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. 2017 Sep;41(6):555-566.
doi: 10.1002/gepi.22056. Epub 2017 Jun 28.

Joint Genotype- And Ancestry-Based Genome-Wide Association Studies in Admixed Populations

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Joint Genotype- And Ancestry-Based Genome-Wide Association Studies in Admixed Populations

Piotr Szulc et al. Genet Epidemiol. .

Erratum in

  • Erratum.
    Genet Epidemiol. 2018 Oct;42(7):749. doi: 10.1002/gepi.22152. Epub 2018 Aug 18. Genet Epidemiol. 2018. PMID: 30311962 No abstract available.

Abstract

In genome-wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand, admixture mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry). Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. Here, we extend this approach for population-based GWAS in the direction of multimarker models. A modified version of the Bayesian information criterion is developed for building a multilocus model that accounts for the differential correlation structure due to linkage disequilibrium (LD) and admixture LD. Simulation studies and a real data example illustrate the advantages of this new approach compared to single-marker analysis or modern model selection strategies based on separately analyzing genotype and ancestry data, as well as to single-marker analysis combining genotypic and ancestry information. Depending on the signal strength, our procedure automatically chooses whether genotypic or locus-specific ancestry markers are added to the model. This results in a good compromise between the power to detect causal mutations and the precision of their localization. The proposed method has been implemented in R and is available at http://www.math.uni.wroc.pl/~mbogdan/admixtures/.

Keywords: admixture mapping; model selection; multiple regression; quantitative trait.

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