Hierarchical Bayesian model of population structure reveals convergent adaptation to high altitude in human populations

摘要: Detecting genes involved in local adaptation is challenging and of fundamental importance evolutionary, quantitative, medical genetics. To this aim, a standard strategy to perform genome scans populations different origins environments, looking for genomic regions high differentiation. Because shared population history or sub-structure may lead an excess false positives, analyses are often done on multiple pairs populations, which leads i) global loss power as compared analysis, ii) the need tests corrections. In order alleviate these problems, we introduce new hierarchical Bayesian method detect markers under selection that can deal with complex demographic histories, where sampled share part their history. Simulations show our approach both more powerful less prone positive loci than approaches based separate those ignoring existing structures. addition, identify occurring at levels (i.e. region-specific adaptation), well convergent regions. We apply analysis large SNP dataset from low- high-altitude human America Asia. The simultaneous two geographic areas allows us several candidate altitudinal selection, evolution among continents has been quite common. addition identifying biological processes altitude adaptation, specific pathways could have evolved counter toxic effects induced by hypoxia.