Open AccessModeling first order additive × additive epistasis improves accuracy of genomic prediction for sclerotinia stem rot resistance in canola
Author(s) -
Derbyshire Mark C,
Khentry Yuphin,
SevernEllis Anita,
Mwape Virginia,
Saad Nur Shuhadah Mohd,
Newman Toby E,
Taiwo Akeem,
Regmi Roshan,
Buchwaldt Lone,
DentonGiles Matthew,
Batley Jacqueline,
Kamphuis Lars G
Publication year - 2021
Publication title -
the plant genome
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 41
ISSN - 1940-3372
DOI - 10.1002/tpg2.20088
Subject(s) - sclerotinia sclerotiorum , canola , biology , epistasis , leptosphaeria maculans , sclerotinia , quantitative trait locus , genetic architecture , best linear unbiased prediction , stem rot , genetics , plant breeding , quantitative genetics , selection (genetic algorithm) , genetic variation , agronomy , botany , machine learning , gene , computer science
The fungus Sclerotinia sclerotiorum infects hundreds of plant species including many crops. Resistance to this pathogen in canola ( Brassica napus L. subsp. napus ) is controlled by numerous quantitative trait loci (QTL). For such polygenic traits, genomic prediction may be useful for breeding as it can capture many QTL at once while also considering nonadditive genetic effects. Here, we test application of common regression models to genomic prediction of S. sclerotiorum resistance in canola in a diverse panel of 218 plants genotyped at 24,634 loci. Disease resistance was scored by infection with an aggressive isolate and monitoring over 3 wk. We found that including first‐order additive × additive epistasis in linear mixed models (LMMs) improved accuracy of breeding value estimation between 3 and 40%, depending on method of assessment, and correlation between phenotypes and predicted total genetic values by 14%. Bayesian models performed similarly to or worse than genomic relationship matrix‐based models for estimating breeding values or overall phenotypes from genetic values. Bayesian ridge regression, which is most similar to the genomic relationship matrix‐based approach in the amount of shrinkage it applies to marker effects, was the most accurate of this family of models. This confirms several studies indicating the highly polygenic nature of sclerotinia stem rot resistance. Overall, our results highlight the use of simple epistasis terms for prediction of breeding values and total genetic values for a complex disease resistance phenotype in canola.