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      Genome-wide coancestry reveals details of ancient and recent male-driven reticulation in baboons

      1 , 2 , 3 , 2 , 4 , 5 , 6 , 7 , 4 , 8 , 9 , 4 , 3 , 10 , 11 , 1 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 2 , 2 , 2 , 1 , 6 , 11 , 23 , 20 , 21 , 22 , 5 , 4 , 24 , 25 , 26 , 1 , 3 , 27 , 2
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      American Association for the Advancement of Science (AAAS)

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          Abstract

          Baboons (genus Papio ) are a morphologically and behaviorally diverse clade of catarrhine monkeys that have experienced hybridization between phenotypically and genetically distinct phylogenetic species. We used high-coverage whole-genome sequences from 225 wild baboons representing 19 geographic localities to investigate population genomics and interspecies gene flow. Our analyses provide an expanded picture of evolutionary reticulation among species and reveal patterns of population structure within and among species, including differential admixture among conspecific populations. We describe the first example of a baboon population with a genetic composition that is derived from three distinct lineages. The results reveal processes, both ancient and recent, that produced the observed mismatch between phylogenetic relationships based on matrilineal, patrilineal, and biparental inheritance. We also identified several candidate genes that may contribute to species-specific phenotypes.

          Abstract

          INTRODUCTION

          As a widespread but comparatively young clade of six parapatric species, the baboons ( Papio sp.) exemplify a frequently observed pattern of mammalian diversity. In particular, they provide analogs for the population structure of the multibranched prehuman lineage that occupied a similar geographic range before the hegemony of “modern” humans, Homo sapiens . Despite phenotypic and genetic differences, interspecies hybridization has been described between baboons at several locations, and population relationships based on mitochondrial DNA (mtDNA) do not correspond with relationships based on phenotype. These previous studies captured the broad outlines of baboon population genetic structure and evolutionary history but necessarily used data that were limited in genomic and geographical coverage and therefore could not adequately document inter- and intrapopulation variation. In this study, we analyzed whole-genome sequences of 225 baboons representing all six species and 19 geographic sites, with 18 local populations represented by multiple individuals.

          RATIONALE

          Recent studies have identified several mammalian species groups in which genetically distinct lineages have hybridized to generate complex reticulate phylogenies. Baboons provide a valuable context for studying processes generating such population and phylogenetic complexity because extant parapatric species form hybrid zones in several regions of Africa, allowing for direct observation of ongoing introgression. Furthermore, prior studies of nuclear and mtDNA and phenotypic diversity have demonstrated gene flow among differentiated lineages but were unable to develop the detailed picture of process and history that is now possible using whole-genome sequences and modern computational methods. To address these questions, we designed a study that would provide a more fine-grained picture of recent and ancient genetic reticulation by comparing phenotypes and autosomal, X and Y chromosomal, and mtDNA sequences, along with polymorphic insertions of repetitive elements across multiple baboon populations.

          RESULTS

          Using deep whole-genome sequence data from 225 baboons representing multiple populations, we identified several previously unknown geographic sites of gene flow between genetically distinct populations. We report that yellow baboons ( P. cynocephalus ) from western Tanzania are the first nonhuman primate found to have received genetic input from three distinct lineages. We compared the ancestry shared among individuals, estimated separately from the X chromosome and autosomes, to distinguish shared ancestry due to ancestral population relationships from coancestry as a result of recent male-biased immigration and gene flow. This reveals directionality and sex bias of recent gene flow in several locations. Analyses of population differences within species quantified different degrees of interspecies introgression among populations with an essentially identical phenotype.

          CONCLUSION

          The population genetic structure and history of introgression among baboon lineages are even more complex than predicted from observed phenotypic diversity and prior studies of limited genetic data. Single populations can carry genetic contributions from more than two ancestral sources. Populations that appear homogeneous on the basis of observable phenotype can display different levels of interspecies introgression. The evolutionary dynamics and current structure of baboon population diversity indicate that other mammals displaying differentiated and geographically separate species may also have more-complex histories than anticipated. This may also be true for the morphologically defined hominin taxa from the past 4 million years.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

              Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                June 02 2023
                June 02 2023
                : 380
                : 6648
                Affiliations
                [1 ]Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark.
                [2 ]Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
                [3 ]Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, 37077 Göttingen, Germany.
                [4 ]Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain.
                [5 ]Artificial Intelligence Lab, Illumina Inc., San Diego, CA 92122, USA.
                [6 ]Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
                [7 ]Institute for Systems Biology, Seattle, WA 98109, USA.
                [8 ]Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria.
                [9 ]Human Evolution and Archaeological Sciences (HEAS), University of Vienna, 1030 Vienna, Austria.
                [10 ]Department of Genetics, Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ 08854, USA.
                [11 ]Department of Anthropology, New York University, New York, NY 10003, USA.
                [12 ]Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, 8000 Aarhus C, Denmark.
                [13 ]Department of Neuroscience, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA.
                [14 ]Department of Anthropology, Washington University in St. Louis, St. Louis, MO 63130, USA.
                [15 ]The Carter Center Ethiopia, Addis Ababa, Ethiopia.
                [16 ]Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85281, USA.
                [17 ]School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA.
                [18 ]Tanzania National Parks, Arusha, Tanzania.
                [19 ]Tanzania Wildlife Research Institute, Arusha, Tanzania.
                [20 ]Cognitive Ethology Laboratory, German Primate Center, Leibniz Institute for Primate Research, 37077 Göttingen, Germany.
                [21 ]Department of Primate Cognition, Georg-August-Universität Göttingen, 37077 Göttingen, Germany.
                [22 ]Leibniz ScienceCampus Primate Cognition, 37077 Göttingen, Germany.
                [23 ]Institute of International Animal Health/One Health, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, 17493 Greifswald–Insel Riems, Germany.
                [24 ]Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluis Companys, 23, 08010 Barcelona, Spain.
                [25 ]CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Baldiri i Reixac 4, 08028 Barcelona, Spain.
                [26 ]Institut Catala de Paleontologia Miquel Crusafont, Universitat Autonoma de Barcelona, Edifici ICTA-ICP, cl Columnes s/n, 08193 Cerdanyola del Valles, Barcelona, Spain.
                [27 ]Gene Bank of Primates, German Primate Center, Leibniz Institute for Primate Research, 37077 Göttingen, Germany.
                Article
                10.1126/science.abn8153
                08d69814-a0b9-487e-b257-ddecc192bff0
                © 2023

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