| Abstrakt: |
Traits such as meat quality and composition are becoming valuable in modern pork production; however, they are difficult to include in genetic evaluations because of the high phenotyping costs. Combining genomic information with multiple‐trait indirect selection with cheaper indicator traits is an alternative for continued cost‐effective genetic improvement. Additionally, gut microbiome information is becoming more affordable to measure using targeted rRNA sequencing, and its applications in animal breeding are becoming relevant. In this paper, we investigated the usefulness of microbial information as a correlated trait in selecting meat quality in swine. This study incorporated phenotypic data encompassing marbling, colour, tenderness, loin muscle and backfat depth, along with the characterization of gut (rectal) microbiota through 16S rRNA sequencing at three distinct time points of the animal's growth curve. Genetic progress estimation and cross‐validation were employed to evaluate the utility of utilizing host genomic and gut microbiota information for selecting expensive‐to‐record traits in crossbred individuals. Initial steps involved variance components estimation using multiple‐trait models on a training dataset, where the top 25 associated operational taxonomic units (OTU) for each meat quality trait and time point were included. The second step compared the predictive ability of multiple‐trait models incorporating different numbers of OTU with single‐trait models in a validation set. Results demonstrated the advantage of including genomic information for some traits, while in some instances, gut microbial information proved advantageous, namely, for marbling and pH. The study suggests further investigation into the shared genetic architecture between microbial features and traits, considering microbial data's compositional and high‐dimensional nature. This research proposes a straightforward method to enhance swine breeding programs for improving costly‐to‐record traits like meat quality by incorporating gut microbiome information. [ABSTRACT FROM AUTHOR] |
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