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      Genetic Parameters for Tolerance to Heat Stress in Crossbred Swine Carcass Traits

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          Abstract

          Data for loin and backfat depth, as well as carcass growth of 126,051 three-way crossbred pigs raised between 2015 and 2019, were combined with climate records of air temperature, relative humidity, and temperature–humidity index. Environmental covariates with the largest impact on the studied traits were incorporated in a random regression model that also included genomic information. Genetic control of tolerance to heat stress and the presence of genotype by environment interaction were detected. Its magnitude was more substantial for loin depth and carcass growth, but all the traits studied showed a different impact of heat stress and different magnitude of genotype by environment interaction. For backfat depth, heritability was larger under comfortable conditions (no heat stress), as compared to heat stress conditions. Genetic correlations between extreme values of environmental conditions were lower (∼0.5 to negative) for growth and loin depth. Based on the solutions obtained from the model, sires were ranked on their breeding value for general performance and tolerance to heat stress. Antagonism between overall performance and tolerance to heat stress was moderate. Still, the models tested can provide valuable information to identify genetic material that is resilient and can perform equally when environmental conditions change. Overall, the results obtained from this study suggest the existence of genotype by environment interaction for carcass traits, as a possible genetic contributor to heat tolerance in swine.

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          MCMC Methods for Multi-Response Generalized Linear Mixed Models: TheMCMCglmmRPackage

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            Economic Losses from Heat Stress by US Livestock Industries

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              A relationship matrix including full pedigree and genomic information.

              Dense molecular markers are being used in genetic evaluation for parts of the population. This requires a two-step procedure where pseudo-data (for instance, daughter yield deviations) are computed from full records and pedigree data and later used for genomic evaluation. This results in bias and loss of information. One way to incorporate the genomic information into a full genetic evaluation is by modifying the numerator relationship matrix. A naive proposal is to substitute the relationships of genotyped animals with the genomic relationship matrix. However, this results in incoherencies because the genomic relationship matrix includes information on relationships among ancestors and descendants. In other words, using the pedigree-derived covariance between genotyped and ungenotyped individuals, with the pretense that genomic information does not exist, leads to inconsistencies. It is proposed to condition the genetic value of ungenotyped animals on the genetic value of genotyped animals via the selection index (e.g., pedigree information), and then use the genomic relationship matrix for the latter. This results in a joint distribution of genotyped and ungenotyped genetic values, with a pedigree-genomic relationship matrix H. In this matrix, genomic information is transmitted to the covariances among all ungenotyped individuals. The matrix is (semi)positive definite by construction, which is not the case for the naive approach. Numerical examples and alternative expressions are discussed. Matrix H is suitable for iteration on data algorithms that multiply a vector times a matrix, such as preconditioned conjugated gradients.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                04 February 2021
                2020
                : 11
                : 612815
                Affiliations
                [1] 1Department of Agricultural Science, University of Sassari , Sassari, Italy
                [2] 2Department of Animal Science, North Carolina State University , Raleigh, NC, United States
                [3] 3Acuity Ag Solutions, LLC , Carlyle, IL, United States
                [4] 4The Maschhoffs, LLC , Carlyle, IL, United States
                Author notes

                Edited by: Denis Milan, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), France

                Reviewed by: Mario Calus, Wageningen University and Research, Netherlands; Marie Lillehammer, Norwegian Institute of Food, Fisheries and Aquaculture Research (Nofima), Norway

                *Correspondence: Francesco Tiezzi, f_tiezzi@ 123456ncsu.edu

                This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2020.612815
                7890262
                33613622
                c8d3a50e-f20a-4aad-aba0-68adfcb668fd
                Copyright © 2021 Usala, Macciotta, Bergamaschi, Maltecca, Fix, Schwab, Shull and Tiezzi.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 September 2020
                : 22 December 2020
                Page count
                Figures: 5, Tables: 2, Equations: 8, References: 37, Pages: 12, Words: 0
                Categories
                Genetics
                Original Research

                Genetics
                heat stress,fat and muscle growth,genotype by environment interaction,heritability,single-step genomic blup

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