Evaluation of Genomic Prediction for Pasmo Resistance in Flax

Abstract

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.

Genome-Wide Association Studies for Pasmo Resistance in Flax (Linum usitatissimum L.)

“…Compared to conventional phenotypic selection, GP can accelerate genetic gains for early selection (Newell and Jannink, 2014). The accuracy and efficiency of GP models for flax PS were evaluated with three sets of QTL: 500 (SNP-500QTL), 134 (SNP-134QTL), and 67 (SNP-67QTL) which are developed in this study (He et al, 2018). The GP model built with SNP-500QTL achieved a prediction accuracy of 0.92 while the use of 134 and 67 QTL yielded accuracies of 0.75 and 0.76, respectively (He et al, 2018).…”

“…The accuracy and efficiency of GP models for flax PS were evaluated with three sets of QTL: 500 (SNP-500QTL), 134 (SNP-134QTL), and 67 (SNP-67QTL) which are developed in this study (He et al, 2018). The GP model built with SNP-500QTL achieved a prediction accuracy of 0.92 while the use of 134 and 67 QTL yielded accuracies of 0.75 and 0.76, respectively (He et al, 2018). The similar accuracies of the two smaller sets were expected because SNP-67QTL is essentially a non-redundant set of SNP-134QTL.…”

Genome-Wide Association Studies for Pasmo Resistance in Flax (<em>Linum Usitatissimum L.</em>)

“…Compared to conventional phenotypic selection, GP can accelerate genetic gains for early selection (Newell and Jannink, 2014). The accuracy and efficiency of GP models for flax PS were evaluated with three sets of QTL: 500 (SNP-500QTL), 134 (SNP-134QTL), and 67 (SNP-67QTL) (He et al, 2018). The GP model built with SNP-500QTL achieved a prediction accuracy of 0.92 while the use of 134 and 67 QTL yielded accuracies of 0.75 and 0.76, respectively (He et al, 2018).…”

“…The accuracy and efficiency of GP models for flax PS were evaluated with three sets of QTL: 500 (SNP-500QTL), 134 (SNP-134QTL), and 67 (SNP-67QTL) (He et al, 2018). The GP model built with SNP-500QTL achieved a prediction accuracy of 0.92 while the use of 134 and 67 QTL yielded accuracies of 0.75 and 0.76, respectively (He et al, 2018). The similar accuracies of the two smaller sets was expected because SNP-67QTL is essentially a non-redundant set of SNP-134QTL.…”