Abstract：
: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 (α). Inferring DFE and α 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 α 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 α 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 α 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 α 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.
journal_name
Geneticsjournal_title
Geneticsauthors
Tataru P,Mollion M,Glémin S,Bataillon Tdoi
10.1534/genetics.117.300323subject
Has Abstractpub_date
2017-11-01 00:00:00pages
1103-1119issue
3eissn
0016-6731issn
1943-2631pii
genetics.117.300323journal_volume
207pub_type
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