bioRxivGeneticsPreprint
The genetic architecture of maize yellow mosaic virus resistance in corn
Erik W. OhlsoniD, Christopher A Nacci, Nitika Khatri, Kristen J. WillieiD, Jennifer R. WilsoniD
Abstract
Maize yellow mosaic virus (MaYMV) is an emerging polerovirus of corn and other grass species. Due to its broad host range and transmission by multiple aphid species, management strategies such as crop rotations, pesticides, and weed control are likely ineffective. Therefore, resistant cultivars are needed. In this study, we characterized the Goodman 282 maize diversity panel for its response to MaYMV. Leaf reddening symptoms were quantified, and diagnostics were performed to assess infection and obtain a semi-quantitative measure of virus titer. Low titer lines and inbreds representing symptomatic and asymptomatic infected phenotypic classes were characterized further by RT-qPCR. Genome-wide association studies were performed using MLM, FarmCPU, and BLINK models to identify SNPs associated with disease. In total, 64% of lines were asymptomatically infected. Although all lines tested positive for infection by MaYMV in at least one experiment, Ky226 had reduced viral titer compared to other lines. Sixteen quantitative trait nucleotides (QTN) were identified, many of which are linked to genes implicated in flavonoid, carotenoid, and phenolic metabolic pathways as well as antiviral defense. Notably, a QTN associated with a chalcone synthase, a key enzyme in the flavonoid biosynthesis pathway, was detected by even the most conservative, MLM model. These results indicate that the genetic architecture of corn in response to MaYMV is complex, and that developing immune cultivars may not be achievable using natural sources of genetic variation. However, this study provides a foundation for breeding maize with improved tolerance and advances our understanding of host response to MaYMV infection.
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: statistical model
- Citations
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