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      Determining the Effect of Natural Selection on Linked Neutral Divergence across Species

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          Abstract

          A major goal in evolutionary biology is to understand how natural selection has shaped patterns of genetic variation across genomes. Studies in a variety of species have shown that neutral genetic diversity (intra-species differences) has been reduced at sites linked to those under direct selection. However, the effect of linked selection on neutral sequence divergence (inter-species differences) remains ambiguous. While empirical studies have reported correlations between divergence and recombination, which is interpreted as evidence for natural selection reducing linked neutral divergence, theory argues otherwise, especially for species that have diverged long ago. Here we address these outstanding issues by examining whether natural selection can affect divergence between both closely and distantly related species. We show that neutral divergence between closely related species (e.g. human-primate) is negatively correlated with functional content and positively correlated with human recombination rate. We also find that neutral divergence between distantly related species (e.g. human-rodent) is negatively correlated with functional content and positively correlated with estimates of background selection from primates. These patterns persist after accounting for the confounding factors of hypermutable CpG sites, GC content, and biased gene conversion. Coalescent models indicate that even when the contribution of ancestral polymorphism to divergence is small, background selection in the ancestral population can still explain a large proportion of the variance in divergence across the genome, generating the observed correlations. Our findings reveal that, contrary to previous intuition, natural selection can indirectly affect linked neutral divergence between both closely and distantly related species. Though we cannot formally exclude the possibility that the direct effects of purifying selection drive some of these patterns, such a scenario would be possible only if more of the genome is under purifying selection than currently believed. Our work has implications for understanding the evolution of genomes and interpreting patterns of genetic variation.

          Author Summary

          Genetic variation at neutral sites can be reduced through linkage to nearby selected sites. This pattern has been used to show the widespread effects of natural selection at shaping patterns of genetic diversity across genomes from a variety of species. However, it is not entirely clear whether natural selection has an effect on neutral divergence between species. Here we show that putatively neutral divergence between closely related species (human and chimp) and between distantly related pairs of species (humans and mice) show signatures consistent with having been affected by linkage to selected sites. Further, our theoretical models and simulations show that natural selection indirectly affecting linked neutral sites can generate these patterns. Unless substantially more of the genome is under the direct effects of purifying selection than currently believed, our results argue that natural selection has played an important role in shaping variation in levels of putatively neutral sequence divergence across the genome. Our findings further suggest that divergence-based estimates of neutral mutation rate variation across the genome as well as certain estimators of population history may be confounded by linkage to selected sites.

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          The hitch-hiking effect of a favourable gene.

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            The effect of deleterious mutations on neutral molecular variation.

            Selection against deleterious alleles maintained by mutation may cause a reduction in the amount of genetic variability at linked neutral sites. This is because a new neutral variant can only remain in a large population for a long period of time if it is maintained in gametes that are free of deleterious alleles, and hence are not destined for rapid elimination from the population by selection. Approximate formulas are derived for the reduction below classical neutral values resulting from such background selection against deleterious mutations, for the mean times to fixation and loss of new mutations, nucleotide site diversity, and number of segregating sites. These formulas apply to random-mating populations with no genetic recombination, and to populations reproducing exclusively asexually or by self-fertilization. For a given selection regime and mating system, the reduction is an exponential function of the total mutation rate to deleterious mutations for the section of the genome involved. Simulations show that the effect decreases rapidly with increasing recombination frequency or rate of outcrossing. The mean time to loss of new neutral mutations and the total number of segregating neutral sites are less sensitive to background selection than the other statistics, unless the population size is of the order of a hundred thousand or more. The stationary distribution of allele frequencies at the neutral sites is correspondingly skewed in favor of rare alleles, compared with the classical neutral result. Observed reductions in molecular variation in low recombination genomic regions of sufficiently large size, for instance in the centromere-proximal regions of Drosophila autosomes or in highly selfing plant populations, may be partly due to background selection against deleterious mutations.
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              ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients.

              Lack of a general matrix formula hampers implementation of the semi-partial correlation, also known as part correlation, to the higher-order coefficient. This is because the higher-order semi-partial correlation calculation using a recursive formula requires an enormous number of recursive calculations to obtain the correlation coefficients. To resolve this difficulty, we derive a general matrix formula of the semi-partial correlation for fast computation. The semi-partial correlations are then implemented on an R package ppcor along with the partial correlation. Owing to the general matrix formulas, users can readily calculate the coefficients of both partial and semi-partial correlations without computational burden. The package ppcor further provides users with the level of the statistical significance with its test statistic.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                10 August 2016
                August 2016
                : 12
                : 8
                : e1006199
                Affiliations
                [1 ]Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, California, United States of America
                [2 ]Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
                [3 ]Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America
                University of Washington, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                • Conceived and designed the experiments: TNP CDH KEL.

                • Analyzed the data: TNP CDH.

                • Contributed reagents/materials/analysis tools: TNP CDH KEL.

                • Wrote the paper: TNP CDH KEL.

                Author information
                http://orcid.org/0000-0002-2267-2604
                Article
                PGENETICS-D-15-03011
                10.1371/journal.pgen.1006199
                4980041
                27508305
                558f877b-95c6-47ee-b425-a5d7ca55af60
                © 2016 Phung et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 11 December 2015
                : 25 June 2016
                Page count
                Figures: 6, Tables: 0, Pages: 27
                Funding
                Funded by: Hellman Faculty fellowship
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000879, Alfred P. Sloan Foundation;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: T32-GM008185
                Award Recipient :
                This research was funded by a UCLA Hellman Faculty Fellowship ( http://www.hellmanfellows.org/fellows/kirk-lohmueller/) and an Alfred P. Sloan Research Fellowship in Computational & Molecular Biology ( http://www.sloan.org/sloan-research-fellowships/) to KEL. TNP was supported by the National Institutes of Health, under Ruth L. Kirschstein National Research Service Award (T32-GM008185). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Genomics
                Animal Genomics
                Mammalian Genomics
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Natural Selection
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Natural Selection
                Biology and Life Sciences
                Genetics
                Population Genetics
                Natural Selection
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Natural Selection
                Biology and life sciences
                Genetics
                DNA
                DNA recombination
                Gene Conversion
                Biology and life sciences
                Biochemistry
                Nucleic acids
                DNA
                DNA recombination
                Gene Conversion
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Population Size
                Biology and Life Sciences
                Genetics
                Genomics
                Functional Genomics
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Biology and Life Sciences
                Genetics
                Population Genetics
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Genetics
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Mammals
                Primates
                Custom metadata
                All data used are already publicly available.

                Genetics
                Genetics

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