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Mol Biol Evol. 2018 Aug 1;35(8):2015-2025. doi: 10.1093/molbev/msy107.

Adaptive Landscape of Protein Variation in Human Exomes.

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Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA.
Department of Biology, Temple University, Philadelphia, PA.
Coriell Institute for Medical Research, Camden, NJ.
Department of Biology, Tokyo Metropolitan University, Tokyo, Japan.
Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA.
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY.
Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.


The human genome contains hundreds of thousands of missense mutations. However, only a handful of these variants are known to be adaptive, which implies that adaptation through protein sequence change is an extremely rare phenomenon in human evolution. Alternatively, existing methods may lack the power to pinpoint adaptive variation. We have developed and applied an Evolutionary Probability Approach (EPA) to discover candidate adaptive polymorphisms (CAPs) through the discordance between allelic evolutionary probabilities and their observed frequencies in human populations. EPA reveals thousands of missense CAPs, which suggest that a large number of previously optimal alleles experienced a reversal of fortune in the human lineage. We explored nonadaptive mechanisms to explain CAPs, including the effects of demography, mutation rate variability, and negative and positive selective pressures in modern humans. Many nonadaptive hypotheses were tested, but failed to explain the data, which suggests that a large proportion of CAP alleles have increased in frequency due to beneficial selection. This suggestion is supported by the fact that a vast majority of adaptive missense variants discovered previously in humans are CAPs, and hundreds of CAP alleles are protective in genotype-phenotype association data. Our integrated phylogenomic and population genetic EPA approach predicts the existence of thousands of nonneutral candidate variants in the human proteome. We expect this collection to be enriched in beneficial variation. The EPA approach can be applied to discover candidate adaptive variation in any protein, population, or species for which allele frequency data and reliable multispecies alignments are available.

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