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

Authors: ADEWALE, SAMUEL - University Of Florida; BABER, MUHAMMAD ALI - University Of Florida; JARQUIN, DIEGO - University Of Florida; KHAN, NAEEM - University Of Florida; ACHARYA, JANAM - University Of Florida; KUNWAR, SUDIP - University Of Florida; MCBREEN, JORDAN - University Of Florida; RIOS, ESTABAN - University Of Florida; HARRISON, STEPHEN - Louisiana State University; DEWITT, NOAH - Louisiana State University; IBRAHIM, AMIR - Texas A&M University; MURPHY, PAUL - North Carolina State University; BOYLES, RICK - Clemson University; Fiedler, Jason; Nandety, Raja Sekhar

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/11/2025
Publication Date: 1/16/2026
Citation: Adewale, S.A., Baber, M., Jarquin, D., Khan, N., Acharya, J.P., Kunwar, S., Mcbreen, J., Rios, E., Harrison, S., Dewitt, N., Ibrahim, A., Murphy, P., Boyles, R., Fiedler, J.D., Nandety, R.S. 2026. Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction for biomass yield improvement in oat (Avena sativa L.). The Plant Genome. 19(1). https://doi.org/10.1002/tpg2.70179.
DOI: https://doi.org/10.1002/tpg2.70179

Interpretive Summary: In the Southern United States, oats have been extensively used as a forage crop for silage, hay, and grazing for animal feed. Nevertheless, collecting trait data for above-ground biomass improvement in oat breeding programs is destructive, labor-intensive, and takes a long time. The use of genomic information to predict traits has been demonstrated to increase genetic gain in plant breeding programs. We investigated two different marker platforms for genomic prediction of oat biomass yield using different models. A low-density 3,000 marker array-based platform displayed similar prediction ability compared to a higher-density sequence-based SNP platform. Thus, the quickly ascertained, durable 3 K array markers could be utilized by breeders for selection of candidate individuals in early generations using multiple traits genomic selection to reduce cost, shorten the length of the breeding cycle, and eventually increase genetic gain in oat forage breeding programs.

Technical Abstract: 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 election of the type of marker systems needed for genomic selection still remains a challenge in several 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 three years and genotyped using two different platforms (3K array and GBS markers). Our results showed similar performance of both the 3K array and the GBS-based SNPs in terms of genetic diversity, marker density, training population optimization, forward prediction, and univariate and multi-trait covariate genomic prediction of forage yield. The genomic best linear unbiased prediction (GBLUP), Bayes-B, and random forest (RF) models returned similar predictive ability for dry matter yield (DMY) in all scenarios. The multi-trait covariate models involving various combinations of secondary traits (simple breeders' field notes and data) resulted in more than two-fold increases in prediction abilities compared to the univariate case. Comparison of the 25% top-performing observed and predicted genotypes showed higher overlap percentage (30.10 – 66.99%) for multi-trait covariate GBLUP models compared to the univariate models (27.18 – 51.46%). This further elucidates the great potential of multi-trait covariate GS models incorporating the durable 3K array SNP markers for improving the genetic gains of forage yield in breeding programs.