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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Using markers with large effect in genetic and genomic predictions.

M S Lopes, H Bovenhuis, M van Son

    Journal of Animal Science
    |February 9, 2017
    PubMed
    Summary

    Marker-assisted selection (MAS) improved prediction accuracy for pig teat number by incorporating genome-wide association study (GWAS) findings. Marker-assisted genomic BLUP (MA-GBLUP) and Bayesian variable selection (BVS) offered the highest accuracies.

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    Area of Science:

    • Animal Breeding and Genetics
    • Quantitative Genetics
    • Genomic Prediction

    Background:

    • Marker-assisted selection (MAS) initially faced challenges due to low-density markers and difficulty identifying quantitative trait loci (QTL).
    • Genome-wide association studies (GWAS) using high-density SNP panels enhance QTL detection, reviving MAS potential.
    • Genomic selection (GS) has become a focus, utilizing all markers without preselection, shifting attention from traditional MAS.

    Purpose of the Study:

    • To evaluate the prediction accuracy of a MAS approach incorporating GWAS findings into marker-assisted BLUP (MA-BLUP) and marker-assisted genomic BLUP (MA-GBLUP) models.
    • To compare the prediction accuracies of these marker-assisted models against traditional BLUP, GBLUP, and a Bayesian variable selection (BVS) model.
    • To validate the findings across four distinct pig populations for the trait 'number of teats'.

    Main Methods:

    • Application of traditional BLUP, MA-BLUP, GBLUP, MA-GBLUP, and BVS models.
    • Inclusion of the most significant SNP from GWAS as a fixed effect in MA-BLUP and MA-GBLUP models.
    • Validation using the trait 'number of teats' in four independent pig populations.

    Main Results:

    • Marker-assisted selection (MAS) incorporating significant GWAS SNPs improved prediction accuracy for 'number of teats' compared to BLUP and GBLUP across all populations.
    • MA-BLUP showed an increase in prediction accuracy of 0.021–0.124 over BLUP; MA-GBLUP showed an increase of 0.003–0.043 over GBLUP.
    • The BVS model yielded prediction accuracies similar to or higher than MA-GBLUP, with BLUP showing the lowest accuracies.
    • MA-GBLUP's superiority over GBLUP was more evident with smaller training populations and lower relationships between training and validation sets.

    Conclusions:

    • Incorporating significant GWAS findings into MAS, specifically via MA-BLUP and MA-GBLUP, enhances prediction accuracy for traits like 'number of teats' in pigs.
    • While BVS models achieve comparable or superior accuracies to MA-GBLUP, MA-GBLUP offers practical implementation advantages for large-scale breeding evaluations.
    • Marker-assisted genomic selection strategies, leveraging GWAS insights, represent a valuable advancement in animal breeding for improving selection efficiency.