bioRxivEvolutionary BiologyPreprint
Drivers of immune-related genetic variation across human populations
Adriana Morales-Guerrero, Keith D. HarrisiD, Nimrod MaromiD, Gili GreenbaumiD
Abstract
Pathogen-mediated disease burden imposes some of the strongest selective pressures on human populations, shaping genetic variation in immune-related genomic regions. Because historical, cultural, ecological, and environmental factors can influence disease burden, associations between these factors and immune-related genomic signatures can provide insight into how pathogen-mediated selection varied among populations. To investigate these associations and identify factors potentially contributing to historical disease burden, we analyzed worldwide variation in the Major Histocompatibility Complex (MHC). We evaluated the relationships between global patterns of genetic variation and a comprehensive set of historical-cultural, ecological, and climatic variables compiled in DisECCO (Disease, Ecological, Cultural, and Climatic Origins dataset). This dataset includes factors potentially associated with historical disease burden, including the timing of major cultural transitions, historical climatic conditions, exposure to domesticated animals, and pre-industrial pathogen stress. Contrary to genome-wide expectations, MHC diversity and variation were not well explained by geographic distance from Africa, suggesting that neutral demographic processes play a limited role in shaping MHC variation. Instead, MHC genetic variation was significantly associated with climatic variables, domestication exposure, and the time lag between the onset of the Neolithic and urbanization, with the strongest explanatory models identified for MHC Class II. These results indicate that cultural transitions and environmental conditions have played a central role in shaping immune-related genetic variation, and may have contributed substantially to changes in disease burden. Overall, our findings show that integrating ecological and cultural-historical variables with genomic data can help explain global patterns of genetic variation in humans.
- organism: human
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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