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      Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds

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          Abstract

          Background

          Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds.

          Methods

          Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10 –6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model.

          Results

          We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN.

          Conclusions

          Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12711-021-00671-w.

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          Most cited references79

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          Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

          Background System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Results Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Conclusions Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.
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            Genome-wide Efficient Mixed Model Analysis for Association Studies

            Linear mixed models have attracted considerable recent attention as a powerful and effective tool for accounting for population stratification and relatedness in genetic association tests. However, existing methods for exact computation of standard test statistics are computationally impractical for even moderate-sized genome-wide association studies. To deal with this several approximate methods have been proposed. Here, we present an efficient exact method that makes these approximations unnecessary in many settings. This method is roughly n times faster than the widely-used exact method EMMA, where n is the sample size, making exact genome-wide association analysis computationally practical for large numbers of individuals.
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              An Expanded View of Complex Traits: From Polygenic to Omnigenic

              A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome-including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an "omnigenic" model.
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                Author and article information

                Contributors
                miguel.marcilla@ed.ac.uk
                buntjer@lombok.nl
                martin.johnsson@slu.se
                loregbatista@gmail.com
                federico.diez@ed.ac.uk
                c.werner@cgiar.org
                ching-yi.chen@genusplc.com
                gregor.gorjanc@roslin.ed.ac.uk
                richard.mellanby@ed.ac.uk
                hickeyjohn@gmail.com
                roger.ros@roslin.ed.ac.uk
                Journal
                Genet Sel Evol
                Genet Sel Evol
                Genetics, Selection, Evolution : GSE
                BioMed Central (London )
                0999-193X
                1297-9686
                22 September 2021
                22 September 2021
                2021
                : 53
                : 76
                Affiliations
                [1 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, The Roslin Institute, , The University of Edinburgh, ; Midlothian, UK
                [2 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, The Royal (Dick) School of Veterinary Studies, , The University of Edinburgh, ; Midlothian, UK
                [3 ]GRID grid.6341.0, ISNI 0000 0000 8578 2742, Department of Animal Breeding and Genetics, , Swedish University of Agricultural Sciences, ; Uppsala, Sweden
                [4 ]The Pig Improvement Company, Genus plc, Hendersonville, TN USA
                [5 ]GRID grid.15043.33, ISNI 0000 0001 2163 1432, Departament de Ciència Animal, , Universitat de Lleida - Agrotecnio-CERCA Center, ; Lleida, Spain
                Author information
                http://orcid.org/0000-0002-3745-6736
                Article
                671
                10.1186/s12711-021-00671-w
                8459476
                33397289
                864ab7e8-e1ee-4ca9-a9c9-47337b75b206
                © The Author(s) 2021

                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
                : 24 March 2021
                : 7 September 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BBS/E/D/30002275
                Award ID: BB/N004736/1
                Award ID: BB/N015339/1
                Award ID: BB/ L020467/1
                Award ID: BB/M009254/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100006041, Innovate UK;
                Award ID: 102271
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001862, Svenska Forskningsrådet Formas;
                Award ID: Dnr 2016-01386
                Award Recipient :
                Funded by: Genus plc
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Genetics
                Genetics

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