutes of Health grants DK53958 and HG-02154 (both to M.D.S.) and MH60343 (to P.M.M.). D.G.C. is a Juvenile Diabetes Research Foundation/Wellcome Trust Principal Research Fellow.
PY - 2003/6/1
Y1 - 2003/6/1
N2 - To control for hidden population stratification in genetic-association studies, statistical methods that use marker genotype data to infer population structure have been proposed as a possible alternative to family-based designs. In principle, it is possible to infer population structure from associations between marker loci and from associations of markers with the trait, even when no information about the demographic background of the population is available. In a model in which the total population is formed by admixture between two or more subpopulations, confounding can be estimated and controlled. Current implementations of this approach have limitations, the most serious of which is that they do not allow for uncertainty in estimations of individual admixture proportions or for lack of identifiability of subpopulations in the model. We describe methods that overcome these limitations by a combination of Bayesian and classical approaches, and we demonstrate the methods by using data from three admixed populations - African American, African Caribbean, and Hispanic American - in which there is extreme confounding of trait-genotype associations because the trait under study (skin pigmentation) varies with admixture proportions. In these data sets, as many as one-third of marker loci show crude associations with the trait. Control for confounding by population stratification eliminates these associations, except at loci that are linked to candidate genes for the trait. With only 32 markers informative for ancestry, the efficiency of the analysis is ∼70%. These methods can deal with both confounding and selection bias in genetic-association studies, making family-based designs unnecessary.
AB - To control for hidden population stratification in genetic-association studies, statistical methods that use marker genotype data to infer population structure have been proposed as a possible alternative to family-based designs. In principle, it is possible to infer population structure from associations between marker loci and from associations of markers with the trait, even when no information about the demographic background of the population is available. In a model in which the total population is formed by admixture between two or more subpopulations, confounding can be estimated and controlled. Current implementations of this approach have limitations, the most serious of which is that they do not allow for uncertainty in estimations of individual admixture proportions or for lack of identifiability of subpopulations in the model. We describe methods that overcome these limitations by a combination of Bayesian and classical approaches, and we demonstrate the methods by using data from three admixed populations - African American, African Caribbean, and Hispanic American - in which there is extreme confounding of trait-genotype associations because the trait under study (skin pigmentation) varies with admixture proportions. In these data sets, as many as one-third of marker loci show crude associations with the trait. Control for confounding by population stratification eliminates these associations, except at loci that are linked to candidate genes for the trait. With only 32 markers informative for ancestry, the efficiency of the analysis is ∼70%. These methods can deal with both confounding and selection bias in genetic-association studies, making family-based designs unnecessary.
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U2 - 10.1086/375613
DO - 10.1086/375613
M3 - Article
C2 - 12817591
AN - SCOPUS:0037648359
SN - 0002-9297
VL - 72
SP - 1492
EP - 1504
JO - American Journal of Human Genetics
JF - American Journal of Human Genetics
IS - 6
ER -