La Trobe
247665_Fikere,M_2018.pdf (3.6 MB)

Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola (Brassica napus L.) Lines

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posted on 2023-05-03, 23:51 authored by Mulusew Fikere, Denise M Barbulescu, Michelle M Malmberg, Fan Shi, Joshua CO Koh, Anthony T Slater, Iona MacLeodIona MacLeod, Philip BowmanPhilip Bowman, Phillip A Salisbury, German SpangenbergGerman Spangenberg, Noel CoganNoel Cogan, Hans DaetwylerHans Daetwyler

Genomic prediction is becoming a popular plant breeding method to predict the genetic merit of lines. While some genomic prediction results have been reported in canola, none have been evaluated for blackleg disease. Here, we report genomic prediction for seedling emergence, survival rate, and internal infection), using 532 Spring and Winter canola lines. These lines were phenotyped in two replicated blackleg disease nurseries grown at Wickliffe and Green Lake, Victoria, Australia. A transcriptome genotyping-by-sequencing approach revealed 98,054 single nucleotide polymorphisms (SNPs) after quality control. We assessed various genomic prediction scenarios based on Genomic Best Linear Unbiased Prediction (GBLUP), BayesR and BayesRC, which can make use of prior quantitative trait loci information, via cross-validation. Clustering based on genomic relationships showed that Winter and Spring lines were genetically distinct, indicating limited gene flow between sets. Genetic correlations within traits between Spring and Winter lines ranged from 0.68 and 0.90 (mean = 0.76). Based on GBLUP in the whole population, moderate to high genomic prediction accuracies were achieved within environments (0.35–0.74) and were reduced across environments (0.28–0.58). Prediction accuracy within the Spring set ranged from 0.30–0.69, and from 0.19–0.71 within the Winter set. The BayesR model resulted in slightly lower accuracy to GBLUP. The proportion of genetic variance explained by known blackleg quantitative trait loci (QTL) was < 30%, indicating that there is a large reservoir of genetic variation in blackleg traits that remains to be discovered, but can be captured with genomic prediction. However, providing prior information of known QTL in the BayesRC method resulted in an increased prediction accuracy for survival and internal infection, particularly with Spring lines. Overall, these promising results indicate that genomic prediction will be a valuable tool to make use of all genetic variation to improve blackleg resistance in canola.


The authors thank Agriculture Victoria Services and Agriculture Victoria for funding. MF gratefully acknowledges PhD scholarship funding from La Trobe University and MM acknowledges her Australian Government Research Training Program Scholarship.


Publication Date



The Plant Genome





Article Number



16p. (p. 1-16)


Crop Science Society of America



Rights Statement

© 2018 Crop Science Society of America This is an open access article distributed under the CC BY-NC-ND license (

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