TY - JOUR

T1 - Testing for Hardy-Weinberg equilibrium in structured populations using genotype or low-depth next generation sequencing data

AU - Meisner, Jonas

AU - Albrechtsen, Anders

N1 - This article is protected by copyright. All rights reserved.

PY - 2019

Y1 - 2019

N2 - Testing for deviations from Hardy-Weinberg Equilibrium (HWE) is a common practice for quality control in genetic studies. Variable sites violating HWE may be identified as technical errors in the sequencing or genotyping process, or they may be of special evolutionary interest. Large-scale genetic studies based on next-generation sequencing (NGS) methods have become more prevalent as cost is decreasing but these methods are still associated with statistical uncertainty. The large-scale studies usually consist of samples from diverse ancestries that make the existence of some degree of population structure almost inevitable. Precautions are therefore needed when analyzing these datasets, as population structure causes deviations from HWE. Here we propose a method that takes population structure into account in the testing for HWE, such that other factors causing deviations from HWE can be detected. We show the effectiveness of PCAngsd in low-depth NGS data, as well as in genotype data, for both simulated and real datasets, where the use of genotype likelihoods enables us to model the uncertainty. This article is protected by copyright. All rights reserved.

AB - Testing for deviations from Hardy-Weinberg Equilibrium (HWE) is a common practice for quality control in genetic studies. Variable sites violating HWE may be identified as technical errors in the sequencing or genotyping process, or they may be of special evolutionary interest. Large-scale genetic studies based on next-generation sequencing (NGS) methods have become more prevalent as cost is decreasing but these methods are still associated with statistical uncertainty. The large-scale studies usually consist of samples from diverse ancestries that make the existence of some degree of population structure almost inevitable. Precautions are therefore needed when analyzing these datasets, as population structure causes deviations from HWE. Here we propose a method that takes population structure into account in the testing for HWE, such that other factors causing deviations from HWE can be detected. We show the effectiveness of PCAngsd in low-depth NGS data, as well as in genotype data, for both simulated and real datasets, where the use of genotype likelihoods enables us to model the uncertainty. This article is protected by copyright. All rights reserved.

U2 - 10.1111/1755-0998.13019

DO - 10.1111/1755-0998.13019

M3 - Journal article

C2 - 30977299

VL - 19

SP - 1144

EP - 1152

JO - Molecular Ecology

JF - Molecular Ecology

SN - 0962-1083

IS - 5

ER -