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" Probabilistic Methods for the Inference of Selection and Demography from Ancient Human Genomes "
Racimo, Fernando
Slatkin, Montgomery
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Latin Dissertation
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English
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903081
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TL4sz3d40b
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Racimo, Fernando
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Probabilistic Methods for the Inference of Selection and Demography from Ancient Human Genomes\ Racimo, FernandoSlatkin, Montgomery
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UC Berkeley
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2016
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2016
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| Abstract
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Recently developed technologies for the recovery and sequencing of ancient DNA have generated an explosion of paleogenomic data in the last five years. In particular, human paleogenomics has become a thriving field for understanding evolutionary patterns of different hominin groups over time. However, there is still a dearth of statistical tools that can allow biologists to discern meaningful patterns from ancient genomes. Here, I present three methods designed for inferring past demographic processes and detecting loci under selection using ancient and modern hominin genomes. First, I describe an algorithm to co-estimate the contamination rate, sequencing error rate and demographic parameters - including drift times and admixture rates - for an ancient nuclear genome obtained from human remains, when the putative contaminating DNA comes from present-day humans. The method is implemented in a C++ program called `Demographic Inference with Contamination and Error' (DICE). Then, I present two methods for downstream analyses of paleogenomic samples, specifically tailored for detecting different types of positive selection. The first of these consists in a series of summary statistics for detecting adaptive introgression (AI). In particular, the number and allelic frequencies of sites that are uniquely shared between archaic humans and specific present-day populations are particularly useful for detecting adaptive pressures on introgressed haplotypes. The second approach for detecting selection is a composite likelihood ratio method called `3P-CLR', and is aimed at locating regions of the genome that were subject to selection before two populations split from each other. I use this method to look for regions under positive selection in the ancestral modern human population after its split from Neanderthals. I validate all of the above methods using simulations and real data, including present-day human genomes from the 1000 Genomes Project and several high- and low-coverage ancient genomes from archaic and early modern humans. I also recover potentially interesting candidate loci that may have been important for various phenotypic adaptations during recent human evolution.
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Slatkin, Montgomery
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UC Berkeley
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