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Predicting Environmental and Ecological Drivers of Human Population Structure.

Evlyn Pless1, Anders M Eckburg1, Brenna M Henn1,2

  • 1Department of Anthropology, Center for Population Biology, University of California, Davis, CA.

Molecular Biology and Evolution
|May 5, 2023
PubMed
Summary

Environmental factors like precipitation and temperature significantly influenced human migration patterns and genetic structure in East Africa over the last 56 generations. Machine learning identified key variables shaping population genetics in this diverse region.

Keywords:
MAPSSPRUCEbarriersgene flowmigrationrandom forest

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Area of Science:

  • Population Genetics
  • Human Evolution
  • Machine Learning Applications

Background:

  • Understanding factors shaping human genetic diversity is crucial.
  • Existing methods struggle to analyze multiple environmental and cultural variables simultaneously.
  • East Africa's rich diversity provides a unique setting to study population structure.

Purpose of the Study:

  • To develop and apply a machine learning method for identifying key drivers of human migration rates.
  • To disentangle the influence of landscape, climate, and tsetse fly presence on genetic patterns.
  • To investigate adaptation signatures in Ethiopian populations.

Main Methods:

  • Utilized a novel machine learning approach to analyze migration rates inferred by the MAPS program.
  • Applied the method to high-density single nucleotide polymorphism data from 30 East African populations.
  • Incorporated over 20 spatial variables including landscape, climate, and tsetse fly distribution.

Main Results:

  • The model explained approximately 40% of the variance in human migration rates over the past 56 generations.
  • Precipitation, minimum temperature of the coldest month, and elevation were identified as the most impactful environmental variables.
  • The fusca tsetse fly group, a vector for trypanosomiasis, also showed significant impact.
  • Signatures of positive selection related to metabolism and disease were found in Ethiopian high-elevation populations, but not well-known high-elevation genes.

Conclusions:

  • Environmental factors play a significant role in shaping human migration and genetic adaptation in East Africa.
  • The study highlights the power of machine learning in dissecting complex population genetic patterns.
  • Unexplained genetic variation suggests the influence of unmeasured cultural or other factors.