Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/26405
Title: On Estimation of Genome Composition in Genetically Admixed Individuals Using Constrained Genomic Regression
Contributor(s): Boerner, Vinzent  (author); Wittenburg, Dorte (author)
Publication Date: 2018-05-29
Open Access: Yes
DOI: 10.3389/fgene.2018.00185Open Access Link
Handle Link: https://hdl.handle.net/1959.11/26405
Abstract: Quantifying the population stratification in genotype samples has become a standard procedure for data manipulation before conducting genome wide association studies, as well as for tracing patterns of migration in humans and animals, and for inference about extinct founder populations. The most widely used approach capable of providing biologically interpretable results is a likelihood formulation which allows for estimation of founder genome proportions and founder allele frequency conditional on the observed genotypes. However, if founder allele frequencies are known and samples are dominated by admixed genotypes this approach may lead to biased inference. In addition, processing time increases drastically with the number of genetic markers. This article describes a simplified approach for obtaining biologically meaningful measures of population stratification at the genotype level conditional on known founder allele frequencies. It was tested on cattle and human data sets with 4,022 and 150,000 genetic markers, respectively, and proved to be very accurate in situations where founder poplations were correctly specified, or under-, over-, and miss-specified. Moreover, processing time was only marginally affected by an increase in the number of markers.
Publication Type: Journal Article
Source of Publication: Frontiers in Genetics, v.9, p. 1-14
Publisher: Frontiers Research Foundation
Place of Publication: Switzerland
ISSN: 1664-8021
Fields of Research (FoR) 2008: 060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
Fields of Research (FoR) 2020: 310506 Gene mapping
Socio-Economic Objective (SEO) 2008: 830301 Beef Cattle
Socio-Economic Objective (SEO) 2020: 100401 Beef cattle
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
Journal Article

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