bioRxivEvolutionary Biology
The Selection Landscape and Genetic Legacy of Ancient Eurasians
E. K. Irving-Pease, A. Refoyo-Martinez, W. Barrie, A. Ingason, A. Pearson, A. Fischer, K.-G. Sjögren, A. S. Halgren, R. Macleod, F. Demeter, R. A. Henriksen, T. Vimala, H. McColl, A. Vaughn, L. Speidel, A. J. Stern, G. Scorrano, A. Ramsoe, A. J. Schork, A. Rosengren, L. Zhao, K. Kristiansen, A. K. N. Iversen, L. Fugger, P. H. Sudmant, , E. Willerslev
Abstract
The Holocene (beginning [~]12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical, and mental health of present-day populations. Using a dataset of >1600 imputed ancient genomes 1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify major selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported, and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in >400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic, and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, while risk alleles for diabetes and Alzheimers disease are enriched for Western Hunter-gatherer ancestry. Our results suggest that ancient selection and migration were major contributors to the distribution of phenotypic diversity in present-day Europeans.
- organism: human
- assay: whole-genome sequencing
- research focus: polygenic risk
- approach: statistical model
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