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      Comparative genomics uncovers the evolutionary history, demography, and molecular adaptations of South American canids

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          Significance

          The diversification of canids in South America represents one of the most striking radiations of carnivorous mammals, comprising both small and large species, as well as hypercarnivorous and frugivorous forms. However, the timing, relationships, and geographic and climatic constraints on this radiation are not well understood. We show that canids colonized that continent from a single ancestral species between 3.9 and 3.5 million years ago. Canids first diversified in eastern South America, followed by a colonization and diversification west of the Andes with demographic histories influenced by habitat shifts during Pleistocene climatic cycles. We show that the phenotypic divergence of the bush dog and maned wolf reflect changes in the regulation and composition of genes underlying dental and skeletal traits.

          Abstract

          The remarkable radiation of South American (SA) canids produced 10 extant species distributed across diverse habitats, including disparate forms such as the short-legged, hypercarnivorous bush dog and the long-legged, largely frugivorous maned wolf. Despite considerable research spanning nearly two centuries, many aspects of their evolutionary history remain unknown. Here, we analyzed 31 whole genomes encompassing all extant SA canid species to assess phylogenetic relationships, interspecific hybridization, historical demography, current genetic diversity, and the molecular bases of adaptations in the bush dog and maned wolf. We found that SA canids originated from a single ancestor that colonized South America 3.9 to 3.5 Mya, followed by diversification east of the Andes and then a single colonization event and radiation of Lycalopex species west of the Andes. We detected extensive historical gene flow between recently diverged lineages and observed distinct patterns of genomic diversity and demographic history in SA canids, likely induced by past climatic cycles compounded by human-induced population declines. Genome-wide scans of selection showed that disparate limb proportions in the bush dog and maned wolf may derive from mutations in genes regulating chondrocyte proliferation and enlargement. Further, frugivory in the maned wolf may have been enabled by variants in genes associated with energy intake from short-chain fatty acids. In contrast, unique genetic variants detected in the bush dog may underlie interdigital webbing and dental adaptations for hypercarnivory. Our analyses shed light on the evolution of a unique carnivoran radiation and how it was shaped by South American topography and climate change.

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          Most cited references133

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          The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

          Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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            PLINK: a tool set for whole-genome association and population-based linkage analyses.

            Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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              PAML 4: phylogenetic analysis by maximum likelihood.

              PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which can be used to estimate parameters in models of sequence evolution and to test interesting biological hypotheses. Uses of the programs include estimation of synonymous and nonsynonymous rates (d(N) and d(S)) between two protein-coding DNA sequences, inference of positive Darwinian selection through phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil calibrations. This note discusses some of the major applications of the package, which includes example data sets to demonstrate their use. The package is written in ANSI C, and runs under Windows, Mac OSX, and UNIX systems. It is available at -- (http://abacus.gene.ucl.ac.uk/software/paml.html).
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                15 August 2022
                23 August 2022
                15 August 2022
                : 119
                : 34
                : e2205986119
                Affiliations
                [1] aDepartment of Ecology and Evolutionary Biology, University of California , Los Angeles, CA 90095;
                [2] bBiodesign Institute, School of Life Sciences, Arizona State University , Tempe, AZ 85287;
                [3] cEfi Arazi School of Computer Science, Reichman University , Herzliya 46150, Israel;
                [4] dCommittee on Evolutionary Biology, University of Chicago , Chicago, IL 60637;
                [5] eData Science Lab, Office of the Chief Information Officer, Smithsonian Institution , Washington, DC 20560;
                [6] fDepartment of Plant and Wildlife Sciences, Brigham Young University , Provo, UT 84602;
                [7] gSmithsonian’s National Zoo and Conservation Biology Institute , Center for Species Survival, Front Royal, VA 22630;
                [8] hSchool of Health and Life Sciences, Pontifical Catholic University of Rio Grande do Sul , Porto Alegre, 90619-900, Brazil;
                [9] iRede de Biodiversidade e Biotecnologia da Amazônia, Curso de Pós-Graduação em Recursos Aquáticos e Pesca, Universidade Estadual do Maranhão , São Luis, 2016-8100, Brazil;
                [10] jCentro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros, Instituto Chico Mendes de Conservação da Biodiversidade , 12952-011, Atibaia, Brazil;
                [11] kDepartment of Genetics, Ecology and Evolution, Universidade Federal de Minas Gerais , Belo Horizonte, 31270-901, Brazil;
                [12] lCentro Nacional de Avaliação da Biodiversidade e de Pesquisa e Conservação do Cerrado, Instituto Chico Mendes de Conservação da Biodiversidade , Brasilia, 70670-350, Brazil;
                [13] mPrivate address, Nova Xavantina, MT, 78690-000, Brazil;
                [14] nInstituto Pró-Carnívoros , Atibaia, 12945-010, Brazil;
                [15] oInstituto Nacional de Ciência e Tecnologia em Ecologia Evolução Conservação da Biodiverside, Universidade Federal de Goiás Goiânia, 74690-900, Brazil;
                [16] pSmithsonian-Mason School of Conservation, George Mason University , Front Royal, VA 22630
                Author notes
                1To whom correspondence may be addressed. Email: dechavezv@ 123456g.ucla.edu or rwayne@ 123456ucla.edu .

                Edited by David Hillis, The University of Texas at Austin, Austin, TX; received April 11, 2022; accepted June 28, 2022

                Author contributions: D.E.C., E.E., K.-P.K., and R.K.W. designed research; D.E.C., I.G., E.E., K.-P.K., and R.K.W. performed research; D.E.C., H.V.F., F.S.G., L.T., R.C.d.P., F.H.G.R., R.S.P.J., E.S.L., N.S., and W.E.J. contributed new reagents/analytic tools; D.E.C., I.G., T.H., R.B.D., and P.B.F. analyzed data; D.E.C., I.G., E.E., K.-P.K., and R.K.W. wrote the paper; R.C.d.P. and F.H.G.R. provided samples and field data on wild-caught maned wolves; R.S.P.J. and E.S.L. provided samples and field data on wild-caught bush dogs; and N.S. and W.E.J. organized and coordinated the sample collection and sequencing of the captive-bred maned wolf and bush dog genomes.

                Author information
                https://orcid.org/0000-0002-4801-7579
                https://orcid.org/0000-0002-8368-7156
                https://orcid.org/0000-0003-1993-1377
                https://orcid.org/0000-0003-1943-8320
                https://orcid.org/0000-0002-9658-0999
                Article
                202205986
                10.1073/pnas.2205986119
                9407222
                35969758
                31a3bd92-87cb-43a0-8871-4a23768d3ad4
                Copyright © 2022 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 28 June 2022
                Page count
                Pages: 12
                Funding
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: DEB20150429
                Award Recipient : Klaus-Peter Koepfli
                Funded by: Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT) 501100004299
                Award ID: 2014-AR2Q4895
                Award Recipient : Daniel Eduardo Chavez
                Funded by: MCTI | CNPq | Instituto Nacional de Ciência e Tecnologia da Criosfera (INCT da Criosfera) 501100010688
                Award ID: 310803/2015-2 and 309068/2019-3
                Award Recipient : Eduardo Eizirik
                Categories
                418
                Biological Sciences
                Evolution

                south america,genomes,canidae,neotropics,positive selection
                south america, genomes, canidae, neotropics, positive selection

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