Back

Improved breeding for Fusarium pseudograminarium (Fusarium crown rot) using qPCR measurement of infection in multi-species winter cereal experiments

Milgate, A.; Baxter, B.; Simpfendorfer, S.; Yang, N.; Orchard, B.; Ovenden, B.

2022-10-17 pathology
10.1101/2022.10.12.512005 bioRxiv
Show abstract

Fusarium crown rot (FCR) causes significant grain yield loss in winter cereals around the world. Breeding for resistance and/or tolerance to FCR has been slow with relatively limited success. In this study, multi-species experiments were used to demonstrate an improved method to quantify FCR infection levels at plant maturity using qPCR, as well as the genotype yield retention using residual regression deviation. Using qPCR to measure FCR infection allowed a higher degree of resolution between genotypes than traditional visual stem basal browning assessments. The results were consistent across three environments with different levels of disease expression. The improved measure of FCR infection along with genotype yield retention allows for partitioning of both tolerance and partial resistance. Together these methods offer new insights to FCR partial resistance and its relative importance to tolerance in bread wheat and barley. This new approach offers a more robust, cost-effective way to select for both FCR traits within breeding programs. Key messageGenetic gain for tolerance and partial resistance against Fusarium crown rot (FCR) in winter cereals has been impeded by laborious and variable visual measures of infection severity. This paper presents results of an improved method to quantify FCR infection that are strongly correlated to yield loss and reveal previously unrecognised partial resistance in barley and wheat varieties.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.