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      Analyzing the genomic and transcriptomic architecture of milk traits in Murciano-Granadina goats

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          Abstract

          Background

          In this study, we aimed to investigate the molecular basis of lactation as well as to identify the genetic factors that influence milk yield and composition in goats. To achieve these two goals, we have analyzed how the mRNA profile of the mammary gland changes in seven Murciano-Granadina goats at each of three different time points, i.e. 78 d (T1, early lactation), 216 d (T2, late lactation) and 285 d (T3, dry period) after parturition. Moreover, we have performed a genome-wide association study (GWAS) for seven dairy traits recorded in the 1st lactation of 822 Murciano-Granadina goats.

          Results

          The expression profiles of the mammary gland in the early (T1) and late (T2) lactation were quite similar (42 differentially expressed genes), while strong transcriptomic differences (more than one thousand differentially expressed genes) were observed between the lactating (T1/T2) and non-lactating (T3) mammary glands. A large number of differentially expressed genes were involved in pathways related with the biosynthesis of amino acids, cholesterol, triglycerides and steroids as well as with glycerophospholipid metabolism, adipocytokine signaling, lipid binding, regulation of ion transmembrane transport, calcium ion binding, metalloendopeptidase activity and complement and coagulation cascades. With regard to the second goal of the study, the performance of the GWAS allowed us to detect 24 quantitative trait loci (QTLs), including three genome-wide significant associations: QTL1 (chromosome 2, 130.72-131.01 Mb) for lactose percentage, QTL6 (chromosome 6, 78.90-93.48 Mb) for protein percentage and QTL17 (chromosome 17, 11.20 Mb) for both protein and dry matter percentages. Interestingly, QTL6 shows positional coincidence with the casein genes, which encode 80% of milk proteins.

          Conclusions

          The abrogation of lactation involves dramatic changes in the expression of genes participating in a broad array of physiological processes such as protein, lipid and carbohydrate metabolism, calcium homeostasis, cell death and tissue remodeling, as well as immunity. We also conclude that genetic variation at the casein genes has a major impact on the milk protein content of Murciano-Granadina goats.

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

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            Benefits and limitations of genome-wide association studies

            Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.
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              Genetic analyses of diverse populations improves discovery for complex traits

              Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.
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                Author and article information

                Contributors
                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
                11 March 2020
                11 March 2020
                2020
                : 11
                : 35
                Affiliations
                [1 ]GRID grid.7080.f, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, , Universitat Autònoma de Barcelona, ; 08193 Bellaterra, Spain
                [2 ]GRID grid.411901.c, ISNI 0000 0001 2183 9102, Departamento de Genética, , Universidad de Córdoba, ; 14071 Córdoba, Spain
                [3 ]GRID grid.7080.f, Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, , Universitat Autònoma de Barcelona, ; 08193 Bellaterra, Spain
                [4 ]Asociación Nacional de Criadores de Caprino de Raza Murciano-Granadina (CAPRIGRAN), 18340 Granada, Spain
                [5 ]GRID grid.7080.f, Servei de Granges i Camps Experimentals, , Universitat Autònoma de Barcelona, ; 08193 Bellaterra, Spain
                Author information
                http://orcid.org/0000-0002-8999-0770
                Article
                435
                10.1186/s40104-020-00435-4
                7065321
                32175082
                653df613-5f26-4818-b2a0-b72054718a99
                © The Author(s) 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                History
                : 19 September 2019
                : 17 February 2020
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Animal science & Zoology
                casein genes,dairy traits,gwas,lactation,qtls,rna-seq
                Animal science & Zoology
                casein genes, dairy traits, gwas, lactation, qtls, rna-seq

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