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      Genomic patterns of homozygosity and inbreeding depression in Murciano-Granadina goats

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          Abstract

          Background

          Inbreeding depression can adversely affect traits related to fitness, reproduction and productive performance. Although current research suggests that inbreeding levels are generally low in most goat breeds, the impact of inbreeding depression on phenotypes of economic interest has only been investigated in a few studies based on genealogical data.

          Results

          We genotyped 1040 goats with the Goat SNP50 BeadChip. This information was used to estimate different molecular inbreeding coefficients and characterise runs of homozygosity and homozygosity patterns. We detected 38 genomic regions with increased homozygosity as well as 8 ROH hotspots mapping to chromosomes 1, 2, 4, 6, 14, 16 and 17. Eight hundred seventeen goats with available records for dairy traits were analysed to evaluate the potential consequences of inbreeding depression on milk phenotypes. Four regions on chromosomes 8 and 25 were significantly associated with inbreeding depression for the natural logarithm of the somatic cell count. Notably, these regions contain several genes related with immunity, such as SYK, IL27, CCL19 and CCL21. Moreover, one region on chromosome 2 was significantly associated with inbreeding depression for milk yield.

          Conclusions

          Although genomic inbreeding levels are low in Murciano-Granadina goats, significant evidence of inbreeding depression for the logarithm of the somatic cell count, a phenotype closely associated with udder health and milk yield, have been detected in this population. Minimising inbreeding would be expected to augment economic gain by increasing milk yield and reducing the incidence of mastitis, which is one of the main causes of dairy goat culling.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40104-022-00684-5.

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          Most cited references75

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Second-generation PLINK: rising to the challenge of larger and richer datasets

            PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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              UniProt: a worldwide hub of protein knowledge

              (2018)
              Abstract The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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                Author and article information

                Contributors
                saura.maria@inia.es
                marcel.amills@uab.cat
                Journal
                J Anim Sci Biotechnol
                J Anim Sci Biotechnol
                Journal of Animal Science and Biotechnology
                BioMed Central (London )
                1674-9782
                2049-1891
                10 March 2022
                10 March 2022
                2022
                : 13
                : 35
                Affiliations
                [1 ]GRID grid.7080.f, ISNI 0000 0001 2296 0625, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, , Universitat Autònoma de Barcelona, ; 08193 Bellaterra, Spain
                [2 ]GRID grid.419190.4, ISNI 0000 0001 2300 669X, Departamento de Mejora Genética Animal, INIA, ; Carretera de la Coruña km 7,5, 28040 Madrid, Spain
                [3 ]GRID grid.411901.c, ISNI 0000 0001 2183 9102, Departamento de Genética, , Universidad de Córdoba, ; 14071 Córdoba, Spain
                [4 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Department of Veterinary Medicine, , University of Bari ‘‘Aldo Moro”, ; 62 per Casamassima km. 3, 70010 Valenzano, SP Italy
                [5 ]GRID grid.7080.f, ISNI 0000 0001 2296 0625, Group of Research in Ruminants (G2R), Department of Animal and Food Science, , Universitat Autònoma de Barcelona (UAB), ; Bellaterra, Barcelona, Spain
                [6 ]GRID grid.7080.f, ISNI 0000 0001 2296 0625, Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, , Universitat Autònoma de Barcelona, ; 08193 Bellaterra, Spain
                Author information
                http://orcid.org/0000-0002-8999-0770
                Article
                684
                10.1186/s40104-022-00684-5
                8908635
                35264251
                1eb3fc03-4d62-4a4f-b5ff-dbdb60eee325
                © The Author(s) 2022

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 3 August 2021
                : 19 January 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100014440, ministerio de ciencia, innovación y universidades;
                Award ID: PID2019-105805RB-I00
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Animal science & Zoology
                goat,inbreeding,milk yield,murciano-granadina,somatic cell score
                Animal science & Zoology
                goat, inbreeding, milk yield, murciano-granadina, somatic cell score

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