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Gopalan S, Smith SP, Korunes K, Hamid I, Ramachandran S, Goldberg A. Human genetic admixture through the lens of population genomics. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200410. [PMID: 35430881 PMCID: PMC9014191 DOI: 10.1098/rstb.2020.0410] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Over the past 50 years, geneticists have made great strides in understanding how our species' evolutionary history gave rise to current patterns of human genetic diversity classically summarized by Lewontin in his 1972 paper, ‘The Apportionment of Human Diversity’. One evolutionary process that requires special attention in both population genetics and statistical genetics is admixture: gene flow between two or more previously separated source populations to form a new admixed population. The admixture process introduces ancestry-based structure into patterns of genetic variation within and between populations, which in turn influences the inference of demographic histories, identification of genetic targets of selection and prediction of complex traits. In this review, we outline some challenges for admixture population genetics, including limitations of applying methods designed for populations without recent admixture to the study of admixed populations. We highlight recent studies and methodological advances that aim to overcome such challenges, leveraging genomic signatures of admixture that occurred in the past tens of generations to gain insights into human history, natural selection and complex trait architecture. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.
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Affiliation(s)
- Shyamalika Gopalan
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Samuel Pattillo Smith
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
- Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, RI 02912, USA
| | - Katharine Korunes
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Iman Hamid
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
- Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, RI 02912, USA
- Data Science Initiative, Brown University, Providence, RI 02912, USA
| | - Amy Goldberg
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
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Gao W, Yu CX, Zhou WW, Zhang BL, Chambers EA, Dahn HA, Jin JQ, Murphy RW, Zhang YP, Che J. Species persistence with hybridization in toad-headed lizards driven by divergent selection and low recombination. Mol Biol Evol 2022; 39:6561330. [PMID: 35356979 PMCID: PMC9007161 DOI: 10.1093/molbev/msac064] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Speciation plays a central role in evolutionary studies, and particularly how reproductive isolation (RI) evolves. The origins and persistence of RI are distinct processes that require separate evaluations. Treating them separately clarifies the drivers of speciation and then it is possible to link the processes to understand large-scale patterns of diversity. Recent genomic studies have focused predominantly on how species or RI originate. However, we know little about how species persist in face of gene flow. Here, we evaluate a contact zone of two closely related toad-headed lizards (Phrynocephalus) using a chromosome-level genome assembly and population genomics. To some extent, recent asymmetric introgression from Phrynocephalus putjatai to P. vlangalii reduces their genomic differences. However, their highly divergent regions (HDRs) have heterogeneous distributions across the genomes. Functional gene annotation indicates that many genes within HDRs are involved in reproduction and RI. Compared with allopatric populations, contact areas exhibit recent divergent selection on the HDRs and a lower population recombination rate. Taken together, this implies that divergent selection and low genetic recombination help maintain RI. This study provides insights into the genomic mechanisms that drive RI and two species persistence in the face of gene flow during the late stage of speciation.
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Affiliation(s)
- Wei Gao
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Chuan-Xin Yu
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Wei-Wei Zhou
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Bao-Lin Zhang
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - E Anne Chambers
- Department of Integrative Biology and Biodiversity Center, University of Texas at Austin, Austin, USA.,Department of Environmental Science, Policy, and Management, Univerity of California, Berkeley, USA
| | - Hollis A Dahn
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Jie-Qiong Jin
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Robert W Murphy
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.,Centre for Biodiversity and Conservation Biology, Royal Ontario Museum, Toronto, Ontario, Canada
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Jing Che
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
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Iasi LNM, Ringbauer H, Peter BM. An Extended Admixture Pulse Model Reveals the Limitations to Human-Neandertal Introgression Dating. Mol Biol Evol 2021; 38:5156-5174. [PMID: 34254144 PMCID: PMC8557420 DOI: 10.1093/molbev/msab210] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Neandertal DNA makes up 2-3% of the genomes of all non-African individuals. The patterns of Neandertal ancestry in modern humans have been used to estimate that this is the result of gene flow that occurred during the expansion of modern humans into Eurasia, but the precise dates of this event remain largely unknown. Here, we introduce an extended admixture pulse model that allows joint estimation of the timing and duration of gene flow. This model leads to simple expressions for both the admixture segment distribution and the decay curve of ancestry linkage disequilibrium, and we show that these two statistics are closely related. In simulations, we find that estimates of the mean time of admixture are largely robust to details in gene flow models, but that the duration of the gene flow can only be recovered if gene flow is very recent and the exact recombination map is known. These results imply that gene flow from Neandertals into modern humans could have happened over hundreds of generations. Ancient genomes from the time around the admixture event are thus likely required to resolve the question when, where, and for how long humans and Neandertals interacted.
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Affiliation(s)
- Leonardo N M Iasi
- Department of Evloutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Harald Ringbauer
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Benjamin M Peter
- Department of Evloutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
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4
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Zimmerman KD, Schurr TG, Chen W, Nayak U, Mychaleckyj JC, Quet Q, Moultrie LH, Divers J, Keene KL, Kamen DL, Gilkeson GS, Hunt KJ, Spruill IJ, Fernandes JK, Aldrich MC, Reich D, Garvey WT, Langefeld CD, Sale MM, Ramos PS. Genetic landscape of Gullah African Americans. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2021; 175:905-919. [PMID: 34008864 PMCID: PMC8286328 DOI: 10.1002/ajpa.24333] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 03/30/2021] [Accepted: 04/17/2021] [Indexed: 01/20/2023]
Abstract
OBJECTIVES Gullah African Americans are descendants of formerly enslaved Africans living in the Sea Islands along the coast of the southeastern U.S., from North Carolina to Florida. Their relatively high numbers and geographic isolation were conducive to the development and preservation of a unique culture that retains deep African features. Although historical evidence supports a West-Central African ancestry for the Gullah, linguistic and cultural evidence of a connection to Sierra Leone has led to the suggestion of this country/region as their ancestral home. This study sought to elucidate the genetic structure and ancestry of the Gullah. MATERIALS AND METHODS We leveraged whole-genome genotype data from Gullah, African Americans from Jackson, Mississippi, African populations from Sierra Leone, and population reference panels from Africa and Europe to infer population structure, ancestry proportions, and global estimates of admixture. RESULTS Relative to non-Gullah African Americans from the Southeast US, the Gullah exhibited higher mean African ancestry, lower European admixture, a similarly small Native American contribution, and increased male-biased European admixture. A slightly tighter bottleneck in the Gullah 13 generations ago suggests a largely shared demographic history with non-Gullah African Americans. Despite a slightly higher relatedness to populations from Sierra Leone, our data demonstrate that the Gullah are genetically related to many West African populations. DISCUSSION This study confirms that subtle differences in African American population structure exist at finer regional levels. Such observations can help to inform medical genetics research in African Americans, and guide the interpretation of genetic data used by African Americans seeking to explore ancestral identities.
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Affiliation(s)
- Kip D. Zimmerman
- Center for Precision MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Theodore G. Schurr
- Department of AnthropologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Wei‐Min Chen
- Center for Public Health GenomicsUniversity of VirginiaCharlottesvilleVirginiaUSA
- Department of Public Health SciencesUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Uma Nayak
- Center for Public Health GenomicsUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Josyf C. Mychaleckyj
- Center for Public Health GenomicsUniversity of VirginiaCharlottesvilleVirginiaUSA
- Department of Public Health SciencesUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Queen Quet
- Gullah/Geechee NationSt. Helena IslandSouth CarolinaUSA
| | - Lee H. Moultrie
- Lee H. Moultrie & AssociatesNorth CharlestonSouth CarolinaUSA
| | - Jasmin Divers
- Department of Health Services ResearchNew York University Winthrop HospitalMineolaNew YorkUSA
| | - Keith L. Keene
- Department of BiologyEast Carolina UniversityGreenvilleNorth CarolinaUSA
- Center for Health DisparitiesEast Carolina University Brody School of MedicineGreenvilleNorth CarolinaUSA
| | - Diane L. Kamen
- Department of MedicineMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Gary S. Gilkeson
- Department of MedicineMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Kelly J. Hunt
- Department of Public Health SciencesMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Ida J. Spruill
- College of NursingMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Jyotika K. Fernandes
- Department of MedicineMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Melinda C. Aldrich
- Department of Thoracic SurgeryVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - David Reich
- Department of GeneticsHarvard Medical SchoolBostonMassachusettsUSA
- Howard Hughes Medical InstituteHarvard Medical SchoolBostonMassachusettsUSA
- Broad Institute of MIT and HarvardCambridgeMassachusettsUSA
- Department of Human Evolutionary BiologyHarvard UniversityCambridgeMassachusettsUSA
| | - W. Timothy Garvey
- Department of Nutrition ScienceUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Carl D. Langefeld
- Center for Precision MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Michèle M. Sale
- Center for Public Health GenomicsUniversity of VirginiaCharlottesvilleVirginiaUSA
- Department of Public Health SciencesUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Paula S. Ramos
- Department of MedicineMedical University of South CarolinaCharlestonSouth CarolinaUSA
- Department of Public Health SciencesMedical University of South CarolinaCharlestonSouth CarolinaUSA
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5
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Leitwein M, Duranton M, Rougemont Q, Gagnaire PA, Bernatchez L. Using Haplotype Information for Conservation Genomics. Trends Ecol Evol 2020; 35:245-258. [DOI: 10.1016/j.tree.2019.10.012] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 10/18/2019] [Accepted: 10/28/2019] [Indexed: 12/19/2022]
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Ni X, Yuan K, Liu C, Feng Q, Tian L, Ma Z, Xu S. MultiWaver 2.0: modeling discrete and continuous gene flow to reconstruct complex population admixtures. Eur J Hum Genet 2019; 27:133-139. [PMID: 30206356 PMCID: PMC6303267 DOI: 10.1038/s41431-018-0259-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 07/12/2018] [Accepted: 08/09/2018] [Indexed: 11/08/2022] Open
Abstract
Our goal in developing the MultiWaver software series was to be able to infer population admixture history under various complex scenarios. The earlier version of MultiWaver considered only discrete admixture models. Here, we report a newly developed version, MultiWaver 2.0, that implements a more flexible framework and is capable of inferring multiple-wave admixture histories under both discrete and continuous admixture models. MultiWaver 2.0 can automatically select an optimal admixture model based on the length distribution of ancestral tracks of chromosomes, and the program can estimate the corresponding parameters under the selected model. Specifically, for discrete admixture models, we used a likelihood ratio test (LRT) to determine the optimal discrete model and an expectation-maximization algorithm to estimate the parameters. In addition, according to the principles of the Bayesian Information Criterion (BIC), we compared the optimal discrete model with several continuous admixture models. In MultiWaver 2.0, we also applied a bootstrapping technique to provide levels of support for the chosen model and the confidence interval (CI) of the estimations of admixture time. Simulation studies validated the reliability and effectiveness of our method. Finally, the program performed well when applied to real datasets of typical admixed populations, such as African Americans, Uyghurs, and Hazaras.
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Affiliation(s)
- Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Kai Yuan
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chang Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qidi Feng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lei Tian
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhiming Ma
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Shuhua Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.
- University of Chinese Academy of Sciences, Beijing, 100049, 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.
- Collaborative Innovation Center of Genetics and Development, Shanghai, 200438, China.
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7
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Chimusa ER, Defo J, Thami PK, Awany D, Mulisa DD, Allali I, Ghazal H, Moussa A, Mazandu GK. Dating admixture events is unsolved problem in multi-way admixed populations. Brief Bioinform 2018; 21:144-155. [PMID: 30462157 DOI: 10.1093/bib/bby112] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 10/12/2018] [Accepted: 10/15/2018] [Indexed: 12/12/2022] Open
Abstract
Advances in human sequencing technologies, coupled with statistical and computational tools, have fostered the development of methods for dating admixture events. These methods have merits and drawbacks in estimating admixture events in multi-way admixed populations. Here, we first provide a comprehensive review and comparison of current methods pertinent to dating admixture events. Second, we assess various admixture dating tools. We do so by performing various simulations. Third, we apply the top two assessed methods to real data of a uniquely admixed population from South Africa. Results reveal that current dating admixture models are not sufficiently equipped to estimate ancient admixtures events and to identify multi-faceted admixture events in complex multi-way admixed populations. We conclude with a discussion of research areas where further work on dating admixture-based methods is needed.
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Affiliation(s)
- Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa
| | - Joel Defo
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa
| | - Prisca K Thami
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.,Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana.,Department of Biological Sciences, University of Botswana, Gaborone, Botswana
| | - Denis Awany
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa
| | - Delesa D Mulisa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa
| | - Imane Allali
- Division of Computational Biology, Department of Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa
| | | | - Ahmed Moussa
- Abdelmalek Essaadi University ENSA, Tangier, Morocco
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.,Division of Computational Biology, Department of Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine,Faculty of Health Sciences, University of Cape Town, Observatory, Cape Town, South Africa.,African Institute for Mathematical Sciences (AIMS),Muizenberg, Cape Town, South Africa
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9
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Modeling Continuous Admixture Using Admixture-Induced Linkage Disequilibrium. Sci Rep 2017; 7:43054. [PMID: 28230170 PMCID: PMC5322361 DOI: 10.1038/srep43054] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 01/18/2017] [Indexed: 11/09/2022] Open
Abstract
Recent migrations and inter-ethnic mating of long isolated populations have resulted in genetically admixed populations. To understand the complex population admixture process, which is critical to both evolutionary and medical studies, here we used admixture induced linkage disequilibrium (LD) to infer continuous admixture events, which is common for most existing admixed populations. Unlike previous studies, we expanded the typical continuous admixture model to a more general scenario with isolation after a certain duration of continuous gene flow. Based on the new models, we developed a method, CAMer, to infer the admixture history considering continuous and complex demographic process of gene flow between populations. We evaluated the performance of CAMer by computer simulation and further applied our method to real data analysis of a few well-known admixed populations.
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