Mixture Density Regression reveals frequent recent adaptation in the human genome

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 prevalence 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.) that complicates the interpretation and distinction of adaptive vs. neutral evolution signals. Here, 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 positive selection, if the latter was common enough in the first place to generate these associations. We find that a MDR model with two Gaussian distributions provides an excellent fit to the genome-wide distribution of a common sweep summary statistic (iHS), with one of the two distributions likely enriched in positive selection. We further find several factors associated with 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 that strong positive selection was relatively common in recent human evolution and highlight MDRs as a powerful tool to make sense of signals of recent genomic adaptation. Author Summary Over the last 50,000 years, human populations have been exposed to selective pressures that can trigger adaptation in the genome. The search for signals of these selective events is however obscured by the substantial variation of factors that are relevant for the prevalence and detection of adaptation across the genome. Here, we analyze the impact of multiple factors on positive selection using a biologically meaningful approach that considers the influence of adaptative and non-adaptive processes in the human genome. Our results show that this novel approach is better suited to find genomic factors associated with recent positive selection compared to the classic regression or correlation approaches. We find multiple genomic functional elements associated with selection across the genome, including novel associations that emerge only after controlling for multiple confounding factors. This strongly suggests that adaptation was common enough in recent evolutionary times to produce a widespread correlation between functional elements and positive selection in the human genome. Note on the language used in this manuscript In this manuscript we use discrete population groups, such as the Yoruba. We want to emphasize that these discrete groups are only used for convenience and clarity when presenting our results, but in fact represent arbitrary human constructions, the same way that the boundaries of countries are arbitrary. There is an unbroken continuum and mixing of geographical ancestries across groups often identified as distinct populations across the world. The discrete groups we use are, as such, by no mean discrete genetic entities. Once grouped together, the grouped individuals only happen to be genetically more similar, with their ancestries coming from specific geographic locations more predominantly than individuals from the other groups. How much more predominantly is completely arbitrary.