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<p>Understanding the genetic basis of human adaptation to environmental pressures is a central question in evolutionary biology. Recent advancements in genome-environment association (GEA) methods have provided novel insights into how genetic variation is shaped by climate, diet, altitude, pathogens, and other environmental factors. However, despite significant progress in non-human studies, the application of GEA in human populations remains underused. This article synthesizes recent GEA methodological advancements, including redundancy analysis (RDA) and machine learning approaches, such as gradient forest (GF), to improve the detection of selection signals while accounting for demographic noise. Furthermore, I discuss the implications of historical GEA projection and genetic offset to human adaptation, particularly in the context of rapid urbanization and climate change. By integrating genomic, environmental, and epidemiological data, GEA has the potential not only to enhance our understanding of past human evolution but also to evaluate how allele–environment relationships may shift under future environmental change. By refining statistical models and expanding studies to diverse populations, GEA can play a critical role in elucidating the mechanisms underlying human adaptation and characterizing how selective forces shape the spatial distribution of adaptive alleles.</p>

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Bringing environment back into human evolution: why human genetics needs genome-environment association studies

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • Pei-Wei Sun

摘要

Understanding the genetic basis of human adaptation to environmental pressures is a central question in evolutionary biology. Recent advancements in genome-environment association (GEA) methods have provided novel insights into how genetic variation is shaped by climate, diet, altitude, pathogens, and other environmental factors. However, despite significant progress in non-human studies, the application of GEA in human populations remains underused. This article synthesizes recent GEA methodological advancements, including redundancy analysis (RDA) and machine learning approaches, such as gradient forest (GF), to improve the detection of selection signals while accounting for demographic noise. Furthermore, I discuss the implications of historical GEA projection and genetic offset to human adaptation, particularly in the context of rapid urbanization and climate change. By integrating genomic, environmental, and epidemiological data, GEA has the potential not only to enhance our understanding of past human evolution but also to evaluate how allele–environment relationships may shift under future environmental change. By refining statistical models and expanding studies to diverse populations, GEA can play a critical role in elucidating the mechanisms underlying human adaptation and characterizing how selective forces shape the spatial distribution of adaptive alleles.