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      Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs

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          Abstract

          Feed efficiency (FE) is a key trait in pig production, as improvement in FE has positive economic and environmental impact. FE is a complex phenotype and testing animals for FE is costly. Therefore, there has been a desire to find functionally relevant single nucleotide polymorphisms (SNPs) as biomarkers, to improve our biological understanding of FE as well as accuracy of genomic prediction for FE. We have performed a cis- and trans- eQTL (expression quantitative trait loci) analysis, in a population of Danbred Durocs (N = 11) and Danbred Landrace (N = 27) using both a linear and ANOVA model based on muscle tissue RNA-seq. We analyzed a total of 1425x19179 or 2.7x10 7 Gene-SNP combinations in eQTL detection models for FE. The 1425 genes were from RNA-Seq based differential gene expression analyses using 25880 genes related to FE and additionally combined with mitochondrial genes. The 19179 SNPs were from applying stringent quality control and linkage disequilibrium filtering on genotype data using a GGP Porcine HD 70k SNP array. We applied 1000 fold bootstrapping and enrichment analysis to further validate and analyze our detected eQTLs. We identified 13 eQTLs with FDR < 0.1, affecting several genes found in previous studies of commercial pig breeds. Examples include MYO19, CPT1B, ACSL1, IER5L, CPT1A, SUCLA2, CSRNP1, PARK7 and MFF. The bootstrapping results showed statistically significant enrichment (p-value<2.2x10 -16) of eQTLs with p-value < 0.01 in both cis and trans-eQTLs. Enrichment analysis of top trans-eQTLs revealed high enrichment for gene categories and gene ontologies associated with genomic context and expression regulation. This included transcription factors (p-value = 1.0x10 -13), DNA-binding (GO:0003677, p-value = 8.9x10 -14), DNA-binding transcription factor activity (GO:0003700,) nucleus gene (GO:0005634, p-value<2.2x10 -16), negative regulation of expression (GO:0010629, p-value<2.2x10 -16). These results would be useful for future genome assisted breeding of pigs to improve FE, and in the improved understanding of the functional mechanism of trans eQTLs.

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          Most cited references48

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors

            Abstract The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to provide the most comprehensive and accurate information for animal transcription factors (TFs) and cofactors. The AnimalTFDB has been maintained and updated for seven years and we will continue to improve it. Recently, we updated the AnimalTFDB to version 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/) with more data and functions to improve it. AnimalTFDB contains 125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes. Besides the expansion in data quantity, some new features and functions have been added. These new features are: (i) more accurate TF family assignment rules; (ii) classification of transcription cofactors; (iii) TF binding sites information; (iv) the GWAS phenotype related information of human TFs; (v) TF expressions in 22 animal species; (vi) a TF binding site prediction tool to identify potential binding TFs for nucleotide sequences; (vii) a separate human TF database web interface (HumanTFDB) was designed for better utilizing the human TFs. The new version of AnimalTFDB provides a comprehensive annotation and classification of TFs and cofactors, and will be a useful resource for studies of TF and transcription regulation.
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              The Post-GWAS Era: From Association to Function

              During the past 12 years, genome-wide association studies (GWASs) have uncovered thousands of genetic variants that influence risk for complex human traits and diseases. Yet functional studies aimed at delineating the causal genetic variants and biological mechanisms underlying the observed statistical associations with disease risk have lagged. In this review, we highlight key advances in the field of functional genomics that may facilitate the derivation of biological meaning post-GWAS. We highlight the evidence suggesting that causal variants underlying disease risk often function through regulatory effects on the expression of target genes and that these expression effects might be modest and cell-type specific. We moreover discuss specific studies as proof-of-principle examples for current statistical, bioinformatic, and empirical bench-based approaches to downstream elucidation of GWAS-identified disease risk loci.

                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                17 September 2020
                2020
                : 15
                : 9
                : e0239143
                Affiliations
                [001]Quantitative Genomics, Bioinformatics and Computational Biology Group, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
                University of Illinois, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-6294-382X
                Article
                PONE-D-20-09228
                10.1371/journal.pone.0239143
                7498092
                32941478
                7cf8e88f-5713-4f1e-bc73-76cf9369cb02
                © 2020 Carmelo, Kadarmideen

                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
                : 8 April 2020
                : 31 August 2020
                Page count
                Figures: 5, Tables: 3, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004836, Det Frie Forskningsråd;
                Award ID: 4184-00268B
                Award Recipient :
                VAOC were funded by the Independent Research Fund Denmark (DFF)( https://dff.dk/en) – Technology and Production (FTP) grant (grant number: 4184-00268B). VAOC received partial Ph.D. stipends from the University of Copenhagen and Technical University of Denmark.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Swine
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Swine
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Biology and Life Sciences
                Genetics
                Biology and Life Sciences
                Genetics
                Single Nucleotide Polymorphisms
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Analysis of Variance
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Analysis of Variance
                Biology and Life Sciences
                Biochemistry
                Bioenergetics
                Energy-Producing Organelles
                Mitochondria
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Energy-Producing Organelles
                Mitochondria
                Biology and Life Sciences
                Genetics
                Genomics
                Custom metadata
                Genotyping data is available at the Gene Expression omnibus with accesion number GSE144064. Expresison data is available at the Gene Expression omnibus with accesion number GSE148889.

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                Uncategorized

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