Goss's bacterial wilt and leaf blight is a disease of maize caused by the gram positive bacterium Clavibacter michiganensis subsp. nebraskensis (Cmn).

First discovered in Nebraska, Goss's wilt has now spread to major maize growing states in the United States and three provinces in Canada. Previous studies conducted using elite maize inbred lines and their hybrids have shown that resistance to Goss's wilt is a quantitative trait. The objective of this study was to further our understanding of the genetic basis of resistance to Goss's wilt by using a combined approach of genome-wide association mapping and gene co-expression network analysis. Genome-wide association analysis was accomplished using a diversity panel consisting of 555 maize inbred lines and a set of 450 recombinant inbred lines (RILs) from three bi-parental mapping populations, providing the most comprehensive screening of Goss's wilt resistance to date. Three SNPs in the diversity panel and 10 SNPs in the combined dataset, including the diversity panel and RILs, were found to be significantly associated with Goss's wilt resistance. Each significant SNP explained 1% to 5% of the phenotypic variation for Goss's wilt (total of 8 - 11%). To augment the results of genome-wide association mapping and help identify candidate genes, a time course RNA sequencing experiment was conducted using resistant (N551) and susceptible (B14A) maize inbred lines. Gene co-expression network analysis of this time course experiment identified one module of 141 correlated genes that showed differential regulation in response to Cmninoculations in both resistant and susceptible lines. SNPs inside and flanking these genes explained 13.3% of the phenotypic variation. Among 1,000 random samples of genes, only 8% of samples explained more phenotypic variance for Goss's wilt resistance than those implicated by the co-expression network analysis. While a statistically significant enrichment was not observed (P < 0.05), these results suggest a possible role for these genes in quantitative resistance at the field level and warrant more research on combining gene co-expression network analysis with quantitative genetic analyses to dissect complex disease resistance traits. The results of the GWAS and co-expression analysis both support the complex nature of resistance to this important disease of maize.

 

See https://www.ncbi.nlm.nih.gov/pubmed/31362973

 

 

Figure 3: Comparison of physical positions of QTL detected in bi-parental linkage 968mapping and GWAS. Bi-parental linkage mapping was conducted by Singh et al. 969(2016),and significant SNPs from GWAS in the combined dataset co-located on970chromosomes 1, 2, and 5. The x-axis of each plot represents the physical positionof each 971SNP,and the y-axis displays the negative log10of p-valuesfor eachSNPincluded in the 972GWAS. Gray colored solid points represent all SNPs used in GWAS. Significant SNPs in 973the GWASare indicated by dark green dots, and 2-LOD support interval ofQTL detected 974by Singh et al. (2016) are shown by the red or blue windows.