Local and global ancestry inference and applications to genetic association analysis for admixed populations

• Verfasst von: Thornton, Timothy A. [VerfasserIn]
• Verfasst von: Lorenzo Bermejo, Justo [VerfasserIn]
• Titel: Local and global ancestry inference and applications to genetic association analysis for admixed populations
• Verf.angabe: Timothy A. Thornton and Justo Lorenzo Bermejo
• E-Jahr: 2014
• Jahr: [2014]
• Umfang: 8 S.
• Fussnoten: First published: 11 August 2014 ; Gesehen am 16.09.2020
• Titel Quelle: Enthalten in: Genetic epidemiology
• Ort Quelle: New York, NY : Wiley-Liss, 1984
• Jahr Quelle: 2014
• Band/Heft Quelle: 38(2014,S1), Seite S5-S12
• ISSN Quelle: 1098-2272
• DOI: doi:10.1002/gepi.21819
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: admixture
• Sach-SW: association
• Sach-SW: genotype imputation
• Sach-SW: GWAS
• Sach-SW: local ancestry
• K10plus-PPN: 1733154175

Abstract

Genetic association studies in recently admixed populations offer exciting opportunities to identify novel variants underlying phenotypic diversity. At the same time, genetic heterogeneity resulting from population admixture has to be accounted for to ensure validity of association tests. The whole-genome sequence data and the genome-wide single-nucleotide polymorphism chip data for Mexican American individuals provided by Genetic Analysis Workshop 18 (GAW18) presents a unique opportunity to evaluate and compare methods for the statistical analysis of admixed genetic data. We summarize here the five contributions from the GAW18 working group on admixture mapping and adjusting for admixture. Although group members considered a variety of research topics, the general theme was inference and consideration of ancestry admixture in genetic analyses. The topics considered can be grouped into three categories: (1) global and local ancestry inference and estimation, (2) association and admixture mapping, and (3) genotype imputation in admixed samples. We describe the approaches that were used and the most relevant findings from each contribution. We also provide insight into the strengths and limitations of the state-of-the-art methods considered for genetic analyses in admixed populations.