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Wisser RJ, Fang Z, Holland JB, Teixeira JEC, Dougherty J, Weldekidan T, de Leon N, Flint-Garcia S, Lauter N, Murray SC, Xu W, Hallauer A. The Genomic Basis for Short-Term Evolution of Environmental Adaptation in Maize. Genetics 2019; 213:1479-1494. [PMID: 31615843 PMCID: PMC6893377 DOI: 10.1534/genetics.119.302780] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 10/04/2019] [Indexed: 12/14/2022] Open
Abstract
Understanding the evolutionary capacity of populations to adapt to novel environments is one of the major pursuits in genetics. Moreover, for plant breeding, maladaptation is the foremost barrier to capitalizing on intraspecific variation in order to develop new breeds for future climate scenarios in agriculture. Using a unique study design, we simultaneously dissected the population and quantitative genomic basis of short-term evolution in a tropical landrace of maize that was translocated to a temperate environment and phenotypically selected for adaptation in flowering time phenology. Underlying 10 generations of directional selection, which resulted in a 26-day mean decrease in female-flowering time, [Formula: see text] of the heritable variation mapped to [Formula: see text] of the genome, where, overall, alleles shifted in frequency beyond the boundaries of genetic drift in the expected direction given their flowering time effects. However, clustering these non-neutral alleles based on their profiles of frequency change revealed transient shifts underpinning a transition in genotype-phenotype relationships across generations. This was distinguished by initial reductions in the frequencies of few relatively large positive effect alleles and subsequent enrichment of many rare negative effect alleles, some of which appear to represent allelic series. With these genomic shifts, the population reached an adapted state while retaining [Formula: see text] of the standing molecular marker variation in the founding population. Robust selection and association mapping tests highlighted several key genes driving the phenotypic response to selection. Our results reveal the evolutionary dynamics of a finite polygenic architecture conditioning a capacity for rapid environmental adaptation in maize.
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Affiliation(s)
- Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716
| | - Zhou Fang
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
| | - James B Holland
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
- US Department of Agriculture-Agricultural Research Service, Raleigh, North Carolina 27695
| | - Juliana E C Teixeira
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716
| | - John Dougherty
- Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware 19714
| | | | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706
| | - Sherry Flint-Garcia
- US Department of Agriculture-Agricultural Research Service, Columbia, Missouri 65211
- Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211
| | - Nick Lauter
- US Department of Agriculture-Agricultural Research Service, Ames, Iowa 50011
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa 50011
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas 77843
| | - Wenwei Xu
- Agricultural Research and Extension Center, Texas A&M AgriLife Research, Lubbock, Texas 79403
| | - Arnel Hallauer
- Department of Agronomy, Iowa State University, Ames, Iowa 50011
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Robledo‐Arnuncio JJ, Unger GM. Measuring viability selection from prospective cohort mortality studies: A case study in maritime pine. Evol Appl 2019; 12:863-877. [PMID: 31080501 PMCID: PMC6503825 DOI: 10.1111/eva.12729] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 10/05/2018] [Accepted: 10/15/2018] [Indexed: 11/27/2022] Open
Abstract
By changing the genetic background available for selection at subsequent life stages, stage-specific selection can define adaptive potential across the life cycle. We propose and evaluate here a neutrality test and a Bayesian method to infer stage-specific viability selection coefficients using sequential random genotypic samples drawn from a longitudinal cohort mortality study, within a generation. The approach is suitable for investigating selective mortality in large natural or experimental cohorts of any organism in which individual tagging and tracking are unfeasible. Numerical simulation results indicate that the method can discriminate loci under strong viability selection, and provided samples are large, yield accurate estimates of the corresponding selection coefficients. Genotypic frequency changes are largely driven by sampling noise under weak selection, however, compromising inference in that case. We apply the proposed methods to analyze viability selection operating at early recruitment stages in a natural maritime pine (Pinus pinaster Ait.) population. We measured temporal genotypic frequency changes at 384 candidate-gene SNP loci among seedlings sampled from the time of emergence in autumn until the summer of the following year, a period with high elimination rates. We detected five loci undergoing allele frequency changes larger than expected from stochastic mortality and sampling, with putative functions that could influence survival at early seedling stages. Our results illustrate how new statistical and sampling schemes can be used to conduct genomic scans of contemporary selection on specific life stages.
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Affiliation(s)
| | - Gregor M. Unger
- Department of Forest Ecology & GeneticsINIA‐CIFORMadridSpain
- Escuela Internacional de DoctoradoUniversidad Rey Juan CarlosMóstolesSpain
- Present address:
Department of Forest GeneticsFederal Research and Training Centre for ForestsNatural Hazards and LandscapeViennaAustria
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Jewett EM, Steinrücken M, Song YS. The Effects of Population Size Histories on Estimates of Selection Coefficients from Time-Series Genetic Data. Mol Biol Evol 2016; 33:3002-3027. [PMID: 27550904 PMCID: PMC5062326 DOI: 10.1093/molbev/msw173] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Many approaches have been developed for inferring selection coefficients from time series data while accounting for genetic drift. These approaches have been motivated by the intuition that properly accounting for the population size history can significantly improve estimates of selective strengths. However, the improvement in inference accuracy that can be attained by modeling drift has not been characterized. Here, by comparing maximum likelihood estimates of selection coefficients that account for the true population size history with estimates that ignore drift by assuming allele frequencies evolve deterministically in a population of infinite size, we address the following questions: how much can modeling the population size history improve estimates of selection coefficients? How much can mis-inferred population sizes hurt inferences of selection coefficients? We conduct our analysis under the discrete Wright–Fisher model by deriving the exact probability of an allele frequency trajectory in a population of time-varying size and we replicate our results under the diffusion model. For both models, we find that ignoring drift leads to estimates of selection coefficients that are nearly as accurate as estimates that account for the true population history, even when population sizes are small and drift is high. This result is of interest because inference methods that ignore drift are widely used in evolutionary studies and can be many orders of magnitude faster than methods that account for population sizes.
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Affiliation(s)
- Ethan M Jewett
- Department of EECS, University of California, Berkeley, CA Department of Statistics, University of California, Berkeley, CA
| | - Matthias Steinrücken
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA
| | - Yun S Song
- Department of EECS, University of California, Berkeley, CA Department of Statistics, University of California, Berkeley, CA Department of Integrative Biology, University of California, Berkeley, CA Department of Biology, University of Pennsylvania, Philadelphia, PA Department of Mathematics, University of Pennsylvania, Philadelphia, PA
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4
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Malaspinas AS. Methods to characterize selective sweeps using time serial samples: an ancient DNA perspective. Mol Ecol 2015; 25:24-41. [DOI: 10.1111/mec.13492] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 11/08/2015] [Accepted: 11/10/2015] [Indexed: 01/20/2023]
Affiliation(s)
- Anna-Sapfo Malaspinas
- Institute of Ecology and Evolution; University of Bern; Baltzerstrasse 6 CH-3012 Bern Switzerland
- Centre for GeoGenetics; Natural History Museum of Denmark; University of Copenhagen; Øster Voldgade 5-7 1350 Copenhagen Denmark
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Thépot S, Restoux G, Goldringer I, Hospital F, Gouache D, Mackay I, Enjalbert J. Efficiently tracking selection in a multiparental population: the case of earliness in wheat. Genetics 2015; 199:609-23. [PMID: 25406468 PMCID: PMC4317666 DOI: 10.1534/genetics.114.169995] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Accepted: 11/11/2014] [Indexed: 11/18/2022] Open
Abstract
Multiparental populations are innovative tools for fine mapping large numbers of loci. Here we explored the application of a wheat Multiparent Advanced Generation Inter-Cross (MAGIC) population for QTL mapping. This population was created by 12 generations of free recombination among 60 founder lines, following modification of the mating system from strict selfing to strict outcrossing using the ms1b nuclear male sterility gene. Available parents and a subset of 380 SSD lines of the resulting MAGIC population were phenotyped for earliness and genotyped with the 9K i-Select SNP array and additional markers in candidate genes controlling heading date. We demonstrated that 12 generations of strict outcrossing rapidly and drastically reduced linkage disequilibrium to very low levels even at short map distances and also greatly reduced the population structure exhibited among the parents. We developed a Bayesian method, based on allelic frequency, to estimate the contribution of each parent in the evolved population. To detect loci under selection and estimate selective pressure, we also developed a new method comparing shifts in allelic frequency between the initial and the evolved populations due to both selection and genetic drift with expectations under drift only. This evolutionary approach allowed us to identify 26 genomic areas under selection. Using association tests between flowering time and polymorphisms, 6 of these genomic areas appeared to carry flowering time QTL, 1 of which corresponds to Ppd-D1, a major gene involved in the photoperiod sensitivity. Frequency shifts at 4 of 6 areas were consistent with earlier flowering of the evolved population relative to the initial population. The use of this new outcrossing wheat population, mixing numerous initial parental lines through multiple generations of panmixia, is discussed in terms of power to detect genes under selection and association mapping. Furthermore we provide new statistical methods for use in future analyses of multiparental populations.
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Affiliation(s)
- Stéphanie Thépot
- Université Paris-Sud, Unité Mixte de Recherche 0320/Unité Mixte de Recherche 8120, Génétique Végétale, F-91190 Gif-sur-Yvette, France Institut National de la Recherche Agronomique, Unité Mixte de Recherche 0320/Unité Mixte de Recherche 8120, Génétique Végétale, F-91190 Gif-sur-Yvette, France
| | - Gwendal Restoux
- Unité d'Ecologie, Systématique et Evolution, Centre National de la Recherche Scientifique Unité Mixte de Recherche 8079, Université Paris-Sud, Orsay, France
| | - Isabelle Goldringer
- Institut National de la Recherche Agronomique, Unité Mixte de Recherche 0320/Unité Mixte de Recherche 8120, Génétique Végétale, F-91190 Gif-sur-Yvette, France
| | - Frédéric Hospital
- Institut National de la Recherche Agronomique, Unité Mixte de Recherche 1313 Génétique Animale et Biologie Intégrative, F-78352 Jouy en Josas, France
| | - David Gouache
- Arvalis, Institut du Végétal, Station Expérimentale, F-91720 Boigneville, France
| | - Ian Mackay
- National Institute of Agricultural Botany, Huntingdon Road, Cambridge CB3 0LE, United Kingdom
| | - Jérôme Enjalbert
- Institut National de la Recherche Agronomique, Unité Mixte de Recherche 0320/Unité Mixte de Recherche 8120, Génétique Végétale, F-91190 Gif-sur-Yvette, France
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Abstract
Longitudinal allele frequency data are becoming increasingly prevalent. Such samples permit statistical inference of the population genetics parameters that influence the fate of mutant variants. To infer these parameters by maximum likelihood, the mutant frequency is often assumed to evolve according to the Wright–Fisher model. For computational reasons, this discrete model is commonly approximated by a diffusion process that requires the assumption that the forces of natural selection and mutation are weak. This assumption is not always appropriate. For example, mutations that impart drug resistance in pathogens may evolve under strong selective pressure. Here, we present an alternative approximation to the mutant-frequency distribution that does not make any assumptions about the magnitude of selection or mutation and is much more computationally efficient than the standard diffusion approximation. Simulation studies are used to compare the performance of our method to that of the Wright–Fisher and Gaussian diffusion approximations. For large populations, our method is found to provide a much better approximation to the mutant-frequency distribution when selection is strong, while all three methods perform comparably when selection is weak. Importantly, maximum-likelihood estimates of the selection coefficient are severely attenuated when selection is strong under the two diffusion models, but not when our method is used. This is further demonstrated with an application to mutant-frequency data from an experimental study of bacteriophage evolution. We therefore recommend our method for estimating the selection coefficient when the effective population size is too large to utilize the discrete Wright–Fisher model.
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