Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction of biomass yield in oat (Avena sativa L.).

Genomic selection (GS) is a promising strategy for accelerating genetic gains of complex traits in breeding programs. Despite the recent advancements in high-throughput genotyping technologies, the selection of the type of marker systems needed for GS remains challenging in breeding programs. In this study, we explored 3K array single nucleotide polymorphisms (SNPs) and genotyping by sequencing (GBS) SNP markers for genomic prediction of oat biomass yield using different statistical and machine learning approaches. An oat panel consisting of 420 lines was phenotyped for biomass-related traits for 3 years and genotyped using two different marker platforms (3K array and GBS). Our results showed 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. The genomic best linear unbiased prediction (GBLUP), Bayes-B, and random forest models gave similar predictive ability for dry matter yield (DMY) in different harvest-year combinations and for 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. Comparison of the 25% top-performing observed and predicted genotypes showed a higher overlap percentage (30.10%-66.99%) for multivariate GBLUP models compared to the univariate models (27.18%-51.46%). This further elucidates 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.