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      Predicted genetic burden and frequency of phenotype-associated variants in the horse

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          Abstract

          Disease-causing variants have been identified for less than 20% of suspected equine genetic diseases. Whole genome sequencing (WGS) allows rapid identification of rare disease causal variants. However, interpreting the clinical variant consequence is confounded by the number of predicted deleterious variants that healthy individuals carry (predicted genetic burden). Estimation of the predicted genetic burden and baseline frequencies of known deleterious or phenotype associated variants within and across the major horse breeds have not been performed. We used WGS of 605 horses across 48 breeds to identify 32,818,945 variants, demonstrate a high predicted genetic burden (median 730 variants/horse, interquartile range: 613–829), show breed differences in predicted genetic burden across 12 target breeds, and estimate the high frequencies of some previously reported disease variants. This large-scale variant catalog for a major and highly athletic domestic animal species will enhance its ability to serve as a model for human phenotypes and improves our ability to discover the bases for important equine phenotypes.

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          Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

          The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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            A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.

            We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w(1118); iso-2; iso-3 strain and the reference y(1); cn(1) bw(1) sp(1) strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in the 5'UTR. We found, as expected, that the SNP frequency is proportional to the recombination frequency (i.e., highest in the middle of chromosome arms). We also found that start-gain or stop-lost SNPs in Drosophila melanogaster often result in additions of N-terminal or C-terminal amino acids that are conserved in other Drosophila species. It appears that the 5' and 3' UTRs are reservoirs for genetic variations that changes the termini of proteins during evolution of the Drosophila genus. As genome sequencing is becoming inexpensive and routine, SnpEff enables rapid analyses of whole-genome sequencing data to be performed by an individual laboratory.
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              A map of human genome variation from population-scale sequencing.

              The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10(-8) per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research.
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                Author and article information

                Contributors
                durwa004@umn.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 April 2024
                10 April 2024
                2024
                : 14
                : 8396
                Affiliations
                [1 ]Department of Veterinary Clinical Sciences, University of Minnesota, ( https://ror.org/017zqws13) C339 VMC, 1353 Boyd Avenue, St. Paul, MN 55108 USA
                [2 ]Department of Veterinary Population Medicine, University of Minnesota, ( https://ror.org/017zqws13) 225 VMC, 1365 Gortner Avenue, St. Paul, MN 55108 USA
                [3 ]Interval Bio LLC, 408 Stierline Road, Mountain View, CA 94043 USA
                [4 ]Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California-Davis, ( https://ror.org/05rrcem69) Davis, CA USA
                [5 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, Population Health and Reproduction and Veterinary Genetics Laboratory, School of Veterinary Medicine, , University of California, ; Davis, CA USA
                [6 ]Department of Veterinary and Biomedical Sciences, University of Minnesota, ( https://ror.org/017zqws13) 295F Animal Science Veterinary Medicine Building, 1988 Fitch Avenue, St. Paul, MN 55108 USA
                Article
                57872
                10.1038/s41598-024-57872-8
                11006912
                38600096
                0cbd659a-16bb-4f29-8e38-f8dad26592ab
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 December 2023
                : 22 March 2024
                Funding
                Funded by: USDA NIFA-AFRI
                Award ID: 2017-67015-26296
                Award ID: 2017-67015-26296
                Award ID: 2017-67015-26296
                Award Recipient :
                Funded by: The American Quarter Horse Association
                Funded by: University of Minnesota Multistate grant
                Funded by: American College of Veterinary Internal Medicine Foundation fellowship
                Funded by: FundRef http://dx.doi.org/10.13039/100016958, Office of Research Infrastructure Programs, National Institutes of Health;
                Award ID: 5T320D010993-12
                Award ID: 5T320D010993-12
                Award Recipient :
                Funded by: Elaine and Bertram Klein Development Award
                Funded by: FundRef http://dx.doi.org/10.13039/100001250, Morris Animal Foundation;
                Award ID: D20EQ-403
                Award Recipient :
                Categories
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                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                biochemistry,diseases,genetics,genomics,medical genetics,population genetics
                Uncategorized
                biochemistry, diseases, genetics, genomics, medical genetics, population genetics

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