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ARS Home » Plains Area » Fargo, North Dakota » Edward T. Schafer Agricultural Research Center » Cereal Crops Research » Research » Research Project #438198

Research Project: Genomic Analysis of Yield and End-use Quality Traits in Hexaploid Oats

Location: Cereal Crops Research

Project Number: 3060-21000-038-028-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2020
End Date: Aug 31, 2025

Oat is a small grain food crop that provides numerous health benefits that include lowering LDL cholesterol levels, reducing cardiovascular disease risks, and increasing satiety and glycemic index stability. Oat is a valuable agronomic crop and can be a key element in crop rotations to naturally improve soil fertility and control weeds. Over the last century, oat acreage has significantly declined in the US and research into breeding of new cultivars has been limited. Fortunately, oat is currently experiencing a renaissance of interest for human consumption, and modern cultivars with optimal performance and quality are required to meet this new need. To rapidly develop new oat germplasm with enhanced disease resistance, high quality and high yield, molecular breeding tools need to be developed. This project aims to provide an infrastructure for these tools by improving the understanding of how the genome influences traits and immediately converting this knowledge into applicable tests for breeder use. The objectives of this research project are to identify regions of the oat genome that influence yield and end-use quality traits and develop molecular markers suitable for marker-assisted selection to improve these traits in breeding efforts, using association mapping in previously evaluated breeding and artificial intercross populations.

The first objective of the study is to identify molecular markers that flag gene regions in the oat genome that are significantly associated with quality and yield traits in NDSU breeding lines. This material has already undergone three years of performance evaluation, and we will perform a genome-wide association study (GWAS) with sequence-based single nucleotide polymorphism (SNP) markers. Several models will be generated, and significant marker-trait associated (MTA) SNPs will be identified from the models that most accurately control for population structure and relatedness of the individuals. Every year, new lines undergoing performance evaluation will be genotyped and this data will be used to validate previously identified MTA SNPs and improve existing GWAS models. The second objective is to create an 8-way intercross population that purposefully mixes all the alleles of the eight parents in four crossing generations and five selfing generations. Parents have already been selected from an existing collection of phenotyped North American breeding lines to maximize the variation of all the important oat breeding traits. Biparental mapping populations of the initial founder crosses will also be spun out from this experiment and developed in tandem. Upon completion, these populations will be evaluated for disease resistance, agronomic, yield, milling, and end-use quality traits. GWAS and linkage mapping will be performed to associate genome-wide markers with the traits to identify new MTA SNPs. Long-read sequencing techniques will be used to fully resolve significant gene regions and identify specific recombinations that occurred in the genome, as well as optimal markers to flag each region. All significant markers identified in these studies will be converted to high-throughput allele-specific PCR markers and validated in breeding material from NDSU as well as regional oat breeding programs in concert with the Uniform Performance Nursery project. Validated markers will immediately be made available to these programs for early-generation screening of breeding germplasm to accelerate genetic gain and line selection. A 4th year of lines from the NDSU breeding program will be added to the GWAS model developed above. These lines will be genotyped with sequence-based marker and combined with phenotypes scored from the program. This data will be integrated with the current GWAS model to increase the predictive ability of identified MTA markers.