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Inference of Distribution of Fitness Effects and Proportion of Adaptive Substitutions from Polymorphism Data
Aarhus Univ, Bioinformat Res Ctr, CF Mollers Alle 8, DK-8000 Aarhus C, Denmark..
Aarhus Univ, Bioinformat Res Ctr, CF Mollers Alle 8, DK-8000 Aarhus C, Denmark..
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Plant Ecology and Evolution. Univ Montpellier, Inst Sci Evolut Montpellier, CNRS, Inst Rech Dev,Ecole Prat Hautes Etud, F-3095 Montpellier, France..ORCID iD: 0000-0001-7260-4573
Aarhus Univ, Bioinformat Res Ctr, CF Mollers Alle 8, DK-8000 Aarhus C, Denmark..
2017 (English)In: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 207, no 3, p. 1103-1119Article in journal (Refereed) Published
Abstract [en]

The distribution of fitness effects (DFE) encompasses the fraction of deleterious, neutral, and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (alpha). Inferring DFE and a from patterns of polymorphism, as given through the site frequency spectrum (SFS) and divergence data, has been a longstanding goal of evolutionary genetics. A widespread assumption shared by previous inference methods is that beneficial mutations only contribute negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and alpha is then predicted by contrasting the SFS with divergence data from an outgroup. We develop a hierarchical probabilistic framework that extends previous methods to infer DFE and alpha from polymorphism data alone. We use extensive simulations to examine the performance of our method. While an outgroup is still needed to obtain an unfolded SFS, we show that both a DFE, comprising both deleterious and beneficial mutations, and alpha can be inferred without using divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and alpha. We compare our framework with one of the most widely used inference methods available and apply it on a recently published chimpanzee exome data set.

Place, publisher, year, edition, pages
2017. Vol. 207, no 3, p. 1103-1119
Keyword [en]
distribution of fitness effects, rate of adaptive molecular evolution, beneficial mutations, polymorphism and divergence data, Poisson random field
National Category
Genetics
Identifiers
URN: urn:nbn:se:uu:diva-341351DOI: 10.1534/genetics.117.300323ISI: 000414265000021PubMedID: 28951530OAI: oai:DiVA.org:uu-341351DiVA: diva2:1182010
Funder
EU, FP7, Seventh Framework Programme, 311341
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-02-12Bibliographically approved

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