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      Genome-Wide Association Study of Meat Quality Traits in Nellore Cattle

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          Abstract

          The objective of this study was to identify genomic regions that are associated with meat quality traits in the Nellore breed. Nellore steers were finished in feedlots and slaughtered at a commercial slaughterhouse. This analysis included 1,822 phenotypic records of tenderness and 1,873 marbling records. After quality control, 1,630 animals genotyped for tenderness, 1,633 animals genotyped for marbling, and 369,722 SNPs remained. The results are reported as the proportion of variance explained by windows of 150 adjacent SNPs. Only windows with largest effects were considered. The genomic regions were located on chromosomes 5, 15, 16 and 25 for marbling and on chromosomes 5, 7, 10, 14 and 21 for tenderness. These windows explained 3,89% and 3,80% of the additive genetic variance for marbling and tenderness, respectively. The genes associated with the traits are related to growth, muscle development and lipid metabolism. The study of these genes in Nellore cattle is the first step in the identification of causal mutations that will contribute to the genetic evaluation of the breed.

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          Most cited references29

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          Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.

          The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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            Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens

            The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined.
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              Genome scan for meat quality traits in Nelore beef cattle.

              Meat quality traits are economically important because they affect consumers' acceptance, which, in turn, influences the demand for beef. However, selection to improve meat quality is limited by the small numbers of animals on which meat tenderness can be evaluated due to the cost of performing shear force analysis and the resultant damage to the carcass. Genome wide-association studies for Warner-Bratzler shear force measured at different times of meat aging, backfat thickness, ribeye muscle area, scanning parameters [lightness, redness (a*), and yellowness] to ascertain color characteristics of meat and fat, water-holding capacity, cooking loss (CL), and muscle pH were conducted using genotype data from the Illumina BovineHD BeadChip array to identify quantitative trait loci (QTL) in all phenotyped Nelore cattle. Phenotype count for these animals ranged from 430 to 536 across traits. Meat quality traits in Nelore are controlled by numerous QTL of small effect, except for a small number of large-effect QTL identified for a*fat, CL, and pH. Genomic regions harboring these QTL and the pathways in which the genes from these regions act appear to differ from those identified in taurine cattle for meat quality traits. These results will guide future QTL mapping studies and the development of models for the prediction of genetic merit to implement genomic selection for meat quality in Nelore cattle.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 June 2016
                2016
                : 11
                : 6
                : e0157845
                Affiliations
                [1 ]Departamento de Zootecnia, Faculdade de Ciências Agrarias e Veterinárias, Jaboticabal, São Paulo, Brazil
                [2 ]Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq, Brasília, Distrito Federal, Brazil
                [3 ]Departamento de Melhoramento e Nutrição Animal, Faculdade de Medicina Veterinária e Zootecnia, Botucatu, São Paulo, Brazil
                University of Bonn, GERMANY
                Author notes

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

                Conceived and designed the experiments: LGA RC FB. Performed the experiments: DGMG RLT RE WBFA TB. Analyzed the data: AFBM GAFJ RBC RMOS. Contributed reagents/materials/analysis tools: LT FLBF LALC. Wrote the paper: AFBM GMFC LGA RC FB.

                [¤a]

                Current address: Universidade Tecnológica do Paraná, Dois Vizinhos, Paraná, Brazil

                [¤b]

                Current address: Departamento de Medicina Veterinária Preventiva e Produção Animal, Universidade Federal da Bahia, Salvador, Bahia, Brazil

                Article
                PONE-D-15-52774
                10.1371/journal.pone.0157845
                4928802
                27359122
                644345cf-5c72-4c1d-a6ee-c3734271f444
                © 2016 Magalhães et al

                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
                : 9 December 2015
                : 6 June 2016
                Page count
                Figures: 2, Tables: 3, Pages: 12
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo;
                Award ID: 2012/21969-7
                Award Recipient : Ana Magalhaes
                Funded by: funder-id http://dx.doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo;
                Award ID: 2009/16118-5
                Award Recipient :
                The authors would like to thank Fapesp (Fundação de Amparo à Pesquisa do Estado de São Paulo < http://www.fapesp.br/>) for the first author’s scholarship (N° 2012/21969-7) and for funding this study (N° 2009/16118-5).
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