The 3K array and GBS-based SNPs performed similarly for training population optimization, forward prediction, and univariate and multivariate genomic prediction of forage yield in oat.
“similar performance of both the 3K array and GBS-based SNPs in terms of training population optimization, forward prediction, and univariate and multivariate genomic prediction of forage yield”
GBLUP, Bayes-B and random forest models gave similar predictive ability for dry matter yield across harvest-year combinations and both marker platforms.
“The multivariate models involving various combinations of secondary traits (simple breeders' field notes and data) resulted in more than twofold increases in predictive abilities compared to the univariate models”
The overlap between top 25% observed and predicted genotypes was higher for multivariate GBLUP models (30.10%-66.99%) than univariate models (27.18%-51.46%).
Multivariate genomic selection models with the robust, reproducible 3K array SNP markers have great potential for improving genetic gains of dry matter yield in oat breeding.
“the great potential of multivariate GS models incorporating the more robust and easily reproducible 3K array SNP markers for improving the genetic gains of DMY in breeding programs”
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