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      Genetic redundancy fuels polygenic adaptation in Drosophila

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          Abstract

          The genetic architecture of adaptive traits is of key importance to predict evolutionary responses. Most adaptive traits are polygenic—i.e., result from selection on a large number of genetic loci—but most molecularly characterized traits have a simple genetic basis. This discrepancy is best explained by the difficulty in detecting small allele frequency changes (AFCs) across many contributing loci. To resolve this, we use laboratory natural selection to detect signatures for selective sweeps and polygenic adaptation. We exposed 10 replicates of a Drosophila simulans population to a new temperature regime and uncovered a polygenic architecture of an adaptive trait with high genetic redundancy among beneficial alleles. We observed convergent responses for several phenotypes—e.g., fitness, metabolic rate, and fat content—and a strong polygenic response (99 selected alleles; mean s = 0.059). However, each of these selected alleles increased in frequency only in a subset of the evolving replicates. We discerned different evolutionary paradigms based on the heterogeneous genomic patterns among replicates. Redundancy and quantitative trait (QT) paradigms fitted the experimental data better than simulations assuming independent selective sweeps. Our results show that natural D. simulans populations harbor a vast reservoir of adaptive variation facilitating rapid evolutionary responses using multiple alternative genetic pathways converging at a new phenotypic optimum. This key property of beneficial alleles requires the modification of testing strategies in natural populations beyond the search for convergence on the molecular level.

          Abstract

          Replicate populations of the fruit fly Drosophila simulans exposed to a novel temperature environment showed convergent evolution on the phenotypic level. On the genetic level, however, no convergence was observed. These results support the “trait optimum” paradigm as the predominant mode of adaptation.

          Author summary

          It is widely assumed that adaptation is mainly polygenic, with the underlying frequency changes being so small that they are difficult to detect and characterize. Taking advantage of laboratory-based natural selection of replicated populations of the fruit fly Drosophila simulans exposed to a high temperature regime, we identified a polygenic response with many (99) selected alleles having pronounced allele frequency changes (AFCs). Despite phenotypic convergence across the 10 replicates, the genomic response was highly heterogeneous, with different subsets of the selected alleles contributing to the convergent phenotype in each of the replicate populations. The observed genetic redundancy not only indicates lack of genetic constraint but also demonstrates that multiple genetic pathways lead to convergent phenotypic outcomes.

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          The hitch-hiking effect of a favourable gene

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            Soft sweeps: molecular population genetics of adaptation from standing genetic variation.

            A population can adapt to a rapid environmental change or habitat expansion in two ways. It may adapt either through new beneficial mutations that subsequently sweep through the population or by using alleles from the standing genetic variation. We use diffusion theory to calculate the probabilities for selective adaptations and find a large increase in the fixation probability for weak substitutions, if alleles originate from the standing genetic variation. We then determine the parameter regions where each scenario-standing variation vs. new mutations-is more likely. Adaptations from the standing genetic variation are favored if either the selective advantage is weak or the selection coefficient and the mutation rate are both high. Finally, we analyze the probability of "soft sweeps," where multiple copies of the selected allele contribute to a substitution, and discuss the consequences for the footprint of selection on linked neutral variation. We find that soft sweeps with weaker selective footprints are likely under both scenarios if the mutation rate and/or the selection coefficient is high.
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              The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation.

              There has long been interest in understanding the genetic basis of human adaptation. To what extent are phenotypic differences among human populations driven by natural selection? With the recent arrival of large genome-wide data sets on human variation, there is now unprecedented opportunity for progress on this type of question. Several lines of evidence argue for an important role of positive selection in shaping human variation and differences among populations. These include studies of comparative morphology and physiology, as well as population genetic studies of candidate loci and genome-wide data. However, the data also suggest that it is unusual for strong selection to drive new mutations rapidly to fixation in particular populations (the 'hard sweep' model). We argue, instead, for alternatives to the hard sweep model: in particular, polygenic adaptation could allow rapid adaptation while not producing classical signatures of selective sweeps. We close by discussing some of the likely opportunities for progress in the field. Copyright 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Writing – review & editing
                Role: Investigation
                Role: Investigation
                Role: Investigation
                Role: Methodology
                Role: Resources
                Role: Software
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                4 February 2019
                February 2019
                4 February 2019
                : 17
                : 2
                : e3000128
                Affiliations
                [1 ] Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
                [2 ] Vienna Graduate School of Population Genetics, Vetmeduni Vienna, Vienna, Austria
                [3 ] Plattform Bioinformatik und Biostatistik, Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
                Georgia Institute of Technology, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                [¤a]

                Current address: Australian Centre for Ancient DNA, School of Biological Sciences, University of Adelaide, Adelaide, Australia

                [¤b]

                Current address: Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States of America

                [¤c]

                Current address: Institut de Biologie de l'Ecole Normale Superieure, Paris, France

                Author information
                http://orcid.org/0000-0003-3700-0971
                http://orcid.org/0000-0002-4603-1473
                http://orcid.org/0000-0001-6159-3418
                http://orcid.org/0000-0003-2087-1914
                Article
                PBIOLOGY-D-18-00070
                10.1371/journal.pbio.3000128
                6375663
                30716062
                a325081d-e269-4326-b253-937b8e396def
                © 2019 Barghi 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
                : 3 July 2018
                : 14 January 2019
                Page count
                Figures: 6, Tables: 0, Pages: 31
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: ArchAdapt
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100002428, Austrian Science Fund;
                Award ID: W1225
                Award Recipient :
                This work was supported by the European Research Council grant “ArchAdapt” and the Austrian Science Fund (FWF, W1225-B20). KAO was supported by a DFG Research Fellowship (OT 532/1-1) and FM was supported by a Marie Skłodowska Curie Individual Fellowship (H2020-MSCA-IF-661149). TT is a recipient of the DOC fellowship of the Austrian Academy of Sciences. 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
                Heredity
                Genetic Mapping
                Haplotypes
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Alleles
                Research and Analysis Methods
                Simulation and Modeling
                Biology and Life Sciences
                Genetics
                Genomics
                Animal Genomics
                Invertebrate Genomics
                Biology and Life Sciences
                Biochemistry
                Lipids
                Fats
                Biology and Life Sciences
                Genetics
                Molecular Genetics
                Biology and Life Sciences
                Molecular Biology
                Molecular Genetics
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Evolutionary Adaptation
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-02-14
                The raw reads for Pool-Seq libraries and evolved haplotypes are available from the European Nucleotide Archive under project PRJEB29281 (accession numbers of reads for each sample are specified in S7 Table). SNP data sets (sync format), the results of CMH and Fisher’s exact tests, haplotype block IDs, TE insertion sites and frequencies, phenotypic data and phased haplotypes from evolved replicates, the estimated selection coefficients and starting frequencies of selected alleles, replicate frequency spectrum and pairwise Jaccard indices of empirical and simulated data, and number of selected alleles in each replicate are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.rr137kn [ 76]. All scripts are available at https://github.com/NedaBarghi/Genetic_redundancy.

                Life sciences
                Life sciences

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