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      Ghost admixture in eastern gorillas

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          Abstract

          Archaic admixture has had a substantial impact on human evolution with multiple events across different clades, including from extinct hominins such as Neanderthals and Denisovans into modern humans. In great apes, archaic admixture has been identified in chimpanzees and bonobos but the possibility of such events has not been explored in other species. Here, we address this question using high-coverage whole-genome sequences from all four extant gorilla subspecies, including six newly sequenced eastern gorillas from previously unsampled geographic regions. Using approximate Bayesian computation with neural networks to model the demographic history of gorillas, we find a signature of admixture from an archaic ‘ghost’ lineage into the common ancestor of eastern gorillas but not western gorillas. We infer that up to 3% of the genome of these individuals is introgressed from an archaic lineage that diverged more than 3 million years ago from the common ancestor of all extant gorillas. This introgression event took place before the split of mountain and eastern lowland gorillas, probably more than 40 thousand years ago and may have influenced perception of bitter taste in eastern gorillas. When comparing the introgression landscapes of gorillas, humans and bonobos, we find a consistent depletion of introgressed fragments on the X chromosome across these species. However, depletion in protein-coding content is not detectable in eastern gorillas, possibly as a consequence of stronger genetic drift in this species.

          Abstract

          Using a Bayesian approach with neural networks the authors model the demographic history of gorillas, finding admixture from an archaic ‘ghost’ lineage into the common ancestor of eastern gorillas, but not western gorillas.

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            The Ensembl Variant Effect Predictor

            The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.
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              A method and server for predicting damaging missense mutations

              To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
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                Author and article information

                Contributors
                tomas.marques@upf.edu
                martin.kuhlwilm@univie.ac.at
                Journal
                Nat Ecol Evol
                Nat Ecol Evol
                Nature Ecology & Evolution
                Nature Publishing Group UK (London )
                2397-334X
                27 July 2023
                27 July 2023
                2023
                : 7
                : 9
                : 1503-1514
                Affiliations
                [1 ]GRID grid.418220.d, ISNI 0000 0004 1756 6019, Institute of Evolutionary Biology (UPF-CSIC), , PRBB, ; Barcelona, Spain
                [2 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Department of Evolutionary Anthropology, , University of Vienna, ; Vienna, Austria
                [3 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Human Evolution and Archaeological Sciences (HEAS), , University of Vienna, ; Wien, Austria
                [4 ]GRID grid.425591.e, ISNI 0000 0004 0605 2864, Department of Bioinformatics and Genetics, Scilifelab, , Swedish Museum of Natural History, ; Stockholm, Sweden
                [5 ]GRID grid.510921.e, Centre for Palaeogenetics, ; Stockholm, Sweden
                [6 ]Institute of Cancer and Genomic Sciences, University of Birmingham, Dubai, United Arab Emirates
                [7 ]GRID grid.420175.5, ISNI 0000 0004 0639 2420, Integrative Genomics Lab, CIC bioGUNE—Centro de Investigación Cooperativa en Biociencias, , Parque Científico Tecnológico de Bizkaia building 801A, ; Derio, Spain
                [8 ]GRID grid.10306.34, ISNI 0000 0004 0606 5382, Wellcome Sanger Institute, ; Hinxton, UK
                [9 ]GRID grid.440425.3, ISNI 0000 0004 1798 0746, Monash University Malaysia Genomics Facility, School of Science, , Monash University Malaysia, ; Selangor Darul Ehsan, Malaysia
                [10 ]GRID grid.205975.c, ISNI 0000 0001 0740 6917, Department of Ecology and Evolutionary Biology, , University of California, ; Santa Cruz, CA USA
                [11 ]GRID grid.83440.3b, ISNI 0000000121901201, UCL Genetics Institute, Department of Genetics, Evolution and Environment, , University College London, ; London, UK
                [12 ]GRID grid.8993.b, ISNI 0000 0004 1936 9457, Animal Ecology, Department of Ecology and Genetics, , Uppsala University, ; Uppsala, Sweden
                [13 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, Institute of Ecology and Evolution, School of Biological Sciences, , University of Edinburgh, ; Edinburgh, UK
                [14 ]GRID grid.452834.c, ISNI 0000 0004 5911 2402, Science for Life Laboratory, ; Uppsala, Sweden
                [15 ]Gorilla Doctors, Kampala, Uganda
                [16 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, Gorilla Doctors, Karen C. Drayer Wildlife Health Center, One Health Institute, University of California Davis, , School of Veterinary Medicine, ; Davis, CA USA
                [17 ]GRID grid.425902.8, ISNI 0000 0000 9601 989X, Catalan Institution of Research and Advanced Studies (ICREA), , Passeig de Lluís Companys, ; Barcelona, Spain
                [18 ]GRID grid.473715.3, ISNI 0000 0004 6475 7299, CNAG-CRG, Centre for Genomic Regulation (CRG), , Barcelona Institute of Science and Technology (BIST), ; Barcelona, Spain
                [19 ]GRID grid.7080.f, ISNI 0000 0001 2296 0625, Institut Català de Paleontologia Miquel Crusafont, , Universitat Autònoma de Barcelona, Edifici ICTA-ICP, ; Barcelona, Spain
                Author information
                http://orcid.org/0009-0001-5461-5161
                http://orcid.org/0000-0002-9918-9602
                http://orcid.org/0000-0003-1170-9915
                http://orcid.org/0000-0002-6113-1042
                http://orcid.org/0000-0003-3048-4280
                http://orcid.org/0000-0003-1912-9667
                http://orcid.org/0000-0003-3291-0917
                http://orcid.org/0009-0007-0059-4178
                http://orcid.org/0000-0002-7731-605X
                http://orcid.org/0000-0003-4076-4227
                http://orcid.org/0000-0002-8493-5457
                http://orcid.org/0000-0002-5597-3075
                http://orcid.org/0000-0002-0115-1797
                Article
                2145
                10.1038/s41559-023-02145-2
                10482688
                37500909
                fbed526a-e81c-4c61-bb76-a791dabb0902
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 December 2022
                : 30 June 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001821, Vienna Science and Technology Fund (Wiener Wissenschafts-, Forschungs- und Technologiefonds);
                Award ID: 10.47379/VRG20001
                Award Recipient :
                Funded by: Formació de Personal Investigador fellowship from Generalitat de Catalunya (FI_B100131)
                Funded by: FPI (Formación de Personal Investigador) PRE2018-083966 from Ministerio de Ciencia, Universidades e Investigación
                Funded by: FundRef https://doi.org/10.13039/100000925, John Templeton Foundation (JTF);
                Award ID: 62178
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001779, Monash University (MU);
                Award ID: STG-000114
                Award Recipient :
                Funded by: María de Maeztu Mobility Fellowship
                Funded by: FundRef https://doi.org/10.13039/501100004359, Vetenskapsrådet (Swedish Research Council);
                Award ID: 2020-03398
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010269, Wellcome Trust (Wellcome);
                Award ID: 098051
                Award ID: 098051
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 864203
                Award Recipient :
                Funded by: PID2021-126004NB-100 (MINECO/FEDER, UE) Secretaria d’Universitats i Recerca and CERCA Program del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2021 SGR 00177)
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                © Springer Nature Limited 2023

                evolutionary genetics,phylogenetics,population genetics

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