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

Mapping Intimacies ◽

10.1101/363887 ◽

2018 ◽

Author(s):

Paula Tataru ◽

Thomas Bataillon

Keyword(s):

Likelihood Ratio ◽

Frequency Spectrum ◽

Likelihood Ratio Tests ◽

Fitness Effects ◽

Site Frequency Spectrum ◽

Genomic Regions

AbstractDistributions 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.

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

Bioinformatics ◽

10.1093/bioinformatics/bty1060 ◽

2019 ◽

Vol 35 (16) ◽

pp. 2868-2869 ◽

Cited By ~ 10

Author(s):

Paula Tataru ◽

Thomas Bataillon

Keyword(s):

Source Code ◽

Likelihood Ratio Tests ◽

Supplementary Information ◽

Supplementary Data ◽

Post Processing ◽

Fitness Effects ◽

Site Frequency Spectrum ◽

Genomic Regions ◽

General Public License ◽

R Functions

Abstract Summary Distribution 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 distinct sources of heterogeneity (chance and genuine difference in DFE) underlying differences in SFS data in these regions. Not accounting for this will result in the spurious detection of DFE differences. Availability and Implementation polyDFEv2.0 is implemented in C and is accompanied by a series of R functions that facilitate post-processing of the output. It is available as source code and compiled binaries under a GNU General Public License v3.0 from https://github.com/paula-tataru/polyDFE. Supplementary information Supplementary data are available at Bioinformatics online.

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On the prospect of identifying adaptive loci in recently bottlenecked populations

10.1101/009456 ◽

2014 ◽

Author(s):

Yu-Ping Poh ◽

Vera S Domingues ◽

Hopi Hoekstra ◽

Jeffrey Jensen

Keyword(s):

Frequency Spectrum ◽

Population Genetic ◽

Null Model ◽

Large Parameter ◽

Independent Evidence ◽

Standing Variation ◽

In The Wild ◽

Site Frequency Spectrum ◽

Genomic Regions ◽

Large Parameter Space

Identifying adaptively important loci in recently bottlenecked populations?be it natural selection acting on a population following the colonization of novel habitats in the wild, or artificial selection during the domestication of a breed?remains a major challenge. Here we report the results of a simulation study examining the performance of available population-genetic tools for identifying genomic regions under selection. To illustrate our findings, we examined the interplay between selection and demography in two species of Peromyscus mice, for which we have independent evidence of selection acting on phenotype as well as functional evidence identifying the underlying genotype. With this unusual information, we tested whether population-genetic-based approaches could have been utilized to identify the adaptive locus. Contrary to published claims, we conclude that the use of the background site frequency spectrum as a null model is largely ineffective in bottlenecked populations. Results are quantified both for site frequency spectrum and linkage disequilibrium-based predictions, and are found to hold true across a large parameter space that encompasses many species and populations currently under study. These results suggest that the genomic footprint left by selection on both new and standing variation in strongly bottlenecked populations will be difficult, if not impossible, to find using current approaches.

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