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

Area of Science:

• Evolutionary biology
• Human genetics
• Population genetics

Background:

• Understanding human genetic adaptation to environmental pressures is crucial.
• Genome-environment association (GEA) methods offer insights into genetic variation shaped by factors like climate and diet.
• GEA applications in human populations are currently underutilized compared to non-human studies.

Purpose of the Study:

• To synthesize recent advancements in GEA methodologies for detecting selection signals in human populations.
• To discuss the implications of GEA for understanding historical adaptation and predicting future evolutionary trajectories under environmental change.
• To highlight the potential of GEA in elucidating adaptive mechanisms and allele distributions.

Main Methods:

• Review and synthesis of recent GEA methodological advancements.
• Application of redundancy analysis (RDA) for detecting selection signals.
• Utilizing machine learning approaches like gradient forest (GF) to account for demographic noise.

Main Results:

• Advanced GEA methods, including RDA and GF, enhance the detection of selection signals.
• GEA can account for demographic noise, improving the accuracy of adaptation studies.
• GEA facilitates the integration of genomic, environmental, and epidemiological data.

Conclusions:

• GEA holds significant potential for understanding past human evolution and predicting future adaptation.
• GEA can characterize how allele-environment relationships shift with urbanization and climate change.
• Refining GEA models and expanding studies to diverse populations are key to advancing the field.