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• W4386793924 abstract "How much genome differences between species reflect neutral or adaptive evolution is a central question in evolutionary genomics. In humans and other mammals, the presence of adaptive versus neutral genomic evolution has proven particularly difficult to quantify. The difficulty notably stems from the highly heterogeneous organization of mammalian genomes at multiple levels (functional sequence density, recombination, etc.) which complicates the interpretation and distinction of adaptive versus neutral evolution signals. In this study, we introduce mixture density regressions (MDRs) for the study of the determinants of recent adaptation in the human genome. MDRs provide a flexible regression model based on multiple Gaussian distributions. We use MDRs to model the association between recent selection signals and multiple genomic factors likely to affect the occurrence/detection of positive selection, if the latter was present in the first place to generate these associations. We find that an MDR model with two Gaussian distributions provides an excellent fit to the genome-wide distribution of a common sweep summary statistic (integrated haplotype score), with one of the two distributions likely enriched in positive selection. We further find several factors associated with signals of recent adaptation, including the recombination rate, the density of regulatory elements in immune cells, GC content, gene expression in immune cells, the density of mammal-wide conserved elements, and the distance to the nearest virus-interacting gene. These results support the presence of strong positive selection in recent human evolution and highlight MDRs as a powerful tool to make sense of signals of recent genomic adaptation." @default.
• W4386793924 created "2023-09-16" @default.
• W4386793924 creator A5019901785 @default.
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• W4386793924 date "2023-09-15" @default.
• W4386793924 modified "2023-10-12" @default.
• W4386793924 title "Assessing the presence of recent adaptation in the human genome with Mixture Density Regression" @default.
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• W4386793924 doi "https://doi.org/10.1093/gbe/evad170" @default.
• W4386793924 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37713622" @default.
• W4386793924 hasPublicationYear "2023" @default.
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