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Zhang S, Zhang R, Yuan K, Yang L, Liu C, Liu Y, Ni X, Xu S. Reconstructing complex admixture history using a hierarchical model. Brief Bioinform 2024; 25:bbad540. [PMID: 38261339 PMCID: PMC10805183 DOI: 10.1093/bib/bbad540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/04/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024] Open
Abstract
Various methods have been proposed to reconstruct admixture histories by analyzing the length of ancestral chromosomal tracts, such as estimating the admixture time and number of admixture events. However, available methods do not explicitly consider the complex admixture structure, which characterizes the joining and mixing patterns of different ancestral populations during the admixture process, and instead assume a simplified one-by-one sequential admixture model. In this study, we proposed a novel approach that considers the non-sequential admixture structure to reconstruct admixture histories. Specifically, we introduced a hierarchical admixture model that incorporated four ancestral populations and developed a new method, called HierarchyMix, which uses the length of ancestral tracts and the number of ancestry switches along genomes to reconstruct the four-way admixture history. By automatically selecting the optimal admixture model using the Bayesian information criterion principles, HierarchyMix effectively estimates the corresponding admixture parameters. Simulation studies confirmed the effectiveness and robustness of HierarchyMix. We also applied HierarchyMix to Uyghurs and Kazakhs, enabling us to reconstruct the admixture histories of Central Asians. Our results highlight the importance of considering complex admixture structures and demonstrate that HierarchyMix is a useful tool for analyzing complex admixture events.
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Affiliation(s)
- Shi Zhang
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, China
| | - 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
| | - 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
| | - Lu Yang
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, 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
| | - Yuting Liu
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, China
| | - Xumin Ni
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032 , China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 201203, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
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2
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Di Santo LN, Quilodrán CS, Currat M. Temporal Variation in Introgressed Segments' Length Statistics Computed from a Limited Number of Ancient Genomes Sheds Light on Past Admixture Pulses. Mol Biol Evol 2023; 40:msad252. [PMID: 37992125 PMCID: PMC10715198 DOI: 10.1093/molbev/msad252] [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: 04/18/2023] [Revised: 10/16/2023] [Accepted: 11/09/2023] [Indexed: 11/24/2023] Open
Abstract
Hybridization is recognized as an important evolutionary force, but identifying and timing admixture events between divergent lineages remain a major aim of evolutionary biology. While this has traditionally been done using inferential tools on contemporary genomes, the latest advances in paleogenomics have provided a growing wealth of temporally distributed genomic data. Here, we used individual-based simulations to generate chromosome-level genomic data for a 2-population system and described temporal neutral introgression patterns under a single- and 2-pulse admixture model. We computed 6 summary statistics aiming to inform the timing and number of admixture pulses between interbreeding entities: lengths of introgressed sequences and their variance within genomes, as well as genome-wide introgression proportions and related measures. The first 2 statistics could confidently be used to infer interlineage hybridization history, peaking at the beginning and shortly after an admixture pulse. Temporal variation in introgression proportions and related statistics provided more limited insights, particularly when considering their application to ancient genomes still scant in number. Lastly, we computed these statistics on Homo sapiens paleogenomes and successfully inferred the hybridization pulse from Neanderthal that occurred approximately 40 to 60 kya. The scarce number of genomes dating from this period prevented more precise inferences, but the accumulation of paleogenomic data opens promising perspectives as our approach only requires a limited number of ancient genomes.
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Affiliation(s)
- Lionel N Di Santo
- Department of Genetics and Evolution, University of Geneva, Geneva CH-1205
| | | | - Mathias Currat
- Department of Genetics and Evolution, University of Geneva, Geneva CH-1205
- Institute of Genetics and Genomics in Geneva (IGE3), University of Geneva, Geneva CH-1205
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3
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Zhang R, Ni X, Yuan K, Pan Y, Xu S. MultiWaverX: modeling latent sex-biased admixture history. Brief Bioinform 2022; 23:6590437. [PMID: 35598333 DOI: 10.1093/bib/bbac179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Sex-biased gene flow has been common in the demographic history of modern humans. However, the lack of sophisticated methods for delineating the detailed sex-biased admixture process prevents insights into complex admixture history and thus our understanding of the evolutionary mechanisms of genetic diversity. Here, we present a novel algorithm, MultiWaverX, for modeling complex admixture history with sex-biased gene flow. Systematic simulations showed that MultiWaverX is a powerful tool for modeling complex admixture history and inferring sex-biased gene flow. Application of MultiWaverX to empirical data of 17 typical admixed populations in America, Central Asia, and the Middle East revealed sex-biased admixture histories that were largely consistent with the historical records. Notably, fine-scale admixture process reconstruction enabled us to recognize latent sex-biased gene flow in certain populations that would likely be overlooked by much of the routine analysis with commonly used methods. An outstanding example in the real world is the Kazakh population that experienced complex admixture with sex-biased gene flow but in which the overall signature has been canceled due to biased gene flow from an opposite direction.
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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
| | - Xumin Ni
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, 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
| | - 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
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai 200438, China.,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.,Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, School of Life Sciences, Jiangsu Normal University, Xuzhou, 221116, China.,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
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4
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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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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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6
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Yang XY, Rakha A, Chen W, Hou J, Qi XB, Shen QK, Dai SS, Sulaiman X, Abdulloevich NT, Afanasevna ME, Ibrohimovich KB, Chen X, Yang WK, Adnan A, Zhao RH, Yao YG, Su B, Peng MS, Zhang YP. Tracing the Genetic Legacy of the Tibetan Empire in the Balti. Mol Biol Evol 2021; 38:1529-1536. [PMID: 33283852 PMCID: PMC8042757 DOI: 10.1093/molbev/msaa313] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The rise and expansion of Tibetan Empire in the 7th to 9th centuries AD affected the course of history across East Eurasia, but the genetic impact of Tibetans on surrounding populations remains undefined. We sequenced 60 genomes for four populations from Pakistan and Tajikistan to explore their demographic history. We showed that the genomes of Balti people from Baltistan comprised 22.6–26% Tibetan ancestry. We inferred a single admixture event and dated it to about 39–21 generations ago, a period that postdated the conquest of Baltistan by the ancient Tibetan Empire. The analyses of mitochondrial DNA, Y, and X chromosome data indicated that both ancient Tibetan males and females were involved in the male-biased dispersal. Given the fact that the Balti people adopted Tibetan language and culture in history, our study suggested the impact of Tibetan Empire on Baltistan involved dominant cultural and minor demic diffusion.
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Affiliation(s)
- Xing-Yan Yang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Life Sciences, Yunnan University, Kunming, China
| | - Allah Rakha
- Department of Forensic Sciences, University of Health Sciences, Lahore, Pakistan.,Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Wei Chen
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan Agricultural University, Kunming, China
| | - Juzhi Hou
- Key Laboratory of Alpine Ecology (LAE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
| | - Xue-Bin Qi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Quan-Kuan Shen
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Shan-Shan Dai
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Xierzhatijiang Sulaiman
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | | | - Manilova Elena Afanasevna
- E.N. Pavlovsky Institute of Zoology and Parasitology, Academy of Sciences of Republic of Tajikistan, Dushanbe, Tajikistan
| | | | - Xi Chen
- Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China.,Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
| | - Wei-Kang Yang
- Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, China.,Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
| | - Atif Adnan
- Department of Human Anatomy, School of Basic Medicine, China Medical University, Shenyang, China
| | - Ruo-Han Zhao
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China.,KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China.,KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Life Sciences, Yunnan University, Kunming, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China.,KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, 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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7
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Yang X, Yuan K, Ni X, Zhou Y, Guo W, Xu S. AdmixSim: A Forward-Time Simulator for Various Complex Scenarios of Population Admixture. Front Genet 2020; 11:601439. [PMID: 33343638 PMCID: PMC7744625 DOI: 10.3389/fgene.2020.601439] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/29/2020] [Indexed: 11/15/2022] Open
Abstract
Background: Population admixture is a common phenomenon in humans, animals, and plants, and it plays a very important role in shaping individual genetic architecture and population genetic diversity. Inference of population admixture, however, is very challenging and typically relies on in silico simulation. We are aware of the lack of a computerized tool for such a purpose. A simulator capable of generating data under various complex admixture scenarios would facilitate the study of recombination, linkage disequilibrium, ancestry tracing, and admixture dynamics in admixed populations. We described such a simulator here. Results: We developed a forward-time simulator (AdmixSim) under the standard Wright Fisher model. It can simulate the following admixed populations: (1) multiple ancestral populations; (2) multiple waves of admixture events; (3) fluctuating population size; and (4) admixtures of fluctuating proportions. Analysis of the simulated data by AdmixSim showed that our simulator can quickly and accurately generate data resembling real-world values. We included in AdmixSim all possible parameters that would allow users to modify and simulate any kind of admixture scenario easily, so it is very flexible. AdmixSim records recombination break points and traces of each chromosomal segment from different ancestral populations, with which users can easily perform further analysis and comparative studies with empirical data. Conclusions:AdmixSim facilitates the study of population admixture by providing a simulation framework with the flexible implementation of various admixture models and parameters.
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Affiliation(s)
- Xiong Yang
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, China
| | - Ying Zhou
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Wei Guo
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai, China
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8
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Goldberg A, Rastogi A, Rosenberg NA. Assortative mating by population of origin in a mechanistic model of admixture. Theor Popul Biol 2020; 134:129-146. [PMID: 32275920 DOI: 10.1016/j.tpb.2020.02.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 02/11/2020] [Accepted: 02/27/2020] [Indexed: 02/01/2023]
Abstract
Populations whose mating pairs have levels of similarity in phenotypes or genotypes that differ systematically from the level expected under random mating are described as experiencing assortative mating. Excess similarity in mating pairs is termed positive assortative mating, and excess dissimilarity is negative assortative mating. In humans, empirical studies suggest that mating pairs from various admixed populations - whose ancestry derives from two or more source populations - possess correlated ancestry components that indicate the occurrence of positive assortative mating on the basis of ancestry. Generalizing a two-sex mechanistic admixture model, we devise a model of one form of ancestry-assortative mating that occurs through preferential mating based on source population. Under the model, we study the moments of the admixture fraction distribution for different assumptions about mating preferences, including both positive and negative assortative mating by population. We demonstrate that whereas the mean admixture under assortative mating is equivalent to that of a corresponding randomly mating population, the variance of admixture depends on the level and direction of assortative mating. We consider two special cases of assortative mating by population: first, a single admixture event, and second, constant contributions to the admixed population over time. In contrast to standard settings in which positive assortment increases variation within a population, certain assortative mating scenarios allow the variance of admixture to decrease relative to a corresponding randomly mating population: with the three populations we consider, the variance-increasing effect of positive assortative mating within a population might be overwhelmed by a variance-decreasing effect emerging from mating preferences involving other pairs of populations. The effect of assortative mating is smaller on the X chromosome than on the autosomes because inheritance of the X in males depends only on the mother's ancestry, not on the mating pair. Because the variance of admixture is informative about the timing of admixture and possibly about sex-biased admixture contributions, the effects of assortative mating are important to consider in inferring features of population history from distributions of admixture values. Our model provides a framework to quantitatively study assortative mating under flexible scenarios of admixture over time.
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Affiliation(s)
- Amy Goldberg
- Department of Evolutionary Anthropology, Duke University, Durham, NC, USA; Department of Biology, Stanford University, Stanford, CA, USA.
| | - Ananya Rastogi
- Department of Systems Immunology & Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
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9
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Duranton M, Bonhomme F, Gagnaire P. The spatial scale of dispersal revealed by admixture tracts. Evol Appl 2019; 12:1743-1756. [PMID: 31548854 PMCID: PMC6752141 DOI: 10.1111/eva.12829] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 05/28/2019] [Indexed: 12/11/2022] Open
Abstract
Evaluating species dispersal across the landscape is essential to design appropriate management and conservation actions. However, technical difficulties often preclude direct measures of individual movement, while indirect genetic approaches rely on assumptions that sometimes limit their application. Here, we show that the temporal decay of admixture tracts lengths can be used to assess genetic connectivity within a population introgressed by foreign haplotypes. We present a proof-of-concept approach based on local ancestry inference in a high gene flow marine fish species, the European sea bass (Dicentrarchus labrax). Genetic admixture in the contact zone between Atlantic and Mediterranean sea bass lineages allows the introgression of Atlantic haplotype tracts within the Mediterranean Sea. Once introgressed, blocks of foreign ancestry are progressively eroded by recombination as they diffuse from the western to the eastern Mediterranean basin, providing a means to estimate dispersal. By comparing the length distributions of Atlantic tracts between two Mediterranean populations located at different distances from the contact zone, we estimated the average per-generation dispersal distance within the Mediterranean lineage to less than 50 km. Using simulations, we showed that this approach is robust to a range of demographic histories and sample sizes. Our results thus support that the length of admixture tracts can be used together with a recombination clock to estimate genetic connectivity in species for which the neutral migration-drift balance is not informative or simply does not exist.
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Affiliation(s)
- Maud Duranton
- ISEM, Univ Montpellier, CNRS, EPHE, IRDMontpellierFrance
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10
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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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Dias-Alves T, Mairal J, Blum MGB. Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species. Mol Biol Evol 2018; 35:2318-2326. [PMID: 29931083 PMCID: PMC6107063 DOI: 10.1093/molbev/msy126] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Admixture between populations provides opportunity to study biological adaptation and phenotypic variation. Admixture studies rely on local ancestry inference for admixed individuals, which consists of computing at each locus the number of copies that originate from ancestral source populations. Existing software packages for local ancestry inference are tuned to provide accurate results on human data and recent admixture events. Here, we introduce Loter, an open-source software package that does not require any biological parameter besides haplotype data in order to make local ancestry inference available for a wide range of species. Using simulations, we compare the performance of Loter to HAPMIX, LAMP-LD, and RFMix. HAPMIX is the only software severely impacted by imperfect haplotype reconstruction. Loter is the less impacted software by increasing admixture time when considering simulated and admixed human genotypes. For simulations of admixed Populus genotypes, Loter and LAMP-LD are robust to increasing admixture times by contrast to RFMix. When comparing length of reconstructed and true ancestry tracts, Loter and LAMP-LD provide results whose accuracy is again more robust than RFMix to increasing admixture times. We apply Loter to individuals resulting from admixture between Populus trichocarpa and Populus balsamifera and lengths of ancestry tracts indicate that admixture took place ∼100 generations ago. We expect that providing a rapid and parameter-free software for local ancestry inference will make more accessible genomic studies about admixture processes.
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Affiliation(s)
| | - Julien Mairal
- CNRS, Institute of Engineering Univ. Grenoble Alpes, LJK, Univ. Grenoble Alpes, Inria, Grenoble, France
| | - Michael G B Blum
- CNRS, TIMC-IMAG UMR 5525, Univ. Grenoble Alpes, Grenoble, France
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12
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Ni X, Yuan K, Yang X, Feng Q, Guo W, Ma Z, Xu S. Inference of multiple-wave admixtures by length distribution of ancestral tracks. Heredity (Edinb) 2018; 121:52-63. [PMID: 29358727 PMCID: PMC5997750 DOI: 10.1038/s41437-017-0041-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 11/23/2017] [Accepted: 11/24/2017] [Indexed: 12/31/2022] Open
Abstract
The ancestral tracks in admixed genomes are valuable for population history inference. While a few methods have been developed to infer admixture history based on ancestral tracks, these methods suffer the same flaw: only population admixture history under some specific models can be inferred. In addition, the inference of history might be biased or even unreliable if the specific model deviates from the real situation. To address this problem, we firstly proposed a general discrete admixture model to describe the admixture history with multiple ancestral populations and multiple-wave admixtures. We next deduced the length distribution of ancestral tracks under the general discrete admixture model. We further developed a new method, MultiWaver, to explore multiple-wave admixture histories. Our method could automatically determine an optimal admixture model based on the length distribution of ancestral tracks, and estimate the corresponding parameters under this optimal model. Specifically, we used a likelihood ratio test (LRT) to determine the number of admixture waves, and implemented an expectation-maximization (EM) algorithm to estimate parameters. We used simulation studies to validate the reliability and effectiveness of our method. Finally, good performance was observed when our method was applied to real data sets of African Americans and Mexicans, and new insights were gained into the admixture history of Uyghurs and Hazaras.
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Affiliation(s)
- Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 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 Institutes for Biological Sciences, CAS, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiong Yang
- 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 Institutes for Biological Sciences, CAS, Shanghai, 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 Institutes for Biological Sciences, CAS, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wei Guo
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Zhiming Ma
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, China.
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 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 Institutes for Biological Sciences, CAS, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
- Collaborative Innovation Center of Genetics and Development, Shanghai, China.
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13
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Jin Y, Yang H, Fu H. An N-(acetoxy)phthalimide motif as a visible-light pro-photosensitizer in photoredox decarboxylative arylthiation. Chem Commun (Camb) 2018; 52:12909-12912. [PMID: 27739553 DOI: 10.1039/c6cc06994k] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
An efficient visible-light photoredox decarboxylative coupling of N-(acetoxy)phthalimides with aryl thiols has been developed. The reaction was performed well at room temperature with good tolerance of functional groups. Importantly, the visible-light photoredox decarboxylative arylthiation did not need an added photocatalyst.
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Affiliation(s)
- Yunhe Jin
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China.
| | - Haijun Yang
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China.
| | - Hua Fu
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China.
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15
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Feng Q, Lu Y, Ni X, Yuan K, Yang Y, Yang X, Liu C, Lou H, Ning Z, Wang Y, Lu D, Zhang C, Zhou Y, Shi M, Tian L, Wang X, Zhang X, Li J, Khan A, Guan Y, Tang K, Wang S, Xu S. Genetic History of Xinjiang’s Uyghurs Suggests Bronze Age Multiple-Way Contacts in Eurasia. Mol Biol Evol 2017; 34:2572-2582. [DOI: 10.1093/molbev/msx177] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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16
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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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Jiang M, Li H, Yang H, Fu H. Room‐Temperature Arylation of Thiols: Breakthrough with Aryl Chlorides. Angew Chem Int Ed Engl 2016; 56:874-879. [DOI: 10.1002/anie.201610414] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Indexed: 02/05/2023]
Affiliation(s)
- Min Jiang
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education) Department of Chemistry Tsinghua University Beijing 100084 China
| | - Haifang Li
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education) Department of Chemistry Tsinghua University Beijing 100084 China
| | - Haijun Yang
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education) Department of Chemistry Tsinghua University Beijing 100084 China
| | - Hua Fu
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education) Department of Chemistry Tsinghua University Beijing 100084 China
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18
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Jiang M, Li H, Yang H, Fu H. Room‐Temperature Arylation of Thiols: Breakthrough with Aryl Chlorides. Angew Chem Int Ed Engl 2016. [DOI: 10.1002/ange.201610414] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Min Jiang
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education) Department of Chemistry Tsinghua University Beijing 100084 China
| | - Haifang Li
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education) Department of Chemistry Tsinghua University Beijing 100084 China
| | - Haijun Yang
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education) Department of Chemistry Tsinghua University Beijing 100084 China
| | - Hua Fu
- Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education) Department of Chemistry Tsinghua University Beijing 100084 China
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