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Hardy–Weinberg Equilibrium Filtering in Population Genomics: Empirical Review and Decision Framework for Improved Practice
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Hardy–Weinberg Equilibrium Filtering in Population Genomics: Empirical Review and Decision Framework for Improved Practice
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Journal Article
Hardy–Weinberg Equilibrium Filtering in Population Genomics: Empirical Review and Decision Framework for Improved Practice
2026
Overview
Hardy–Weinberg equilibrium (HWE) filtering remains widely used in population genomics, but its application remains inconsistent, often lacking detailed justification, and not always aligned with biological context. To evaluate whether conceptual awareness has translated into methodological change, we review empirical studies citing Pearman et al. (2022), a representative study testing the impacts of different grouping approaches for HWE filtering. While pooled filtering is becoming rare, we found a decreasing but still considerable heterogeneity in the decision of filtering schemes, limited reporting of thresholds, and few explicit justifications for applied approaches. These patterns suggest that awareness of HWE filtering limitations is increasing but has not yet led to consistent practice. We synthesise the biological and technical causes of HWE deviation, review recent advances, including population‐aware and structure‐informed filtering tools, and propose a transparent decision framework for population genomic studies. Rather than a default quality‐control step, HWE filtering should be applied as a hypothesis‐aware decision that reflects study aims and biological context. A citation‐based mini‐survey and decision workflow are provided to support biologically informed and reproducible applications. Hardy–Weinberg equilibrium (HWE) filtering is widely used in population genomics, but its application remains inconsistent and often lacks biological justification. This review went through recent empirical studies that demonstrate awareness of these issues to assess whether conceptual understanding has led to improved practice. This review synthesises the causes of HWE deviation and proposes a biologically informed decision workflow to support transparent filtering applications.
Publisher
John Wiley & Sons, Inc,Wiley
