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      Genetic architecture of acute hyperthermia resistance in juvenile rainbow trout ( Oncorhynchus mykiss) and genetic correlations with production traits

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          Abstract

          Background

          Selective breeding is a promising solution to reduce the vulnerability of fish farms to heat waves, which are predicted to increase in intensity and frequency. However, limited information about the genetic architecture of acute hyperthermia resistance in fish is available. Two batches of sibs from a rainbow trout commercial line were produced: the first (N = 1382) was phenotyped for acute hyperthermia resistance at nine months of age and the second (N = 1506) was phenotyped for main production traits (growth, body length, muscle fat content and carcass yield) at 20 months of age. Fish were genotyped on a 57 K single nucleotide polymorphism (SNP) array and their genotypes were imputed to high-density based on the parent’s genotypes from a 665 K SNP array.

          Results

          The heritability estimate of resistance to acute hyperthermia was 0.29 ± 0.05, confirming the potential of selective breeding for this trait. Since genetic correlations of acute hyperthermia resistance with the main production traits near harvest age were all close to zero, selecting for acute hyperthermia resistance should not impact the main production traits, and vice-versa. A genome-wide association study revealed that resistance to acute hyperthermia is a highly polygenic trait, with six quantitative trait loci (QTL) detected, but explaining less than 5% of the genetic variance. Two of these QTL, including the most significant one, may explain differences in acute hyperthermia resistance across INRAE isogenic lines of rainbow trout. Differences in mean acute hyperthermia resistance phenotypes between homozygotes at the most significant SNP was 69% of the phenotypic standard deviation, showing promising potential for marker-assisted selection. We identified 89 candidate genes within the QTL regions, among which the most convincing functional candidates are dnajc7, hsp70b, nkiras2, cdk12, phb, fkbp10, ddx5, cygb1, enpp7, pdhx and acly.

          Conclusions

          This study provides valuable insight into the genetic architecture of acute hyperthermia resistance in juvenile rainbow trout. We show that the selection potential for this trait is substantial and selection for this trait should not be too detrimental to improvement of other traits of interest. Identified functional candidate genes provide new knowledge on the physiological mechanisms involved in acute hyperthermia resistance, such as protein chaperoning, oxidative stress response, homeostasis maintenance and cell survival.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12711-023-00811-4.

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            Efficient methods to compute genomic predictions.

            P VanRaden (2008)
            Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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              Hsp70 chaperones: Cellular functions and molecular mechanism

              Abstract. Hsp70 proteins are central components of the cellular network of molecular chaperones and folding catalysts. They assist a large variety of protein folding processes in the cell by transient association of their substrate binding domain with short hydrophobic peptide segments within their substrate proteins. The substrate binding and release cycle is driven by the switching of Hsp70 between the low-affinity ATP bound state and the high-affinity ADP bound state. Thus, ATP binding and hydrolysis are essential in vitro and in vivo for the chaperone activity of Hsp70 proteins. This ATPase cycle is controlled by co-chaperones of the family of J-domain proteins, which target Hsp70s to their substrates, and by nucleotide exchange factors, which determine the lifetime of the Hsp70-substrate complex. Additional co-chaperones fine-tune this chaperone cycle. For specific tasks the Hsp70 cycle is coupled to the action of other chaperones, such as Hsp90 and Hsp100.
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                Author and article information

                Contributors
                henri.lagarde@inrae.fr
                delphine.lallias@inrae.fr
                pierre.Patrice@inrae.fr
                audrey.Dehaullon@inrae.fr
                mprchal@frov.jcu.cz
                yoannah.francois@anses.fr
                jonathan.Dambrosio@inrae.fr
                emilien.segret@viviersaqua.com
                ana@sarrance.com
                fred.cachelou@hotmail.fr
                pierrick.haffray@inrae.fr
                mathilde.dupont-nivet@inrae.fr
                florence.phocas@inrae.fr
                Journal
                Genet Sel Evol
                Genet Sel Evol
                Genetics, Selection, Evolution : GSE
                BioMed Central (London )
                0999-193X
                1297-9686
                12 June 2023
                12 June 2023
                2023
                : 55
                : 39
                Affiliations
                [1 ]GRID grid.420312.6, ISNI 0000 0004 0452 7969, Université Paris-Saclay, INRAE, AgroParisTech, GABI, ; 78350 Jouy-en-Josas, France
                [2 ]SYSAAF, French Poultry, Aquaculture and Insect Breeders Association, 35042 Rennes, France
                [3 ]GRID grid.14509.39, ISNI 0000 0001 2166 4904, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, , University of South Bohemia in České Budějovice, ; Zátiší 728/II, 389 25 Vodňany, Czech Republic
                [4 ]Viviers de Sarrance, Pisciculture Labedan, 64490 Sarrance, France
                Author information
                http://orcid.org/0000-0003-1161-3665
                Article
                811
                10.1186/s12711-023-00811-4
                10259007
                37308823
                06bf81ac-180b-4f30-b33f-81dae37a493d
                © The Author(s) 2023

                Open Access This 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
                : 18 November 2022
                : 11 May 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100014510, European Maritime and Fisheries Fund;
                Award ID: FEA470019FA1000016
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003199, France AgriMer;
                Award ID: FEA470019FAA1000016
                Award Recipient :
                Categories
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                © ’Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE) 2023

                Genetics
                Genetics

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