Principal Investigator: A. R. McElroy
Research Institution: PhytoGene Resources Inc.
Timeline: April 2017 – March 2018
- Determine if molecular markers called single nucleotide polymorphisms (SNPs) included in the in silico array chip (‘6K Chip’) are associated with variation for kernel number per panicle and the percent unfilled kernels in 50 elite oat lines.
- The identification of molecular markers associated with grain fill will provide a valuable tool to enhance the efficiency of oat breeding.
- The development of more effective breeding protocols may lead to an increase in both the yield potential and quality of oat, increasing profitability and making this valuable rotation crop more attractive to producers.
Oat is a valuable rotation crop in Ontario and there are good markets for high-quality grain for both feed and milling. However, low yields, compared to some other cereals, and low test weight (‘light oats’) diminish its popularity. Both problems relate to grain fill. PhytoGene Resources has determined that the number of kernels per panicle is the major yield determinant, and that the proportion of unfilled kernels – a phenomenon not related to stress during the grain filling period – affects yield, and particularly average seed mass and test weight. Both parameters have been shown to be heritable. Evaluating these traits is laborious and expensive, since individual panicles must be threshed, and the seed cleaned prior to counting the filled and unfilled kernels. The use of molecular markers would increase the efficiency of screening for these two traits. An in silico array chip has been developed for oat and contains approximately 6,000 molecular markers called single nucleotide polymorphisms (SNPs). This ‘6K chip’ may contain SNPs associated with quantitative trait loci (QTLs) that are related to kernel number per panicle and the percent of unfilled kernels.
The project tested whether the ‘6K Chip’ can be used to develop molecular markers to enhance selection for kernel development in oat. The study was augmented to include two tests of 30 elite lines, with four additional check varieties in each. Trials were grown in 3-rep, randomized complete blocks in Cumberland, ON. Panicles with uniform heading date were tagged in each plot; these were then hand-harvested and evaluated for filled and unfilled kernels, as well as number of kernels per panicle.
The identification of molecular markers requires data from lines that differ for the trait in question, and very precise evaluation of these lines (phenotypic assessment). This project exceeded expectations in both aspects. Highly significant (P<0.01) differences among lines were found for TKW and % unfilled kernels in both tests.
The range for TKW of filled kernels was 32.8 to 46.6 g and 32.5 to 49.6 g for tests A and B, respectively. These values are consistent with those normally found in high yield oat trials, and suggests ‘normal’ grain development in this study.
The range for unfilled kernels was 4.3 to 45.0% (SE = 3.24) and 3.3 to 29.3% (SE =3.29) for tests A and B, respectively. These values are consistent with those normally found in our breeding nurseries (unpublished data) and confirm the importance of this trait as a selection criterion.
Despite the wide differences among lines and the fairly low standard errors no statistically-significant relationship between the SNP’s and these traits could be identified. There are a couple possible explanations for this result.
First, it is possible that, if major loci affect these traits, they are not covered by the 6K chip, and therefore could not be detected. The 6 K SNPs cover only a portion of the oat genome. In contrast the array size for barley exceeds 40K and that of wheat is 660K. A much larger SNP array may be necessary to identify loci associated with these traits in oat.
Second, it is possible that multiple loci may be responsible for these traits, and therefore could not be detected using this approach. Multiple loci interactions are likely to influence TKW. There is very little information about the heritability of % unfilled kernels. However, despite the very wide range among line means, there was a continuum of values, suggesting that this trait is also multigenic. In short, a ‘silver-bullet’ may not exist.
Going forward, an option that may offer a better chance of success is the development of closely-related populations which are segregating for grain fill traits, as they might be better at isolating individual loci than the more diverse population used in this study. Likewise, different genotyping techniques, coupled with recently released genomic maps of oat may increase precision and introduce new regions unused in this study.
Despite the results from this study, we strongly feel that a component-based approach for yield and grain quality improvement in oat remains promising. Work to refine the methods is ongoing, with the objective of developing superior oat varieties for eastern Canada.
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