Implementing multi-trait genomic selection to improve grain milling quality in oat

Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products and for supporting oat growers. Test weight, thins percentage, and groat percentage are traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and impossible in early generations. Multi-trait genomic selection (MTGS) combines genomics and phenomics using genome-wide markers and phenotypic informationm from relatives and selection candidates to predict the breeding values. MTGS use phenotypic information on economically important primary trait and secondary traits that are genetically correlated with the primary trait. MTGS enables intensive phenotyping and significantly accelerates the rate of genetic gain for milling quality. The objective of this study was to evaluate different MTGS models that use morphometric traits to improve accuracy for primary oat grain quality traits for their potential to enhance breeding for oat grain quality. We evaluated 558 breeding lines from the University of Illinois at Urbana-Champaign Oat Breeding Program across two years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated (rg> 0.3) with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59% respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. When using kernel morphometric traits from a single replicate, MTGS was 36% and 55% more accurate than the single-trait model for test weight and thin percentage, respectively. Overall, we found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thin percentage in oat. However, further research is needed to enhance the genomic selection for groat percentage.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Crop Sciences

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Dhakal, Anup

Contributors dc:contributor

• Arbelaez, Juan David
• Juvik, John A
• Rutkoski, Jessica Elaine

• Oat
• Milling Quality
• Multi-trait Genomic Selection

Statement dc:rights

• Copyright 2023 Anup Dhakal

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/122245

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F122245