Genomic Selection, a New Era for Pork Quality Improvement View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-06

AUTHORS

Younes Miar, Graham Plastow, Zhiquan Wang

ABSTRACT

Traditional breeding approaches apply sophisticated statistical tools such as best linear unbiased prediction (BLUP) to evaluate the genetic potential of animals for economically important traits using phenotype and pedigree information observed on the animal. However, the genetic gain achieved is relatively slow for traits with low-to-moderate heritability, or expensive to measure traits, such as those determined post-mortem e.g., pork quality. Nowadays, the availability of dense panels of DNA markers covering the whole genome along with powerful statistical tools has made genomic selection (GS) feasible in pigs. The large number of single nucleotide polymorphisms generated by high-throughput technologies can be used in GS to select superior animals with better meat quality. Many quantitative trait loci (QTL) affecting meat quality traits have been detected in pigs demonstrating the potential for this improvement. Genomic selection uses genome-wide markers so that all QTL are likely to be in linkage disequilibrium with at least one marker. Genomic selection sums the effects of markers covering the whole genome so that potentially all the genetic variance associated with the traits and explained by the markers are considered. This can greatly improve selection accuracy to accelerate genetic gain for pork quality traits. In this review, we discuss the genetic component underlying pork quality variation, statistical approaches for pork quality genomic prediction, and present recent highlights for their application in swine breeding programs. Firstly, we review how pork quality is integrated into breeding objectives. Secondly, we present approaches for application of molecular genetics in meat quality improvement. Additionally, we discuss the statistical methods for genomic prediction including ridge regression, Bayesian approaches, GBLUP, and combining genomic and traditional information using single-step BLUP. Finally, we review the strategies for their use in swine genetic improvement and management. In particular, we review the strategy for implementing GS in swine breeding programs to improve pork quality. More... »

PAGES

27-37

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  • Journal

    TITLE

    Springer Science Reviews

    ISSUE

    1

    VOLUME

    3

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s40362-015-0029-3

    DOI

    http://dx.doi.org/10.1007/s40362-015-0029-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1052953397


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