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      Genetic characterization of outbred Sprague Dawley rats and utility for genome-wide association studies

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          Abstract

          Sprague Dawley ( SD) rats are among the most widely used outbred laboratory rat populations. Despite this, the genetic characteristics of SD rats have not been clearly described, and SD rats are rarely used for experiments aimed at exploring genotype-phenotype relationships. In order to use SD rats to perform a genome-wide association study ( GWAS), we collected behavioral data from 4,625 SD rats that were predominantly obtained from two commercial vendors, Charles River Laboratories and Harlan Sprague Dawley Inc. Using double-digest genotyping-by-sequencing ( ddGBS), we obtained dense, high-quality genotypes at 291,438 SNPs across 4,061 rats. This genetic data allowed us to characterize the variation present in Charles River vs. Harlan SD rats. We found that the two populations are highly diverged (F ST > 0.4). Furthermore, even for rats obtained from the same vendor, there was strong population structure across breeding facilities and even between rooms at the same facility. We performed multiple separate GWAS by fitting a linear mixed model that accounted for population structure and using meta-analysis to jointly analyze all cohorts. Our study examined Pavlovian conditioned approach ( PavCA) behavior, which assesses the propensity for rats to attribute incentive salience to reward-associated cues. We identified 46 significant associations for the various metrics used to define PavCA. The surprising degree of population structure among SD rats from different sources has important implications for their use in both genetic and non-genetic studies.

          Author summary

          Outbred Sprague Dawley rats are among the most commonly used rats for neuroscience, physiology and pharmacological research; in the year 2020, 4,188 publications contained the keyword “Sprague Dawley”. Rats identified as “Sprague Dawley” are sold by several commercial vendors, including Charles River Laboratories and Harlan Sprague Dawley Inc. (now Envigo). Despite their widespread use, little is known about the genetic diversity of SD. We genotyped more than 4,000 SD rats, which we used for a genome-wide association study ( GWAS) and to characterize genetic differences between SD rats from Charles River Laboratories and Harlan. Our analysis revealed extensive population structure both between and within vendors. The GWAS for Pavlovian conditioned approach ( PavCA) identified a number of genome-wide significant loci for that complex behavioral trait. Our results demonstrate that, despite sharing an identical name, SD rats that are obtained from different vendors are very different. Future studies should carefully define the exact source of SD rats being used and may exploit their genetic diversity for genetic studies of complex traits.

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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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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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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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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Software
                Role: Data curationRole: InvestigationRole: Methodology
                Role: Investigation
                Role: Investigation
                Role: Investigation
                Role: Methodology
                Role: Software
                Role: ConceptualizationRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                31 May 2022
                May 2022
                : 18
                : 5
                : e1010234
                Affiliations
                [1 ] Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
                [2 ] Department of Psychiatry, University of California, San Diego, California, United States of America
                [3 ] Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, United States of America
                [4 ] Biological Sciences Collegiate Division, University of Chicago, Chicago, Illinois, United States of America
                [5 ] Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States of America
                [6 ] Department of Psychology and Neuroscience Program, University of Michigan, Ann Arbor, Michigan, United States of America
                [7 ] Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan, United States of America
                [8 ] Institute for Genomic Medicine, University of California, San Diego, California, United States of America
                University of Illinois at Urbana-Champaign, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-7793-8043
                https://orcid.org/0000-0003-1709-9214
                https://orcid.org/0000-0001-5465-6601
                https://orcid.org/0000-0002-8133-2298
                https://orcid.org/0000-0001-5712-3616
                https://orcid.org/0000-0001-5289-0410
                https://orcid.org/0000-0001-9281-8528
                https://orcid.org/0000-0002-3530-4544
                https://orcid.org/0000-0001-7861-3737
                https://orcid.org/0000-0002-7309-9908
                https://orcid.org/0000-0003-3634-0747
                Article
                PGENETICS-D-21-01074
                10.1371/journal.pgen.1010234
                9187121
                35639796
                628365c4-fcfa-4809-9b5e-ae2140da0ed8
                © 2022 Gileta et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 16 August 2021
                : 3 May 2022
                Page count
                Figures: 4, Tables: 1, Pages: 29
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA036672
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA037844
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA039638-02
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: GM007197
                Award Recipient :
                This work was funded by the National Institute of Drug Addiction (NIDA; https://www.drugabuse.gov/) through R21 DA036672 (AAP; TER; SBF), P50 DA037844 (AAP; TER; SBF), and F31 DA039638-02 (AFG), as well as training grant T32 GM007197 (AFG) from the National Institute of General Medical Sciences (NIGMS; https://www.nigms.nih.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Genome-Wide Association Studies
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Model Organisms
                Rats
                Research and Analysis Methods
                Model Organisms
                Rats
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Animal Models
                Rats
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Rodents
                Rats
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
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                Rodents
                Rats
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Metaanalysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Metaanalysis
                Social Sciences
                Economics
                Commerce
                Vendors
                Biology and Life Sciences
                Genetics
                Single Nucleotide Polymorphisms
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Biology and Life Sciences
                Genetics
                Biology and Life Sciences
                Genetics
                Genomics
                Custom metadata
                vor-update-to-uncorrected-proof
                2022-06-10
                Data files too large to be included as supplemental content were uploaded to a central online repository through the UCSD Library System: https://doi.org/10.6075/J0XS5V8K. This DOI can be followed to access (1) a VCF file containing all raw SNP calls with dosage data in the original set of 4,625 Sprague Dawley rats, (2) filtered variants in BIM/BED/FAM format for each of the seven SD subgroups, and (3) summary files for the GWAS results for all 55 metric x day combinations for all analyses, individual subgroups and meta-analyses. In addition, FASTQ files from the original ddGBS and light WGS sequencing runs were submitted to the NCBI Sequence Read Archive (SRA) under BioProject identifier PRJNA779552 ( https://www.ncbi.nlm.nih.gov/sra/PRJNA779552). Instructions on how to download the data can be found here: https://www.ncbi.nlm.nih.gov/sra/docs/sradownload/#download-metadata-associated-wit.

                Genetics
                Genetics

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