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      Genomics Reveals Complex Population History and Unexpected Diversity of Eurasian Otters ( Lutra lutra) in Britain Relative to Genetic Methods

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          Abstract

          Conservation genetic analyses of many endangered species have been based on genotyping of microsatellite loci and sequencing of short fragments of mtDNA. The increase in power and resolution afforded by whole genome approaches may challenge conclusions made on limited numbers of loci and maternally inherited haploid markers. Here, we provide a matched comparison of whole genome sequencing versus microsatellite and control region (CR) genotyping for Eurasian otters ( Lutra lutra). Previous work identified four genetically differentiated “stronghold” populations of otter in Britain, derived from regional populations that survived the population crash of the 1950s–1980s. Using whole genome resequencing data from 45 samples from across the British stronghold populations, we confirmed some aspects of population structure derived from previous marker-driven studies. Importantly, we showed that genomic signals of the population crash bottlenecks matched evidence from otter population surveys. Unexpectedly, two strongly divergent mitochondrial lineages were identified that were undetectable using CR fragments, and otters in the east of England were genetically distinct and surprisingly variable. We hypothesize that this previously unsuspected variability may derive from past releases of Eurasian otters from other, non-British source populations in England around the time of the population bottleneck. Our work highlights that even reasonably well-studied species may harbor genetic surprises, if studied using modern high-throughput sequencing methods.

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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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            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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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Mol Biol Evol
                Mol Biol Evol
                molbev
                Molecular Biology and Evolution
                Oxford University Press (US )
                0737-4038
                1537-1719
                November 2023
                15 September 2023
                15 September 2023
                : 40
                : 11
                : msad207
                Affiliations
                School of Biosciences, Cardiff University , Cardiff, UK
                Tree of Life, Wellcome Sanger Institute , Cambridge, UK
                Smithsonian-Mason School of Conservation, George Mason University , Front Royal, VA, USA
                Centre for Species Survival, Smithsonian's National Zoo and Conservation Biology Institute , Washington, DC, USA
                School of Biosciences, Cardiff University , Cardiff, UK
                School of Biosciences, Cardiff University , Cardiff, UK
                Author notes
                Author information
                https://orcid.org/0000-0002-0239-0482
                https://orcid.org/0000-0002-2340-1726
                Article
                msad207
                10.1093/molbev/msad207
                10630326
                37713621
                d71dcde2-e574-42bc-ba6c-4499ff11f2dc
                © The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Pages: 17
                Categories
                Discoveries
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01180

                Molecular biology
                population genomics,bottleneck,demographic history,reintroductions,genetic tools
                Molecular biology
                population genomics, bottleneck, demographic history, reintroductions, genetic tools

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