Multivariate genome-wide association studies and genomic predictions in multiple breeds and crossbred animals
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The aim of the first research project is to design a commercialized genomic test that strongly predicts meat tenderness and other carcass traits in multiple breeds and crossbred cattle. We have identified single nucleotide polymorphisms (SNPs) that have significant association with several carcass traits. Using a multivariate analysis should have increased power in the presence of genetic correlation between different traits. We seek to make a haplotype based genomic prediction that can accurately predict tenderness and other meat quality traits in multiple breeds and crossbred animals in the second research project. Using GEMMA, we fit traits in a Bayesian Sparse Linear Mixed Models (BSLMM). The SNPs with the largest effects are found by calculating allele substitution effect from the SNP based BSLMM analysis. Using the four flanking SNPs of each identified core SNP we construct five SNP blocks that are used to select haplotypes. Using GEMMA, we fit this new haplotype matrix as the random effects in a BSLMM analysis. We use a three-fold cross-validation to validate predictions. We find the correlation and accuracy of each breed and across breeds using the haplotype based models.