Background: Meat quality and composition traits have become valuable in modern pork production; how‐ ever, genetic improvement has been slow due to high phenotyping costs. Combining genomic information with multi‐trait indirect selection based on cheaper indicator traits is an alternative for continued cost‐effective genetic improvement. Methods: Data from an ongoing breeding program were used in this study. Phenotypic and genomic information was collected on three‐way crossbred and purebred Duroc animals belonging to 28 half‐sib families. We applied dif‐ ferent methods to assess the value of using purebred and crossbred information (both genomic and phenotypic) to predict expensive‐to‐record traits measured on crossbred individuals. Estimation of multi‐trait variance components set the basis for comparing the different scenarios, together with a fourfold cross‐validation approach to validate the phenotyping schemes under four genotyping strategies. Results: The benefit of including genomic information for multi‐trait prediction depended on the breeding goal trait, the indicator traits included, and the source of genomic information. While some traits benefitted significantly from genotyping crossbreds (e.g., loin intramuscular fat content, backfat depth, and belly weight), multi‐trait prediction was advantageous for some traits even in the absence of genomic information (e.g., loin muscle weight, subjective color, and subjective firmness). Conclusions: Our results show the value of using different sources of phenotypic and genomic information. For most of the traits studied, including crossbred genomic information was more beneficial than performing multi‐trait prediction. Thus, we recommend including crossbred individuals in the reference population when these are pheno‐ typed for the breeding objective.

Genotyping and phenotyping strategies for genetic improvement of meat quality and carcass composition in swine / Emmanuel André Lozada‐Soto , Daniela Lourenco, Christian Maltecca, Justin Fix, Clint Schwab, Caleb Shull, Francesco Tiezzi. - In: GENETICS SELECTION EVOLUTION. - ISSN 1297-9686. - ELETTRONICO. - (2022), pp. 1-16. [10.1186/s12711-022-00736-4]

Genotyping and phenotyping strategies for genetic improvement of meat quality and carcass composition in swine

Francesco Tiezzi
2022

Abstract

Background: Meat quality and composition traits have become valuable in modern pork production; how‐ ever, genetic improvement has been slow due to high phenotyping costs. Combining genomic information with multi‐trait indirect selection based on cheaper indicator traits is an alternative for continued cost‐effective genetic improvement. Methods: Data from an ongoing breeding program were used in this study. Phenotypic and genomic information was collected on three‐way crossbred and purebred Duroc animals belonging to 28 half‐sib families. We applied dif‐ ferent methods to assess the value of using purebred and crossbred information (both genomic and phenotypic) to predict expensive‐to‐record traits measured on crossbred individuals. Estimation of multi‐trait variance components set the basis for comparing the different scenarios, together with a fourfold cross‐validation approach to validate the phenotyping schemes under four genotyping strategies. Results: The benefit of including genomic information for multi‐trait prediction depended on the breeding goal trait, the indicator traits included, and the source of genomic information. While some traits benefitted significantly from genotyping crossbreds (e.g., loin intramuscular fat content, backfat depth, and belly weight), multi‐trait prediction was advantageous for some traits even in the absence of genomic information (e.g., loin muscle weight, subjective color, and subjective firmness). Conclusions: Our results show the value of using different sources of phenotypic and genomic information. For most of the traits studied, including crossbred genomic information was more beneficial than performing multi‐trait prediction. Thus, we recommend including crossbred individuals in the reference population when these are pheno‐ typed for the breeding objective.
2022
1
16
Emmanuel André Lozada‐Soto , Daniela Lourenco, Christian Maltecca, Justin Fix, Clint Schwab, Caleb Shull, Francesco Tiezzi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1277322
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