Novel gene loci associated with susceptibility or cryptic quantitative resistance to Pyrenopeziza brassicae in Brassica napus

Abstract

Quantitative disease resistance (QDR) can provide durable control of pathogens in crops in contrast to resistance (R) gene-mediated resistance which can break down due to pathogen evolution. QDR is therefore a desirable trait in crop improvement but little is known about the causative genes and so it is difficult to incorporate into breeding programmes. Light leaf spot, caused by Pyrenopeziza brassicae, is an important disease of oilseed rape (canola, Brassica napus). To identify new QDR gene loci, we used a high-throughput screening pathosystem with P. brassicae on 196 lines of B. napus combined with an association transcriptomics platform. We show that all resistance against P. brassicae was associated with QDR and not R gene-mediated. We used genome-wide association analysis with an improved B. napus population structure to reveal four gene loci significantly (P = 0.0001) associated with QDR in regions showing linkage disequilibrium. On chromosome A09, enhanced resistance was associated with heterozygosity for a cytochrome P450 gene co-localising with a previously-described locus for seed glucosinolate content. In addition, eight significant gene expression markers with a false discovery rate of 0.001 were associated with QDR against P. brassicae. For seven of these, expression was positively correlated with resistance whereas for one, a HXXXD-type acyl-transferase, negative correlation indicated a potential susceptibility gene. The study identifies novel QDR loci for susceptibility and resistance, including novel cryptic QDR genes associated with heterozygosity, that will inform future crop improvement.

Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus)

“…The P. brassicae methods and phenotype dataset used were the same as in our previous publication (Fell et al ., 2022). Disease severity was scored on a scale of 1 to 6—with a score of 1 for no sporulation and 6 for the most sporulation—and was calculated for the 129 genotypes.…”

“…Genome-Wide Association (GWA) and GEM mapping were done using the R-based GWA and GEM Automation (GAGA) pipeline (Nichols, 2022), which utilizes GAPIT Version 3 (Lipka et al ., 2012; Wang and Zhang, 2021). GAGA was run using our recently updated population structure (Fell et al ., 2022) and B. napus Pantranscriptome version 11 (Havlickova et al ., 2018). For GWA analyses, generalized linear model (GLM), Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) (Huang et al ., 2019), and Fixed and random model Circulating Probability Unification (FarmCPU) (Liu et al ., 2016) models were compared using QQ plots to select the best-fitting model.…”

“…The R-based GAGA pipeline is available at https://github.com/bsnichols/GAGA. Our population structure is available in Fell et al . (2022).…”

“…Disease susceptibility significantly impacts B. napus yields so enhanced resistance is a major breeding objective. Employing associative transcriptomics (AT) (Harper et al ., 2012; Havlickova et al ., 2018), we previously identified single nucleotide polymorphism (SNP) and gene expression marker (GEM) candidate loci linked to B. napus QDR against Pyrenopeziza brassicae (Fell et al, 2022). The GEM analysis utilizes transcriptomic data representing a baseline pre-infection expression level—a foundation for investigating constitutive QDR.…”