Abstract
Testing 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." 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.
Highlights
Testing for Hardy–Weinberg equilibrium (HWE) is an important component in almost all analyses of population genetic data
We show that there are no systematic differences in structural HWE” (sHWE) P-values when single nucleotide polymorphism (SNP) are separated by annotations or minor allele frequency
We extended the Pearson x2 test of HWE to allow for population structure, called the sHWE test
Summary
Testing for Hardy–Weinberg equilibrium (HWE) is an important component in almost all analyses of population genetic data. Population structure is ubiquitous in human populations (Novembre and Peter 2016) and they are likely to violate the no-structure assumption of HWE This typically results in the appearance that the large proportions of markers deviate from HWE, obfuscating the important deviations such as those resulting from genotyping error or evolutionary selection. Test results are aggregated at each marker, and some criteria accounting for the separate tests are applied to determine whether HWE is violated This often appears in studies where there are known population labels for the samples (for example, Li et al 2008; Coop et al 2009). The goal of these approaches is to reduce the number of markers violating HWE to ensure that genotyping errors are removed
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