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Simon Aeschbacher
Researcher at University of California, Davis
Publications - 16
Citations - 846
Simon Aeschbacher is an academic researcher from University of California, Davis. The author has contributed to research in topics: Population & Background selection. The author has an hindex of 8, co-authored 12 publications receiving 698 citations. Previous affiliations of Simon Aeschbacher include Institute of Science and Technology Austria & University of Bern.
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Journal ArticleDOI
The Strength of Selection against Neanderthal Introgression.
TL;DR: A novel method is developed for estimating the genome-wide average strength of selection and the density of selected sites using estimates of Neanderthal allele frequency along the genomes of modern-day humans, suggesting that differences in effective population size may play a far more important role in shaping levels of introgression than previously thought.
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A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation
TL;DR: An approach for choosing summary statistics based on boosting, a technique from the machine-learning literature, is proposed and it is found that ABC with summary statistics chosen locally via boosting with the L2-loss performs best.
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Population-genomic inference of the strength and timing of selection against gene flow.
TL;DR: A theoretical framework is provided for linking genome-wide patterns of divergence and recombination with the underlying evolutionary mechanisms that drive this differentiation and a procedure for parameter estimation is developed that accounts for the confounding effect of background selection.
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Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences.
Fanny Pouyet,Fanny Pouyet,Simon Aeschbacher,Simon Aeschbacher,Simon Aeschbacher,Alexandre Thiéry,Alexandre Thiéry,Laurent Excoffier,Laurent Excoffier +8 more
TL;DR: High-quality human genomic data is used to show that purifying selection at linked sites and GC-biased gene conversion together affect as much as 95% of the variants of the genome, and identifies a set of SNPs that are mostly unaffected by BGS or gBGC.
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The evolution of genomic islands by increased establishment probability of linked alleles.
TL;DR: It is shown how the interplay between migration and selection affects the establishment probability of linked vs. unlinked alleles, the expected maximum size of genomic islands, and the expected time required for their evolution.