On detection of population stratification in genotype samples using spacial clustering and non-linear optimization

Author(s)
Boerner, Vinzent
Publication Date
2018
Abstract
Accounting for population stratification in genotype samples is important to avoid false inference from genome wide association studies. It is usually quantified using model-based ancestry estimation (e.g. ADMIXTURE; Alexander et al. (2009)), which has disadvantages with regard to model assumptions and processing time. This article describes a two step procedure for estimating population stratification. In the first step a spacial cluster algorithm is used to detect clusters of genetically homogeneous animals. In a subsequent step genotypes are described as linear functions of within-cluster allele frequencies. The approach was tested on a cattle data set which consisted of 11,639 real genotypes from 11 breeds and 5,000 artificially generated cross-bred genotypes (F1 to F5). It outperformed results obtained from ADMIXTURE in terms of speed and accuracy.
Citation
Proceedings of the World Congress on Genetics Applied to Livestock Production, p. 24-27
Link
Publisher
Massey University
Title
On detection of population stratification in genotype samples using spacial clustering and non-linear optimization
Type of document
Conference Publication
Entity Type
Publication

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