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      Polygenic discrimination of migratory phenotypes in an estuarine forage fish

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          Abstract

          Migration is a complex phenotypic trait with some species containing migratory and nonmigratory individuals. Such life history variation may be attributed in part to plasticity, epigenetics, or genetics. Although considered semianadromous, recent studies using otolith geochemistry have revealed life history variation within the critically endangered Delta Smelt. Broadly categorizable as migratory or freshwater residents, we examined Restriction site Associated DNA sequencing data to test for a relationship between genetic variation and migratory behaviors. As previously shown, we found no evidence for neutral population genetic structure within Delta Smelt; however, we found significant evidence for associations between genetic variants and life history phenotypes. Furthermore, discriminant analysis of principal components, hierarchical clustering, and machine learning resulted in accurate assignment of fish into the freshwater resident or migratory classes based on their genotypes. These results suggest the presence of adaptive genetic variants relating to life history variation within a panmictic population. Mechanisms that may lead to this observation are genotype dependent habitat choice and spatially variable selection, both of which could operate each generation and are not exclusive. Given that the population of cultured Delta Smelt are being used as a refugial population for conservation, as a supply for wild population supplementation, and currently represent the majority of all living individuals of this species, we recommend that the hatchery management strategy consider the frequencies of life history-associated alleles and how to maintain this important aspect of Delta Smelt biological variation while under captive propagation.

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          Most cited references75

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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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              adegenet: a R package for the multivariate analysis of genetic markers.

              The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers. It extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. Data can be imported from common population genetics software and exported to other software and R packages. adegenet also implements standard population genetics tools along with more original approaches for spatial genetics and hybridization. Stable version is available from CRAN: http://cran.r-project.org/mirrors.html. Development version is available from adegenet website: http://adegenet.r-forge.r-project.org/. Both versions can be installed directly from R. adegenet is distributed under the GNU General Public Licence (v.2).
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press
                2160-1836
                August 2022
                30 May 2022
                30 May 2022
                : 12
                : 8
                : jkac133
                Affiliations
                Genomic Variation Laboratory, Department of Animal Science, University of California, Davis , Davis, CA 95616, USA
                Genomic Variation Laboratory, Department of Animal Science, University of California, Davis , Davis, CA 95616, USA
                Genomic Variation Laboratory, Department of Animal Science, University of California, Davis , Davis, CA 95616, USA
                Institute of Marine Sciences, UC Santa Cruz , Santa Cruz, CA 95064, USA
                National Marine Fisheries Service, Southwest Fisheries Science Center , Santa Cruz, CA 95064, USA
                Otolith Geochemistry and Fish Ecology Lab, Department of Wildlife, Fish and Conservation Biology, University of California, Davis , Davis, CA 95616, USA
                Otolith Geochemistry and Fish Ecology Lab, Department of Wildlife, Fish and Conservation Biology, University of California, Davis , Davis, CA 95616, USA
                Genomic Variation Laboratory, Department of Animal Science, University of California, Davis , Davis, CA 95616, USA
                Author notes
                Corresponding author: Genomic Variation Laboratory, 2403 Meyer Hall, Department of Animal Science, University of California Davis, One Shields Avenue, Davis, CA 95616, USA. Email: ajfinger@ 123456ucdavis.edu ; Corresponding author: Genomic Variation Laboratory, Department of Animal Science, University of California Davis, 2403 Meyer Hall, One Shields Avenue, Davis, CA 95616, USA. Email: maccampbell@ 123456ucdavis.edu
                Author information
                https://orcid.org/0000-0002-5826-0329
                https://orcid.org/0000-0003-0416-2069
                https://orcid.org/0000-0001-8734-5324
                https://orcid.org/0000-0002-4052-9000
                https://orcid.org/0000-0002-9664-9386
                https://orcid.org/0000-0003-3850-3685
                Article
                jkac133
                10.1093/g3journal/jkac133
                9339312
                35640553
                2cafc021-6385-4cb1-954a-8118ffed86f2
                © The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.

                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
                : 1 March 2022
                : 14 May 2022
                : 09 June 2022
                Page count
                Pages: 10
                Funding
                Funded by: DNA sequencing and genome assembly of Delta Smelt came from the US Bureau of Reclamation;
                Award ID: R20AC00027
                Funded by: State Water Contractors research agreement;
                Award ID: A19-1844
                Funded by: California Department of Fish and Wildlife contracts;
                Award ID: E1183004
                Award ID: D1583004
                Award ID: P1696005
                Funded by: US Bureau of Reclamation;
                Award ID: R13AP20022
                Award ID: R17AC00129
                Funded by: Delta Stewardship Council via postdoctoral fellowships to MW;
                Award ID: 1167
                Award ID: 2279
                Award ID: 5298
                Categories
                Investigation
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140
                AcademicSubjects/SCI00010
                AcademicSubjects/SCI00960

                Genetics
                adaptive genetic variation,delta smelt,migration,osmeridae,resident ecotype,semianadromy
                Genetics
                adaptive genetic variation, delta smelt, migration, osmeridae, resident ecotype, semianadromy

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