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Beaman JE, Gates K, Saltré F, Hogg CJ, Belov K, Ashman K, da Silva KB, Beheregaray LB, Bradshaw CJA. A Guide for Developing Demo-Genetic Models to Simulate Genetic Rescue. Evol Appl 2025; 18:e70092. [PMID: 40371097 PMCID: PMC12076008 DOI: 10.1111/eva.70092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 01/27/2025] [Accepted: 01/28/2025] [Indexed: 05/16/2025] Open
Abstract
Genetic rescue is a conservation management strategy that reduces the negative effects of genetic drift and inbreeding in small and isolated populations. However, such populations might already be vulnerable to random fluctuations in growth rates (demographic stochasticity). Therefore, the success of genetic rescue depends not only on the genetic composition of the source and target populations but also on the emergent outcome of interacting demographic processes and other stochastic events. Developing predictive models that account for feedback between demographic and genetic processes ('demo-genetic feedback') is therefore necessary to guide the implementation of genetic rescue to minimize the risk of extinction of threatened populations. Here, we explain how the mutual reinforcement of genetic drift, inbreeding, and demographic stochasticity increases extinction risk in small populations. We then describe how these processes can be modelled by parameterizing underlying mechanisms, including deleterious mutations with partial dominance and demographic rates with variances that increase as abundance declines. We combine our suggestions of model parameterization with a comparison of the relevant capability and flexibility of five open-source programs designed for building genetically explicit, individual-based simulations. Using one of the programs, we provide a heuristic model to demonstrate that simulated genetic rescue can delay extinction of small virtual populations that would otherwise be exposed to greater extinction risk due to demo-genetic feedback. We then use a case study of threatened Australian marsupials to demonstrate that published genetic data can be used in one or all stages of model development and application, including parameterization, calibration, and validation. We highlight that genetic rescue can be simulated with either virtual or empirical sequence variation (or a hybrid approach) and suggest that model-based decision-making should be informed by ranking the sensitivity of predicted probability/time to extinction to variation in model parameters (e.g., translocation size, frequency, source populations) among different genetic-rescue scenarios.
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Affiliation(s)
- Julian E. Beaman
- Global Ecology | Partuyarta Ngadluku Wardli Kuu, College of Science and EngineeringFlinders UniversityAdelaideSouth AustraliaAustralia
| | - Katie Gates
- Molecular Ecology Laboratory, College of Science and EngineeringFlinders UniversityAdelaideSouth AustraliaAustralia
| | - Frédérik Saltré
- Global Ecology | Partuyarta Ngadluku Wardli Kuu, College of Science and EngineeringFlinders UniversityAdelaideSouth AustraliaAustralia
- Biogeography Ecology & Modelling, School of Life SciencesUniversity Technology SydneySydneyNew South WalesAustralia
- Australian Museum, Research InstituteAustralian MuseumSydneyNew South WalesAustralia
| | - Carolyn J. Hogg
- School of Life and Environmental SciencesThe University of SydneySydneyNew South WalesAustralia
| | - Katherine Belov
- School of Life and Environmental SciencesThe University of SydneySydneyNew South WalesAustralia
| | - Kita Ashman
- World Wide Fund for Nature AustraliaMelbourneVictoriaAustralia
| | - Karen Burke da Silva
- Conservation and Symbiosis Lab, College of Science and EngineeringFlinders UniversityAdelaideSouth AustraliaAustralia
| | - Luciano B. Beheregaray
- Molecular Ecology Laboratory, College of Science and EngineeringFlinders UniversityAdelaideSouth AustraliaAustralia
| | - Corey J. A. Bradshaw
- Global Ecology | Partuyarta Ngadluku Wardli Kuu, College of Science and EngineeringFlinders UniversityAdelaideSouth AustraliaAustralia
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Chevy ET, Min J, Caudill V, Champer SE, Haller BC, Rehmann CT, Smith CCR, Tittes S, Messer PW, Kern AD, Ramachandran S, Ralph PL. Population Genetics Meets Ecology: A Guide to Individual-Based Simulations in Continuous Landscapes. Ecol Evol 2025; 15:e71098. [PMID: 40235724 PMCID: PMC11997375 DOI: 10.1002/ece3.71098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2024] [Revised: 02/13/2025] [Accepted: 02/21/2025] [Indexed: 04/17/2025] Open
Abstract
Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. We additionally discuss natural selection-in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).
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Affiliation(s)
- Elizabeth T. Chevy
- Center for Computational Molecular BiologyBrown UniversityProvidenceRhode IslandUSA
| | - Jiseon Min
- Institute of Ecology and EvolutionUniversity of OregonEugeneOregonUSA
| | - Victoria Caudill
- Institute of Ecology and EvolutionUniversity of OregonEugeneOregonUSA
| | - Samuel E. Champer
- Department of Computational BiologyCornell UniversityIthacaNew YorkUSA
| | | | - Clara T. Rehmann
- Institute of Ecology and EvolutionUniversity of OregonEugeneOregonUSA
| | - Chris C. R. Smith
- Institute of Ecology and EvolutionUniversity of OregonEugeneOregonUSA
| | - Silas Tittes
- Institute of Ecology and EvolutionUniversity of OregonEugeneOregonUSA
| | - Philipp W. Messer
- Department of Computational BiologyCornell UniversityIthacaNew YorkUSA
| | - Andrew D. Kern
- Institute of Ecology and EvolutionUniversity of OregonEugeneOregonUSA
- Department of BiologyUniversity of OregonEugeneOregonUSA
| | - Sohini Ramachandran
- Center for Computational Molecular BiologyBrown UniversityProvidenceRhode IslandUSA
| | - Peter L. Ralph
- Institute of Ecology and EvolutionUniversity of OregonEugeneOregonUSA
- Department of Data ScienceUniversity of OregonEugeneOregonUSA
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3
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Pongsanarm T, Panthum T, Budi T, Wongloet W, Chaiyes A, Thatukan C, Jaito W, Patta C, Singchat W, Duengkae P, Muangmai N, Wangwon K, Srikulnath K. Genetic and geographical insights call for early conservation of Mae Hong Son's blue mahseer to prevent population crisis. PLoS One 2025; 20:e0313505. [PMID: 39937793 DOI: 10.1371/journal.pone.0313505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Accepted: 10/24/2024] [Indexed: 02/14/2025] Open
Abstract
Ecosystems are being disrupted by climate change and habitat fragmentation, which affect species survival through altered mating, feeding, and migration patterns. Mae Hong Son Province, Thailand, harbors a unique hydrological network that supports rich freshwater fish biodiversity. Blue mahseer (Neolissochilus stracheyi), which is restricted to headwater streams in Mae Hong Son, is particularly sensitive to habitat disturbances and has experienced population decline. Despite their vulnerability to climate change and habitat fragmentation, information on the genetic diversity, population structure, and environmental drivers of their distribution remains limited. In this study, microsatellite genotyping and mitochondrial DNA displacement loop sequence analysis were used to assess the genetic diversity and population structure of five blue mahseer populations in Mae Hong Son, with the aim of identifying reliable conservation units for effective management. Low genetic diversity levels across populations were identified (expected heterozygosity = 0.452 ± 0.037; allelic richness = 3.150 ± 0.506) with no evidence of inbreeding or outbreeding. A forecasted drop in heterozygosity below 0.1 within 50 years indicated the urgency of conservation attention. The five blue mahseer populations were clustered into three evolutionarily significant units (ESUs) based on historical isolation, phylogenetic distinctness, and significant genetic differentiation. Habitat suitability was assessed using MaxEnt species distribution modeling, which identified distance to rivers and annual mean total precipitation as significant environmental variables. The correlation between genetic differentiation and geographical distance suggested that habitat conditions primarily influence population genetic structure. Stocking between ESUs with differing genetic stocks is discouraged to avoid negative genetic effects. A comprehensive understanding of blue mahseer population dynamics, informed by the integration of genetic and ecological data, is needed to inform conservation strategies for resource management in Mae Hong Son.
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Affiliation(s)
- Tavun Pongsanarm
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Laboratory of Animal Cytogenetics and Comparative Genomics (ACCG), Department of Genetics, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Thitipong Panthum
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forest, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Trifan Budi
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Wongsathit Wongloet
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Laboratory of Animal Cytogenetics and Comparative Genomics (ACCG), Department of Genetics, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Aingorn Chaiyes
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- School of Agriculture and Cooperatives, Sukhothai Thammathirat Open University, Nonthaburi, Thailand
| | - Chadaphon Thatukan
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Sciences for Industry, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Wattanawan Jaito
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Sciences for Industry, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Chananya Patta
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Sciences for Industry, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Worapong Singchat
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forest, Kasetsart University, Chatuchak, Bangkok, Thailand
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Prateep Duengkae
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forest, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Narongrit Muangmai
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forest, Kasetsart University, Chatuchak, Bangkok, Thailand
- Department of Fishery Biology, Faculty of Fisheries, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Kiatisak Wangwon
- Tham Pla-Namtok Pha Suea National Park, Huai Pha, Mueang Mae Hong Son, Mae Hong Son, Thailand
| | - Kornsorn Srikulnath
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Laboratory of Animal Cytogenetics and Comparative Genomics (ACCG), Department of Genetics, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forest, Kasetsart University, Chatuchak, Bangkok, Thailand
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
- Biodiversity Center Kasetsart University (BDCKU), Chatuchak, Bangkok, Thailand
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4
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Justen HC, Easton WE, Delmore KE. Mapping seasonal migration in a songbird hybrid zone -- heritability, genetic correlations, and genomic patterns linked to speciation. Proc Natl Acad Sci U S A 2024; 121:e2313442121. [PMID: 38648483 PMCID: PMC11067064 DOI: 10.1073/pnas.2313442121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 03/19/2024] [Indexed: 04/25/2024] Open
Abstract
Seasonal migration is a widespread behavior relevant for adaptation and speciation, yet knowledge of its genetic basis is limited. We leveraged advances in tracking and sequencing technologies to bridge this gap in a well-characterized hybrid zone between songbirds that differ in migratory behavior. Migration requires the coordinated action of many traits, including orientation, timing, and wing morphology. We used genetic mapping to show these traits are highly heritable and genetically correlated, explaining how migration has evolved so rapidly in the past and suggesting future responses to climate change may be possible. Many of these traits mapped to the same genomic regions and small structural variants indicating the same, or tightly linked, genes underlie them. Analyses integrating transcriptomic data indicate cholinergic receptors could control multiple traits. Furthermore, analyses integrating genomic differentiation further suggested genes underlying migratory traits help maintain reproductive isolation in this hybrid zone.
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Affiliation(s)
- Hannah C. Justen
- Biology Department, Texas Agricultural and Mechanical University, TAMUCollege Station, TX3528
| | - Wendy E. Easton
- Environment and Climate Change Canada, Canadian Wildlife Service-Pacific Region, Delta, BCV4K 3N2, Canada
| | - Kira E. Delmore
- Biology Department, Texas Agricultural and Mechanical University, TAMUCollege Station, TX3528
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5
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Amado A, Li J, Bank C. STUN: forward-time simulation on TUnable fitNess landscapes in recombining populations. BIOINFORMATICS ADVANCES 2023; 3:vbad164. [PMID: 38075480 PMCID: PMC10703519 DOI: 10.1093/bioadv/vbad164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/26/2023] [Accepted: 11/15/2023] [Indexed: 11/30/2024]
Abstract
MOTIVATION Understanding the population genetics of complex polygenic traits during adaptation is challenging. RESULTS Here, we implement a forward-in-time population-genetic simulator (STUN) based on Wright-Fisher dynamics. STUN is a flexible and user-friendly software package for simulating the polygenic adaptation of recombining haploid populations using either new mutations or standing genetic variation. STUN assumes that populations adapt to sudden environmental changes by undergoing selection on a new fitness landscape. With pre-implemented fitness landscape models like Rough Mount Fuji, NK, Block, additive, and House-of-Cards, users can explore the effect of different levels of epistasis (ruggedness of the fitness landscape). Custom fitness landscapes and recombination maps can also be defined. STUN empowers both experimentalists and advanced programmers to study the evolution of complex polygenic traits and to dissect the adaptation process. AVAILABILITY AND IMPLEMENTATION STUN is implemented in Rust. Its source code is available at https://github.com/banklab/STUN and archived on Zenodo under doi: 10.5281/zenodo.10246377. The repository includes a link to the software's manual and binary files for Linux, macOS and Windows.
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Affiliation(s)
- André Amado
- Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland
- Swiss Institute for Bioinformatics, Quartier Sorge Bâtiment Amphipôle, 1015 Lausanne, Switzerland
| | - Juan Li
- Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland
- Swiss Institute for Bioinformatics, Quartier Sorge Bâtiment Amphipôle, 1015 Lausanne, Switzerland
| | - Claudia Bank
- Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland
- Swiss Institute for Bioinformatics, Quartier Sorge Bâtiment Amphipôle, 1015 Lausanne, Switzerland
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6
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Roux C, Vekemans X, Pannell J. Inferring the Demographic History and Inheritance Mode of Tetraploid Species Using ABC. Methods Mol Biol 2023; 2545:325-348. [PMID: 36720821 DOI: 10.1007/978-1-0716-2561-3_17] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Genomic patterns of diversity and divergence are impacted by certain life history traits, reproductive systems, and demographic history. The latter is characterized by fluctuations in population sizes over time, as well as by temporal patterns of introgression. For a given organism, identifying a demographic history that deviates from the standard neutral model allows a better understanding of its evolution but also helps to reduce the risk of false positives when screening for molecular targets of natural selection. Tetraploid organisms and beyond have demographic histories that are complicated by the mode of polyploidization, the mode of inheritance, and different scenarios of gene flow between sub-genomes and diploid parental species. Here we provide guidelines for experimenters wishing to address these issues through a flexible statistical framework: approximate Bayesian computation (ABC). The emphasis is on the general philosophy of the approach to encourage future users to exploit the enormous flexibility of ABC beyond the limitations imposed by generalist data analysis pipelines.
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Affiliation(s)
- Camille Roux
- Univ. Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, Lille, France.
| | | | - John Pannell
- Department of Ecology and Evolution, Biophore Building, University of Lausanne, Lausanne, Switzerland
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7
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Rouger B, Goldringer I, Barbillon P, Miramon A, Naino Jika AK, Thomas M. Sensitivity analysis of a crop metapopulation model. Ecol Modell 2023. [DOI: 10.1016/j.ecolmodel.2022.110174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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8
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Zhang QS, Goudet J, Weir BS. Rank-invariant estimation of inbreeding coefficients. Heredity (Edinb) 2022; 128:1-10. [PMID: 34824382 PMCID: PMC8733021 DOI: 10.1038/s41437-021-00471-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 09/05/2021] [Accepted: 09/05/2021] [Indexed: 11/18/2022] Open
Abstract
The two alleles an individual carries at a locus are identical by descent (ibd) if they have descended from a single ancestral allele in a reference population, and the probability of such identity is the inbreeding coefficient of the individual. Inbreeding coefficients can be predicted from pedigrees with founders constituting the reference population, but estimation from genetic data is not possible without data from the reference population. Most inbreeding estimators that make explicit use of sample allele frequencies as estimates of allele probabilities in the reference population are confounded by average kinships with other individuals. This means that the ranking of those estimates depends on the scope of the study sample and we show the variation in rankings for common estimators applied to different subdivisions of 1000 Genomes data. Allele-sharing estimators of within-population inbreeding relative to average kinship in a study sample, however, do have invariant rankings across all studies including those individuals. They are unbiased with a large number of SNPs. We discuss how allele sharing estimates are the relevant quantities for a range of empirical applications.
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Affiliation(s)
- Qian S Zhang
- Department of Biostatistics, University of Washington, Seattle, WA, 98195-1617, USA
| | - Jérôme Goudet
- Department of Ecology and Evolution, University of Lausanne, CH-1015, Lausanne, Switzerland
| | - Bruce S Weir
- Department of Biostatistics, University of Washington, Seattle, WA, 98195-1617, USA.
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9
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Zhang R, Liu C, Yuan K, Ni X, Pan Y, Xu S. AdmixSim 2: a forward-time simulator for modeling complex population admixture. BMC Bioinformatics 2021; 22:506. [PMID: 34663213 PMCID: PMC8522168 DOI: 10.1186/s12859-021-04415-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 09/30/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Computer simulations have been widely applied in population genetics and evolutionary studies. A great deal of effort has been made over the past two decades in developing simulation tools. However, there are not many simulation tools suitable for studying population admixture. RESULTS We here developed a forward-time simulator, AdmixSim 2, an individual-based tool that can flexibly and efficiently simulate population genomics data under complex evolutionary scenarios. Unlike its previous version, AdmixSim 2 is based on the extended Wright-Fisher model, and it implements many common evolutionary parameters to involve gene flow, natural selection, recombination, and mutation, which allow users to freely design and simulate any complex scenario involving population admixture. AdmixSim 2 can be used to simulate data of dioecious or monoecious populations, autosomes, or sex chromosomes. To our best knowledge, there are no similar tools available for the purpose of simulation of complex population admixture. Using empirical or previously simulated genomic data as input, AdmixSim 2 provides phased haplotype data for the convenience of further admixture-related analyses such as local ancestry inference, association studies, and other applications. We here evaluate the performance of AdmixSim 2 based on simulated data and validated functions via comparative analysis of simulated data and empirical data of African American, Mexican, and Uyghur populations. CONCLUSIONS AdmixSim 2 is a flexible simulation tool expected to facilitate the study of complex population admixture in various situations.
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Affiliation(s)
- Rui Zhang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Chang Liu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. .,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China. .,Human Phenome Institute, Fudan University, Shanghai, 201203, China. .,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China. .,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, 450052, China.
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10
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Paril JF, Balding DJ, Fournier-Level A. Optimizing sampling design and sequencing strategy for the genomic analysis of quantitative traits in natural populations. Mol Ecol Resour 2021; 22:137-152. [PMID: 34192415 DOI: 10.1111/1755-0998.13458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 05/02/2021] [Accepted: 06/25/2021] [Indexed: 11/27/2022]
Abstract
Mapping the genes underlying ecologically relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterization of a species' genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine, through simulation, the sampling scheme that maximizes the power to map the genetic basis of a complex trait in an outbreeding species across an idealized landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that quantitative trait locus detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost-effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.
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Affiliation(s)
- Jefferson F Paril
- School of Biosciences, The University of Melbourne, Parkville, Victoria, Australia
| | - David J Balding
- School of Biosciences, The University of Melbourne, Parkville, Victoria, Australia.,Melbourne Integrative Genomics, The University of Melbourne, Parkville, Victoria, Australia.,School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Alexandre Fournier-Level
- School of Biosciences, The University of Melbourne, Parkville, Victoria, Australia.,Melbourne Integrative Genomics, The University of Melbourne, Parkville, Victoria, Australia
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11
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Bourgeois YXC, Warren BH. An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes. Mol Ecol 2021; 30:6036-6071. [PMID: 34009688 DOI: 10.1111/mec.15989] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 04/26/2021] [Accepted: 05/11/2021] [Indexed: 01/01/2023]
Abstract
Characterizing the population history of a species and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The constant improvement of high-throughput sequencing techniques has facilitated the production of whole genome data in a wide range of species. Population genomics now provides tools to better integrate selection into a historical framework, and take into account selection when reconstructing demographic history. However, this improvement has come with a profusion of analytical tools that can confuse and discourage users. Such confusion limits the amount of information effectively retrieved from complex genomic data sets, and impairs the diffusion of the most recent analytical tools into fields such as conservation biology. It may also lead to redundancy among methods. To address these isssues, we propose an overview of more than 100 state-of-the-art methods that can deal with whole genome data. We summarize the strategies they use to infer demographic history and selection, and discuss some of their limitations. A website listing these methods is available at www.methodspopgen.com.
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Affiliation(s)
| | - Ben H Warren
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, UA, CP 51, Paris, France
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12
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Prunier JG, Poesy C, Dubut V, Veyssière C, Loot G, Poulet N, Blanchet S. Quantifying the individual impact of artificial barriers in freshwaters: A standardized and absolute genetic index of fragmentation. Evol Appl 2020; 13:2566-2581. [PMID: 33294009 PMCID: PMC7691472 DOI: 10.1111/eva.13044] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 06/02/2020] [Accepted: 06/09/2020] [Indexed: 12/27/2022] Open
Abstract
Fragmentation by artificial barriers is an important threat to freshwater biodiversity. Mitigating the negative aftermaths of fragmentation is of crucial importance, and it is now essential for environmental managers to benefit from a precise estimate of the individual impact of weirs and dams on river connectivity. Although the indirect monitoring of fragmentation using molecular data constitutes a promising approach, it is plagued with several constraints preventing a standardized quantification of barrier effects. Indeed, observed levels of genetic differentiation GD depend on both the age of the obstacle and the effective size of the populations it separates, making comparisons of the actual barrier effect of different obstacles difficult. Here, we developed a standardized genetic index of fragmentation (F INDEX), allowing an absolute and independent assessment of the individual effects of obstacles on connectivity. The F INDEX is the standardized ratio between the observed GD between pairs of populations located on either side of an obstacle and the GD expected if this obstacle completely prevented gene flow. The expected GD is calculated from simulations taking into account two parameters: the number of generations since barrier creation and the expected heterozygosity of the populations, a proxy for effective population size. Using both simulated and empirical datasets, we explored the validity and the limits of the F INDEX. We demonstrated that it allows quantifying effects of fragmentation only from a few generations after barrier creation and provides valid comparisons among obstacles of different ages and populations (or species) of different effective sizes. The F INDEX requires a minimum amount of fieldwork and genotypic data and solves some of the difficulties inherent to the study of artificial fragmentation in rivers and potentially in other ecosystems. This makes the F INDEX promising to support the management of freshwater species affected by barriers, notably for planning and evaluating restoration programs.
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Affiliation(s)
- Jérôme G. Prunier
- Centre National de la Recherche Scientifique (CNRS)Université Paul Sabatier (UPS)UMR 5321Station d’Ecologie Théorique et ExpérimentaleMoulisFrance
| | - Camille Poesy
- Centre National de la Recherche Scientifique (CNRS)Université Paul Sabatier (UPS)UMR 5321Station d’Ecologie Théorique et ExpérimentaleMoulisFrance
| | - Vincent Dubut
- CNRSIRDAvignon UniversitéIMBEAix Marseille UnivMarseille UniversitéFrance
| | - Charlotte Veyssière
- CNRSUPSUMR 5174 EDB (Laboratoire Évolution & Diversité Biologique)École Nationale de Formation Agronomique (ENFA)Toulouse Cedex 4France
| | - Géraldine Loot
- CNRSUPSUMR 5174 EDB (Laboratoire Évolution & Diversité Biologique)École Nationale de Formation Agronomique (ENFA)Toulouse Cedex 4France
| | - Nicolas Poulet
- DRAS, Pôle R&D écohydraulique OFBIMFT‐PPRIMEOffice Français de la BiodiversitéToulouseFrance
| | - Simon Blanchet
- Centre National de la Recherche Scientifique (CNRS)Université Paul Sabatier (UPS)UMR 5321Station d’Ecologie Théorique et ExpérimentaleMoulisFrance
- CNRSUPSUMR 5174 EDB (Laboratoire Évolution & Diversité Biologique)École Nationale de Formation Agronomique (ENFA)Toulouse Cedex 4France
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13
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Galimberti M, Leuenberger C, Wolf B, Szilágyi SM, Foll M, Wegmann D. Detecting Selection from Linked Sites Using an F-Model. Genetics 2020; 216:1205-1215. [PMID: 33067324 PMCID: PMC7768260 DOI: 10.1534/genetics.120.303780] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 10/03/2020] [Indexed: 11/18/2022] Open
Abstract
Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F-model, which measures differences in allele frequencies by population specific FST coefficients. This model readily accounts for multiple evolutionary forces by partitioning FST coefficients into locus- and population-specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM), which characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations, we show that the statistical power of our method is up to twofold higher than that of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).
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Affiliation(s)
- Marco Galimberti
- Department of Biology, University of Fribourg, 1700, Switzerland
- Swiss Institute of Bioinformatics, Fribourg, 1700, Switzerland
| | | | - Beat Wolf
- iCoSys, University of Applied Sciences Western Switzerland, Fribourg, 1700 Switzerland
| | - Sándor Miklós Szilágyi
- Department of Informatics, University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Târgu Mureş, 540139, Romania
| | - Matthieu Foll
- International Agency for Research on Cancer (IARC/WHO), Section of Genetics, 69372 Lyon, France
| | - Daniel Wegmann
- Department of Biology, University of Fribourg, 1700, Switzerland
- Swiss Institute of Bioinformatics, Fribourg, 1700, Switzerland
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14
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Andrello M, Noirot C, Débarre F, Manel S. MetaPopGen 2.0: A multilocus genetic simulator to model populations of large size. Mol Ecol Resour 2020; 21:596-608. [PMID: 33030758 DOI: 10.1111/1755-0998.13270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 08/05/2020] [Accepted: 09/23/2020] [Indexed: 11/27/2022]
Abstract
Multilocus genetic processes in subdivided populations can be complex and difficult to interpret using theoretical population genetics models. Genetic simulators offer a valid alternative to study multilocus genetic processes in arbitrarily complex scenarios. However, the use of forward-in-time simulators in realistic scenarios involving high numbers of individuals distributed in multiple local populations is limited by computation time and memory requirements. These limitations increase with the number of simulated individuals. We developed a genetic simulator, MetaPopGen 2.0, to model multilocus population genetic processes in subdivided populations of arbitrarily large size. It allows for spatial and temporal variation in demographic parameters, age structure, adult and propagule dispersal, variable mutation rates and selection on survival and fecundity. We developed MetaPopGen 2.0 in the R environment to facilitate its use by non-modeler ecologists and evolutionary biologists. We illustrate the capabilities of MetaPopGen 2.0 for studying adaptation to water salinity in the striped red mullet Mullus surmuletus.
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Affiliation(s)
- Marco Andrello
- MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France
| | - Christelle Noirot
- CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
| | - Florence Débarre
- Sorbonne Université, CNRS, INRAE, IRD, UPEC, Institut d'Ecologie et des Sciences de l'Environnement de Paris (iEES-Paris), UMR 7618, Paris, France
| | - Stéphanie Manel
- CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
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15
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Yannic G, Hagen O, Leugger F, Karger DN, Pellissier L. Harnessing paleo-environmental modeling and genetic data to predict intraspecific genetic structure. Evol Appl 2020; 13:1526-1542. [PMID: 32684974 PMCID: PMC7359836 DOI: 10.1111/eva.12986] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 04/15/2020] [Accepted: 04/21/2020] [Indexed: 12/18/2022] Open
Abstract
Spatially explicit simulations of gene flow within complex landscapes could help forecast the responses of populations to global and anthropological changes. Simulating how past climate change shaped intraspecific genetic variation can provide a validation of models in anticipation of their use to predict future changes. We review simulation models that provide inferences on population genetic structure. Existing simulation models generally integrate complex demographic and genetic processes but are less focused on the landscape dynamics. In contrast to previous approaches integrating detailed demographic and genetic processes and only secondarily landscape dynamics, we present a model based on parsimonious biological mechanisms combining habitat suitability and cellular processes, applicable to complex landscapes. The simulation model takes as input (a) the species dispersal capacities as the main biological parameter, (b) the species habitat suitability, and (c) the landscape structure, modulating dispersal. Our model emphasizes the role of landscape features and their temporal dynamics in generating genetic differentiation among populations within species. We illustrate our model on caribou/reindeer populations sampled across the entire species distribution range in the Northern Hemisphere. We show that simulations over the past 21 kyr predict a population genetic structure that matches empirical data. This approach looking at the impact of historical landscape dynamics on intraspecific structure can be used to forecast population structure under climate change scenarios and evaluate how species range shifts might induce erosion of genetic variation within species.
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Affiliation(s)
- Glenn Yannic
- Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS LECA Grenoble France
| | - Oskar Hagen
- Landscape Ecology Department of Environmental Systems Sciensce Institute of Terrestrial Ecosystems ETH Zürich Zürich Switzerland.,Swiss Federal Institute for Forest, Snow and Landscape Research Birmensdorf Switzerland
| | - Flurin Leugger
- Landscape Ecology Department of Environmental Systems Sciensce Institute of Terrestrial Ecosystems ETH Zürich Zürich Switzerland.,Swiss Federal Institute for Forest, Snow and Landscape Research Birmensdorf Switzerland
| | - Dirk N Karger
- Swiss Federal Institute for Forest, Snow and Landscape Research Birmensdorf Switzerland
| | - Loïc Pellissier
- Landscape Ecology Department of Environmental Systems Sciensce Institute of Terrestrial Ecosystems ETH Zürich Zürich Switzerland.,Swiss Federal Institute for Forest, Snow and Landscape Research Birmensdorf Switzerland
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16
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Landguth EL, Forester BR, Eckert AJ, Shirk AJ, Menon M, Whipple A, Day CC, Cushman SA. Modelling multilocus selection in an individual‐based, spatially‐explicit landscape genetics framework. Mol Ecol Resour 2019; 20:605-615. [DOI: 10.1111/1755-0998.13121] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 10/28/2019] [Accepted: 11/12/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Erin L. Landguth
- School of Public and Community Health Sciences University of Montana Missoula MT USA
| | | | - Andrew J. Eckert
- Department of Biology Virginia Commonwealth University Richmond VA USA
| | - Andrew J. Shirk
- Climate Impacts Group College of the Environment University of Washington Seattle WA USA
| | - Mitra Menon
- Integrative Life Sciences Virginian Commonwealth University Richmond VA USA
| | - Amy Whipple
- Department of Biological Sciences and Merriam‐Powell Center for Environmental Research Northern Arizona University Flagstaff AZ USA
| | - Casey C. Day
- School of Public and Community Health Sciences University of Montana Missoula MT USA
| | - Samuel A. Cushman
- USDA Forest Service Rocky Mountain Research Station Flagstaff AZ USA
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17
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Saunders PA, Neuenschwander S, Perrin N. Impact of deleterious mutations, sexually antagonistic selection, and mode of recombination suppression on transitions between male and female heterogamety. Heredity (Edinb) 2019; 123:419-428. [PMID: 31028370 DOI: 10.1038/s41437-019-0225-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 03/18/2019] [Accepted: 03/23/2019] [Indexed: 01/19/2023] Open
Abstract
Deleterious mutations accumulating on non-recombining Y chromosomes can drive XY to XY turnovers, as they allow to replace the old mutation-loaded Y by a new mutation-free one. The same process is thought to prevent XY to ZW turnovers, because the latter requires fixation of the ancestral Y, assuming dominance of the emergent feminizing mutation. Using individual-based simulations, we explored whether and how an epistatically dominant W allele can spread in a young XY system that gradually accumulates deleterious mutations. We also investigated how sexually antagonistic (SA) polymorphism on the ancestral sex chromosomes and the mechanism controlling X-Y recombination suppression affect these transitions. In contrast with XY to XY turnovers, XY to ZW turnovers cannot be favored by Y chromosome mutation load. If the arrest of X-Y recombination depends on genotypic sex, transitions are strongly hindered by deleterious mutations, and totally suppressed by very small SA cost, because deleterious mutations and female-detrimental SA alleles would have to fix with the Y. If, however, the arrest of X-Y recombination depends on phenotypic sex, X and Y recombine in XY ZW females, allowing for the purge of Y-linked deleterious mutations and loss of the SA polymorphism, causing XY to ZW turnovers to occur at the same rate as in the absence of deleterious and sex-antagonistic mutations. We generalize our results to other types of turnovers (e.g., triggered by non-dominant sex-determining mutations) and discuss their empirical relevance.
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Affiliation(s)
- Paul A Saunders
- Department of Ecology and Evolution, University of Lausanne, 1015, Lausanne, Switzerland.
| | - Samuel Neuenschwander
- Department of Ecology and Evolution, University of Lausanne, 1015, Lausanne, Switzerland.,Vital-IT, Swiss Institute of Bioinformatics, University of Lausanne, 1015, Lausanne, Switzerland
| | - Nicolas Perrin
- Department of Ecology and Evolution, University of Lausanne, 1015, Lausanne, Switzerland
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