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Daigle A, Johri P. Hill-Robertson interference may bias the inference of fitness effects of new mutations in highly selfing species. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.579142. [PMID: 38370745 PMCID: PMC10871249 DOI: 10.1101/2024.02.06.579142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The accurate estimation of the distribution of fitness effects (DFE) of new mutations is critical for population genetic inference but remains a challenging task. While various methods have been developed for DFE inference using the site frequency spectrum of putatively neutral and selected sites, their applicability in species with diverse life history traits and complex demographic scenarios is not well understood. Selfing is common among eukaryotic species and can lead to decreased effective recombination rates, increasing the effects of selection at linked sites, including interference between selected alleles. We employ forward simulations to investigate the limitations of current DFE estimation approaches in the presence of selfing and other model violations, such as linkage, departures from semidominance, population structure, and uneven sampling. We find that distortions of the site frequency spectrum due to Hill-Robertson interference in highly selfing populations lead to mis-inference of the deleterious DFE of new mutations. Specifically, when inferring the distribution of selection coefficients, there is an overestimation of nearly neutral and strongly deleterious mutations and an underestimation of mildly deleterious mutations when interference between selected alleles is pervasive. In addition, the presence of cryptic population structure with low rates of migration and uneven sampling across subpopulations leads to the false inference of a deleterious DFE skewed towards effectively neutral/mildly deleterious mutations. Finally, the proportion of adaptive substitutions estimated at high rates of selfing is substantially overestimated. Our observations apply broadly to species and genomic regions with little/no recombination and where interference might be pervasive.
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2
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Smith ML, Hahn MW. Selection leads to false inferences of introgression using popular methods. Genetics 2024; 227:iyae089. [PMID: 38805070 DOI: 10.1093/genetics/iyae089] [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: 10/28/2023] [Revised: 10/28/2023] [Accepted: 05/21/2024] [Indexed: 05/29/2024] Open
Abstract
Detecting introgression between closely related populations or species is a fundamental objective in evolutionary biology. Existing methods for detecting migration and inferring migration rates from population genetic data often assume a neutral model of evolution. Growing evidence of the pervasive impact of selection on large portions of the genome across diverse taxa suggests that this assumption is unrealistic in most empirical systems. Further, ignoring selection has previously been shown to negatively impact demographic inferences (e.g. of population size histories). However, the impacts of biologically realistic selection on inferences of migration remain poorly explored. Here, we simulate data under models of background selection, selective sweeps, balancing selection, and adaptive introgression. We show that ignoring selection sometimes leads to false inferences of migration in popularly used methods that rely on the site frequency spectrum. Specifically, balancing selection and some models of background selection result in the rejection of isolation-only models in favor of isolation-with-migration models and lead to elevated estimates of migration rates. BPP, a method that analyzes sequence data directly, showed false positives for all conditions at recent divergence times, but balancing selection also led to false positives at medium-divergence times. Our results suggest that such methods may be unreliable in some empirical systems, such that new methods that are robust to selection need to be developed.
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Affiliation(s)
- Megan L Smith
- Department of Biological Sciences, Mississippi State University, Starkville, MS 39762, USA
- Department of Biology, Indiana University, Bloomington, IN 47405, USA
| | - Matthew W Hahn
- Department of Biology, Indiana University, Bloomington, IN 47405, USA
- Department of Computer Science, Indiana University, Bloomington, IN 47405, USA
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3
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Ruiz-García M, Escobar-Armel P, Martínez-Agüero M, Gaviria M, Álvarez D, Pinedo M, Shostell JM. Are There Barriers Separating the Pink River Dolphin Populations ( Inia boliviensis, Iniidae, Cetacea) within the Mamoré-Iténez River Basins (Bolivia)? An Analysis of Its Genetic Structure by Means of Mitochondrial and Nuclear DNA Markers. Genes (Basel) 2024; 15:1012. [PMID: 39202372 PMCID: PMC11353456 DOI: 10.3390/genes15081012] [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: 07/02/2024] [Revised: 07/27/2024] [Accepted: 07/28/2024] [Indexed: 09/03/2024] Open
Abstract
The pink river dolphin, or bufeo, is one of the dolphins which lives in the rivers of the Orinoco and Amazon basins in South America. The Bolivian bufeo population is considered a differentiated species (Inia boliviensis) from the Amazon and Orinoco species (Inia geoffrensis). Until now, no study has completed an extensive population genetics analysis of the bufeo in Bolivian rivers. We analyzed 82 bufeos from different rivers from the Mamoré and Iténez (Guaporé) river basins for the mt control region (CR), nuclear microsatellites, and DQB-1 gene sequences to determine if the inner rapids of these Bolivian river basins have some influence on the genetic structure of this species. The first relevant result was that the genetic diversity for CR, and the microsatellites were substantially lower in the Bolivian bufeos than in the dolphins studied in other areas of the Amazon and Orinoco. However, the DQB-1 gene sequences yielded similar genetic diversity to those found in other areas. The second relevant result is the existence of some significant genetic heterogeneity among the bufeo populations within Bolivia, although in a small degree, but this differentiation is independent of the inner rapids of the Bolivian rivers we sampled. The third relevant result was the existence of significant isolation by distance for the CR, but not for microsatellites and DQB-1 gene sequences. This was related to differential gene flow capacity of females (philopatric) and males (less philopatric and more migrants) and, possibly, to different selective patterns affecting the molecular markers studied. The fourth relevant result was related to diverse demographic changes of these bufeos. At least two or three bottleneck events and one or two population expansions have occurred in the Bolivian bufeo population. The major part of these events occurred during the Pleistocene.
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Affiliation(s)
- Manuel Ruiz-García
- Laboratorio de Genética de Poblaciones Molecular-Biología Evolutiva, Unidad de Genética, Departamento de Biología, Facultad de Ciencias, Pontificia Universidad Javeriana, Carrera 7A No 43-82, Bogotá 110311, DC, Colombia; (P.E.-A.); (M.G.); (D.Á.); (M.P.)
| | - Pablo Escobar-Armel
- Laboratorio de Genética de Poblaciones Molecular-Biología Evolutiva, Unidad de Genética, Departamento de Biología, Facultad de Ciencias, Pontificia Universidad Javeriana, Carrera 7A No 43-82, Bogotá 110311, DC, Colombia; (P.E.-A.); (M.G.); (D.Á.); (M.P.)
| | - María Martínez-Agüero
- Facultad de Ciencias Naturales y Matemáticas, Universidad del Rosario, Bogotá 111321, DC, Colombia;
| | - Magda Gaviria
- Laboratorio de Genética de Poblaciones Molecular-Biología Evolutiva, Unidad de Genética, Departamento de Biología, Facultad de Ciencias, Pontificia Universidad Javeriana, Carrera 7A No 43-82, Bogotá 110311, DC, Colombia; (P.E.-A.); (M.G.); (D.Á.); (M.P.)
| | - Diana Álvarez
- Laboratorio de Genética de Poblaciones Molecular-Biología Evolutiva, Unidad de Genética, Departamento de Biología, Facultad de Ciencias, Pontificia Universidad Javeriana, Carrera 7A No 43-82, Bogotá 110311, DC, Colombia; (P.E.-A.); (M.G.); (D.Á.); (M.P.)
| | - Myreya Pinedo
- Laboratorio de Genética de Poblaciones Molecular-Biología Evolutiva, Unidad de Genética, Departamento de Biología, Facultad de Ciencias, Pontificia Universidad Javeriana, Carrera 7A No 43-82, Bogotá 110311, DC, Colombia; (P.E.-A.); (M.G.); (D.Á.); (M.P.)
| | - Joseph Mark Shostell
- Math, Science and Technology Department, University of Minnesota Crookston, Crookston, MN 56716, USA;
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4
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Marsh JI, Johri P. Biases in ARG-Based Inference of Historical Population Size in Populations Experiencing Selection. Mol Biol Evol 2024; 41:msae118. [PMID: 38874402 PMCID: PMC11245712 DOI: 10.1093/molbev/msae118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/05/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ancestral recombination graph (ARG)-based approaches to demographic inference in typical empirical analyses are susceptible to misinference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying, and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, although it could cause misinference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion, which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid misinference due to the linked effects of selection.
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Affiliation(s)
- Jacob I Marsh
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Parul Johri
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
- Integrative Program for Biological and Genome Sciences, University of North Carolina, Chapel Hill, NC 27599, USA
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5
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Hartfield M, Glémin S. Polygenic selection to a changing optimum under self-fertilisation. PLoS Genet 2024; 20:e1011312. [PMID: 39018328 DOI: 10.1371/journal.pgen.1011312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/29/2024] [Accepted: 05/21/2024] [Indexed: 07/19/2024] Open
Abstract
Many traits are polygenic, affected by multiple genetic variants throughout the genome. Selection acting on these traits involves co-ordinated allele-frequency changes at these underlying variants, and this process has been extensively studied in random-mating populations. Yet many species self-fertilise to some degree, which incurs changes to genetic diversity, recombination and genome segregation. These factors cumulatively influence how polygenic selection is realised in nature. Here, we use analytical modelling and stochastic simulations to investigate to what extent self-fertilisation affects polygenic adaptation to a new environment. Our analytical solutions show that while selfing can increase adaptation to an optimum, it incurs linkage disequilibrium that can slow down the initial spread of favoured mutations due to selection interference, and favours the fixation of alleles with opposing trait effects. Simulations show that while selection interference is present, high levels of selfing (at least 90%) aids adaptation to a new optimum, showing a higher long-term fitness. If mutations are pleiotropic then only a few major-effect variants fix along with many neutral hitchhikers, with a transient increase in linkage disequilibrium. These results show potential advantages to self-fertilisation when adapting to a new environment, and how the mating system affects the genetic composition of polygenic selection.
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Affiliation(s)
- Matthew Hartfield
- Institute of Ecology and Evolution, The University of Edinburgh, Edinburgh, United Kingdom
| | - Sylvain Glémin
- Université de Rennes, Centre National de la Recherche Scientifique (CNRS), ECOBIO (Ecosystèmes, Biodiversité, Evolution) - Unité Mixte de Recherche (UMR) 6553, Rennes, France
- Department of Ecology and Evolution, Evolutionary Biology Center, Uppsala University, Uppsala, Sweden
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6
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Murga-Moreno J, Casillas S, Barbadilla A, Uricchio L, Enard D. An efficient and robust ABC approach to infer the rate and strength of adaptation. G3 (BETHESDA, MD.) 2024; 14:jkae031. [PMID: 38365205 PMCID: PMC11090462 DOI: 10.1093/g3journal/jkae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 10/10/2023] [Accepted: 01/29/2024] [Indexed: 02/18/2024]
Abstract
Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in nonmodel species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to nonmodel genomes. We apply ABC-MK to the human proteome and a set of known virus interacting proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.
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Affiliation(s)
- Jesús Murga-Moreno
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85719, USA
| | - Sònia Casillas
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Antonio Barbadilla
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | | | - David Enard
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85719, USA
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Zurita AMI, Kyriazis CC, Lohmueller KE. The impact of non-neutral synonymous mutations when inferring selection on non-synonymous mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.07.579314. [PMID: 38370782 PMCID: PMC10871344 DOI: 10.1101/2024.02.07.579314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The distribution of fitness effects (DFE) describes the proportions of new mutations that have different effects on reproductive fitness. Accurate measurements of the DFE are important because the DFE is a fundamental parameter in evolutionary genetics and has implications for our understanding of other phenomena like complex disease or inbreeding depression. Current computational methods to infer the DFE for nonsynonymous mutations from natural variation first estimate demographic parameters from synonymous variants to control for the effects of demography and background selection. Then, conditional on these parameters, the DFE is then inferred for nonsynonymous mutations. This approach relies on the assumption that synonymous variants are neutrally evolving. However, some evidence points toward synonymous mutations having measurable effects on fitness. To test whether selection on synonymous mutations affects inference of the DFE of nonsynonymous mutations, we simulated several possible models of selection on synonymous mutations using SLiM and attempted to recover the DFE of nonsynonymous mutations using Fit∂a∂i, a common method for DFE inference. Our results show that the presence of selection on synonymous variants leads to incorrect inferences of recent population growth. Furthermore, under certain parameter combinations, inferences of the DFE can have an inflated proportion of highly deleterious nonsynonymous mutations. However, this bias can be eliminated if the correct demographic parameters are used for DFE inference instead of the biased ones inferred from synonymous variants. Our work demonstrates how unmodeled selection on synonymous mutations may affect downstream inferences of the DFE.
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Affiliation(s)
- Aina Martinez I Zurita
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, USA
| | - Christopher C Kyriazis
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, USA
| | - Kirk E Lohmueller
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, USA
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, USA
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, USA
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8
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Galtier N. Half a Century of Controversy: The Neutralist/Selectionist Debate in Molecular Evolution. Genome Biol Evol 2024; 16:evae003. [PMID: 38311843 PMCID: PMC10839204 DOI: 10.1093/gbe/evae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/01/2024] [Indexed: 02/06/2024] Open
Abstract
The neutral and nearly neutral theories, introduced more than 50 yr ago, have raised and still raise passionate discussion regarding the forces governing molecular evolution and their relative importance. The debate, initially focused on the amount of within-species polymorphism and constancy of the substitution rate, has spread, matured, and now underlies a wide range of topics and questions. The neutralist/selectionist controversy has structured the field and influences the way molecular evolutionary scientists conceive their research.
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Affiliation(s)
- Nicolas Galtier
- ISEM, CNRS, IRD, Université de Montpellier, Montpellier, France
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9
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Wang ZF, Fu L, Yu EP, Zhu WG, Zeng SJ, Cao HL. Chromosome-level genome assembly and demographic history of Euryodendron excelsum in monotypic genus endemic to China. DNA Res 2024; 31:dsad028. [PMID: 38147541 PMCID: PMC10781514 DOI: 10.1093/dnares/dsad028] [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: 10/19/2023] [Revised: 12/04/2023] [Accepted: 12/22/2023] [Indexed: 12/28/2023] Open
Abstract
Euryodendron excelsum is in a monotypic genus Euryodendron, endemic to China. It has intermediate morphisms in the Pentaphylacaceae or Theaceae families, which make it distinct. Due to anthropogenic disturbance, E. excelsum is currently found in very restricted and fragmented areas with extremely small populations. Although much research and effort has been applied towards its conservation, its long-term survival mechanisms and evolutionary history remain elusive, especially from a genomic aspect. Therefore, using a combination of long/short whole genome sequencing, RNA sequencing reads, and Hi-C data, we assembled and annotated a high-quality genome for E. excelsum. The genome assembly of E. excelsum comprised 1,059,895,887 bp with 99.66% anchored into 23 pseudo-chromosomes and a 99.0% BUSCO completeness. Comparative genomic analysis revealed the expansion of terpenoid and flavonoid secondary metabolite genes, and displayed a tandem and/or proximal duplication framework of these genes. E. excelsum also displayed genes associated with growth, development, and defence adaptation from whole genome duplication. Demographic analysis indicated that its fluctuations in population size and its recent population decline were related to cold climate changes. The E. excelsum genome assembly provides a highly valuable resource for evolutionary and ecological research in the future, aiding its conservation, management, and restoration.
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Affiliation(s)
- Zheng-Feng Wang
- Guangdong Provincial Key Laboratory of Applied Botany, Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- South China National Botanical Garden, Guangzhou, Guangdong 510650, China
| | - Lin Fu
- Guangdong Provincial Key Laboratory of Applied Botany, Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- South China National Botanical Garden, Guangzhou, Guangdong 510650, China
| | - En-Ping Yu
- Guangdong Provincial Key Laboratory of Applied Botany, Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- South China National Botanical Garden, Guangzhou, Guangdong 510650, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei-Guang Zhu
- Guangdong Provincial Key Laboratory of Applied Botany, Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- South China National Botanical Garden, Guangzhou, Guangdong 510650, China
| | - Song-Jun Zeng
- Guangdong Provincial Key Laboratory of Applied Botany, Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- South China National Botanical Garden, Guangzhou, Guangdong 510650, China
| | - Hong-Lin Cao
- Guangdong Provincial Key Laboratory of Applied Botany, Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- South China National Botanical Garden, Guangzhou, Guangdong 510650, China
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10
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Cousins T, Tabin D, Patterson N, Reich D, Durvasula A. Accurate inference of population history in the presence of background selection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576291. [PMID: 38313273 PMCID: PMC10838404 DOI: 10.1101/2024.01.18.576291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
All published methods for learning about demographic history make the simplifying assumption that the genome evolves neutrally, and do not seek to account for the effects of natural selection on patterns of variation. This is a major concern, as ample work has demonstrated the pervasive effects of natural selection and in particular background selection (BGS) on patterns of genetic variation in diverse species. Simulations and theoretical work have shown that methods to infer changes in effective population size over time (Ne(t)) become increasingly inaccurate as the strength of linked selection increases. Here, we introduce an extension to the Pairwise Sequentially Markovian Coalescent (PSMC) algorithm, PSMC+, which explicitly co-models demographic history and natural selection. We benchmark our method using forward-in-time simulations with BGS and find that our approach improves the accuracy of effective population size inference. Leveraging a high resolution map of BGS in humans, we infer considerable changes in the magnitude of inferred effective population size relative to previous reports. Finally, we separately infer Ne(t) on the X chromosome and on the autosomes in diverse great apes without making a correction for selection, and find that the inferred ratio fluctuates substantially through time in a way that differs across species, showing that uncorrected selection may be an important driver of signals of genetic difference on the X chromosome and autosomes.
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Affiliation(s)
- Trevor Cousins
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Daniel Tabin
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Nick Patterson
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - David Reich
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Boston, MA, USA
| | - Arun Durvasula
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, 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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11
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Thom G, Moreira LR, Batista R, Gehara M, Aleixo A, Smith BT. Genomic Architecture Predicts Tree Topology, Population Structuring, and Demographic History in Amazonian Birds. Genome Biol Evol 2024; 16:evae002. [PMID: 38236173 PMCID: PMC10823491 DOI: 10.1093/gbe/evae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/26/2023] [Accepted: 12/12/2023] [Indexed: 01/19/2024] Open
Abstract
Geographic barriers are frequently invoked to explain genetic structuring across the landscape. However, inferences on the spatial and temporal origins of population variation have been largely limited to evolutionary neutral models, ignoring the potential role of natural selection and intrinsic genomic processes known as genomic architecture in producing heterogeneity in differentiation across the genome. To test how variation in genomic characteristics (e.g. recombination rate) impacts our ability to reconstruct general patterns of differentiation between species that cooccur across geographic barriers, we sequenced the whole genomes of multiple bird populations that are distributed across rivers in southeastern Amazonia. We found that phylogenetic relationships within species and demographic parameters varied across the genome in predictable ways. Genetic diversity was positively associated with recombination rate and negatively associated with species tree support. Gene flow was less pervasive in genomic regions of low recombination, making these windows more likely to retain patterns of population structuring that matched the species tree. We further found that approximately a third of the genome showed evidence of selective sweeps and linked selection, skewing genome-wide estimates of effective population sizes and gene flow between populations toward lower values. In sum, we showed that the effects of intrinsic genomic characteristics and selection can be disentangled from neutral processes to elucidate spatial patterns of population differentiation.
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Affiliation(s)
- Gregory Thom
- Department of Ornithology, American Museum of Natural History, New York, NY, USA
- Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Lucas Rocha Moreira
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Department of Vertebrate Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Romina Batista
- Programa de Coleções Biológicas, Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil
- School of Science, Engineering and Environment, University of Salford, Manchester, UK
| | - Marcelo Gehara
- Department of Earth and Environmental Sciences, Rutgers University, Newark, NJ, USA
| | - Alexandre Aleixo
- Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland
- Department of Environmental Genomics, Instituto Tecnológico Vale, Belém, Brazil
| | - Brian Tilston Smith
- Department of Ornithology, American Museum of Natural History, New York, NY, USA
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12
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Panigrahi M, Rajawat D, Nayak SS, Ghildiyal K, Sharma A, Jain K, Lei C, Bhushan B, Mishra BP, Dutt T. Landmarks in the history of selective sweeps. Anim Genet 2023; 54:667-688. [PMID: 37710403 DOI: 10.1111/age.13355] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023]
Abstract
Half a century ago, a seminal article on the hitchhiking effect by Smith and Haigh inaugurated the concept of the selection signature. Selective sweeps are characterised by the rapid spread of an advantageous genetic variant through a population and hence play an important role in shaping evolution and research on genetic diversity. The process by which a beneficial allele arises and becomes fixed in a population, leading to a increase in the frequency of other linked alleles, is known as genetic hitchhiking or genetic draft. Kimura's neutral theory and hitchhiking theory are complementary, with Kimura's neutral evolution as the 'null model' and positive selection as the 'signal'. Both are widely accepted in evolution, especially with genomics enabling precise measurements. Significant advances in genomic technologies, such as next-generation sequencing, high-density SNP arrays and powerful bioinformatics tools, have made it possible to systematically investigate selection signatures in a variety of species. Although the history of selection signatures is relatively recent, progress has been made in the last two decades, owing to the increasing availability of large-scale genomic data and the development of computational methods. In this review, we embark on a journey through the history of research on selective sweeps, ranging from early theoretical work to recent empirical studies that utilise genomic data.
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Affiliation(s)
- Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | | | - Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Anurodh Sharma
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Karan Jain
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Chuzhao Lei
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Bishnu Prasad Mishra
- Division of Animal Biotechnology, ICAR-National Bureau of Animal Genetic Resources, Karnal, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Bareilly, India
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13
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Feng S, DeGrey SP, Guédot C, Schoville SD, Pool JE. Genomic Diversity Illuminates the Environmental Adaptation of Drosophila suzukii. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.03.547576. [PMID: 37461625 PMCID: PMC10349955 DOI: 10.1101/2023.07.03.547576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Biological invasions carry substantial practical and scientific importance, and represent natural evolutionary experiments on contemporary timescales. Here, we investigated genomic diversity and environmental adaptation of the crop pest Drosophila suzukii using whole-genome sequencing data and environmental metadata for 29 population samples from its native and invasive range. Through a multifaceted analysis of this population genomic data, we increase our understanding of the D. suzukii genome, its diversity and its evolution, and we identify an appropriate genotype-environment association pipeline for our data set. Using this approach, we detect genetic signals of local adaptation associated with nine distinct environmental factors related to altitude, wind speed, precipitation, temperature, and human land use. We uncover unique functional signatures for each environmental variable, such as a prevalence of cuticular genes associated with annual precipitation. We also infer biological commonalities in the adaptation to diverse selective pressures, particularly in terms of the apparent contribution of nervous system evolution to enriched processes (ranging from neuron development to circadian behavior) and to top genes associated with all nine environmental variables. Our findings therefore depict a finer-scale adaptive landscape underlying the rapid invasion success of this agronomically important species.
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Affiliation(s)
- Siyuan Feng
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Samuel P. DeGrey
- Department of Entomology, University of Wisconsin-Madison, Madison, WI, USA
| | - Christelle Guédot
- Department of Entomology, University of Wisconsin-Madison, Madison, WI, USA
| | - Sean D. Schoville
- Department of Entomology, University of Wisconsin-Madison, Madison, WI, USA
| | - John E. Pool
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
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14
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Medina-Muñoz SG, Ortega-Del Vecchyo D, Cruz-Hervert LP, Ferreyra-Reyes L, García-García L, Moreno-Estrada A, Ragsdale AP. Demographic modeling of admixed Latin American populations from whole genomes. Am J Hum Genet 2023; 110:1804-1816. [PMID: 37725976 PMCID: PMC10577084 DOI: 10.1016/j.ajhg.2023.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/17/2023] [Accepted: 08/23/2023] [Indexed: 09/21/2023] Open
Abstract
Demographic models of Latin American populations often fail to fully capture their complex evolutionary history, which has been shaped by both recent admixture and deeper-in-time demographic events. To address this gap, we used high-coverage whole-genome data from Indigenous American ancestries in present-day Mexico and existing genomes from across Latin America to infer multiple demographic models that capture the impact of different timescales on genetic diversity. Our approach, which combines analyses of allele frequencies and ancestry tract length distributions, represents a significant improvement over current models in predicting patterns of genetic variation in admixed Latin American populations. We jointly modeled the contribution of European, African, East Asian, and Indigenous American ancestries into present-day Latin American populations. We infer that the ancestors of Indigenous Americans and East Asians diverged ∼30 thousand years ago, and we characterize genetic contributions of recent migrations from East and Southeast Asia to Peru and Mexico. Our inferred demographic histories are consistent across different genomic regions and annotations, suggesting that our inferences are robust to the potential effects of linked selection. In conjunction with published distributions of fitness effects for new nonsynonymous mutations in humans, we show in large-scale simulations that our models recover important features of both neutral and deleterious variation. By providing a more realistic framework for understanding the evolutionary history of Latin American populations, our models can help address the historical under-representation of admixed groups in genomics research and can be a valuable resource for future studies of populations with complex admixture and demographic histories.
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Affiliation(s)
- Santiago G Medina-Muñoz
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato 36824, Mexico
| | - Diego Ortega-Del Vecchyo
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de Mexico, Juriquilla, Querétaro 76230, Mexico
| | | | | | | | - Andrés Moreno-Estrada
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato 36824, Mexico.
| | - Aaron P Ragsdale
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato 36824, Mexico; Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA.
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15
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Murga-Moreno J, Casillas S, Barbadilla A, Uricchio L, Enard D. An efficient and robust ABC approach to infer the rate and strength of adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.29.555322. [PMID: 37693550 PMCID: PMC10491248 DOI: 10.1101/2023.08.29.555322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in non-model species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to non-model genomes. We apply ABC-MK to the human proteome and a set of known Virus Interacting Proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.
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Affiliation(s)
- Jesús Murga-Moreno
- University of Arizona Department of Ecology and Evolutionary Biology, Tucson, USA
| | - Sònia Casillas
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Antonio Barbadilla
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | | | - David Enard
- University of Arizona Department of Ecology and Evolutionary Biology, Tucson, USA
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16
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Kuderna LFK, Gao H, Janiak MC, Kuhlwilm M, Orkin JD, Bataillon T, Manu S, Valenzuela A, Bergman J, Rousselle M, Silva FE, Agueda L, Blanc J, Gut M, de Vries D, Goodhead I, Harris RA, Raveendran M, Jensen A, Chuma IS, Horvath JE, Hvilsom C, Juan D, Frandsen P, Schraiber JG, de Melo FR, Bertuol F, Byrne H, Sampaio I, Farias I, Valsecchi J, Messias M, da Silva MNF, Trivedi M, Rossi R, Hrbek T, Andriaholinirina N, Rabarivola CJ, Zaramody A, Jolly CJ, Phillips-Conroy J, Wilkerson G, Abee C, Simmons JH, Fernandez-Duque E, Kanthaswamy S, Shiferaw F, Wu D, Zhou L, Shao Y, Zhang G, Keyyu JD, Knauf S, Le MD, Lizano E, Merker S, Navarro A, Nadler T, Khor CC, Lee J, Tan P, Lim WK, Kitchener AC, Zinner D, Gut I, Melin AD, Guschanski K, Schierup MH, Beck RMD, Umapathy G, Roos C, Boubli JP, Rogers J, Farh KKH, Marques Bonet T. A global catalog of whole-genome diversity from 233 primate species. Science 2023; 380:906-913. [PMID: 37262161 DOI: 10.1126/science.abn7829] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 02/06/2023] [Indexed: 06/03/2023]
Abstract
The rich diversity of morphology and behavior displayed across primate species provides an informative context in which to study the impact of genomic diversity on fundamental biological processes. Analysis of that diversity provides insight into long-standing questions in evolutionary and conservation biology and is urgent given severe threats these species are facing. Here, we present high-coverage whole-genome data from 233 primate species representing 86% of genera and all 16 families. This dataset was used, together with fossil calibration, to create a nuclear DNA phylogeny and to reassess evolutionary divergence times among primate clades. We found within-species genetic diversity across families and geographic regions to be associated with climate and sociality, but not with extinction risk. Furthermore, mutation rates differ across species, potentially influenced by effective population sizes. Lastly, we identified extensive recurrence of missense mutations previously thought to be human specific. This study will open a wide range of research avenues for future primate genomic research.
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Affiliation(s)
- Lukas F K Kuderna
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra. PRBB, C. Doctor Aiguader N88, 08003 Barcelona, Spain
- Illumina Artificial Intelligence Laboratory, Illumina Inc., Foster City, CA 94404, USA
| | - Hong Gao
- Illumina Artificial Intelligence Laboratory, Illumina Inc., Foster City, CA 94404, USA
| | - Mareike C Janiak
- School of Science, Engineering & Environment, University of Salford, Salford M5 4WT, UK
| | - Martin Kuhlwilm
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra. PRBB, C. Doctor Aiguader N88, 08003 Barcelona, Spain
- Department of Evolutionary Anthropology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Austria
| | - Joseph D Orkin
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra. PRBB, C. Doctor Aiguader N88, 08003 Barcelona, Spain
- Département d'anthropologie, Université de Montréal, 3150 Jean-Brillant, Montréal, QC H3T 1N8, Canada
| | - Thomas Bataillon
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Shivakumara Manu
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
- Laboratory for the Conservation of Endangered Species, CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500007, India
| | - Alejandro Valenzuela
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra. PRBB, C. Doctor Aiguader N88, 08003 Barcelona, Spain
| | - Juraj Bergman
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark
| | | | - Felipe Ennes Silva
- Research Group on Primate Biology and Conservation, Mamirauá Institute for Sustainable Development, Estrada da Bexiga 2584, CEP 69553-225, Tefé, Amazonas, Brazil
- Evolutionary Biology and Ecology (EBE), Département de Biologie des Organismes, Université libre de Bruxelles (ULB), Av. Franklin D. Roosevelt 50, CP 160/12, B-1050 Brussels Belgium
| | - Lidia Agueda
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri I Reixac 4, 08028 Barcelona, Spain
| | - Julie Blanc
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri I Reixac 4, 08028 Barcelona, Spain
| | - Marta Gut
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri I Reixac 4, 08028 Barcelona, Spain
| | - Dorien de Vries
- School of Science, Engineering & Environment, University of Salford, Salford M5 4WT, UK
| | - Ian Goodhead
- School of Science, Engineering & Environment, University of Salford, Salford M5 4WT, UK
| | - R Alan Harris
- Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Muthuswamy Raveendran
- Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Axel Jensen
- Department of Ecology and Genetics, Animal Ecology, Uppsala University, SE-75236 Uppsala, Sweden
| | | | - Julie E Horvath
- North Carolina Museum of Natural Sciences, Raleigh, NC 27601, USA
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, NC 27707, USA
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - David Juan
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra. PRBB, C. Doctor Aiguader N88, 08003 Barcelona, Spain
| | | | - Joshua G Schraiber
- Illumina Artificial Intelligence Laboratory, Illumina Inc., Foster City, CA 94404, USA
| | | | - Fabrício Bertuol
- Universidade Federal do Amazonas, Departamento de Genética, Laboratório de Evolução e Genética Animal (LEGAL), Manaus, Amazonas 69080-900, Brazil
| | - Hazel Byrne
- Department of Anthropology, University of Utah, Salt Lake City. UT 84102, USA
| | | | - Izeni Farias
- Universidade Federal do Amazonas, Departamento de Genética, Laboratório de Evolução e Genética Animal (LEGAL), Manaus, Amazonas 69080-900, Brazil
| | - João Valsecchi
- Research Group on Terrestrial Vertebrate Ecology, Mamirauá Institute for Sustainable Development, Tefé, Amazonas, Brazil
- Rede de Pesquisa para Estudos sobre Diversidade, Conservação e Uso da Fauna na Amazônia - RedeFauna, Manaus, Amazonas, Brazil
- Comunidad de Manejo de Fauna Silvestre en la Amazonía y en Latinoamérica - ComFauna, Iquitos, Loreto, Peru
| | - Malu Messias
- Universidade Federal de Rondônia, Porto Velho, Rondônia, Brazil
| | | | - Mihir Trivedi
- Laboratory for the Conservation of Endangered Species, CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500007, India
| | - Rogerio Rossi
- Instituto de Biociências, Universidade Federal do Mato Grosso, Cuiabá, MT, Brazil
| | - Tomas Hrbek
- Universidade Federal do Amazonas, Departamento de Genética, Laboratório de Evolução e Genética Animal (LEGAL), Manaus, Amazonas 69080-900, Brazil
- Department of Biology, Trinity University, San Antonio, TX 78212, USA
| | - Nicole Andriaholinirina
- Life Sciences and Environment, Technology and Environment of Mahajanga, University of Mahajanga, Mahajanga, Madagascar
| | - Clément J Rabarivola
- Life Sciences and Environment, Technology and Environment of Mahajanga, University of Mahajanga, Mahajanga, Madagascar
| | - Alphonse Zaramody
- Life Sciences and Environment, Technology and Environment of Mahajanga, University of Mahajanga, Mahajanga, Madagascar
| | - Clifford J Jolly
- Department of Anthropology, New York University, New York, NY 10003, USA
| | - Jane Phillips-Conroy
- Department of Neuroscience, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Gregory Wilkerson
- Keeling Center for Comparative Medicine and Research, MD Anderson Cancer Center, Bastrop TX 78602, USA
| | - Christian Abee
- Keeling Center for Comparative Medicine and Research, MD Anderson Cancer Center, Bastrop TX 78602, USA
| | - Joe H Simmons
- Keeling Center for Comparative Medicine and Research, MD Anderson Cancer Center, Bastrop TX 78602, USA
| | | | - Sree Kanthaswamy
- School of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85004, USA
| | - Fekadu Shiferaw
- Guinea Worm Eradication Program, The Carter Center Ethiopia, Addis Ababa, Ethiopia
| | - Dongdong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Long Zhou
- Center for Evolutionary and Organismal Biology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Yong Shao
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Guojie Zhang
- Center for Evolutionary and Organismal Biology, Zhejiang University School of Medicine, Hangzhou 310058, China
- Villum Centre for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
- Liangzhu Laboratory, Zhejiang University Medical Center, 1369 West Wenyi Road, Hangzhou 311121, China
- Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Shangcheng District, Hangzhou 310006, China
| | - Julius D Keyyu
- Tanzania Wildlife Research Institute (TAWIRI), Head Office, P.O. Box 661, Arusha, Tanzania
| | - Sascha Knauf
- Institute of International Animal Health/One Health, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, 17493 Greifswald-Insel Riems, Germany
| | - Minh D Le
- Department of Environmental Ecology, Faculty of Environmental Sciences, University of Science and Central Institute for Natural Resources and Environmental Studies, Vietnam National University, Hanoi, Vietnam
| | - Esther Lizano
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra. PRBB, C. Doctor Aiguader N88, 08003 Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Stefan Merker
- Department of Zoology, State Museum of Natural History Stuttgart, Stuttgart, Germany
| | - Arcadi Navarro
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra. PRBB, C. Doctor Aiguader N88, 08003 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA) and Universitat Pompeu Fabra. Pg. Luís Companys 23, 08010 Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Av. Doctor Aiguader, N88, 08003 Barcelona, Spain
- BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation, C. Wellington 30, 08005 Barcelona, Spain
| | - Tilo Nadler
- Cuc Phuong Commune, Nho Quan District, Ninh Binh Province, Vietnam
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | - Jessica Lee
- Mandai Nature, 80 Mandai Lake Road, Singapore
| | - Patrick Tan
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
- SingHealth Duke-NUS Institute of Precision Medicine (PRISM), Singapore
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore
| | - Weng Khong Lim
- SingHealth Duke-NUS Institute of Precision Medicine (PRISM), Singapore
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore
- SingHealth Duke-NUS Genomic Medicine Centre, Singapore
| | - Andrew C Kitchener
- Department of Natural Sciences, National Museums Scotland, Chambers Street, Edinburgh EH1 1JF, UK, and School of Geosciences, Drummond Street, Edinburgh EH8 9XP, UK
| | - Dietmar Zinner
- Cognitive Ethology Laboratory, Germany Primate Center, Leibniz Institute for Primate Research, 37077 Göttingen, Germany
- Department of Primate Cognition, Georg-August-Universität Göttingen, 37077 Göttingen, Germany
- Leibniz ScienceCampus Primate Cognition, 37077 Göttingen, Germany
| | - Ivo Gut
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri I Reixac 4, 08028 Barcelona, Spain
| | - Amanda D Melin
- Department of Anthropology and Archaeology, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
- Department of Medical Genetics, University of Calgary, 3330 Hospital Drive NW, HMRB 202, Calgary, AB T2N 4N1, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, 3330 Hospital Drive NW, HMRB 202, Calgary, AB T2N 4N1, Canada
| | - Katerina Guschanski
- Department of Ecology and Genetics, Animal Ecology, Uppsala University, SE-75236 Uppsala, Sweden
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Robin M D Beck
- School of Science, Engineering & Environment, University of Salford, Salford M5 4WT, UK
| | - Govindhaswamy Umapathy
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
- Laboratory for the Conservation of Endangered Species, CSIR-Centre for Cellular and Molecular Biology, Hyderabad 500007, India
| | - Christian Roos
- Gene Bank of Primates and Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Jean P Boubli
- School of Science, Engineering & Environment, University of Salford, Salford M5 4WT, UK
| | - Jeffrey Rogers
- Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kyle Kai-How Farh
- Illumina Artificial Intelligence Laboratory, Illumina Inc., Foster City, CA 94404, USA
| | - Tomas Marques Bonet
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra. PRBB, C. Doctor Aiguader N88, 08003 Barcelona, Spain
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri I Reixac 4, 08028 Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA) and Universitat Pompeu Fabra. Pg. Luís Companys 23, 08010 Barcelona, Spain
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17
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Mooney JA, Marsden CD, Yohannes A, Wayne RK, Lohmueller KE. Long-term Small Population Size, Deleterious Variation, and Altitude Adaptation in the Ethiopian Wolf, a Severely Endangered Canid. Mol Biol Evol 2023; 40:msac277. [PMID: 36585842 PMCID: PMC9847632 DOI: 10.1093/molbev/msac277] [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: 08/18/2022] [Revised: 11/07/2022] [Accepted: 12/22/2022] [Indexed: 01/01/2023] Open
Abstract
Ethiopian wolves, a canid species endemic to the Ethiopian Highlands, have been steadily declining in numbers for decades. Currently, out of 35 extant species, it is now one of the world's most endangered canids. Most conservation efforts have focused on preventing disease, monitoring movements and behavior, and assessing the geographic ranges of sub-populations. Here, we add an essential layer by determining the Ethiopian wolf's demographic and evolutionary history using high-coverage (∼40×) whole-genome sequencing from 10 Ethiopian wolves from the Bale Mountains. We observe exceptionally low diversity and enrichment of weakly deleterious variants in the Ethiopian wolves in comparison with two North American gray wolf populations and four dog breeds. These patterns are consequences of long-term small population size, rather than recent inbreeding. We infer the demographic history of the Ethiopian wolf and find it to be concordant with historic records and previous genetic analyses, suggesting Ethiopian wolves experienced a series of both ancient and recent bottlenecks, resulting in a census population size of fewer than 500 individuals and an estimated effective population size of approximately 100 individuals. Additionally, long-term small population size may have limited the accumulation of strongly deleterious recessive mutations. Finally, as the Ethiopian wolves have inhabited high-altitude areas for thousands of years, we searched for evidence of high-altitude adaptation, finding evidence of positive selection at a transcription factor in a hypoxia-response pathway [CREB-binding protein (CREBBP)]. Our findings are pertinent to continuing conservation efforts and understanding how demography influences the persistence of deleterious variation in small populations.
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Affiliation(s)
- Jazlyn A Mooney
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Clare D Marsden
- Department of Ecology & Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Abigail Yohannes
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Robert K Wayne
- Department of Ecology & Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Kirk E Lohmueller
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Ecology & Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, USA
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18
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Turba R, Richmond JQ, Fitz-Gibbon S, Morselli M, Fisher RN, Swift CC, Ruiz-Campos G, Backlin AR, Dellith C, Jacobs DK. Genetic structure and historic demography of endangered unarmoured threespine stickleback at southern latitudes signals a potential new management approach. Mol Ecol 2022; 31:6515-6530. [PMID: 36205603 PMCID: PMC10092051 DOI: 10.1111/mec.16722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 09/05/2022] [Accepted: 09/29/2022] [Indexed: 01/13/2023]
Abstract
Habitat loss, flood control infrastructure, and drought have left most of southern California and northern Baja California's native freshwater fish near extinction, including the endangered unarmoured threespine stickleback (Gasterosteus aculeatus williamsoni). This subspecies, an unusual morph lacking the typical lateral bony plates of the G. aculeatus complex, occurs at arid southern latitudes in the eastern Pacific Ocean and survives in only three inland locations. Managers have lacked molecular data to answer basic questions about the ancestry and genetic distinctiveness of unarmoured populations. These data could be used to prioritize conservation efforts. We sampled G. aculeatus from 36 localities and used microsatellites and whole genome data to place unarmoured populations within the broader evolutionary context of G. aculeatus across southern California/northern Baja California. We identified three genetic groups with none consisting solely of unarmoured populations. Unlike G. aculeatus at northern latitudes, where Pleistocene glaciation has produced similar historical demographic profiles across populations, we found markedly different demographics depending on sampling location, with inland unarmoured populations showing steeper population declines and lower heterozygosity compared to low armoured populations in coastal lagoons. One exception involved the only high elevation population in the region, where the demography and alleles of unarmoured fish were similar to low armoured populations near the coast, exposing one of several cases of artificial translocation. Our results suggest that the current "management-by-phenotype" approach, based on lateral plates, is incidentally protecting the most imperilled populations; however, redirecting efforts toward evolutionary units, regardless of phenotype, may more effectively preserve adaptive potential.
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Affiliation(s)
- Rachel Turba
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA
| | | | - Sorel Fitz-Gibbon
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA
| | - Marco Morselli
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, California, USA
| | | | - Camm C Swift
- Emeritus, Section of Fishes, Natural History Museum of Los Angeles County, Los Angeles, California, USA
| | - Gorgonio Ruiz-Campos
- Ichthyological Collection, Facultad de Ciencias, Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico
| | - Adam R Backlin
- U.S. Geological Survey, Western Ecological Research Center, San Diego Field Station-Santa Ana Office, Santa Ana, California, USA
| | - Chris Dellith
- U.S. Fish and Wildlife Service, Ventura, California, USA
| | - David K Jacobs
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA
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19
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Wilder AP, Dudchenko O, Curry C, Korody M, Turbek SP, Daly M, Misuraca A, Gaojianyong WANG, Khan R, Weisz D, Fronczek J, Aiden EL, Houck ML, Shier DM, Ryder OA, Steiner CC. A chromosome-length reference genome for the endangered Pacific pocket mouse reveals recent inbreeding in a historically large population. Genome Biol Evol 2022; 14:6650481. [PMID: 35894178 PMCID: PMC9348616 DOI: 10.1093/gbe/evac122] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
High-quality reference genomes are fundamental tools for understanding population history, and can provide estimates of genetic and demographic parameters relevant to the conservation of biodiversity. The federally endangered Pacific pocket mouse (PPM), which persists in three small, isolated populations in southern California, is a promising model for studying how demographic history shapes genetic diversity, and how diversity in turn may influence extinction risk. To facilitate these studies in PPM, we combined PacBio HiFi long reads with Omni-C and Hi-C data to generate a de novo genome assembly, and annotated the genome using RNAseq. The assembly comprised 28 chromosome-length scaffolds (N50 = 72.6 MB) and the complete mitochondrial genome, and included a long heterochromatic region on chromosome 18 not represented in the previously available short-read assembly. Heterozygosity was highly variable across the genome of the reference individual, with 18% of windows falling in runs of homozygosity (ROH) >1 MB, and nearly 9% in tracts spanning >5 MB. Yet outside of ROH, heterozygosity was relatively high (0.0027), and historical Ne estimates were large. These patterns of genetic variation suggest recent inbreeding in a formerly large population. Currently the most contiguous assembly for a heteromyid rodent, this reference genome provides insight into the past and recent demographic history of the population, and will be a critical tool for management and future studies of outbreeding depression, inbreeding depression, and genetic load.
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Affiliation(s)
- Aryn P Wilder
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA
| | - Olga Dudchenko
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, USA.,Center for Theoretical Biological Physics and Department of Computer Science, Rice University, USA
| | - Caitlin Curry
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA
| | - Marisa Korody
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA
| | - Sheela P Turbek
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA.,Ecology and Evolutionary Biology, University of Colorado, Boulder, USA
| | | | - Ann Misuraca
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA
| | - W A N G Gaojianyong
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Ruqayya Khan
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, USA
| | - David Weisz
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, USA
| | - Julie Fronczek
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA
| | - Erez Lieberman Aiden
- The Center for Genome Architecture, Department of Molecular and Human Genetics, Baylor College of Medicine, USA.,Center for Theoretical Biological Physics and Department of Computer Science, Rice University, USA.,UWA School of Agriculture and Environment, The University of Western Australia, Australia.,Broad Institute of MIT and Harvard, USA.,Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech, China
| | - Marlys L Houck
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA
| | - Debra M Shier
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA.,Department of Ecology & Evolutionary Biology, University of California Los Angeles, USA
| | - Oliver A Ryder
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA
| | - Cynthia C Steiner
- Conservation Science Wildlife Health, San Diego Zoo Wildlife Alliance, USA
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20
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Brachmann MK, Parsons K, Skúlason S, Gaggiotti O, Ferguson M. Variation in the genomic basis of parallel phenotypic and ecological divergence in benthic and pelagic morphs of Icelandic Arctic charr (Salvelinus alpinus). Mol Ecol 2022; 31:4688-4706. [PMID: 35861579 DOI: 10.1111/mec.16625] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 06/22/2022] [Accepted: 07/06/2022] [Indexed: 11/28/2022]
Abstract
Sympatric adaptive phenotypic divergence should be underlain by genomic differentiation between sub-populations. When divergence drives similar patterns of phenotypic and ecological variation within species we expect evolution to draw on common allelic variation. We investigated divergence histories and genomic signatures of adaptive divergence between benthic and pelagic morphs of Icelandic Arctic charr. Divergence histories for each of four populations were reconstructed using coalescent modelling and 14,187 single nucleotide polymorphisms. Sympatric divergence with continuous gene flow was supported in two populations while allopatric divergence with secondary contact was supported in one population; we could not differentiate between demographic models in the fourth population. We detected parallel patterns of phenotypic divergence along benthic-pelagic evolutionary trajectories among populations. Patterns of genomic differentiation between benthic and pelagic morphs were characterized by outlier loci in many narrow peaks of differentiation throughout the genome, which may reflect the eroding effects of gene flow on nearby neutral loci. We then used genome-wide association analyses to relate both phenotypic (body shape and size) and ecological (carbon and nitrogen stable isotopes) variation to patterns of genomic differentiation. Many peaks of genomic differentiation were associated with phenotypic and ecological variation in the three highly divergent populations, suggesting a genomic basis for adaptive divergence. We detected little evidence for a parallel genomic basis of differentiation as most regions and outlier loci were not shared among populations. Our results show that adaptive divergence can have varied genomic consequences in populations with relatively recent common origins, similar divergence histories, and parallel phenotypic divergence.
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Affiliation(s)
| | - Kevin Parsons
- Institute of Biodiversity, Animal Health and Comparative Medicine, School of Life Science, University of Glasgow, Glasgow, UK
| | - Skúli Skúlason
- Department of Aquaculture and Fish Biology, Hólar University, Saudárkrókur, Iceland.,Icelandic Museum of Natural History, Reykjavik, Iceland
| | - Oscar Gaggiotti
- School of biology, Scottish Oceans Institute, University of St. Andrews, St. Andrews, UK
| | - Moira Ferguson
- Department of Integrative Biology, University of Guelph, Guelph, ON, Canada
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21
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Johri P, Eyre-Walker A, Gutenkunst RN, Lohmueller KE, Jensen JD. On the prospect of achieving accurate joint estimation of selection with population history. Genome Biol Evol 2022; 14:evac088. [PMID: 35675379 PMCID: PMC9254643 DOI: 10.1093/gbe/evac088] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2022] [Indexed: 11/15/2022] Open
Abstract
As both natural selection and population history can affect genome-wide patterns of variation, disentangling the contributions of each has remained as a major challenge in population genetics. We here discuss historical and recent progress towards this goal-highlighting theoretical and computational challenges that remain to be addressed, as well as inherent difficulties in dealing with model complexity and model violations-and offer thoughts on potentially fruitful next steps.
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Affiliation(s)
- Parul Johri
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | | | - Ryan N Gutenkunst
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Kirk E Lohmueller
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, CA, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
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22
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Ruiz-García M, Cáceres AM, Luengas-Villamil K, Aliaga-Rossel E, Zeballos H, Singh MD, Shostell JM. Mitogenomic phylogenetics and population genetics of several taxa of agouties (Dasyprocta sp., Dasyproctidae, Rodentia): molecular nonexistence of some claimed endemic taxa. MAMMAL RES 2022. [DOI: 10.1007/s13364-022-00626-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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23
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Dittberner H, Tellier A, de Meaux J. Approximate Bayesian computation untangles signatures of contemporary and historical hybridization between two endangered species. Mol Biol Evol 2022; 39:6516021. [PMID: 35084503 PMCID: PMC8826969 DOI: 10.1093/molbev/msac015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Contemporary gene flow, when resumed after a period of isolation, can have crucial consequences for endangered species, as it can both increase the supply of adaptive alleles and erode local adaptation. Determining the history of gene flow and thus the importance of contemporary hybridization, however, is notoriously difficult. Here, we focus on two endangered plant species, Arabis nemorensis and A. sagittata, which hybridize naturally in a sympatric population located on the banks of the Rhine. Using reduced genome sequencing, we determined the phylogeography of the two taxa but report only a unique sympatric population. Molecular variation in chloroplast DNA indicated that A. sagittata is the principal receiver of gene flow. Applying classical D-statistics and its derivatives to whole-genome data of 35 accessions, we detect gene flow not only in the sympatric population but also among allopatric populations. Using an Approximate Bayesian computation approach, we identify the model that best describes the history of gene flow between these taxa. This model shows that low levels of gene flow have persisted long after speciation. Around 10 000 years ago, gene flow stopped and a period of complete isolation began. Eventually, a hotspot of contemporary hybridization was formed in the unique sympatric population. Occasional sympatry may have helped protect these lineages from extinction in spite of their extremely low diversity.
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Affiliation(s)
- Hannes Dittberner
- Institute of Plant Sciences,University of Cologne, Zülpicher str. 47b, Germany
| | - Aurelien Tellier
- Department of Life Science Systems, Technical University of Munich, Freising, Germany
| | - Juliette de Meaux
- Institute of Plant Sciences,University of Cologne, Zülpicher str. 47b, Germany
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24
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Boitard S, Arredondo A, Chikhi L, Mazet O. Heterogeneity in effective size across the genome: effects on the inverse instantaneous coalescence rate (IICR) and implications for demographic inference under linked selection. Genetics 2022; 220:6512058. [PMID: 35100421 PMCID: PMC8893248 DOI: 10.1093/genetics/iyac008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/01/2022] [Indexed: 01/22/2023] Open
Abstract
The relative contribution of selection and neutrality in shaping species genetic diversity is one of the most central and controversial questions in evolutionary theory. Genomic data provide growing evidence that linked selection, i.e. the modification of genetic diversity at neutral sites through linkage with selected sites, might be pervasive over the genome. Several studies proposed that linked selection could be modeled as first approximation by a local reduction (e.g. purifying selection, selective sweeps) or increase (e.g. balancing selection) of effective population size (Ne). At the genome-wide scale, this leads to variations of Ne from one region to another, reflecting the heterogeneity of selective constraints and recombination rates between regions. We investigate here the consequences of such genomic variations of Ne on the genome-wide distribution of coalescence times. The underlying motivation concerns the impact of linked selection on demographic inference, because the distribution of coalescence times is at the heart of several important demographic inference approaches. Using the concept of inverse instantaneous coalescence rate, we demonstrate that in a panmictic population, linked selection always results in a spurious apparent decrease of Ne along time. Balancing selection has a particularly large effect, even when it concerns a very small part of the genome. We also study more general models including genuine population size changes, population structure or transient selection and find that the effect of linked selection can be significantly reduced by that of population structure. The models and conclusions presented here are also relevant to the study of other biological processes generating apparent variations of Ne along the genome.
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Affiliation(s)
- Simon Boitard
- CBGP, Université de Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montferrier-sur-Lez 34988, France
- Corresponding author: Université de Montpellier, CIRAD, INRAE, Institut Agro, IRD, 755 Avenue du Campus Agropolis, CS 30016, Montferrier-sur-Lez 34988, France.
| | - Armando Arredondo
- Institut National des Sciences Appliquées, Institut de Mathématiques de Toulouse, Université de Toulouse,Toulouse 31062, France
| | - Lounès Chikhi
- Instituto Gulbenkian de Ciência, Oeiras P-2780-156, Portugal
- Laboratoire Évolution & Diversité Biologique (EDB UMR 5174), CNRS, IRD, UPS, Université de Toulouse Midi-Pyrénées, Toulouse 31062, France
| | - Olivier Mazet
- Institut National des Sciences Appliquées, Institut de Mathématiques de Toulouse, Université de Toulouse,Toulouse 31062, France
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25
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Louis M, Galimberti M, Archer F, Berrow S, Brownlow A, Fallon R, Nykänen M, O'Brien J, Roberston KM, Rosel PE, Simon-Bouhet B, Wegmann D, Fontaine MC, Foote AD, Gaggiotti OE. Selection on ancestral genetic variation fuels repeated ecotype formation in bottlenose dolphins. SCIENCE ADVANCES 2021; 7:eabg1245. [PMID: 34705499 PMCID: PMC8550227 DOI: 10.1126/sciadv.abg1245] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 09/08/2021] [Indexed: 05/27/2023]
Abstract
Studying repeated adaptation can provide insights into the mechanisms allowing species to adapt to novel environments. Here, we investigate repeated evolution driven by habitat specialization in the common bottlenose dolphin. Parapatric pelagic and coastal ecotypes of common bottlenose dolphins have repeatedly formed across the oceans. Analyzing whole genomes of 57 individuals, we find that ecotype evolution involved a complex reticulated evolutionary history. We find parallel linked selection acted upon ancient alleles in geographically distant coastal populations, which were present as standing genetic variation in the pelagic populations. Candidate loci evolving under parallel linked selection were found in ancient tracts, suggesting recurrent bouts of selection through time. Therefore, despite the constraints of small effective population size and long generation time on the efficacy of selection, repeated adaptation in long-lived social species can be driven by a combination of ecological opportunities and selection acting on ancestral standing genetic variation.
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Affiliation(s)
- Marie Louis
- Scottish Oceans Institute, University of St Andrews, East Sands, St Andrews KY16 8LB, Scotland, UK
- Centre d'Etudes Biologiques de Chize, La Rochelle Université, 17000 La Rochelle, France
- Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, PO Box 11103 CC, Groningen, Netherlands
- Globe Institute, University of Copenhagen, Øster Voldgade 5, 1350 Copenhagen, Denmark
| | - Marco Galimberti
- Department of Biology, University of Fribourg, Fribourg 1700, Switzerland
- Swiss Institute of Bioinformatics, Fribourg 1700, Switzerland
| | - Frederick Archer
- National Marine Fisheries Service, Southwest Fisheries Science Center, NOAA, 8901 La Jolla Shores Drive, La Jolla, CA 92037, USA
- Scripps Institution of Oceanography, UC San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Simon Berrow
- Irish Whale and Dolphin Group, Kilrush, Co Clare, Ireland
- Marine and Freshwater Research Centre, Department of Natural Sciences, School of Science and Computing, Galway-Mayo Institute of Technology, Dublin Road, H91 T8NW Galway, Ireland
| | - Andrew Brownlow
- Scottish Marine Animal Stranding Scheme, Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Ramon Fallon
- School of Medicine, University of St Andrews, North Haugh, St Andrews, Fife KY16 9TF, Scotland, UK
| | | | - Joanne O'Brien
- Irish Whale and Dolphin Group, Kilrush, Co Clare, Ireland
- Marine and Freshwater Research Centre, Department of Natural Sciences, School of Science and Computing, Galway-Mayo Institute of Technology, Dublin Road, H91 T8NW Galway, Ireland
| | - Kelly M Roberston
- National Marine Fisheries Service, Southwest Fisheries Science Center, NOAA, 8901 La Jolla Shores Drive, La Jolla, CA 92037, USA
| | - Patricia E Rosel
- National Marine Fisheries Service, Southeast Fisheries Science Center, NOAA, 646 Cajundome Boulevard, Lafayette, LA 70506, USA
| | - Benoit Simon-Bouhet
- Centre d'Etudes Biologiques de Chize, La Rochelle Université, 17000 La Rochelle, France
| | - Daniel Wegmann
- Department of Biology, University of Fribourg, Fribourg 1700, Switzerland
- Swiss Institute of Bioinformatics, Fribourg 1700, Switzerland
| | - Michael C Fontaine
- Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, PO Box 11103 CC, Groningen, Netherlands
- MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France
- Centre de Recherche en Écologie et Évolution de la Santé (CREES), Montpellier, France
| | - Andrew D Foote
- Molecular Ecology and Evolution Bangor, Environment Centre Wales, School of Natural Sciences, Bangor University, Bangor, UK
- Department of Natural History, University Museum, Norwegian University of Science and Technology (NTNU), Erling Skakkes gate 47A, Trondheim 7012, Norway
| | - Oscar E Gaggiotti
- Scottish Oceans Institute, University of St Andrews, East Sands, St Andrews KY16 8LB, Scotland, UK
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26
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Gompert Z, Feder JL, Nosil P. Natural selection drives genome-wide evolution via chance genetic associations. Mol Ecol 2021; 31:467-481. [PMID: 34704650 DOI: 10.1111/mec.16247] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 11/29/2022]
Abstract
Understanding selection's impact on the genome is a major theme in biology. Functionally neutral genetic regions can be affected indirectly by natural selection, via their statistical association with genes under direct selection. The genomic extent of such indirect selection, particularly across loci not physically linked to those under direct selection, remains poorly understood, as does the time scale at which indirect selection occurs. Here, we use field experiments and genomic data in stick insects, deer mice and stickleback fish to show that widespread statistical associations with genes known to affect fitness cause many genetic loci across the genome to be impacted indirectly by selection. This includes regions physically distant from those directly under selection. Then, focusing on the stick insect system, we show that statistical associations between SNPs and other unknown, causal variants result in additional indirect selection in general and specifically within genomic regions of physically linked loci. This widespread indirect selection necessarily makes aspects of evolution more predictable. Thus, natural selection combines with chance genetic associations to affect genome-wide evolution across linked and unlinked loci and even in modest-sized populations. This process has implications for the application of evolutionary principles in basic and applied science.
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Affiliation(s)
- Zachariah Gompert
- Department of Biology, Utah State University, Logan, Utah, USA.,Ecology Center, Utah State University, Logan, Utah, USA
| | - Jeffrey L Feder
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Patrik Nosil
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
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27
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Ruiz-García M, Pinilla-Beltrán D, Murillo-García OE, Pinto CM, Brito J, Shostell JM. Comparative mitogenome phylogeography of two anteater genera ( Tamandua and Myrmecophaga; Myrmecophagidae, Xenarthra): Evidence of discrepant evolutionary traits. Zool Res 2021; 42:525-547. [PMID: 34313411 PMCID: PMC8455474 DOI: 10.24272/j.issn.2095-8137.2020.365] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/23/2021] [Indexed: 11/07/2022] Open
Abstract
The species within Xenarthra (sloths, anteaters, and armadillos) are quintessential South American mammals. Of the three groups, Vermilingua (anteaters) contains the fewest extant and paleontological species. Here, we sampled and sequenced the entire mitochondrial genomes (mitogenomes) of two Tamandua species (Tamandua tetradactyla and Tamandua mexicana) (n=74) from Central and South America, as well as Myrmecophaga tridactyla (n=41) from South America. Within Tamandua, we detected three different haplogroups. The oldest (THI) contained many specimens with the T. tetradactyla morphotype (but also several with the T. mexicana morphotype) and originated in southeastern South America (currently Uruguay) before moving towards northern South America, where the THII haplogroup originated. THII primarily contained specimens with the T. mexicana morphotype (but also several with the T. tetradactyla morphotype) and was distributed in Central America, Colombia, and Ecuador. THI and THII yielded a genetic distance of 4%. THII originated in either northern South America or "in situ" in Central America with haplogroup THIII, which consisted of ~50% T. mexicana and 50% T. tetradactyla phenotypes. THIII was mostly located in the same areas as THII, i.e., Central America, Ecuador, and Colombia, though mainly in the latter. The three haplogroups overlapped in Colombia and Ecuador. Thus, T. tetradactyla and T. mexicana were not reciprocally monophyletic. For this reason, we considered that a unique species of Tamandua likely exists, i.e., T. tetradactyla. In contrast to Tamandua, M. tridactyla did not show different morphotypes throughout its geographical range in the Neotropics. However, two very divergent genetic haplogroups (MHI and MHII), with a genetic distance of ~10%, were detected. The basal haplogroup, MHI, originated in northwestern South America, whereas the more geographically derived haplogroup, MHII, overlapped with MHI, but also expanded into central and southern South America. Thus, Tamandua migrated from south to north whereas Myrmecophaga migrated from north to south. Our results also showed that temporal mitochondrial diversification for Tamandua began during the Late Pliocene and Upper Pleistocene, but for Myrmecophaga began during the Late Miocene. Furthermore, both taxa showed elevated levels of mitochondrial genetic diversity. Tamandua showed more evidence of female population expansion than Myrmecophaga. Tamandua experienced population expansion ~0.6-0.17 million years ago (Mya), whereas Myrmecophaga showed possible population expansion ~0.3-0.2 Mya. However, both taxa experienced a conspicuous female decline in the last 10 000-20 000 years. Our results also showed little spatial genetic structure for both taxa. However, several analyses revealed higher spatial structure in Tamandua than in Myrmecophaga. Therefore, Tamandua and Myrmecophaga were not subjected to the same biogeographical, geological, or climatological events in shaping their genetic structures.
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Affiliation(s)
- Manuel Ruiz-García
- Laboratorio de Genética de Poblaciones Molecular-Biología Evolutiva, Departamento de Biología, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá DC 110231, Colombia. E-mail:
| | - Daniel Pinilla-Beltrán
- Laboratorio de Genética de Poblaciones Molecular-Biología Evolutiva, Departamento de Biología, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá DC 110231, Colombia
| | - Oscar E Murillo-García
- Grupo de Investigación en Ecología Animal, Departamento de Biología, Facultad de Ciencias Naturales y Exactas, Universidad del Valle, Apartado Aéreo, Cali 25360, Colombia
| | | | - Jorge Brito
- Instituto Nacional de Biodiversidad (INABIO), Quito 170135, Ecuador
| | - Joseph Mark Shostell
- Math, Science and Technology Department, University of Minnesota Crookston, Crookston, MN 56716, USA
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28
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Hardigan MA, Lorant A, Pincot DDA, Feldmann MJ, Famula RA, Acharya CB, Lee S, Verma S, Whitaker VM, Bassil N, Zurn J, Cole GS, Bird K, Edger PP, Knapp SJ. Unraveling the Complex Hybrid Ancestry and Domestication History of Cultivated Strawberry. Mol Biol Evol 2021; 38:2285-2305. [PMID: 33507311 PMCID: PMC8136507 DOI: 10.1093/molbev/msab024] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Cultivated strawberry (Fragaria × ananassa) is one of our youngest domesticates, originating in early eighteenth-century Europe from spontaneous hybrids between wild allo-octoploid species (Fragaria chiloensis and Fragaria virginiana). The improvement of horticultural traits by 300 years of breeding has enabled the global expansion of strawberry production. Here, we describe the genomic history of strawberry domestication from the earliest hybrids to modern cultivars. We observed a significant increase in heterozygosity among interspecific hybrids and a decrease in heterozygosity among domesticated descendants of those hybrids. Selective sweeps were found across the genome in early and modern phases of domestication—59–76% of the selectively swept genes originated in the three less dominant ancestral subgenomes. Contrary to the tenet that genetic diversity is limited in cultivated strawberry, we found that the octoploid species harbor massive allelic diversity and that F. × ananassa harbors as much allelic diversity as either wild founder. We identified 41.8 M subgenome-specific DNA variants among resequenced wild and domesticated individuals. Strikingly, 98% of common alleles and 73% of total alleles were shared between wild and domesticated populations. Moreover, genome-wide estimates of nucleotide diversity were virtually identical in F. chiloensis,F. virginiana, and F. × ananassa (π = 0.0059–0.0060). We found, however, that nucleotide diversity and heterozygosity were significantly lower in modern F. × ananassa populations that have experienced significant genetic gains and have produced numerous agriculturally important cultivars.
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Affiliation(s)
- Michael A Hardigan
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Anne Lorant
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Dominique D A Pincot
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Mitchell J Feldmann
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Randi A Famula
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Charlotte B Acharya
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Seonghee Lee
- IFAS Gulf Coast Research and Education Center, Department of Horticulture, University of Florida, Wimauma, FL 33598, USA
| | - Sujeet Verma
- IFAS Gulf Coast Research and Education Center, Department of Horticulture, University of Florida, Wimauma, FL 33598, USA
| | - Vance M Whitaker
- IFAS Gulf Coast Research and Education Center, Department of Horticulture, University of Florida, Wimauma, FL 33598, USA
| | - Nahla Bassil
- USDA-ARS, National Clonal Germplasm Repository, Corvallis, OR 92182, USA
| | - Jason Zurn
- USDA-ARS, National Clonal Germplasm Repository, Corvallis, OR 92182, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Kevin Bird
- Department of Horticultural Science, Michigan State University, East Lansing, MI 48824, USA
| | - Patrick P Edger
- Department of Horticultural Science, Michigan State University, East Lansing, MI 48824, USA
| | - Steven J Knapp
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
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29
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Walton W, Stone GN, Lohse K. Discordant Pleistocene population size histories in a guild of hymenopteran parasitoids. Mol Ecol 2021; 30:4538-4550. [PMID: 34252238 DOI: 10.1111/mec.16074] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 06/23/2021] [Accepted: 07/06/2021] [Indexed: 01/03/2023]
Abstract
Signatures of past changes in population size have been detected in genome-wide variation in many species. However, the causes of such demographic changes and the extent to which they are shared across co-distributed species remain poorly understood. During Pleistocene glacial maxima, many temperate European species were confined to southern refugia. While vicariance and range expansion processes associated with glacial cycles have been widely documented, it is unclear whether refugial populations of co-distributed species have experienced shared histories of population size change. We analyse whole-genome sequence data to reconstruct and compare demographic histories during the Quaternary for Iberian refuge populations in a single ecological guild (seven species of chalcid parasitoid wasps associated with oak cynipid galls). For four of these species, we find support for large changes in effective population size (Ne ) through the Pleistocene that coincide with major climate events. However, there is little evidence that the timing, direction and magnitude of demographic change are shared across species, suggesting that demographic histories in this guild are largely idiosyncratic, even at the scale of a single glacial refugium.
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Affiliation(s)
- William Walton
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Graham N Stone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Konrad Lohse
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
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30
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Improving mosquito control strategies with population genomics. Trends Parasitol 2021; 37:907-921. [PMID: 34074606 DOI: 10.1016/j.pt.2021.05.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/06/2021] [Accepted: 05/06/2021] [Indexed: 01/01/2023]
Abstract
Mosquito control strategies increasingly apply knowledge from population genomics research. This review highlights recent applications to three research domains: mosquito invasions, insecticide resistance evolution, and rear and release programs. Current research trends follow developments in reference assemblies, either as improvements to existing assemblies (particularly Aedes) or assemblies for new taxa (particularly Anopheles). With improved assemblies, studies of invasive and rear and release target populations are better able to incorporate adaptive as well as demographic hypotheses. New reference assemblies are aiding comparisons of insecticide resistance across sister taxa while helping resolve taxon boundaries amidst frequent introgression. Anopheles gene drive deployments and improved Aedes genome assemblies should lead to a convergence in research aims for Anopheles and Aedes in the coming years.
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31
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Szpiech ZA, Novak TE, Bailey NP, Stevison LS. Application of a novel haplotype-based scan for local adaptation to study high-altitude adaptation in rhesus macaques. Evol Lett 2021; 5:408-421. [PMID: 34367665 PMCID: PMC8327953 DOI: 10.1002/evl3.232] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 02/24/2021] [Accepted: 05/04/2021] [Indexed: 12/17/2022] Open
Abstract
When natural populations split and migrate to different environments, they may experience different selection pressures that can lead to local adaptation. To capture the genomic patterns of a local selective sweep, we develop XP-nSL, a genomic scan for local adaptation that compares haplotype patterns between two populations. We show that XP-nSL has power to detect ongoing and recently completed hard and soft sweeps, and we then apply this statistic to search for evidence of adaptation to high altitude in rhesus macaques. We analyze the whole genomes of 23 wild rhesus macaques captured at high altitude (mean altitude > 4000 m above sea level) to 22 wild rhesus macaques captured at low altitude (mean altitude < 500 m above sea level) and find evidence of local adaptation in the high-altitude population at or near 303 known genes and several unannotated regions. We find the strongest signal for adaptation at EGLN1, a classic target for convergent evolution in several species living in low oxygen environments. Furthermore, many of the 303 genes are involved in processes related to hypoxia, regulation of ROS, DNA damage repair, synaptic signaling, and metabolism. These results suggest that, beyond adapting via a beneficial mutation in one single gene, adaptation to high altitude in rhesus macaques is polygenic and spread across numerous important biological systems.
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Affiliation(s)
- Zachary A Szpiech
- Department of Biology Pennsylvania State University University Park Pennsylvania 16801.,Institute for Computational and Data Sciences Pennsylvania State University University Park Pennsylvania 16801.,Department of Biological Sciences Auburn University Auburn Ala 36842 USA
| | - Taylor E Novak
- Department of Biological Sciences Auburn University Auburn Ala 36842 USA
| | - Nick P Bailey
- Department of Biological Sciences Auburn University Auburn Ala 36842 USA
| | - Laurie S Stevison
- Department of Biological Sciences Auburn University Auburn Ala 36842 USA
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32
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Bourgeois YXC, Warren BH. An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes. Mol Ecol 2021; 30:6036-6071. [PMID: 34009688 DOI: 10.1111/mec.15989] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 04/26/2021] [Accepted: 05/11/2021] [Indexed: 01/01/2023]
Abstract
Characterizing the population history of a species and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The constant improvement of high-throughput sequencing techniques has facilitated the production of whole genome data in a wide range of species. Population genomics now provides tools to better integrate selection into a historical framework, and take into account selection when reconstructing demographic history. However, this improvement has come with a profusion of analytical tools that can confuse and discourage users. Such confusion limits the amount of information effectively retrieved from complex genomic data sets, and impairs the diffusion of the most recent analytical tools into fields such as conservation biology. It may also lead to redundancy among methods. To address these isssues, we propose an overview of more than 100 state-of-the-art methods that can deal with whole genome data. We summarize the strategies they use to infer demographic history and selection, and discuss some of their limitations. A website listing these methods is available at www.methodspopgen.com.
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Affiliation(s)
| | - Ben H Warren
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, UA, CP 51, Paris, France
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33
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Sellinger TPP, Abu-Awad D, Tellier A. Limits and convergence properties of the sequentially Markovian coalescent. Mol Ecol Resour 2021; 21:2231-2248. [PMID: 33978324 DOI: 10.1111/1755-0998.13416] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/19/2021] [Accepted: 04/29/2021] [Indexed: 02/07/2023]
Abstract
Several methods based on the sequentially Markovian coalescent (SMC) make use of full genome sequence data from samples to infer population demographic history including past changes in population size, admixture, migration events and population structure. More recently, the original theoretical framework has been extended to allow the simultaneous estimation of population size changes along with other life history traits such as selfing or seed banking. The latter developments enhance the applicability of SMC methods to nonmodel species. Although convergence proofs have been given using simulated data in a few specific cases, an in-depth investigation of the limitations of SMC methods is lacking. In order to explore such limits, we first develop a tool inferring the best case convergence of SMC methods assuming the true underlying coalescent genealogies are known. This tool can be used to quantify the amount and type of information that can be confidently retrieved from given data sets prior to the analysis of the real data. Second, we assess the inference accuracy when the assumptions of SMC approaches are violated due to departures from the model, namely the presence of transposable elements, variable recombination and mutation rates along the sequence, and SNP calling errors. Third, we deliver a new interpretation of SMC methods by highlighting the importance of the transition matrix, which we argue can be used as a set of summary statistics in other statistical inference methods, uncoupling the SMC from hidden Markov models (HMMs). We finally offer recommendations to better apply SMC methods and build adequate data sets under budget constraints.
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Affiliation(s)
| | - Diala Abu-Awad
- Department of Life Science Systems, Technical University of Munich, Munchen, Germany
| | - Aurélien Tellier
- Department of Life Science Systems, Technical University of Munich, Munchen, Germany
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34
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Wang Z, Wang J, Kourakos M, Hoang N, Lee HH, Mathieson I, Mathieson S. Automatic inference of demographic parameters using generative adversarial networks. Mol Ecol Resour 2021; 21:2689-2705. [PMID: 33745225 PMCID: PMC8596911 DOI: 10.1111/1755-0998.13386] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 03/05/2021] [Indexed: 12/12/2022]
Abstract
Population genetics relies heavily on simulated data for validation, inference and intuition. In particular, since the evolutionary ‘ground truth’ for real data is always limited, simulated data are crucial for training supervised machine learning methods. Simulation software can accurately model evolutionary processes but requires many hand‐selected input parameters. As a result, simulated data often fail to mirror the properties of real genetic data, which limits the scope of methods that rely on it. Here, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method, pg‐gan, is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation‐with‐migration model. We then apply our method to human data from the 1000 Genomes Project and show that we can accurately recapitulate the features of real data.
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Affiliation(s)
- Zhanpeng Wang
- Department of Computer Science, Haverford College, Haverford, PA, USA
| | - Jiaping Wang
- Department of Computer Science, Haverford College, Haverford, PA, USA
| | - Michael Kourakos
- Department of Computer Science, Swarthmore College, Swarthmore, PA, USA
| | - Nhung Hoang
- Department of Computer Science, Swarthmore College, Swarthmore, PA, USA
| | - Hyong Hark Lee
- Department of Computer Science, Swarthmore College, Swarthmore, PA, USA
| | - Iain Mathieson
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Sara Mathieson
- Department of Computer Science, Haverford College, Haverford, PA, USA
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35
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Momigliano P, Florin AB, Merilä J. Biases in Demographic Modeling Affect Our Understanding of Recent Divergence. Mol Biol Evol 2021; 38:2967-2985. [PMID: 33624816 PMCID: PMC8233503 DOI: 10.1093/molbev/msab047] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Testing among competing demographic models of divergence has become an important component of evolutionary research in model and non-model organisms. However, the effect of unaccounted demographic events on model choice and parameter estimation remains largely unexplored. Using extensive simulations, we demonstrate that under realistic divergence scenarios, failure to account for population size (Ne) changes in daughter and ancestral populations leads to strong biases in divergence time estimates as well as model choice. We illustrate these issues reconstructing the recent demographic history of North Sea and Baltic Sea turbots (Scophthalmus maximus) by testing 16 isolation with migration (IM) and 16 secondary contact (SC) scenarios, modeling changes in Ne as well as the effects of linked selection and barrier loci. Failure to account for changes in Ne resulted in selecting SC models with long periods of strict isolation and divergence times preceding the formation of the Baltic Sea. In contrast, models accounting for Ne changes suggest recent (<6 kya) divergence with constant gene flow. We further show how interpreting genomic landscapes of differentiation can help discerning among competing models. For example, in the turbot data, islands of differentiation show signatures of recent selective sweeps, rather than old divergence resisting secondary introgression. The results have broad implications for the study of population divergence by highlighting the potential effects of unmodeled changes in Ne on demographic inference. Tested models should aim at representing realistic divergence scenarios for the target taxa, and extreme caution should always be exercised when interpreting results of demographic modeling.
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Affiliation(s)
- Paolo Momigliano
- Ecological Genetics Research Unit, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
| | - Ann-Britt Florin
- Department of Aquatic Resources, Institute of Coastal Research, Swedish University of Agricultural Sciences, Öregrund, Sweden
| | - Juha Merilä
- Ecological Genetics Research Unit, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland.,Division of Ecology and Biodiversity, Faculty of Science, The University of Hong Kong, Hong Kong SAR
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36
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Hayes K, Barton HJ, Zeng K. A Study of Faster-Z Evolution in the Great Tit (Parus major). Genome Biol Evol 2021; 12:210-222. [PMID: 32119100 PMCID: PMC7144363 DOI: 10.1093/gbe/evaa044] [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] [Accepted: 02/26/2020] [Indexed: 12/17/2022] Open
Abstract
Sex chromosomes contribute substantially to key evolutionary processes such as speciation and adaptation. Several theories suggest that evolution could occur more rapidly on sex chromosomes, but currently our understanding of whether and how this occurs is limited. Here, we present an analysis of the great tit (Parus major) genome, aiming to detect signals of faster-Z evolution. We find mixed evidence of faster divergence on the Z chromosome than autosomes, with significantly higher divergence being found in ancestral repeats, but not at 4- or 0-fold degenerate sites. Interestingly, some 4-fold sites appear to be selectively constrained, which may mislead analyses that use these sites as the neutral reference (e.g., dN/dS). Consistent with other studies in birds, the mutation rate is significantly higher in males than females, and the long-term Z-to-autosome effective population size ratio is only 0.5, significantly lower than the expected value of 0.75. These are indicative of male-driven evolution and high variance in male reproductive success, respectively. We find no evidence for an increased efficacy of positive selection on the Z chromosome. In contrast, the Z chromosome in great tits appears to be affected by increased genetic drift, which has led to detectable signals of weakened intensity of purifying selection. These results provide further evidence that the Z chromosome often has a low effective population size, and that this has important consequences for its evolution. They also highlight the importance of considering multiple factors that can affect the rate of evolution and effective population sizes of sex chromosomes.
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Affiliation(s)
- Kai Hayes
- Department of Animal and Plant Sciences, University of Sheffield, United Kingdom
| | - Henry J Barton
- Department of Animal and Plant Sciences, University of Sheffield, United Kingdom.,Organismal and Evolutionary Biology Research Program, University of Helsinki, Finland
| | - Kai Zeng
- Department of Animal and Plant Sciences, University of Sheffield, United Kingdom
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37
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Johri P, Riall K, Becher H, Excoffier L, Charlesworth B, Jensen JD. The Impact of Purifying and Background Selection on the Inference of Population History: Problems and Prospects. Mol Biol Evol 2021; 38:2986-3003. [PMID: 33591322 PMCID: PMC8233493 DOI: 10.1093/molbev/msab050] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Current procedures for inferring population history generally assume complete neutrality—that is, they neglect both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically. We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the distribution of fitness effect as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy.
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Affiliation(s)
- Parul Johri
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Kellen Riall
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Hannes Becher
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Laurent Excoffier
- Institute of Ecology and Evolution, University of Berne, Berne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Charlesworth
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
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38
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Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, Taliun SAG, Corvelo A, Gogarten SM, Kang HM, Pitsillides AN, LeFaive J, Lee SB, Tian X, Browning BL, Das S, Emde AK, Clarke WE, Loesch DP, Shetty AC, Blackwell TW, Smith AV, Wong Q, Liu X, Conomos MP, Bobo DM, Aguet F, Albert C, Alonso A, Ardlie KG, Arking DE, Aslibekyan S, Auer PL, Barnard J, Barr RG, Barwick L, Becker LC, Beer RL, Benjamin EJ, Bielak LF, Blangero J, Boehnke M, Bowden DW, Brody JA, Burchard EG, Cade BE, Casella JF, Chalazan B, Chasman DI, Chen YDI, Cho MH, Choi SH, Chung MK, Clish CB, Correa A, Curran JE, Custer B, Darbar D, Daya M, de Andrade M, DeMeo DL, Dutcher SK, Ellinor PT, Emery LS, Eng C, Fatkin D, Fingerlin T, Forer L, Fornage M, Franceschini N, Fuchsberger C, Fullerton SM, Germer S, Gladwin MT, Gottlieb DJ, Guo X, Hall ME, He J, Heard-Costa NL, Heckbert SR, Irvin MR, Johnsen JM, Johnson AD, Kaplan R, Kardia SLR, Kelly T, Kelly S, Kenny EE, Kiel DP, Klemmer R, Konkle BA, Kooperberg C, Köttgen A, Lange LA, Lasky-Su J, Levy D, Lin X, Lin KH, Liu C, Loos RJF, Garman L, Gerszten R, Lubitz SA, Lunetta KL, Mak ACY, Manichaikul A, Manning AK, Mathias RA, McManus DD, McGarvey ST, Meigs JB, Meyers DA, Mikulla JL, Minear MA, Mitchell BD, Mohanty S, Montasser ME, Montgomery C, Morrison AC, Murabito JM, Natale A, Natarajan P, Nelson SC, North KE, O'Connell JR, Palmer ND, Pankratz N, Peloso GM, Peyser PA, Pleiness J, Post WS, Psaty BM, Rao DC, Redline S, Reiner AP, Roden D, Rotter JI, Ruczinski I, Sarnowski C, Schoenherr S, Schwartz DA, Seo JS, Seshadri S, Sheehan VA, Sheu WH, Shoemaker MB, Smith NL, Smith JA, Sotoodehnia N, Stilp AM, Tang W, Taylor KD, Telen M, Thornton TA, Tracy RP, Van Den Berg DJ, Vasan RS, Viaud-Martinez KA, Vrieze S, Weeks DE, Weir BS, Weiss ST, Weng LC, Willer CJ, Zhang Y, Zhao X, Arnett DK, Ashley-Koch AE, Barnes KC, Boerwinkle E, Gabriel S, Gibbs R, Rice KM, Rich SS, Silverman EK, Qasba P, Gan W, Papanicolaou GJ, Nickerson DA, Browning SR, Zody MC, Zöllner S, Wilson JG, Cupples LA, Laurie CC, Jaquish CE, Hernandez RD, O'Connor TD, Abecasis GR. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 2021; 590:290-299. [PMID: 33568819 PMCID: PMC7875770 DOI: 10.1038/s41586-021-03205-y] [Citation(s) in RCA: 941] [Impact Index Per Article: 313.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 01/07/2021] [Indexed: 02/08/2023]
Abstract
The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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Affiliation(s)
- Daniel Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Daniel N Harris
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Michael D Kessler
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jedidiah Carlson
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Zachary A Szpiech
- Department of Biology, Pennsylvania State University, University Park, PA, USA
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA
| | - Raul Torres
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah A Gagliano Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | | | - Hyun Min Kang
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | - Jonathon LeFaive
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Seung-Been Lee
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Xiaowen Tian
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Brian L Browning
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | - Sayantan Das
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | | | - Douglas P Loesch
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Amol C Shetty
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Albert V Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xiaoming Liu
- USF Genomics, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Dean M Bobo
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | | | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Paul L Auer
- Zilber School of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, USA
| | | | - R Graham Barr
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
- Department of Epidemiology, Columbia University Medical Center, New York, NY, USA
| | | | | | - Rebecca L Beer
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Emelia J Benjamin
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Lawrence F Bielak
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Brian E Cade
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James F Casella
- Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
- Division of Pediatric Hematology, Johns Hopkins University, Baltimore, MD, USA
| | - Brandon Chalazan
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Mina K Chung
- Department of Cardiovascular Medicine, Heart & Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Clary B Clish
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Joanne E Curran
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Brian Custer
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Dawood Darbar
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Michelle Daya
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Susan K Dutcher
- McDonnell Genome Institute, Washington University, St Louis, MO, USA
- Department of Genetics, Washington University, St Louis, MO, USA
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Diane Fatkin
- Molecular Cardiology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
- Faculty of Medicine, University of New South Wales, Kensington, New South Wales, Australia
- Cardiology Department, St Vincent's Hospital, Darlinghurst, New South Wales, Australia
| | - Tasha Fingerlin
- National Jewish Health, Center for Genes, Environment and Health, Denver, CO, USA
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Christian Fuchsberger
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Stephanie M Fullerton
- Department of Bioethics & Humanities, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Mark T Gladwin
- Pittsburgh Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel J Gottlieb
- VA Boston Healthcare System, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Michael E Hall
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jiang He
- Department of Epidemiology, Tulane University, New Orleans, LA, USA
- Tulane University Translational Science Institute, Tulane University, New Orleans, LA, USA
| | - Nancy L Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jill M Johnsen
- Department of Medicine, University of Washington, Seattle, WA, USA
- Bloodworks Northwest Research Institute, Seattle, WA, USA
| | - Andrew D Johnson
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, USA
| | - Robert Kaplan
- Albert Einstein College of Medicine, New York, NY, USA
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University, New Orleans, LA, USA
| | - Shannon Kelly
- Department of Epidemiology, Vitalant Research Institute, San Francisco, CA, USA
- Department of Pediatrics, UCSF Benioff Children's Hospital, Oakland, CA, USA
- Division of Pediatric Hematology, UCSF Benioff Children's Hospital, Oakland, CA, USA
| | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Douglas P Kiel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert Klemmer
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Barbara A Konkle
- Department of Medicine, University of Washington, Seattle, WA, USA
- Bloodworks Northwest Research Institute, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Leslie A Lange
- Department of Medicine, University of Colorado at Denver, Aurora, CO, USA
| | - Jessica Lasky-Su
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Daniel Levy
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, USA
| | - Xihong Lin
- Biostatistics and Statistics, Harvard University, Boston, MA, USA
| | - Keng-Han Lin
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lori Garman
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | | | | | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Alisa K Manning
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
- Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David D McManus
- Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Stephen T McGarvey
- International Health Institute, Brown University, Providence, RI, USA
- Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Anthropology, Brown University, Providence, RI, USA
| | - James B Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, The Broad Institute of MIT and Harvard, Boston, MA, USA
| | | | - Julie L Mikulla
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mollie A Minear
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Braxton D Mitchell
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Sanghamitra Mohanty
- Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
- Department of Internal Medicine, Dell Medical School, Austin, TX, USA
| | - May E Montasser
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Courtney Montgomery
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joanne M Murabito
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Andrea Natale
- Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sarah C Nelson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jeffrey R O'Connell
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jacob Pleiness
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - D C Rao
- Division of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Dan Roden
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Sebastian Schoenherr
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Jeong-Sun Seo
- Precision Medicine Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Macrogen Inc, Seoul, Republic of Korea
- Gong Wu Genomic Medicine Institute, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center at San Antonio, San Antonio, TX, USA
| | - Vivien A Sheehan
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Wayne H Sheu
- Taichung Veterans General Hospital Taiwan, Taichung City, Taiwan
| | | | - Nicholas L Smith
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | | | - Russell P Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - David J Van Den Berg
- Center for Genetic Epidemiology, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ramachandran S Vasan
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | | | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Daniel E Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bruce S Weir
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Scott T Weiss
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | | | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine-Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Yingze Zhang
- Pittsburgh Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xutong Zhao
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Donna K Arnett
- Department of Epidemiology, University of Kentucky, Lexington, KY, USA
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Kathleen C Barnes
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Eric Boerwinkle
- University of Texas Health Science Center at Houston, Houston, TX, USA
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Stacey Gabriel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Richard Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Pankaj Qasba
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Weiniu Gan
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - George J Papanicolaou
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Northwest Genomics Center, Seattle, WA, USA
- Brotman Baty Institute, Seattle, WA, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
- Framingham Heart Study, Framingham, MA, USA.
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Cashell E Jaquish
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
| | - Timothy D O'Connor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Gonçalo R Abecasis
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
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Benestan LM, Rougemont Q, Senay C, Normandeau E, Parent E, Rideout R, Bernatchez L, Lambert Y, Audet C, Parent GJ. Population genomics and history of speciation reveal fishery management gaps in two related redfish species ( Sebastes mentella and Sebastes fasciatus). Evol Appl 2021; 14:588-606. [PMID: 33664797 PMCID: PMC7896722 DOI: 10.1111/eva.13143] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 08/27/2020] [Accepted: 09/01/2020] [Indexed: 12/18/2022] Open
Abstract
Understanding the processes shaping population structure and reproductive isolation of marine organisms can improve their management and conservation. Using genomic markers combined with estimation of individual ancestries, assignment tests, spatial ecology, and demographic modeling, we (i) characterized the contemporary population structure, (ii) assessed the influence of space, fishing depth, and sampling years on contemporary distribution, and (iii) reconstructed the speciation history of two cryptic redfish species, Sebastes mentella and S. fasciatus. We genotyped 860 individuals in the Northwest Atlantic Ocean using 24,603 filtered single nucleotide polymorphisms (SNPs). Our results confirmed the clear genetic distinctiveness of the two species and identified three ecotypes within S. mentella and five populations in S. fasciatus. Multivariate analyses highlighted the influence of spatial distribution and depth on the overall genomic variation, while demographic modeling revealed that secondary contact models best explained inter- and intragenomic divergence. These species, ecotypes, and populations can be considered as a rare and wide continuum of genomic divergence in the marine environment. This acquired knowledge pertaining to the evolutionary processes driving population divergence and reproductive isolation will help optimizing the assessment of demographic units and possibly to refine fishery management units.
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Affiliation(s)
- Laura M. Benestan
- CEFEUniv Montpellier, CNRS, EPHE‐PSL UniversityIRD, Univ Paul Valéry Montpellier 3MontpellierFrance
| | - Quentin Rougemont
- Département de biologie, Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecQCCanada
| | - Caroline Senay
- Fisheries and Oceans CanadaMaurice‐Lamontagne InstituteMont‐JoliQCCanada
| | - Eric Normandeau
- Département de biologie, Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecQCCanada
| | - Eric Parent
- Fisheries and Oceans CanadaMaurice‐Lamontagne InstituteMont‐JoliQCCanada
| | - Rick Rideout
- Fisheries and Oceans CanadaNorthwest Atlantic Fisheries CentreN.L.St. John’sCanada
| | - Louis Bernatchez
- Département de biologie, Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecQCCanada
| | - Yvan Lambert
- Fisheries and Oceans CanadaMaurice‐Lamontagne InstituteMont‐JoliQCCanada
| | - Céline Audet
- Institut des sciences de la mer de RimouskiUniversité du Québec à RimouskiRimouskiQCCanada
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40
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Johri P, Riall K, Becher H, Excoffier L, Charlesworth B, Jensen JD. The impact of purifying and background selection on the inference of population history: problems and prospects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021. [PMID: 33501439 PMCID: PMC7836109 DOI: 10.1101/2020.04.28.066365] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Current procedures for inferring population history generally assume complete neutrality - that is, they neglect both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects (DFE) and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically. We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the DFE as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy.
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Affiliation(s)
- Parul Johri
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Kellen Riall
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Hannes Becher
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, EH9 3FL, United Kingdom
| | - Laurent Excoffier
- Institute of Ecology and Evolution, University of Berne, Berne 3012, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Brian Charlesworth
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, EH9 3FL, United Kingdom
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
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41
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Fraïsse C, Popovic I, Mazoyer C, Spataro B, Delmotte S, Romiguier J, Loire É, Simon A, Galtier N, Duret L, Bierne N, Vekemans X, Roux C. DILS: Demographic inferences with linked selection by using ABC. Mol Ecol Resour 2021; 21:2629-2644. [PMID: 33448666 DOI: 10.1111/1755-0998.13323] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 12/09/2020] [Accepted: 12/21/2020] [Indexed: 01/21/2023]
Abstract
We present DILS, a deployable statistical analysis platform for conducting demographic inferences with linked selection from population genomic data using an Approximate Bayesian Computation framework. DILS takes as input single-population or two-population data sets (multilocus fasta sequences) and performs three types of analyses in a hierarchical manner, identifying: (a) the best demographic model to study the importance of gene flow and population size change on the genetic patterns of polymorphism and divergence, (b) the best genomic model to determine whether the effective size Ne and migration rate N, m are heterogeneously distributed along the genome (implying linked selection) and (c) loci in genomic regions most associated with barriers to gene flow. Also available via a Web interface, an objective of DILS is to facilitate collaborative research in speciation genomics. Here, we show the performance and limitations of DILS by using simulations and finally apply the method to published data on a divergence continuum composed by 28 pairs of Mytilus mussel populations/species.
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Affiliation(s)
- Christelle Fraïsse
- Institute of Science and Technology Austria, Klosterneuœburg, Austria.,Univ. Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, Lille, France
| | - Iva Popovic
- School of Biological Sciences, University of Queensland, St Lucia, Qld, Australia
| | | | - Bruno Spataro
- Laboratoire de Biologie et Biométrie Évolutive CNRS UMR 5558, Université Claude Bernard, Lyon, France
| | - Stéphane Delmotte
- Laboratoire de Biologie et Biométrie Évolutive CNRS UMR 5558, Université Claude Bernard, Lyon, France
| | | | - Étienne Loire
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR, ASTRE, Montpellier, France
| | - Alexis Simon
- ISEM, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Nicolas Galtier
- ISEM, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Laurent Duret
- Laboratoire de Biologie et Biométrie Évolutive CNRS UMR 5558, Université Claude Bernard, Lyon, France
| | - Nicolas Bierne
- ISEM, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | | | - Camille Roux
- Univ. Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, Lille, France
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42
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Genomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps. Proc Natl Acad Sci U S A 2020; 117:30554-30565. [PMID: 33199636 DOI: 10.1073/pnas.2015987117] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Numerous studies of emerging species have identified genomic "islands" of elevated differentiation against a background of relative homogeneity. The causes of these islands remain unclear, however, with some signs pointing toward "speciation genes" that locally restrict gene flow and others suggesting selective sweeps that have occurred within nascent species after speciation. Here, we examine this question through the lens of genome sequence data for five species of southern capuchino seedeaters, finch-like birds from South America that have undergone a species radiation during the last ∼50,000 generations. By applying newly developed statistical methods for ancestral recombination graph inference and machine-learning methods for the prediction of selective sweeps, we show that previously identified islands of differentiation in these birds appear to be generally associated with relatively recent, species-specific selective sweeps, most of which are predicted to be soft sweeps acting on standing genetic variation. Many of these sweeps coincide with genes associated with melanin-based variation in plumage, suggesting a prominent role for sexual selection. At the same time, a few loci also exhibit indications of possible selection against gene flow. These observations shed light on the complex manner in which natural selection shapes genome sequences during speciation.
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43
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Schrider DR. Background Selection Does Not Mimic the Patterns of Genetic Diversity Produced by Selective Sweeps. Genetics 2020; 216:499-519. [PMID: 32847814 PMCID: PMC7536861 DOI: 10.1534/genetics.120.303469] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 08/04/2020] [Indexed: 12/28/2022] Open
Abstract
It is increasingly evident that natural selection plays a prominent role in shaping patterns of diversity across the genome. The most commonly studied modes of natural selection are positive selection and negative selection, which refer to directional selection for and against derived mutations, respectively. Positive selection can result in hitchhiking events, in which a beneficial allele rapidly replaces all others in the population, creating a valley of diversity around the selected site along with characteristic skews in allele frequencies and linkage disequilibrium among linked neutral polymorphisms. Similarly, negative selection reduces variation not only at selected sites but also at linked sites, a phenomenon called background selection (BGS). Thus, discriminating between these two forces may be difficult, and one might expect efforts to detect hitchhiking to produce an excess of false positives in regions affected by BGS. Here, we examine the similarity between BGS and hitchhiking models via simulation. First, we show that BGS may somewhat resemble hitchhiking in simplistic scenarios in which a region constrained by negative selection is flanked by large stretches of unconstrained sites, echoing previous results. However, this scenario does not mirror the actual spatial arrangement of selected sites across the genome. By performing forward simulations under more realistic scenarios of BGS, modeling the locations of protein-coding and conserved noncoding DNA in real genomes, we show that the spatial patterns of variation produced by BGS rarely mimic those of hitchhiking events. Indeed, BGS is not substantially more likely than neutrality to produce false signatures of hitchhiking. This holds for simulations modeled after both humans and Drosophila, and for several different demographic histories. These results demonstrate that appropriately designed scans for hitchhiking need not consider BGS's impact on false-positive rates. However, we do find evidence that BGS increases the false-negative rate for hitchhiking, an observation that demands further investigation.
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Affiliation(s)
- Daniel R Schrider
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27514
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44
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Capblancq T, Butnor JR, Deyoung S, Thibault E, Munson H, Nelson DM, Fitzpatrick MC, Keller SR. Whole-exome sequencing reveals a long-term decline in effective population size of red spruce ( Picea rubens). Evol Appl 2020; 13:2190-2205. [PMID: 33005218 PMCID: PMC7513712 DOI: 10.1111/eva.12985] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 02/26/2020] [Accepted: 04/09/2020] [Indexed: 01/02/2023] Open
Abstract
Understanding the factors influencing the current distribution of genetic diversity across a species range is one of the main questions of evolutionary biology, especially given the increasing threat to biodiversity posed by climate change. Historical demographic processes such as population expansion or bottlenecks and decline are known to exert a predominant influence on past and current levels of genetic diversity, and revealing this demo-genetic history can have immediate conservation implications. We used a whole-exome capture sequencing approach to analyze polymorphism across the gene space of red spruce (Picea rubens Sarg.), an endemic and emblematic tree species of eastern North America high-elevation forests that are facing the combined threat of global warming and increasing human activities. We sampled a total of 340 individuals, including populations from the current core of the range in northeastern USA and southeastern Canada and from the southern portions of its range along the Appalachian Mountains, where populations occur as highly fragmented mountaintop "sky islands." Exome capture baits were designed from the closely relative white spruce (P. glauca Voss) transcriptome, and sequencing successfully captured most regions on or near our target genes, resulting in the generation of a new and expansive genomic resource for studying standing genetic variation in red spruce applicable to its conservation. Our results, based on over 2 million exome-derived variants, indicate that red spruce is structured into three distinct ancestry groups that occupy different geographic regions of its highly fragmented range. Moreover, these groups show small Ne , with a temporal history of sustained population decline that has been ongoing for thousands (or even hundreds of thousands) of years. These results demonstrate the broad potential of genomic studies for revealing details of the demographic history that can inform management and conservation efforts of nonmodel species with active restoration programs, such as red spruce.
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Affiliation(s)
| | - John R Butnor
- USDA Forest Service Southern Research Station University of Vermont Burlington VT USA
| | - Sonia Deyoung
- Department of Plant Biology University of Vermont Burlington VT USA
| | - Ethan Thibault
- Department of Plant Biology University of Vermont Burlington VT USA
| | - Helena Munson
- Department of Plant Biology University of Vermont Burlington VT USA
| | - David M Nelson
- Appalachian Laboratory University of Maryland Center for Environmental Science Frostburg MD USA
| | - Matthew C Fitzpatrick
- Appalachian Laboratory University of Maryland Center for Environmental Science Frostburg MD USA
| | - Stephen R Keller
- Department of Plant Biology University of Vermont Burlington VT USA
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45
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Horscroft C, Ennis S, Pengelly RJ, Sluckin TJ, Collins A. Sequencing era methods for identifying signatures of selection in the genome. Brief Bioinform 2020; 20:1997-2008. [PMID: 30053138 DOI: 10.1093/bib/bby064] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 05/16/2018] [Indexed: 12/12/2022] Open
Abstract
Insights into genetic loci which are under selection and their functional roles contribute to increased understanding of the patterns of phenotypic variation we observe today. The availability of whole-genome sequence data, for humans and other species, provides opportunities to investigate adaptation and evolution at unprecedented resolution. Many analytical methods have been developed to interrogate these large data sets and characterize signatures of selection in the genome. We review here recently developed methods and consider the impact of increased computing power and data availability on the detection of selection signatures. Consideration of demography, recombination and other confounding factors is important, and use of a range of methods in combination is a powerful route to resolving different forms of selection in genome sequence data. Overall, a substantial improvement in methods for application to whole-genome sequencing is evident, although further work is required to develop robust and computationally efficient approaches which may increase reproducibility across studies.
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Affiliation(s)
- Clare Horscroft
- Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.,Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK
| | - Sarah Ennis
- Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.,Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK
| | - Reuben J Pengelly
- Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.,Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK
| | - Timothy J Sluckin
- Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK.,Mathematical Sciences, University of Southampton, Highfield, Southampton, UK
| | - Andrew Collins
- Genetic Epidemiology and Bioinformatics, Faculty of Medicine, University of Southampton, Duthie Building (808), Tremona Road, Southampton, UK.,Institute for Life Sciences, University of Southampton, Life Sciences Building (85), Highfield, Southampton, UK
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46
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The phylogeographic structure of the mountain coati (Nasuella olivacea; Procyonidae, Carnivora), and its phylogenetic relationships with other coati species (Nasua nasua and Nasua narica) as inferred by mitochondrial DNA. Mamm Biol 2020. [DOI: 10.1007/s42991-020-00050-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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47
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Perrier C, Rougemont Q, Charmantier A. Demographic history and genomics of local adaptation in blue tit populations. Evol Appl 2020; 13:1145-1165. [PMID: 32684952 PMCID: PMC7359843 DOI: 10.1111/eva.13035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 05/11/2020] [Accepted: 05/18/2020] [Indexed: 12/24/2022] Open
Abstract
Understanding the genomic processes underlying local adaptation is a central aim of modern evolutionary biology. This task requires identifying footprints of local selection but also estimating spatio‐temporal variations in population demography and variations in recombination rate and in diversity along the genome. Here, we investigated these parameters in blue tit populations inhabiting deciduous versus evergreen forests, and insular versus mainland areas, in the context of a previously described strong phenotypic differentiation. Neighboring population pairs of deciduous and evergreen habitats were weakly genetically differentiated (FST = 0.003 on average), nevertheless with a statistically significant effect of habitat type on the overall genetic structure. This low differentiation was consistent with the strong and long‐lasting gene flow between populations inferred by demographic modeling. In turn, insular and mainland populations were moderately differentiated (FST = 0.08 on average), in line with the inference of moderate ancestral migration, followed by isolation since the end of the last glaciation. Effective population sizes were large, yet smaller on the island than on the mainland. Weak and nonparallel footprints of divergent selection between deciduous and evergreen populations were consistent with their high connectivity and the probable polygenic nature of local adaptation in these habitats. In turn, stronger footprints of divergent selection were identified between long isolated insular versus mainland birds and were more often found in regions of low recombination, as expected from theory. Lastly, we identified a genomic inversion on the mainland, spanning 2.8 Mb. These results provide insights into the demographic history and genetic architecture of local adaptation in blue tit populations at multiple geographic scales.
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Affiliation(s)
- Charles Perrier
- Centre d'Ecologie Fonctionnelle et Evolutive UMR 5175 CNRS Univ Montpellier CNRS EPHE IRD Univ Paul Valéry Montpellier 3 Montpellier France.,Centre de Biologie pour la Gestion des Populations UMR CBGP INRAE CIRAD IRD Montpellier SupAgro Univ Montpellier Montpellier France
| | - Quentin Rougemont
- Département de Biologie Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec Québec Canada
| | - Anne Charmantier
- Centre d'Ecologie Fonctionnelle et Evolutive UMR 5175 CNRS Univ Montpellier CNRS EPHE IRD Univ Paul Valéry Montpellier 3 Montpellier France
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48
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Osborne OG, Kafle T, Brewer T, Dobreva MP, Hutton I, Savolainen V. Sympatric speciation in mountain roses ( Metrosideros) on an oceanic island. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190542. [PMID: 32654651 DOI: 10.1098/rstb.2019.0542] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Shifts in flowering time have the potential to act as strong prezygotic reproductive barriers in plants. We investigate the role of flowering time divergence in two species of mountain rose (Metrosideros) endemic to Lord Howe Island, Australia, a minute and isolated island in the Tasman Sea. Metrosideros nervulosa and M. sclerocarpa are sister species and have divergent ecological niches on the island but grow sympatrically for much of their range, and likely speciated in situ on the island. We used flowering time and population genomic analyses of population structure and selection, to investigate their evolution, with a particular focus on the role of flowering time in their speciation. Population structure analyses showed the species are highly differentiated and appear to be in the very late stages of speciation. We found flowering times of the species to be significantly displaced, with M. sclerocarpa flowering 53 days later than M. nervulosa. Furthermore, the analyses of selection showed that flowering time genes are under selection between the species. Thus, prezygotic reproductive isolation is mediated by flowering time shifts in the species, and likely evolved under selection, to drive the completion of speciation within a small geographical area. This article is part of the theme issue 'Towards the completion of speciation: the evolution of reproductive isolation beyond the first barriers'.
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Affiliation(s)
- Owen G Osborne
- Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot SL5 7PY, UK
| | - Tane Kafle
- Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot SL5 7PY, UK
| | - Tom Brewer
- Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot SL5 7PY, UK
| | - Mariya P Dobreva
- Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot SL5 7PY, UK
| | - Ian Hutton
- Lord Howe Island Museum, Lord Howe Island, NSW 2898, Australia
| | - Vincent Savolainen
- Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot SL5 7PY, UK
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49
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Humble E, Dobrynin P, Senn H, Chuven J, Scott AF, Mohr DW, Dudchenko O, Omer AD, Colaric Z, Lieberman Aiden E, Al Dhaheri SS, Wildt D, Oliaji S, Tamazian G, Pukazhenthi B, Ogden R, Koepfli KP. Chromosomal-level genome assembly of the scimitar-horned oryx: Insights into diversity and demography of a species extinct in the wild. Mol Ecol Resour 2020; 20:1668-1681. [PMID: 32365406 DOI: 10.1111/1755-0998.13181] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 04/09/2020] [Accepted: 04/24/2020] [Indexed: 01/04/2023]
Abstract
Captive populations provide a valuable insurance against extinctions in the wild. However, they are also vulnerable to the negative impacts of inbreeding, selection and drift. Genetic information is therefore considered a critical aspect of conservation management. Recent developments in sequencing technologies have the potential to improve the outcomes of management programmes; however, the transfer of these approaches to applied conservation has been slow. The scimitar-horned oryx (Oryx dammah) is a North African antelope that has been extinct in the wild since the early 1980s and is the focus of a large-scale and long-term reintroduction project. To enable the selection of suitable founder individuals, facilitate post-release monitoring and improve captive breeding management, comprehensive genomic resources are required. Here, we used 10X Chromium sequencing together with Hi-C contact mapping to develop a chromosomal-level genome assembly for the species. The resulting assembly contained 29 chromosomes with a scaffold N50 of 100.4 Mb, and displayed strong chromosomal synteny with the cattle genome. Using resequencing data from six additional individuals, we demonstrated relatively high genetic diversity in the scimitar-horned oryx compared to other mammals, despite it having experienced a strong founding event in captivity. Additionally, the level of diversity across populations varied according to management strategy. Finally, we uncovered a dynamic demographic history that coincided with periods of climate variation during the Pleistocene. Overall, our study provides a clear example of how genomic data can uncover valuable insights into captive populations and contributes important resources to guide future management decisions of an endangered species.
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Affiliation(s)
- Emily Humble
- Royal (Dick) School of Veterinary Studies and the Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Pavel Dobrynin
- Smithsonian Conservation Biology Institute, Center for Species Survival, National Zoological Park, Front Royal, VA, USA.,Smithsonian Conservation Biology Institute, Center for Species Survival, National Zoological Park, Washington, DC, USA.,Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia
| | - Helen Senn
- RZSS WildGenes Laboratory, Conservation Department, Royal Zoological Society of Scotland, Edinburgh, UK
| | - Justin Chuven
- Terrestrial & Marine Biodiversity Sector, Environment Agency, Abu Dhabi, United Arab Emirates
| | - Alan F Scott
- Genetic Resources Core Facility, McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David W Mohr
- Genetic Resources Core Facility, McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Olga Dudchenko
- The Center for Genome Architecture, Department of Molecular and Human Genetics Baylor College of Medicine, Houston, TX, USA.,Department of Computer Science, Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA.,Center for Theoretical and Biological Physics, Rice University, Houston, TX, USA
| | - Arina D Omer
- The Center for Genome Architecture, Department of Molecular and Human Genetics Baylor College of Medicine, Houston, TX, USA.,Department of Computer Science, Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | - Zane Colaric
- The Center for Genome Architecture, Department of Molecular and Human Genetics Baylor College of Medicine, Houston, TX, USA.,Department of Computer Science, Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | - Erez Lieberman Aiden
- The Center for Genome Architecture, Department of Molecular and Human Genetics Baylor College of Medicine, Houston, TX, USA.,Department of Computer Science, Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA.,Center for Theoretical and Biological Physics, Rice University, Houston, TX, USA.,Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China
| | | | - David Wildt
- Smithsonian Conservation Biology Institute, Center for Species Survival, National Zoological Park, Front Royal, VA, USA.,Smithsonian Conservation Biology Institute, Center for Species Survival, National Zoological Park, Washington, DC, USA
| | - Shireen Oliaji
- Royal (Dick) School of Veterinary Studies and the Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Gaik Tamazian
- Computer Technologies Laboratory, ITMO University, St. Petersburg, Russia
| | - Budhan Pukazhenthi
- Smithsonian Conservation Biology Institute, Center for Species Survival, National Zoological Park, Front Royal, VA, USA.,Smithsonian Conservation Biology Institute, Center for Species Survival, National Zoological Park, Washington, DC, USA
| | - Rob Ogden
- Royal (Dick) School of Veterinary Studies and the Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Klaus-Peter Koepfli
- Smithsonian Conservation Biology Institute, Center for Species Survival, National Zoological Park, Front Royal, VA, USA.,Smithsonian Conservation Biology Institute, Center for Species Survival, National Zoological Park, Washington, DC, USA
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50
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Gagnaire PA. Comparative genomics approach to evolutionary process connectivity. Evol Appl 2020; 13:1320-1334. [PMID: 32684961 PMCID: PMC7359831 DOI: 10.1111/eva.12978] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 04/02/2020] [Accepted: 04/03/2020] [Indexed: 01/01/2023] Open
Abstract
The influence of species life history traits and historical demography on contemporary connectivity is still poorly understood. However, these factors partly determine the evolutionary responses of species to anthropogenic landscape alterations. Genetic connectivity and its evolutionary outcomes depend on a variety of spatially dependent evolutionary processes, such as population structure, local adaptation, genetic admixture, and speciation. Over the last years, population genomic studies have been interrogating these processes with increasing resolution, revealing a large diversity of species responses to spatially structured landscapes. In parallel, multispecies meta-analyses usually based on low-genome coverage data have provided fundamental insights into the ecological determinants of genetic connectivity, such as the influence of key life history traits on population structure. However, comparative studies still lack a thorough integration of macro- and micro-evolutionary scales to fully realize their potential. Here, I present how a comparative genomics framework may provide a deeper understanding of evolutionary process connectivity. This framework relies on coupling the inference of long-term demographic and selective history with an assessment of the contemporary consequences of genetic connectivity. Standardizing this approach across several species occupying the same landscape should help understand how spatial environmental heterogeneity has shaped the diversity of historical and contemporary connectivity patterns in different taxa with contrasted life history traits. I will argue that a reasonable amount of genome sequence data can be sufficient to resolve and connect complex macro- and micro-evolutionary histories. Ultimately, implementing this framework in varied taxonomic groups is expected to improve scientific guidelines for conservation and management policies.
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