Evaluation of genomic prediction for Pasmo resistance in flax


He, L., Xiao, J., Rashid, K.Y., Jia, G., Li, P., Yao, Z., Wang, X., Cloutier, S., You, F.M. (2019). Evaluation of genomic prediction for Pasmo resistance in flax. International Journal of Molecular Sciences, [online] 20(2), http://dx.doi.org/10.3390/ijms20020359

Plain language summary

Flax has long been cultivated for its seeds and fibers. The pasmo fungal disease threatens both the yield and quality of flax harvests. Cultivar genetic improvement for fungal disease resistance is the major approach to control the disease. The breeding efficiency of the fungal resistance can be improved by increasing the accuracy of genomic prediction. Genomic prediction can predict the genetic potential of an organism based on scoring DNA markers without the need to measure the trait directly. Using the pasmo disease severity data sets collected over a five year period along with identified marker sets, the accuracy of the genomic prediction reached above 90%. The high accuracy of the genomic prediction suggests that the genomic prediction is highly effective molecular breeding method for fungal disease resistance prediction.


Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

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