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Biology subjects

Tataru, P.

Publications and source records attributed to Tataru, P..

2 recordsLinked to original sources

Comparison of the full distribution of fitness effects of new amino acid mutations across great apes

The distribution of fitness effects (DFE) is central to many questions in evolutionary biology. However, little is known about the differences in DFEs between closely related species. We use more than 9,000 coding genes orthologous one-to-one across great apes, gibbons, and macaques to assess the stability of the DFE across great apes. We use the unfolded site frequency spectrum of polymorphic mutations (n = 8 haploid chromosomes per population) to estimate the DFE. We find that the shape of the deleterious DFE is strikingly similar across great apes. We confirm that effective population size (Ne) is a strong predictor of the strength of negative selection, consistent with the Nearly Neutral Theory. However, we also find that the strength of negative selection varies more than expected given the differences in Ne between species. Across species, mean fitness effects of new deleterious mutations co-varies with Ne, consistent with positive epistasis among deleterious mutations. We find that the strength of negative selection for the smallest populations: bonobos and western chimpanzees, is higher than expected given their Ne. This may result from a more efficient purging of strongly deleterious recessive variants in these populations. Forward simulations confirm that these findings are not artifacts of the way we are inferring Ne and DFE parameters. All findings are replicated using only GC-conservative mutations, thereby confirming that GC-biased gene conversion is not affecting our conclusions.

evolutionary biology↗

polyDFEv2.0: Testing for invariance of the distribution of fitness effects within and across species

Distributions of fitness effects (DFE) of mutations can be inferred from site frequency spectrum (SFS) data. There is mounting interest to determine whether distinct genomic regions and/or species share a common DFE, or whether evidence exists for differences among them. polyDFEv2.0 fits multiple SFS datasets at once and provides likelihood ratio tests for DFE invariance across datasets. Simulations show that testing for DFE invariance across genomic regions within a species requires models accounting for heterogeneous genealogical histories underlying SFS data in these regions. Not accounting for these heterogeneities will result in the spurious detection of DFE differences.

bioinformatics↗