1
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Sun Q, Rowland BT, Chen J, Mikhaylova AV, Avery C, Peters U, Lundin J, Matise T, Buyske S, Tao R, Mathias RA, Reiner AP, Auer PL, Cox NJ, Kooperberg C, Thornton TA, Raffield LM, Li Y. Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI. Nat Commun 2024; 15:1016. [PMID: 38310129 PMCID: PMC10838303 DOI: 10.1038/s41467-024-45135-z] [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: 10/07/2022] [Accepted: 01/16/2024] [Indexed: 02/05/2024] Open
Abstract
Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Bryce T Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Anna V Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Christy Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jessica Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Tara Matise
- Department of Genetics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Timothy A Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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2
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Dinh BL, Tang E, Taparra K, Nakatsuka N, Chen F, Chiang CWK. Recombination map tailored to Native Hawaiians may improve robustness of genomic scans for positive selection. Hum Genet 2024; 143:85-99. [PMID: 38157018 PMCID: PMC10794367 DOI: 10.1007/s00439-023-02625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 11/25/2023] [Indexed: 01/03/2024]
Abstract
Recombination events establish the patterns of haplotypic structure in a population and estimates of recombination rates are used in several downstream population and statistical genetic analyses. Using suboptimal maps from distantly related populations may reduce the efficacy of genomic analyses, particularly for underrepresented populations such as the Native Hawaiians. To overcome this challenge, we constructed recombination maps using genome-wide array data from two study samples of Native Hawaiians: one reflecting the current admixed state of Native Hawaiians (NH map) and one based on individuals of enriched Polynesian ancestries (PNS map) with the potential to be used for less admixed Polynesian populations such as the Samoans. We found the recombination landscape to be less correlated with those from other continental populations (e.g. Spearman's rho = 0.79 between PNS and CEU (Utah residents with Northern and Western European ancestry) compared to 0.92 between YRI (Yoruba in Ibadan, Nigeria) and CEU at 50 kb resolution), likely driven by the unique demographic history of the Native Hawaiians. PNS also shared the fewest recombination hotspots with other populations (e.g. 8% of hotspots shared between PNS and CEU compared to 27% of hotspots shared between YRI and CEU). We found that downstream analyses in the Native Hawaiian population, such as local ancestry inference, imputation, and IBD segment and relatedness detections, would achieve similar efficacy when using the NH map compared to an omnibus map. However, for genome scans of adaptive loci using integrated haplotype scores, we found several loci with apparent genome-wide significant signals (|Z-score|> 4) in Native Hawaiians that would not have been significant when analyzed using NH-specific maps. Population-specific recombination maps may therefore improve the robustness of haplotype-based statistics and help us better characterize the evolutionary history that may underlie Native Hawaiian-specific health conditions that persist today.
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Affiliation(s)
- Bryan L Dinh
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Echo Tang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Kekoa Taparra
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | | | - Fei Chen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Charleston W K Chiang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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3
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Fetter KC, Keller SR. Admixture mapping and selection scans identify genomic regions associated with stomatal patterning and disease resistance in hybrid poplars. Ecol Evol 2023; 13:e10579. [PMID: 37881228 PMCID: PMC10597741 DOI: 10.1002/ece3.10579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 10/27/2023] Open
Abstract
Variation in fitness components can be linked in some cases to variation in key traits. Metric traits that lie at the intersection of development, defense, and ecological interactions may be expected to experience environmental selection, informing our understanding of evolutionary and ecological processes. Here, we use quantitative genetic and population genomic methods to investigate disease dynamics in hybrid and non-hybrid populations. We focus our investigation on morphological and ecophysiological traits which inform our understanding of physiology, growth, and defense against a pathogen. In particular, we investigate stomata, microscopic pores on the surface of a leaf that regulate gas exchange during photosynthesis and are sites of entry for various plant pathogens. Stomatal patterning traits were highly predictive of disease risk. Admixture mapping identified a polygenic basis of disease resistance. Candidate genes for stomatal and disease resistance map to the same genomic regions and experienced positive selection. Genes with functions to guard cell homeostasis, the plant immune system, components of constitutive defenses, and growth-related transcription factors were identified. Our results indicate positive selection acted on candidate genes for stomatal patterning and disease resistance, potentially acting in concert to structure their variation in naturally formed backcrossing hybrid populations.
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Affiliation(s)
- Karl C. Fetter
- Department of Plant BiologyUniversity of VermontBurlingtonVermontUSA
- Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsConnecticutUSA
| | - Stephen R. Keller
- Department of Plant BiologyUniversity of VermontBurlingtonVermontUSA
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4
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Dinh BL, Tang E, Taparra K, Nakatsuka N, Chen F, Chiang CWK. Recombination map tailored to Native Hawaiians improves robustness of genomic scans for positive selection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548735. [PMID: 37503129 PMCID: PMC10370006 DOI: 10.1101/2023.07.12.548735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Recombination events establish the patterns of haplotypic structure in a population and estimates of recombination rates are used in several downstream population and statistical genetic analyses. Using suboptimal maps from distantly related populations may reduce the efficacy of genomic analyses, particularly for underrepresented populations such as the Native Hawaiians. To overcome this challenge, we constructed recombination maps using genome-wide array data from two study samples of Native Hawaiians: one reflecting the current admixed state of Native Hawaiians (NH map), and one based on individuals of enriched Polynesian ancestries (PNS map) with the potential to be used for less admixed Polynesian populations such as the Samoans. We found the recombination landscape to be less correlated with those from other continental populations (e.g. Spearman's rho = 0.79 between PNS and CEU (Utah residents with Northern and Western European ancestry) compared to 0.92 between YRI (Yoruba in Ibadan, Nigeria) and CEU at 50 kb resolution), likely driven by the unique demographic history of the Native Hawaiians. PNS also shared the fewest recombination hotspots with other populations (e.g. 8% of hotspots shared between PNS and CEU compared to 27% of hotspots shared between YRI and CEU). We found that downstream analyses in the Native Hawaiian population, such as local ancestry inference, imputation, and IBD segment and relatedness detections, would achieve similar efficacy when using the NH map compared to an omnibus map. However, for genome scans of adaptive loci using integrated haplotype scores, we found several loci with apparent genome-wide significant signals (|Z-score| > 4) in Native Hawaiians that would not have been significant when analyzed using NH-specific maps. Population-specific recombination maps may therefore improve the robustness of haplotype-based statistics and help us better characterize the evolutionary history that may underlie Native Hawaiian-specific health conditions that persist today.
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Affiliation(s)
- Bryan L Dinh
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Echo Tang
- Department of Quantitative and Computational Biology, University of Southern California
| | - Kekoa Taparra
- Department of Radiation Oncology, Stanford University, Palo Alto, California
| | | | - Fei Chen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Charleston W K Chiang
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
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5
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Tan T, Atkinson EG. Strategies for the Genomic Analysis of Admixed Populations. Annu Rev Biomed Data Sci 2023; 6:105-127. [PMID: 37127050 PMCID: PMC10871708 DOI: 10.1146/annurev-biodatasci-020722-014310] [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] [Indexed: 05/03/2023]
Abstract
Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations-the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.
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Affiliation(s)
- Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
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6
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Wegmann D, Eckel R. Human evolution: When admixture met selection. Curr Biol 2023; 33:R259-R261. [PMID: 37040705 DOI: 10.1016/j.cub.2023.02.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Admixture has been a major force during human evolution. Two new studies using ancient DNA now show how two key admixture events in the evolutionary history of Europeans altered their adaptive trajectories and facilitated rapid evolution.
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Affiliation(s)
- Daniel Wegmann
- Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland; Swiss Institute of Bioinformatics, 1700 Fribourg, Switzerland.
| | - Raphael Eckel
- Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland; Swiss Institute of Bioinformatics, 1700 Fribourg, Switzerland
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7
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Browning SR, Waples RK, Browning BL. Fast, accurate local ancestry inference with FLARE. Am J Hum Genet 2023; 110:326-335. [PMID: 36610402 PMCID: PMC9943733 DOI: 10.1016/j.ajhg.2022.12.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 12/13/2022] [Indexed: 01/09/2023] Open
Abstract
Local ancestry is the source ancestry at each point in the genome of an admixed individual. Inferred local ancestry is used for admixture mapping and population genetic analyses. We present FLARE (fast local ancestry estimation), a method for local ancestry inference. FLARE achieves high accuracy through the use of an extended Li and Stephens model, and it achieves exceptional computational performance through incorporation of computational techniques developed for genotype imputation. Memory requirements are reduced through on-the-fly compression of reference haplotypes and stored checkpoints. Computation time is reduced through the use of composite reference haplotypes. These techniques allow FLARE to scale to datasets with hundreds of thousands of sequenced individuals and to provide superior accuracy on large-scale data. FLARE is open source and available at https://github.com/browning-lab/flare.
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Affiliation(s)
- Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Ryan K Waples
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Brian L Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.
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8
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Caliebe A, Tekola‐Ayele F, Darst BF, Wang X, Song YE, Gui J, Sebro RA, Balding DJ, Saad M, Dubé M. Including diverse and admixed populations in genetic epidemiology research. Genet Epidemiol 2022; 46:347-371. [PMID: 35842778 PMCID: PMC9452464 DOI: 10.1002/gepi.22492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/25/2022]
Abstract
The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.
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Affiliation(s)
- Amke Caliebe
- Institute of Medical Informatics and StatisticsKiel University and University Hospital Schleswig‐HolsteinKielGermany
| | - Fasil Tekola‐Ayele
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMarylandUSA
| | - Burcu F. Darst
- Center for Genetic EpidemiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleWashingtonUSA
| | - Xuexia Wang
- Department of MathematicsUniversity of North TexasDentonTexasUSA
| | - Yeunjoo E. Song
- Department of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | - Jiang Gui
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth CollegeOne Medical Center Dr.LebanonNew HampshireUSA
| | | | - David J. Balding
- Melbourne Integrative Genomics, Schools of BioSciences and of Mathematics & StatisticsUniversity of MelbourneMelbourneAustralia
| | - Mohamad Saad
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
- Neuroscience Research Center, Faculty of Medical SciencesLebanese UniversityBeirutLebanon
| | - Marie‐Pierre Dubé
- Department of Medicine, and Social and Preventive MedicineUniversité de MontréalMontréalQuébecCanada
- Beaulieu‐Saucier Pharmacogenomcis CentreMontreal Heart InstituteMontrealCanada
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9
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van Eeden G, Uren C, Pless E, Mastoras M, van der Spuy GD, Tromp G, Henn BM, Möller M. The recombination landscape of the Khoe-San likely represents the upper limits of recombination divergence in humans. Genome Biol 2022; 23:172. [PMID: 35945619 PMCID: PMC9361568 DOI: 10.1186/s13059-022-02744-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 08/01/2022] [Indexed: 11/10/2022] Open
Abstract
Background Recombination maps are important resources for epidemiological and evolutionary analyses; however, there are currently no recombination maps representing any African population outside of those with West African ancestry. We infer the demographic history for the Nama, an indigenous Khoe-San population of southern Africa, and derive a novel, population-specific recombination map from the whole genome sequencing of 54 Nama individuals. We hypothesise that there are no publicly available recombination maps representative of the Nama, considering the deep population divergence and subsequent isolation of the Khoe-San from other African groups. Results We show that the recombination landscape of the Nama does not cluster with any continental groups with publicly available representative recombination maps. Finally, we use selection scans as an example of how fine-scale differences between the Nama recombination map and the combined Phase II HapMap recombination map can impact the outcome of selection scans. Conclusions Fine-scale differences in recombination can meaningfully alter the results of a selection scan. The recombination map we infer likely represents an upper bound on the extent of divergence we expect to see for a recombination map in humans and would be of interest to any researcher that wants to test the sensitivity of population genetic or GWAS analysis to recombination map input. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02744-5.
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Affiliation(s)
- Gerald van Eeden
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Caitlin Uren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, 7602, South Africa
| | - Evlyn Pless
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California (UC) Davis, Davis, CA, USA
| | - Mira Mastoras
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California (UC) Davis, Davis, CA, USA
| | - Gian D van der Spuy
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, 7602, South Africa.,SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, Cape Town, South Africa
| | - Gerard Tromp
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, 7602, South Africa.,SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, Cape Town, South Africa
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California (UC) Davis, Davis, CA, USA
| | - Marlo Möller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa. .,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, 7602, South Africa.
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10
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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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11
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Fine human genetic map based on UK10K data set. Hum Genet 2022; 141:273-281. [DOI: 10.1007/s00439-021-02415-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 12/03/2021] [Indexed: 11/04/2022]
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12
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Abstract
Over the past decade, genomic analyses of single cells-the fundamental units of life-have become possible. Single-cell DNA sequencing has shed light on biological questions that were previously inaccessible across diverse fields of research, including somatic mutagenesis, organismal development, genome function, and microbiology. Single-cell DNA sequencing also promises significant future biomedical and clinical impact, spanning oncology, fertility, and beyond. While single-cell approaches that profile RNA and protein have greatly expanded our understanding of cellular diversity, many fundamental questions in biology and important biomedical applications require analysis of the DNA of single cells. Here, we review the applications and biological questions for which single-cell DNA sequencing is uniquely suited or required. We include a discussion of the fields that will be impacted by single-cell DNA sequencing as the technology continues to advance.
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Affiliation(s)
- Gilad D Evrony
- Center for Human Genetics and Genomics, Grossman School of Medicine, New York University, New York, NY 10016, USA;
| | - Anjali Gupta Hinch
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom;
| | - Chongyuan Luo
- Department of Human Genetics, University of California, Los Angeles, California 90095, USA;
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13
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van Eeden G, Uren C, van der Spuy G, Tromp G, Möller M. Local ancestry inference in heterogeneous populations-Are recent recombination events more relevant? Brief Bioinform 2021; 22:6337894. [PMID: 34343255 DOI: 10.1093/bib/bbab300] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/29/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
To date, numerous software tools have been developed to infer recombination maps. Many of these software tools infer the recombination rate from linkage disequilibrium, and therefore they infer recombination many generations into the past. Other recently developed methods rely on the inference of recent recombination events to determine the recombination rate, such as identity by descent- and local ancestry inference (LAI)-based tools. Methods that mainly use recent recombination events to infer the recombination rate might be more relevant for certain analyses like LAI. We therefore describe a protocol for creating high-resolution, population-specific recombination maps using methods that mainly use recent recombination events and a method that uses recent and distant recombination events for recombination rate inference. Subsequently, we compared the effect of using maps inferred by these two paradigms on LAI accuracy.
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Affiliation(s)
| | | | - Gian van der Spuy
- Department of Molecular Biology and Human Genetics, Stellenbosch University, South Africa
| | - Gerard Tromp
- South African Tuberculosis Bioinformatics Initiative (SATBBI), South Africa
| | - Marlo Möller
- Department of Molecular Biology and Human Genetics, Stellenbosch University, South Africa
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14
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Frayer ME, Payseur BA. Demographic history shapes genomic ancestry in hybrid zones. Ecol Evol 2021; 11:10290-10302. [PMID: 34367575 PMCID: PMC8328415 DOI: 10.1002/ece3.7833] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 12/26/2022] Open
Abstract
Demographic factors such as migration rate and population size can impede or facilitate speciation. In hybrid zones, reproductive boundaries between species are tested and demography mediates the opportunity for admixture between lineages that are partially isolated. Genomic ancestry is a powerful tool for revealing the history of admixed populations, but models and methods based on local ancestry are rarely applied to structured hybrid zones. To understand the effects of demography on ancestry in hybrids zones, we performed individual-based simulations under a stepping-stone model, treating migration rate, deme size, and hybrid zone age as parameters. We find that the number of ancestry junctions (the transition points between genomic regions with different ancestries) and heterogenicity (the genomic proportion heterozygous for ancestry) are often closely connected to demographic history. Reducing deme size reduces junction number and heterogenicity. Elevating migration rate increases heterogenicity, but migration affects junction number in more complex ways. We highlight the junction frequency spectrum as a novel and informative summary of ancestry that responds to demographic history. A substantial proportion of junctions are expected to fix when migration is limited or deme size is small, changing the shape of the spectrum. Our findings suggest that genomic patterns of ancestry could be used to infer demographic history in hybrid zones.
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Affiliation(s)
- Megan E. Frayer
- Laboratory of GeneticsUniversity of Wisconsin MadisonMadisonWIUSA
| | - Bret A. Payseur
- Laboratory of GeneticsUniversity of Wisconsin MadisonMadisonWIUSA
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15
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Abstract
Throughout human history, large-scale migrations have facilitated the formation of populations with ancestry from multiple previously separated populations. This process leads to subsequent shuffling of genetic ancestry through recombination, producing variation in ancestry between populations, among individuals in a population, and along the genome within an individual. Recent methodological and empirical developments have elucidated the genomic signatures of this admixture process, bringing previously understudied admixed populations to the forefront of population and medical genetics. Under this theme, we present a collection of recent PLOS Genetics publications that exemplify recent progress in human genetic admixture studies, and we discuss potential areas for future work.
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Affiliation(s)
- Katharine L. Korunes
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, United States of America
| | - Amy Goldberg
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, United States of America
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16
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Shastry V, Adams PE, Lindtke D, Mandeville EG, Parchman TL, Gompert Z, Buerkle CA. Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy. Mol Ecol Resour 2021; 21:1434-1451. [PMID: 33482035 DOI: 10.1111/1755-0998.13330] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 12/11/2020] [Accepted: 01/11/2021] [Indexed: 11/29/2022]
Abstract
Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can use low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low-depth sequencing across a larger fraction of the genome than with high-depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.
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Affiliation(s)
| | - Paula E Adams
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL, USA
| | - Dorothea Lindtke
- Institute of Plant Sciences, University of Bern, Bern, Switzerland
| | | | | | | | - C Alex Buerkle
- Department of Botany, University of Wyoming, Laramie, WY, USA
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17
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Sun H, Lin M, Russell EM, Minster RL, Chan TF, Dinh BL, Naseri T, Reupena MS, Lum-Jones A, Cheng I, Wilkens LR, Le Marchand L, Haiman CA, Chiang CWK. The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians. PLoS Genet 2021; 17:e1009273. [PMID: 33571193 PMCID: PMC7877570 DOI: 10.1371/journal.pgen.1009273] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 11/18/2020] [Indexed: 12/17/2022] Open
Abstract
Epidemiological studies of obesity, Type-2 diabetes (T2D), cardiovascular diseases and several common cancers have revealed an increased risk in Native Hawaiians compared to European- or Asian-Americans living in the Hawaiian islands. However, there remains a gap in our understanding of the genetic factors that affect the health of Native Hawaiians. To fill this gap, we studied the genetic risk factors at both the chromosomal and sub-chromosomal scales using genome-wide SNP array data on ~4,000 Native Hawaiians from the Multiethnic Cohort. We estimated the genomic proportion of Native Hawaiian ancestry ("global ancestry," which we presumed to be Polynesian in origin), as well as this ancestral component along each chromosome ("local ancestry") and tested their respective association with binary and quantitative cardiometabolic traits. After attempting to adjust for non-genetic covariates evaluated through questionnaires, we found that per 10% increase in global Polynesian genetic ancestry, there is a respective 8.6%, and 11.0% increase in the odds of being diabetic (P = 1.65×10-4) and having heart failure (P = 2.18×10-4), as well as a 0.059 s.d. increase in BMI (P = 1.04×10-10). When testing the association of local Polynesian ancestry with risk of disease or biomarkers, we identified a chr6 region associated with T2D. This association was driven by an uniquely prevalent variant in Polynesian ancestry individuals. However, we could not replicate this finding in an independent Polynesian cohort from Samoa due to the small sample size of the replication cohort. In conclusion, we showed that Polynesian ancestry, which likely capture both genetic and lifestyle risk factors, is associated with an increased risk of obesity, Type-2 diabetes, and heart failure, and that larger cohorts of Polynesian ancestry individuals will be needed to replicate the putative association on chr6 with T2D.
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Affiliation(s)
- Hanxiao Sun
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Meng Lin
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Emily M. Russell
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Ryan L. Minster
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Tsz Fung Chan
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Bryan L. Dinh
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
| | - Take Naseri
- Ministry of Health, Government of Samoa, Apia, Samoa
| | | | - Annette Lum-Jones
- Epidemiology Program, University of Hawai‘i Cancer Center, University of Hawai‘i, Manoa, Honolulu, Hawaii, United States of America
| | | | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
| | - Lynne R. Wilkens
- Epidemiology Program, University of Hawai‘i Cancer Center, University of Hawai‘i, Manoa, Honolulu, Hawaii, United States of America
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawai‘i Cancer Center, University of Hawai‘i, Manoa, Honolulu, Hawaii, United States of America
| | - Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Charleston W. K. Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
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18
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Atkinson EG, Maihofer AX, Kanai M, Martin AR, Karczewski KJ, Santoro ML, Ulirsch JC, Kamatani Y, Okada Y, Finucane HK, Koenen KC, Nievergelt CM, Daly MJ, Neale BM. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat Genet 2021; 53:195-204. [PMID: 33462486 PMCID: PMC7867648 DOI: 10.1038/s41588-020-00766-y] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 12/15/2020] [Indexed: 12/26/2022]
Abstract
Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.
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Affiliation(s)
- Elizabeth G Atkinson
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Adam X Maihofer
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Marcos L Santoro
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Departamento de Psiquiatria, Universidade Federal de São Paulo, São Paulo, Brazil
- Departamento de Morfologia e Genética, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Jacob C Ulirsch
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Graduate School of Medicine, Osaka University, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
| | - Hilary K Finucane
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Karestan C Koenen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Mark J Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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19
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van Eeden G, Uren C, Möller M, Henn BM. Inferring recombination patterns in African populations. Hum Mol Genet 2021; 30:R11-R16. [PMID: 33445180 DOI: 10.1093/hmg/ddab020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/04/2021] [Accepted: 01/06/2021] [Indexed: 11/14/2022] Open
Abstract
Although several high-resolution recombination maps exist for European-descent populations, the recombination landscape of African populations remains relatively understudied. Given that there is high genetic divergence among groups in Africa, it is possible that recombination hotspots also diverge significantly. Both limitations and opportunities exist for developing recombination maps for these populations. In this review, we discuss various recombination inference methods, and the strengths and weaknesses of these methods in analyzing recombination in African-descent populations. Furthermore, we provide a decision tree and recommendations for which inference method to use in various research contexts. Establishing an appropriate methodology for recombination rate inference in a particular study will improve the accuracy of various downstream analyses including but not limited to local ancestry inference, haplotype phasing, fine-mapping of GWAS loci and genome assemblies.
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Affiliation(s)
- Gerald van Eeden
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
| | - Caitlin Uren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch 7602, South Africa
| | - Marlo Möller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch 7602, South Africa
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California Davis, Davis, CA 95616, USA
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20
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Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium. EBioMedicine 2021; 63:103157. [PMID: 33418499 PMCID: PMC7804602 DOI: 10.1016/j.ebiom.2020.103157] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/10/2020] [Accepted: 11/18/2020] [Indexed: 01/05/2023] Open
Abstract
Background Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. Methods We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. Findings When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10−11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10−9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10−9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10−9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10−9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. Interpretation This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.
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21
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Nait Saada J, Kalantzis G, Shyr D, Cooper F, Robinson M, Gusev A, Palamara PF. Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations. Nat Commun 2020; 11:6130. [PMID: 33257650 PMCID: PMC7704644 DOI: 10.1038/s41467-020-19588-x] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/02/2020] [Indexed: 12/14/2022] Open
Abstract
Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of analyses. We develop FastSMC, an IBD detection algorithm that combines a fast heuristic search with accurate coalescent-based likelihood calculations. FastSMC enables biobank-scale detection and dating of IBD segments within several thousands of years in the past. We apply FastSMC to 487,409 UK Biobank samples and detect ~214 billion IBD segments transmitted by shared ancestors within the past 1500 years, obtaining a fine-grained picture of genetic relatedness in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the use of genomic data to localize a sample's birth coordinates with a median error of 45 km. We seek evidence of recent positive selection by identifying loci with unusually strong shared ancestry and detect 12 genome-wide significant signals. We devise an IBD-based test for association between phenotype and ultra-rare loss-of-function variation, identifying 29 association signals in 7 blood-related traits.
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Affiliation(s)
| | | | - Derek Shyr
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Fergus Cooper
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Alexander Gusev
- Brigham & Women's Hospital, Division of Genetics, Boston, MA, 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Pier Francesco Palamara
- Department of Statistics, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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22
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Zhou Y, Browning BL, Browning SR. Population-Specific Recombination Maps from Segments of Identity by Descent. Am J Hum Genet 2020; 107:137-148. [PMID: 32533945 PMCID: PMC7332656 DOI: 10.1016/j.ajhg.2020.05.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 05/20/2020] [Indexed: 12/26/2022] Open
Abstract
Recombination rates vary significantly across the genome, and estimates of recombination rates are needed for downstream analyses such as haplotype phasing and genotype imputation. Existing methods for recombination rate estimation are limited by insufficient amounts of informative genetic data or by high computational cost. We present a method and software, called IBDrecomb, for using segments of identity by descent to infer recombination rates. IBDrecomb can be applied to sequenced population cohorts to obtain high-resolution, population-specific recombination maps. In simulated admixed data, IBDrecomb obtains higher accuracy than admixture-based estimation of recombination rates. When applied to 2,500 simulated individuals, IBDrecomb obtains similar accuracy to a linkage-disequilibrium (LD)-based method applied to 96 individuals (the largest number for which computation is tractable). Compared to LD-based maps, our IBD-based maps have the advantage of estimating recombination rates in the recent past rather than the distant past. We used IBDrecomb to generate new recombination maps for European Americans and for African Americans from TOPMed sequence data from the Framingham Heart Study (1,626 unrelated individuals) and the Jackson Heart Study (2,046 unrelated individuals), and we compare them to LD-based, admixture-based, and family-based maps.
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Affiliation(s)
- Ying Zhou
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
| | - Brian L Browning
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
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23
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Bresadola L, Link V, Buerkle CA, Lexer C, Wegmann D. Estimating and accounting for genotyping errors in RAD‐seq experiments. Mol Ecol Resour 2020; 20:856-870. [DOI: 10.1111/1755-0998.13153] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 02/21/2020] [Accepted: 02/26/2020] [Indexed: 12/26/2022]
Affiliation(s)
- Luisa Bresadola
- Department of Biology University of Fribourg Fribourg Switzerland
| | - Vivian Link
- Department of Biology University of Fribourg Fribourg Switzerland
- Swiss Institute of Bioinformatics Fribourg Switzerland
| | | | - Christian Lexer
- Department of Botany and Biodiversity Research University of Vienna Vienna Austria
| | - Daniel Wegmann
- Department of Biology University of Fribourg Fribourg Switzerland
- Swiss Institute of Bioinformatics Fribourg Switzerland
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24
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Spence JP, Song YS. Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations. SCIENCE ADVANCES 2019; 5:eaaw9206. [PMID: 31681842 PMCID: PMC6810367 DOI: 10.1126/sciadv.aaw9206] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 09/13/2019] [Indexed: 05/28/2023]
Abstract
Fine-scale rates of meiotic recombination vary by orders of magnitude across the genome and differ between species and even populations. Studying cross-population differences has been stymied by the confounding effects of demographic history. To address this problem, we developed a demography-aware method to infer fine-scale recombination rates and applied it to 26 diverse human populations, inferring population-specific recombination maps. These maps recapitulate many aspects of the history of these populations including signatures of the trans-Atlantic slave trade and the Iberian colonization of the Americas. We also investigated modulators of the local recombination rate, finding further evidence that Polycomb group proteins and the trimethylation of H3K27 elevate recombination rates. Further differences in the recombination landscape across the genome and between populations are driven by variation in the gene that encodes the DNA binding protein PRDM9, and we quantify the weak effect of meiotic drive acting to remove its binding sites.
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Affiliation(s)
- Jeffrey P. Spence
- Graduate Group in Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Yun S. Song
- Computer Science Division and Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
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25
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Chen LY, VanBuren R, Paris M, Zhou H, Zhang X, Wai CM, Yan H, Chen S, Alonge M, Ramakrishnan S, Liao Z, Liu J, Lin J, Yue J, Fatima M, Lin Z, Zhang J, Huang L, Wang H, Hwa TY, Kao SM, Choi JY, Sharma A, Song J, Wang L, Yim WC, Cushman JC, Paull RE, Matsumoto T, Qin Y, Wu Q, Wang J, Yu Q, Wu J, Zhang S, Boches P, Tung CW, Wang ML, Coppens d'Eeckenbrugge G, Sanewski GM, Purugganan MD, Schatz MC, Bennetzen JL, Lexer C, Ming R. The bracteatus pineapple genome and domestication of clonally propagated crops. Nat Genet 2019; 51:1549-1558. [PMID: 31570895 DOI: 10.1038/s41588-019-0506-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 08/28/2019] [Indexed: 11/09/2022]
Abstract
Domestication of clonally propagated crops such as pineapple from South America was hypothesized to be a 'one-step operation'. We sequenced the genome of Ananas comosus var. bracteatus CB5 and assembled 513 Mb into 25 chromosomes with 29,412 genes. Comparison of the genomes of CB5, F153 and MD2 elucidated the genomic basis of fiber production, color formation, sugar accumulation and fruit maturation. We also resequenced 89 Ananas genomes. Cultivars 'Smooth Cayenne' and 'Queen' exhibited ancient and recent admixture, while 'Singapore Spanish' supported a one-step operation of domestication. We identified 25 selective sweeps, including a strong sweep containing a pair of tandemly duplicated bromelain inhibitors. Four candidate genes for self-incompatibility were linked in F153, but were not functional in self-compatible CB5. Our findings support the coexistence of sexual recombination and a one-step operation in the domestication of clonally propagated crops. This work guides the exploration of sexual and asexual domestication trajectories in other clonally propagated crops.
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Affiliation(s)
- Li-Yu Chen
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Robert VanBuren
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Department of Horticulture, Michigan State University, East Lansing, MI, USA
| | - Margot Paris
- Department of Biology, University of Fribourg, Fribourg, Switzerland
| | - Hongye Zhou
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Xingtan Zhang
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Ching Man Wai
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Hansong Yan
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Shuai Chen
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Michael Alonge
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | | | - Zhenyang Liao
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Juan Liu
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jishan Lin
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jingjing Yue
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Mahpara Fatima
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Zhicong Lin
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jisen Zhang
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Lixian Huang
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Hao Wang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Teh-Yang Hwa
- Department of Agronomy, National Taiwan University, Taipei, ROC
| | - Shu-Min Kao
- Department of Agronomy, National Taiwan University, Taipei, ROC
| | - Jae Young Choi
- Department of Biology, Center for Genomics and Systems Biology, New York University, NY, New York, USA
| | - Anupma Sharma
- Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX, USA
| | - Jian Song
- Department of Agronomy, University of Florida, Gainesville, FL, USA
| | - Lulu Wang
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Won C Yim
- Department of Biochemistry and Molecular Biology, MS330, University of Nevada, Reno, NV, USA
| | - John C Cushman
- Department of Biochemistry and Molecular Biology, MS330, University of Nevada, Reno, NV, USA
| | - Robert E Paull
- Department of Tropical Plant and Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Tracie Matsumoto
- USDA-ARS, Pacific Basin Agricultural Research Center, Hilo, HI, USA
| | - Yuan Qin
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Qingsong Wu
- South Subtropical Crops Research Institute, CATAS, Zhanjiang, China
| | - Jianping Wang
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China.,Department of Agronomy, University of Florida, Gainesville, FL, USA
| | - Qingyi Yu
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China.,Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX, USA
| | - Jun Wu
- Centre of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Shaoling Zhang
- Centre of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Peter Boches
- USDA-ARS, Pacific Basin Agricultural Research Center, Hilo, HI, USA
| | - Chih-Wei Tung
- Department of Agronomy, National Taiwan University, Taipei, ROC
| | - Ming-Li Wang
- Hawaii Agriculture Research Center, Kunia, HI, USA
| | - Geo Coppens d'Eeckenbrugge
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement, UMR AGAP, Montpellier, France.,AGAP, Université de Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | - Garth M Sanewski
- Queensland Department of Agriculture and Fisheries, Nambour, Queensland, Australia
| | - Michael D Purugganan
- Department of Biology, Center for Genomics and Systems Biology, New York University, NY, New York, USA
| | - Michael C Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | | | - Christian Lexer
- Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria.
| | - Ray Ming
- FAFU and UIUC-SIB Joint Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, China. .,Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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26
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Bresadola L, Caseys C, Castiglione S, Buerkle CA, Wegmann D, Lexer C. Admixture mapping in interspecific Populus hybrids identifies classes of genomic architectures for phytochemical, morphological and growth traits. THE NEW PHYTOLOGIST 2019; 223:2076-2089. [PMID: 31104343 PMCID: PMC6771622 DOI: 10.1111/nph.15930] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Accepted: 05/06/2019] [Indexed: 05/03/2023]
Abstract
The genomic architecture of functionally important traits is key to understanding the maintenance of reproductive barriers and trait differences when divergent populations or species hybridize. We conducted a genome-wide association study (GWAS) to study trait architecture in natural hybrids of two ecologically divergent Populus species. We genotyped 472 seedlings from a natural hybrid zone of Populus alba and Populus tremula for genome-wide markers from reduced representation sequencing, phenotyped the plants in common gardens for 46 phytochemical (phenylpropanoid), morphological and growth traits, and used a Bayesian polygenic model for mapping. We detected three classes of genomic architectures: traits with finite, detectable associations of genetic loci with phenotypic variation in addition to highly polygenic heritability; traits with indications for polygenic heritability only; and traits with no detectable heritability. For the first class, we identified genome regions with plausible candidate genes for phenylpropanoid biosynthesis or its regulation, including MYB transcription factors and glycosyl transferases. GWAS in natural, recombinant hybrids represent a promising step towards resolving the genomic architecture of phenotypic traits in long-lived species. This facilitates the fine-mapping and subsequent functional characterization of genes and networks causing differences in hybrid performance and fitness.
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Affiliation(s)
- Luisa Bresadola
- Department of BiologyUniversity of FribourgChemin du Musée 101700FribourgSwitzerland
| | - Céline Caseys
- Department of BiologyUniversity of FribourgChemin du Musée 101700FribourgSwitzerland
- Department of Plant SciencesUniversity of California DavisOne Shields AvenueDavisCA95616USA
| | - Stefano Castiglione
- Department of Chemistry and Biology ‘A. Zambelli’University of SalernoVia Giovanni Paolo II 13284084Fisciano, SalernoItaly
| | - C. Alex Buerkle
- Department of BotanyUniversity of Wyoming1000 E. University Ave.LaramieWY82071USA
| | - Daniel Wegmann
- Department of BiologyUniversity of FribourgChemin du Musée 101700FribourgSwitzerland
- Swiss Institute of Bioinformatics1700FribourgSwitzerland
| | - Christian Lexer
- Department of BiologyUniversity of FribourgChemin du Musée 101700FribourgSwitzerland
- Department of Botany and Biodiversity ResearchFaculty of Life SciencesUniversity of ViennaRennweg 12A‐1030ViennaAustria
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27
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Abstract
Context: Africa's role in the narrative of human evolution is indisputably emphasised in the emergence of Homo sapiens. However, once humans dispersed beyond Africa, the history of those who stayed remains vastly under-studied, lacking the proper attention the birthplace of both modern and archaic humans deserves. The sequencing of Neanderthal and Denisovan genomes has elucidated evidence of admixture between archaic and modern humans outside of Africa, but has not aided efforts in answering whether archaic admixture happened within Africa. Objectives: This article reviews the state of research for archaic introgression in African populations and discusses recent insights into this topic. Methods: Gathering published sources and recently released preprints, this review reports on the different methods developed for detecting archaic introgression. Particularly it discusses how relevant these are when implemented on African populations and what findings these studies have shown so far. Results: Methods for detecting archaic introgression have been predominantly developed and implemented on non-African populations. Recent preprints present new methods considering African populations. While a number of studies using these methods suggest archaic introgression in Africa, without an African archaic genome to validate these results, such findings remain as putative archaic introgression. Conclusion: In light of the caveats with implementing current archaic introgression detection methods in Africa, we recommend future studies to concentrate on unravelling the complicated demographic history of Africa through means of ancient DNA where possible and through more focused efforts to sequence modern DNA from more representative populations across the African continent.
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Affiliation(s)
- Cindy Santander
- a Department of Zoology , University of Oxford , Oxford , UK
| | - Francesco Montinaro
- a Department of Zoology , University of Oxford , Oxford , UK.,b Estonian Biocentre , University of Tartu , Tartu , Estonia
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28
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Abstract
Introgression is emerging as an important source of novel genetic variation, alongside standing variation and mutation. It is adaptive when such introgressed alleles are maintained by natural selection. Recently, there has been an explosion in the number of studies on adaptive introgression. In this review, we take a plant perspective centred on four lines of evidence: (i) introgression, (ii) selection, (iii) phenotype and (iv) fitness. While advances in genomics have contributed to our understanding of introgression and porous species boundaries (task 1), and the detection of signatures of selection in introgression (task 2), the investigation of adaptive introgression critically requires links to phenotypic variation and fitness (tasks 3 and 4). We also discuss the conservation implications of adaptive introgression in the face of climate change. Adaptive introgression is particularly important in rapidly changing environments, when standing genetic variation and mutation alone may only offer limited potential for adaptation. We conclude that clarifying the magnitude and fitness effects of introgression with improved statistical techniques, coupled with phenotypic evidence, has great potential for conservation and management efforts.
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Affiliation(s)
| | - Christian Lexer
- Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria
| | - Quentin C B Cronk
- Department of Botany, University of British Columbia, Vancouver, Canada
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29
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Janzen GM, Wang L, Hufford MB. The extent of adaptive wild introgression in crops. THE NEW PHYTOLOGIST 2019; 221:1279-1288. [PMID: 30368812 DOI: 10.1111/nph.15457] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Accepted: 08/24/2018] [Indexed: 05/05/2023]
Abstract
The study of crop evolution has focused primarily on the process of initial domestication. Post-domestication adaptation during the expansion of crops from their centers of origin has received considerably less attention. Recent research has revealed that, in at least some instances, crops have received introgression from their wild relatives that has facilitated adaptation to novel conditions encountered during expansion. Such adaptive introgression could have an important impact on the basic study of domestication, affecting estimates of several evolutionary processes of interest (e.g. the strength of the domestication bottleneck, the timing of domestication, the targets of selection during domestication). Identification of haplotypes introgressed from the wild may also help in the identification of alleles that are beneficial under particular environmental conditions. Here we review mounting evidence for substantial adaptive wild introgression in several crops and consider the implications of such gene flow to our understanding of crop histories.
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Affiliation(s)
- Garrett M Janzen
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Li Wang
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
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30
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Veller C, Kleckner N, Nowak MA. A rigorous measure of genome-wide genetic shuffling that takes into account crossover positions and Mendel's second law. Proc Natl Acad Sci U S A 2019; 116:1659-1668. [PMID: 30635424 PMCID: PMC6358705 DOI: 10.1073/pnas.1817482116] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Comparative studies in evolutionary genetics rely critically on evaluation of the total amount of genetic shuffling that occurs during gamete production. Such studies have been hampered by the absence of a direct measure of this quantity. Existing measures consider crossing-over by simply counting the average number of crossovers per meiosis. This is qualitatively inadequate, because the positions of crossovers along a chromosome are also critical: a crossover toward the middle of a chromosome causes more shuffling than a crossover toward the tip. Moreover, traditional measures fail to consider shuffling from independent assortment of homologous chromosomes (Mendel's second law). Here, we present a rigorous measure of genome-wide shuffling that does not suffer from these limitations. We define the parameter [Formula: see text] as the probability that the alleles at two randomly chosen loci are shuffled during gamete production. This measure can be decomposed into separate contributions from crossover number and position and from independent assortment. Intrinsic implications of this metric include the fact that [Formula: see text] is larger when crossovers are more evenly spaced, which suggests a selective advantage of crossover interference. Utilization of [Formula: see text] is enabled by powerful emergent methods for determining crossover positions either cytologically or by DNA sequencing. Application of our analysis to such data from human male and female reveals that (i) [Formula: see text] in humans is close to its maximum possible value of 1/2 and that (ii) this high level of shuffling is due almost entirely to independent assortment, the contribution of which is ∼30 times greater than that of crossovers.
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Affiliation(s)
- Carl Veller
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138
| | - Nancy Kleckner
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138;
| | - Martin A Nowak
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138
- Department of Mathematics, Harvard University, Cambridge, MA 02138
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31
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Pouyet F, Aeschbacher S, Thiéry A, Excoffier L. Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences. eLife 2018; 7:e36317. [PMID: 30125248 PMCID: PMC6177262 DOI: 10.7554/elife.36317] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 08/17/2018] [Indexed: 12/15/2022] Open
Abstract
Disentangling the effect on genomic diversity of natural selection from that of demography is notoriously difficult, but necessary to properly reconstruct the history of species. Here, we use high-quality human genomic data to show that purifying selection at linked sites (i.e. background selection, BGS) and GC-biased gene conversion (gBGC) together affect as much as 95% of the variants of our genome. We find that the magnitude and relative importance of BGS and gBGC are largely determined by variation in recombination rate and base composition. Importantly, synonymous sites and non-transcribed regions are also affected, albeit to different degrees. Their use for demographic inference can lead to strong biases. However, by conditioning on genomic regions with recombination rates above 1.5 cM/Mb and mutation types (C↔G, A↔T), we identify a set of SNPs that is mostly unaffected by BGS or gBGC, and that avoids these biases in the reconstruction of human history.
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Affiliation(s)
- Fanny Pouyet
- Computational and Molecular Population Genetics, Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
| | - Simon Aeschbacher
- Computational and Molecular Population Genetics, Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
| | - Alexandre Thiéry
- Computational and Molecular Population Genetics, Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
| | - Laurent Excoffier
- Computational and Molecular Population Genetics, Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
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32
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High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nat Genet 2018; 50:1311-1317. [PMID: 30104759 PMCID: PMC6145075 DOI: 10.1038/s41588-018-0177-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 06/21/2018] [Indexed: 12/19/2022]
Abstract
Interest in reconstructing demographic histories has motivated the development of methods to estimate locus-specific pairwise coalescence times from whole-genome sequence data. Here we introduce a powerful new method, ASMC, that can estimate coalescence times using only SNP array data, and is orders of magnitude faster than previous approaches. We applied ASMC to detect recent positive selection in 113,851 phased British samples from the UK Biobank, and detected 12 genome-wide significant signals, including 6 novel loci. We also applied ASMC to sequencing data from 498 Dutch individuals to detect background selection at deeper time scales. We detected strong heritability enrichment in regions of high background selection in an analysis of 20 independent diseases and complex traits using stratified LD score regression, conditioned on a broad set of functional annotations (including other background selection annotations). These results underscore the widespread effects of background selection on the genetic architecture of complex traits.
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33
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Leroy G, Carroll EL, Bruford MW, DeWoody JA, Strand A, Waits L, Wang J. Next-generation metrics for monitoring genetic erosion within populations of conservation concern. Evol Appl 2018; 11:1066-1083. [PMID: 30026798 PMCID: PMC6050182 DOI: 10.1111/eva.12564] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 10/11/2017] [Indexed: 12/26/2022] Open
Abstract
Genetic erosion is a major threat to biodiversity because it can reduce fitness and ultimately contribute to the extinction of populations. Here, we explore the use of quantitative metrics to detect and monitor genetic erosion. Monitoring systems should not only characterize the mechanisms and drivers of genetic erosion (inbreeding, genetic drift, demographic instability, population fragmentation, introgressive hybridization, selection) but also its consequences (inbreeding and outbreeding depression, emergence of large-effect detrimental alleles, maladaptation and loss of adaptability). Technological advances in genomics now allow the production of data the can be measured by new metrics with improved precision, increased efficiency and the potential to discriminate between neutral diversity (shaped mainly by population size and gene flow) and functional/adaptive diversity (shaped mainly by selection), allowing the assessment of management-relevant genetic markers. The requirements of such studies in terms of sample size and marker density largely depend on the kind of population monitored, the questions to be answered and the metrics employed. We discuss prospects for the integration of this new information and metrics into conservation monitoring programmes.
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Affiliation(s)
- Gregoire Leroy
- Food and Agriculture Organization (FAO) of the United Nations, Animal Production and Health DivisionRomeItaly
| | - Emma L. Carroll
- Scottish Oceans Institute and School of BiologyUniversity of St AndrewsSt AndrewsUK
| | - Mike W. Bruford
- Cardiff School of Biosciences and Sustainable Places InstituteCardiff UniversityCardiffUK
| | - J. Andrew DeWoody
- Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteINUSA
- Department of Biological SciencesPurdue UniversityWest LafayetteINUSA
| | - Allan Strand
- Department of BiologyGrice Marine Laboratory, College of CharlestonCharlestonSCUSA
| | - Lisette Waits
- Department of Fish and Wildlife SciencesUniversity of IdahoMoscowIDUSA
| | - Jinliang Wang
- Institute of ZoologyZoological Society of LondonLondonUK
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34
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Hvala JA, Frayer ME, Payseur BA. Signatures of hybridization and speciation in genomic patterns of ancestry. Evolution 2018; 72:10.1111/evo.13509. [PMID: 29806154 PMCID: PMC6261709 DOI: 10.1111/evo.13509] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 05/03/2018] [Indexed: 12/27/2022]
Abstract
Genomes sampled from hybrid zones between nascent species provide important clues into the speciation process. With advances in genome sequencing and single nucleotide polymorphism (SNP) genotyping, it is now feasible to measure variation in gene flow with high genomic resolution. This progress motivates the development of conceptual and analytical frameworks for hybrid zones that complement well-established cline approaches. We extend the perspective that genomic distributions of ancestry are sensitive indicators of hybridization history. We use simulations to examine the behavior of the number of ancestry junctions-a simple summary of genomic patterns-in hybrid zones under increasingly realistic scenarios. Neutral simulations revealed that ancestry junction number is shaped by population structure, migration rate, and population size. Modeling multiple genetic architectures of hybrid dysfunction, with an emphasis on epistatic hybrid incompatibilities, showed that selection reduces junction number near loci that confer reproductive barriers. The magnitude of this signature was affected by the form of selection, dominance, and genomic location (autosome vs. sex chromosome) of incompatible loci. Our results suggest that researchers can identify loci involved in reproductive isolation by scanning hybrid genomes for local reductions in junction number. We outline necessary directions for future theory and method development to realize this goal.
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Affiliation(s)
- John A. Hvala
- Laboratory of Genetics, University of Wisconsin-Madison
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35
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Suarez-Gonzalez A, Hefer CA, Lexer C, Cronk QCB, Douglas CJ. Scale and direction of adaptive introgression between black cottonwood (Populus trichocarpa) and balsam poplar (P. balsamifera). Mol Ecol 2018; 27:1667-1680. [DOI: 10.1111/mec.14561] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 02/17/2018] [Accepted: 02/23/2018] [Indexed: 12/31/2022]
Affiliation(s)
| | - Charles A. Hefer
- Department of Botany; University of British Columbia; Vancouver BC Canada
| | - Christian Lexer
- Department of Botany and Biodiversity Research; University of Vienna; Vienna Austria
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36
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Duan Q, Xu Z, Raffield L, Chang S, Wu D, Lange EM, Reiner AP, Li Y. A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations. Genet Epidemiol 2018; 42:288-302. [PMID: 29226381 PMCID: PMC5851818 DOI: 10.1002/gepi.22104] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 09/07/2017] [Accepted: 10/20/2017] [Indexed: 12/23/2022]
Abstract
Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two-step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry-specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups' previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA.
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Affiliation(s)
- Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of North Carolina, Chapel Hill, NC, USA
| | - Zheng Xu
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE
- Initiative of Quantitative Life Sciences, University of Nebraska-Lincoln, Lincoln, NE
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Suhua Chang
- Institute of Psychology, Chinese Academy of Science, Beijing, China
| | - Di Wu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Periodontology, University of North Carolina, Chapel Hill, NC, USA
| | - Ethan M. Lange
- Department of Medicine, University of Colorado at Denver, Anschutz Medical Campus, Aurora, CO, USA
- Department of Biostatistics and Informatics, University of Colorado at Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Alex P. Reiner
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
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37
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Abstract
The haplotypes of a beneficial allele carry information about its history that can shed light on its age and the putative cause for its increase in frequency. Specifically, the signature of an allele's age is contained in the pattern of variation that mutation and recombination impose on its haplotypic background. We provide a method to exploit this pattern and infer the time to the common ancestor of a positively selected allele following a rapid increase in frequency. We do so using a hidden Markov model which leverages the length distribution of the shared ancestral haplotype, the accumulation of derived mutations on the ancestral background, and the surrounding background haplotype diversity. Using simulations, we demonstrate how the inclusion of information from both mutation and recombination events increases accuracy relative to approaches that only consider a single type of event. We also show the behavior of the estimator in cases where data do not conform to model assumptions, and provide some diagnostics for assessing and improving inference. Using the method, we analyze population-specific patterns in the 1000 Genomes Project data to estimate the timing of adaptation for several variants which show evidence of recent selection and functional relevance to diet, skin pigmentation, and morphology in humans.
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Affiliation(s)
- Joel Smith
- Department of Ecology and Evolution, University of Chicago, Chicago, IL
| | - Graham Coop
- Center for Population Biology, Department of Evolution and Ecology, University of California, Davis, Davis, CA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL
- Department of Statistics, University of Chicago, Chicago, IL
| | - John Novembre
- Department of Ecology and Evolution, University of Chicago, Chicago, IL
- Department of Human Genetics, University of Chicago, Chicago, IL
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38
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Suarez-Gonzalez A, Hefer CA, Lexer C, Douglas CJ, Cronk QCB. Introgression from Populus balsamifera underlies adaptively significant variation and range boundaries in P. trichocarpa. THE NEW PHYTOLOGIST 2018; 217:416-427. [PMID: 29124769 DOI: 10.1111/nph.14779] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Accepted: 08/03/2017] [Indexed: 06/07/2023]
Abstract
Introgression can be an important source of adaptive phenotypes, although conversely it can have deleterious effects. Evidence for adaptive introgression is accumulating but information on the genetic architecture of introgressed traits lags behind. Here we determine trait architecture in Populus trichocarpa under introgression from P. balsamifera using admixture mapping and phenotypic analyses. Our results reveal that admixture is a key driver of clinal adaptation and suggest that the northern range extension of P. trichocarpa depends, at least in part, on introgression from P. balsamifera. However, admixture with P. balsamifera can lead to potentially maladaptive early phenology, and a reduction in growth and disease resistance in P. trichocarpa. Strikingly, an introgressed chromosome 9 haplotype block from P. balsamifera restores the late phenology and high growth parental phenotype in admixed P. trichocarpa. This epistatic restorer block may be strongly advantageous in maximizing carbon assimilation and disease resistance in the southernmost populations where admixture has been detected. We also confirm a previously demonstrated case of adaptive introgression in chromosome 15 and show that introgression generates a transgressive chlorophyll-content phenotype. We provide strong support that introgression provides a reservoir of genetic variation associated with adaptive characters that allows improved survival in new environments.
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Affiliation(s)
| | - Charles A Hefer
- Department of Botany, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4
- Biotechnology Platform, Agricultural Research Council, Private Bag X05, Onderstepoort, 0110, South Africa
| | - Christian Lexer
- Department of Botany and Biodiversity Research, University of Vienna, Vienna, 1030, Austria
| | - Carl J Douglas
- Department of Botany, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4
| | - Quentin C B Cronk
- Department of Botany, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4
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40
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Nadeau NJ, Kawakami T. Population Genomics of Speciation and Admixture. POPULATION GENOMICS 2018. [DOI: 10.1007/13836_2018_24] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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41
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Sherwin WB, Chao A, Jost L, Smouse PE. Information Theory Broadens the Spectrum of Molecular Ecology and Evolution. Trends Ecol Evol 2017; 32:948-963. [PMID: 29126564 DOI: 10.1016/j.tree.2017.09.012] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 09/22/2017] [Accepted: 09/26/2017] [Indexed: 01/18/2023]
Abstract
Information or entropy analysis of diversity is used extensively in community ecology, and has recently been exploited for prediction and analysis in molecular ecology and evolution. Information measures belong to a spectrum (or q profile) of measures whose contrasting properties provide a rich summary of diversity, including allelic richness (q=0), Shannon information (q=1), and heterozygosity (q=2). We present the merits of information measures for describing and forecasting molecular variation within and among groups, comparing forecasts with data, and evaluating underlying processes such as dispersal. Importantly, information measures directly link causal processes and divergence outcomes, have straightforward relationship to allele frequency differences (including monotonicity that q=2 lacks), and show additivity across hierarchical layers such as ecology, behaviour, cellular processes, and nongenetic inheritance.
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Affiliation(s)
- W B Sherwin
- Evolution and Ecology Research Centre, School of Biological Earth and Environmental Science, University of New South Wales, Sydney, NSW 2052, Australia; Murdoch University Cetacean Research Unit, Murdoch University, South Road, Murdoch, WA 6150, Australia.
| | - A Chao
- Institute of Statistics, National Tsing Hua University, Hsin-Chu 30043, Taiwan
| | - L Jost
- EcoMinga Foundation, Via a Runtun, Baños, Tungurahua, Ecuador
| | - P E Smouse
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ 08901-8551, USA
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42
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Gompert Z, Mandeville EG, Buerkle CA. Analysis of Population Genomic Data from Hybrid Zones. ANNUAL REVIEW OF ECOLOGY EVOLUTION AND SYSTEMATICS 2017. [DOI: 10.1146/annurev-ecolsys-110316-022652] [Citation(s) in RCA: 118] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Zachariah Gompert
- Department of Biology and Ecology Center, Utah State University, Logan, Utah 84322
| | - Elizabeth G. Mandeville
- Department of Botany and Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming, Laramie, Wyoming 82071
| | - C. Alex Buerkle
- Department of Botany and Program in Ecology, University of Wyoming, Laramie, Wyoming 82071
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43
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Joukhadar R, Daetwyler HD, Bansal UK, Gendall AR, Hayden MJ. Genetic Diversity, Population Structure and Ancestral Origin of Australian Wheat. FRONTIERS IN PLANT SCIENCE 2017; 8:2115. [PMID: 29312381 PMCID: PMC5733070 DOI: 10.3389/fpls.2017.02115] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Accepted: 11/28/2017] [Indexed: 05/22/2023]
Abstract
Since the introduction of wheat into Australia by the First Fleet settlers, germplasm from different geographical origins has been used to adapt wheat to the Australian climate through selection and breeding. In this paper, we used 482 cultivars, representing the breeding history of bread wheat in Australia since 1840, to characterize their diversity and population structure and to define the geographical ancestral background of Australian wheat germplasm. This was achieved by comparing them to a global wheat collection using in-silico chromosome painting based on SNP genotyping. The global collection involved 2,335 wheat accessions which was divided into 23 different geographical subpopulations. However, the whole set was reduced to 1,544 accessions to increase the differentiation and decrease the admixture among different global subpopulations to increase the power of the painting analysis. Our analysis revealed that the structure of Australian wheat germplasm and its geographic ancestors have changed significantly through time, especially after the Green Revolution. Before 1920, breeders used cultivars from around the world, but mainly Europe and Africa, to select potential cultivars that could tolerate Australian growing conditions. Between 1921 and 1970, a dependence on African wheat germplasm became more prevalent. Since 1970, a heavy reliance on International Maize and Wheat Improvement Center (CIMMYT) germplasm has persisted. Combining the results from linkage disequilibrium, population structure and in-silico painting revealed that the dependence on CIMMYT materials has varied among different Australian States, has shrunken the germplasm effective population size and produced larger linkage disequilibrium blocks. This study documents the evolutionary history of wheat breeding in Australia and provides an understanding for how the wheat genome has been adapted to local growing conditions. This information provides a guide for industry to assist with maintaining genetic diversity for long-term selection gains and to plan future breeding programs.
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Affiliation(s)
- Reem Joukhadar
- Department of Animal, Plant and Soil Sciences, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria Research, AgriBio, Centre for Agribioscience, Bundoora, VIC, Australia
- *Correspondence: Reem Joukhadar
| | - Hans D. Daetwyler
- Agriculture Victoria Research, AgriBio, Centre for Agribioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Urmil K. Bansal
- School of Life and Environmental Sciences, The University of Sydney Plant Breeding Institute, Cobbitty, NSW, Australia
| | - Anthony R. Gendall
- Department of Animal, Plant and Soil Sciences, La Trobe University, Bundoora, VIC, Australia
| | - Matthew J. Hayden
- Agriculture Victoria Research, AgriBio, Centre for Agribioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- Matthew J. Hayden
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44
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Affiliation(s)
- Yun S Song
- Computer Science Division and Department of Statistics, University of California, Berkeley, California 94720, Department of Mathematics and Department of Biology and University of Pennsylvania, Philadelphia, Pennsylvania 19104
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45
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Kamm JA, Spence JP, Chan J, Song YS. Two-Locus Likelihoods Under Variable Population Size and Fine-Scale Recombination Rate Estimation. Genetics 2016; 203:1381-99. [PMID: 27182948 PMCID: PMC4937484 DOI: 10.1534/genetics.115.184820] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 05/06/2016] [Indexed: 01/06/2023] Open
Abstract
Two-locus sampling probabilities have played a central role in devising an efficient composite-likelihood method for estimating fine-scale recombination rates. Due to mathematical and computational challenges, these sampling probabilities are typically computed under the unrealistic assumption of a constant population size, and simulation studies have shown that resulting recombination rate estimates can be severely biased in certain cases of historical population size changes. To alleviate this problem, we develop here new methods to compute the sampling probability for variable population size functions that are piecewise constant. Our main theoretical result, implemented in a new software package called LDpop, is a novel formula for the sampling probability that can be evaluated by numerically exponentiating a large but sparse matrix. This formula can handle moderate sample sizes ([Formula: see text]) and demographic size histories with a large number of epochs ([Formula: see text]). In addition, LDpop implements an approximate formula for the sampling probability that is reasonably accurate and scales to hundreds in sample size ([Formula: see text]). Finally, LDpop includes an importance sampler for the posterior distribution of two-locus genealogies, based on a new result for the optimal proposal distribution in the variable-size setting. Using our methods, we study how a sharp population bottleneck followed by rapid growth affects the correlation between partially linked sites. Then, through an extensive simulation study, we show that accounting for population size changes under such a demographic model leads to substantial improvements in fine-scale recombination rate estimation.
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Affiliation(s)
- John A Kamm
- Department of Statistics, University of California, Berkeley, California 94720 Computer Science Division, University of California, Berkeley, California 94720
| | - Jeffrey P Spence
- Computational Biology Graduate Group, University of California, Berkeley, California 94720
| | - Jeffrey Chan
- Computer Science Division, University of California, Berkeley, California 94720
| | - Yun S Song
- Department of Statistics, University of California, Berkeley, California 94720 Computer Science Division, University of California, Berkeley, California 94720 Department of Integrative Biology, University of California, Berkeley, California 94720 Departments of Mathematics and Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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46
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Payseur BA, Rieseberg LH. A genomic perspective on hybridization and speciation. Mol Ecol 2016; 25:2337-60. [PMID: 26836441 PMCID: PMC4915564 DOI: 10.1111/mec.13557] [Citation(s) in RCA: 292] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Revised: 01/18/2016] [Accepted: 01/25/2016] [Indexed: 12/13/2022]
Abstract
Hybridization among diverging lineages is common in nature. Genomic data provide a special opportunity to characterize the history of hybridization and the genetic basis of speciation. We review existing methods and empirical studies to identify recent advances in the genomics of hybridization, as well as issues that need to be addressed. Notable progress has been made in the development of methods for detecting hybridization and inferring individual ancestries. However, few approaches reconstruct the magnitude and timing of gene flow, estimate the fitness of hybrids or incorporate knowledge of recombination rate. Empirical studies indicate that the genomic consequences of hybridization are complex, including a highly heterogeneous landscape of differentiation. Inferred characteristics of hybridization differ substantially among species groups. Loci showing unusual patterns - which may contribute to reproductive barriers - are usually scattered throughout the genome, with potential enrichment in sex chromosomes and regions of reduced recombination. We caution against the growing trend of interpreting genomic variation in summary statistics across genomes as evidence of differential gene flow. We argue that converting genomic patterns into useful inferences about hybridization will ultimately require models and methods that directly incorporate key ingredients of speciation, including the dynamic nature of gene flow, selection acting in hybrid populations and recombination rate variation.
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Affiliation(s)
- Bret A. Payseur
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Loren H. Rieseberg
- Department of Botany, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Biology, Indiana University, Bloomington, IN 47405, USA
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47
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Stevison LS, Woerner AE, Kidd JM, Kelley JL, Veeramah KR, McManus KF, Bustamante CD, Hammer MF, Wall JD. The Time Scale of Recombination Rate Evolution in Great Apes. Mol Biol Evol 2016; 33:928-45. [PMID: 26671457 PMCID: PMC5870646 DOI: 10.1093/molbev/msv331] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
We present three linkage-disequilibrium (LD)-based recombination maps generated using whole-genome sequence data from 10 Nigerian chimpanzees, 13 bonobos, and 15 western gorillas, collected as part of the Great Ape Genome Project (Prado-Martinez J, et al. 2013. Great ape genetic diversity and population history. Nature 499:471-475). We also identified species-specific recombination hotspots in each group using a modified LDhot framework, which greatly improves statistical power to detect hotspots at varying strengths. We show that fewer hotspots are shared among chimpanzee subspecies than within human populations, further narrowing the time scale of complete hotspot turnover. Further, using species-specific PRDM9 sequences to predict potential binding sites (PBS), we show higher predicted PRDM9 binding in recombination hotspots as compared to matched cold spot regions in multiple great ape species, including at least one chimpanzee subspecies. We found that correlations between broad-scale recombination rates decline more rapidly than nucleotide divergence between species. We also compared the skew of recombination rates at centromeres and telomeres between species and show a skew from chromosome means extending as far as 10-15 Mb from chromosome ends. Further, we examined broad-scale recombination rate changes near a translocation in gorillas and found minimal differences as compared to other great ape species perhaps because the coordinates relative to the chromosome ends were unaffected. Finally, on the basis of multiple linear regression analysis, we found that various correlates of recombination rate persist throughout the African great apes including repeats, diversity, and divergence. Our study is the first to analyze within- and between-species genome-wide recombination rate variation in several close relatives.
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Affiliation(s)
- Laurie S Stevison
- Institute for Human Genetics, University of California San Francisco Department of Biological Sciences, Auburn University
| | - August E Woerner
- Arizona Research Laboratories, Division of Biotechnology, University of Arizona Department of Genetics, University of Arizona
| | - Jeffrey M Kidd
- Department of Human Genetics, University of Michigan Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Joanna L Kelley
- School of Biological Sciences, Washington State University Department of Genetics, Stanford University
| | - Krishna R Veeramah
- Arizona Research Laboratories, Division of Biotechnology, University of Arizona Department of Ecology and Evolution, Stony Brook University
| | - Kimberly F McManus
- Department of Biology, Stanford University Department of Biomedical Informatics, Stanford University
| | | | - Michael F Hammer
- Arizona Research Laboratories, Division of Biotechnology, University of Arizona Department of Ecology and Evolutionary Biology, University of Arizona Department of Anthropology, University of Arizona
| | - Jeffrey D Wall
- Institute for Human Genetics, University of California San Francisco Department of Epidemiology & Biostatistics, University of California San Francisco
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48
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Christe C, Stölting KN, Bresadola L, Fussi B, Heinze B, Wegmann D, Lexer C. Selection against recombinant hybrids maintains reproductive isolation in hybridizingPopulusspecies despite F1fertility and recurrent gene flow. Mol Ecol 2016; 25:2482-98. [DOI: 10.1111/mec.13587] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 01/31/2016] [Accepted: 02/02/2016] [Indexed: 12/31/2022]
Affiliation(s)
- Camille Christe
- Department of Biology; University of Fribourg; Chemin du Musée 10 CH-1700 Fribourg Switzerland
| | - Kai N. Stölting
- Department of Biology; University of Fribourg; Chemin du Musée 10 CH-1700 Fribourg Switzerland
| | - Luisa Bresadola
- Department of Biology; University of Fribourg; Chemin du Musée 10 CH-1700 Fribourg Switzerland
| | - Barbara Fussi
- Applied Forest Genetics; Bavarian Office for Forest Seeding and Planting; Forstamtsplatz 1 83317 Teisendorf Germany
| | - Berthold Heinze
- Department of Genetics; Austrian Federal Research and Training Centre for Forests; Natural Hazards and Landscape; Seckendorff-Gudent-Weg 8 A-1130 Vienna Austria
| | - Daniel Wegmann
- Department of Biology; University of Fribourg; Chemin du Musée 10 CH-1700 Fribourg Switzerland
| | - Christian Lexer
- Department of Biology; University of Fribourg; Chemin du Musée 10 CH-1700 Fribourg Switzerland
- Department of Botany and Biodiversity Research; University of Vienna; Rennweg 14 A-1030 Vienna Austria
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49
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Suarez-Gonzalez A, Hefer CA, Christe C, Corea O, Lexer C, Cronk QCB, Douglas CJ. Genomic and functional approaches reveal a case of adaptive introgression fromPopulus balsamifera(balsam poplar) inP. trichocarpa(black cottonwood). Mol Ecol 2016; 25:2427-42. [DOI: 10.1111/mec.13539] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Revised: 01/02/2016] [Accepted: 01/06/2016] [Indexed: 01/14/2023]
Affiliation(s)
| | - Charles A. Hefer
- Department of Botany; University of British Columbia; Vancouver BC V6T 1Z4 Canada
| | - Camille Christe
- Unit of Ecology & Evolution; Department of Biology; University of Fribourg; CH-1700 Fribourg Switzerland
| | - Oliver Corea
- Department of Biology and Centre for Forest Biology; University of Victoria; Victoria BC V8W 3N5 Canada
| | - Christian Lexer
- Unit of Ecology & Evolution; Department of Biology; University of Fribourg; CH-1700 Fribourg Switzerland
- Department of Botany and Biodiversity Research; University of Vienna; A-1030 Vienna Austria
| | - Quentin C. B. Cronk
- Department of Botany; University of British Columbia; Vancouver BC V6T 1Z4 Canada
| | - Carl J. Douglas
- Department of Botany; University of British Columbia; Vancouver BC V6T 1Z4 Canada
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50
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Ward BJ, van Oosterhout C. HYBRIDCHECK: software for the rapid detection, visualization and dating of recombinant regions in genome sequence data. Mol Ecol Resour 2015; 16:534-9. [PMID: 26394708 DOI: 10.1111/1755-0998.12469] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 09/09/2015] [Accepted: 09/16/2015] [Indexed: 11/29/2022]
Abstract
HYBRIDCHECK is a software package to visualize the recombination signal in large DNA sequence data set, and it can be used to analyse recombination, genetic introgression, hybridization and horizontal gene transfer. It can scan large (multiple kb) contigs and whole-genome sequences of three or more individuals. HYBRIDCHECK is written in the r software for OS X, Linux and Windows operating systems, and it has a simple graphical user interface. In addition, the r code can be readily incorporated in scripts and analysis pipelines. HYBRIDCHECK implements several ABBA-BABA tests and visualizes the effects of hybridization and the resulting mosaic-like genome structure in high-density graphics. The package also reports the following: (i) the breakpoint positions, (ii) the number of mutations in each introgressed block, (iii) the probability that the identified region is not caused by recombination and (iv) the estimated age of each recombination event. The divergence times between the donor and recombinant sequence are calculated using a JC, K80, F81, HKY or GTR correction, and the dating algorithm is exceedingly fast. By estimating the coalescence time of introgressed blocks, it is possible to distinguish between hybridization and incomplete lineage sorting. HYBRIDCHECK is libré software and it and its manual are free to download from http://ward9250.github.io/HybridCheck/.
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Affiliation(s)
- Ben J Ward
- School of Environmental Sciences, Norwich Research Park, University of East Anglia, Norwich, NR4 7TJ, UK
| | - Cock van Oosterhout
- School of Environmental Sciences, Norwich Research Park, University of East Anglia, Norwich, NR4 7TJ, UK
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