The Genetic Architecture of Milling Quality in Spring Oat Lines of the Collaborative Oat Research Enterprise

Klos, Kathy Esvelt; Admassu-Yimer, Belayneh; Howarth, Catherine; McMullen, Marcia; Sorrells, Mark E.; Tinker, Nicholas A.; Yan, Weikai; Beattie, Aaron D.

doi:10.3390/foods10102479

Cited by 10 publications

(9 citation statements)

References 32 publications

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“…The negative genetic correlation between test weight and thins indicates that undeveloped and undersized kernels negatively impact the test weight. A large germplasm set of 501 spring oats showed a similar negative relationship (Esvelt Klos et al, 2021). The positive relationship between the test weight and groat percentage has been reported in previous research (Doehlert et al, 2009;Souza & Sorrells, 1988).…”

Section: Discussionsupporting

confidence: 84%

“…In many cases, individual factors have been explained independently. In this research, three primary traits have different genetic architectures (Esvelt Klos et al, 2021), which might be one reason for the variation in the prediction accuracy across different traits despite using the same set of secondary traits. We did not see any population structure (Figure S10) among the germplasm as all the lines were from the UIUC oat breeding program, so there is a minimum effect of the population structure on the prediction accuracy.…”

Section: Discussionmentioning

confidence: 97%

“…Previous linkage studies using biparental population or association mapping using diversity panels have identified some quantitative trait loci (QTLs)/markers associated with the milling traits and grain morphometric traits (De Koeyer et al, 2004;Esvelt Klos et al, 2021;Groh et al, 2001;Herrmann et al, 2014;Siripoonwiwat et al, 1996;Yan et al, 2023;Zimmer et al, 2021). Siripoonwiwat et al (1996) using biparental inbred lines (Kanota × Ogle) found that 25 and 21 markers controlled test weight and groat percentage, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…For oats to be certified as food‐grade, they must fulfill different physical, chemical, and agronomical attributes. The requirement differs at each level of the value chain, but the revenue obtained by farmers is determined by grain milling industry standards (Esvelt Klos et al., 2021). Milling requirements vary depending on the production area and the individual mill needs (Girardet & Webster, 2011; USDA, 2020; Winkler & Murphy, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Implementing multi‐trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

Dhakal,

Poland,

Adhikari

et al. 2024

The Plant Genome

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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. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food‐grade oats. Conventional selection for milling quality is costly and burdensome. Multi‐trait genomic selection (MTGS) combines information from genome‐wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 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 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. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Implementing multi‐trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

Dhakal,

Poland,

Adhikari

et al. 2024

The Plant Genome

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“…Syntenic relationship with the barley genome identified three major genomic region of interest, which may accelerate genetic progress of oat β-glucan content in subtropical regions. Klos et al (2021) genotyped 501 lines from the CORE (Collaborative Oat Research Enterprise) oat panel with six key milling traits using the oat iSelect 6K-Beadchip array and GBS approaches; 57 major and minor QTLs influencing one or more of the milling quality related traits were identified. Tinker et al (2022) validated QTLs in a different mapping populations and developed KASP (Kompetitive Allele Specific PCR) markers using genotyping-by-sequencing approach and reported six, three and five major QTLs for oil content, β-glucan content, and number of days to 50% heading, respectively.…”

Section: Molecular Markers and Genomicsmentioning

confidence: 99%

Health benefits of oat (Avena sativa) and nutritional improvement through plant breeding interventions

Sood

Sanadya²,

Kumar³

et al. 2022

Crop & Pasture Science

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Since the Bronze Age, oat (Avena sativa L.) has been used mainly as an animal feed. Currently, it is in high demand for human consumption because of its nutritional properties, which improve health and wellbeing. Oat is a good source of protein, carbohydrates, lipid, minerals, vitamins and phenolic compounds. However, quality traits are usually polygenic and subjected to non-heritable factors, making quality improvement difficult. Several conventional breeding approaches such as pure line selection, pedigree selection, mutagenesis, wide crosses and polyploidy have been extensively used to develop new and improved oat varieties, commonly for forage purposes. Molecular approaches such as use of molecular markers, QTL mapping, genome-wide association studies, genetic engineering, genomic selection and speed breeding are being utilised to identify traits/genes of interest, produce plants carrying the desired agronomic and climatic resilience traits, and accelerate genetic gain. There has been minimal focus on nutrient enrichment and the development of high-quality, enriched oat genetic resources. Herein, we address and compile much-needed, up-to-date information on comparative analysis of oat nutritional and phytochemical properties with those of other cereals. We also consider the importance and involvement of conventional breeding in the modern approaches. This updated information provides guidance for oat breeders to develop nutrient-enriched varieties and points to future prospects towards oat quality improvement.

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A finite mixture distribution to model genetic architecture of image‐based oat grain morphology

Berro,

Yandell,

Gutiérrez

2024

Crop Science

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The multi‐floral oat (Avena sativa L.) inflorescence influences grain size and shape distributions, affecting the physical attributes of grain quality such as plumpness, size, and uniformity. While the grain size and shape distribution has been characterized as multi‐modal, very little is known about the genetic determinants of those distributions and their properties. The goal of this study was to model grain size and shape distribution using a finite mixture distribution approach and propose new distributional traits (i.e., emerging distributional traits) to characterize genotypes. We evaluated 47 oat genotypes in four highly replicated field experiments. Grains of three panicles per plot were individually threshed and scanned. Grain area, length, width, and roundness were obtained from each grain‐based image, while emerging distributional traits were evaluated using a finite mixture distribution approach. Finally, grain size distributions from hand‐threshed panicles (representing the full biological distribution) were compared with the grain size distributions of grains harvested with a combine harvester representing commercial harvest where small grains may be blown out. The heritability of all grain traits was high (0.89–0.94), and trait distributions differed among genotypes. Grain area and length show bi‐ and trimodal distributions, while grain width and roundness are uni‐ and bimodal. Although the full biological distribution of grains differed from the combine‐harvested grains, their genetic correlations were high, suggesting the combine‐harvested distributions can be used as a proxy for full biological distributions. This study proposes a straightforward methodological approach to model grain attributes that can aid in quality evaluations for genetic studies, breeding decisions, and industry characterization.

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The Genetic Architecture of Milling Quality in Spring Oat Lines of the Collaborative Oat Research Enterprise

Cited by 10 publications

References 32 publications

Implementing multi‐trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

Implementing multi‐trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

Health benefits of oat (Avena sativa) and nutritional improvement through plant breeding interventions

A finite mixture distribution to model genetic architecture of image‐based oat grain morphology

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