Extending Tests of Hardy-Weinberg Equilibrium to Structured Populations

AbstractTesting for Hardy-Weinberg equilibrium (HWE) is an important component in almost all analyses of population genetic data. Genetic markers that violate HWE are often treated as special cases; for example, they may be flagged as possible genotyping errors or they may be investigated more closely for evolutionary signatures of interest. The presence of population structure is one reason why genetic markers may fail a test of HWE. This is problematic because almost all natural populations studied in the modern setting show some degree of structure. Therefore, it is important to be able to detect deviations from HWE for reasons other than structure. To this end, we extend statistical tests of HWE to allow for population structure, which we call a test of “structural HWE” (sHWE). Additionally, our new test allows one to automatically choose tuning parameters and identify accurate models of structure. We demonstrate our approach on several important studies, provide theoretical justification for the test, and present empirical evidence for its utility. We anticipate the proposed test will be useful in a broad range of analyses of genome-wide population genetic data.

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Commonly used Hardy-Weinberg equilibrium filtering schemes impact population structure inferences using RADseq data

10.1101/2021.06.15.448615 ◽

2021 ◽

Author(s):

William S Pearman ◽

Lara Urban ◽

Alana Alexander

Keyword(s):

Population Structure ◽

Population Genetic ◽

Best Practice ◽

Careful Consideration ◽

Model Organisms ◽

Reduced Representation ◽

Weinberg Equilibrium ◽

Hardy Weinberg Equilibrium ◽

Population Genetic Inference ◽

Filtering Approach

Reduced representation sequencing (RRS) is a widely used method to assay the diversity of genetic loci across the genome of an organism. The dominant class of RRS approaches assay loci associated with restriction sites within the genome (restriction site associated DNA sequencing, or RADseq). RADseq is frequently applied to non-model organisms since it enables population genetic studies without relying on well-characterized reference genomes. However, RADseq requires the use of many bioinformatic filters to ensure the quality of genotyping calls. These filters can have direct impacts on population genetic inference, and therefore require careful consideration. One widely used filtering approach is the removal of loci which do not conform to expectations of Hardy-Weinberg equilibrium (HWE). Despite being widely used, we show that this filtering approach is rarely described in sufficient detail to enable replication. Furthermore, through analyses of in silico and empirical datasets we show that some of the most widely used HWE filtering approaches dramatically impact inference of population structure. In particular, the removal of loci exhibiting departures from HWE after pooling across samples significantly reduces the degree of inferred population structure within a dataset (despite this approach being widely used). Based on these results, we provide recommendations for best practice regarding the implementation of HWE filtering for RADseq datasets.

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Testing for Hardy-Weinberg Equilibrium in Structured Populations using NGS Data

10.1101/468611 ◽

2018 ◽

Author(s):

Jonas Meisner ◽

Anders Albrechtsen

Keyword(s):

Population Structure ◽

Large Scale ◽

Structured Populations ◽

Sequencing Data ◽

Genetic Studies ◽

Weinberg Equilibrium ◽

Technical Errors ◽

Hardy Weinberg Equilibrium ◽

Next Generation Sequencing Ngs ◽

Ngs Data

AbstractTesting for 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 our method in 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 for low-depth sequencing data.

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