Genome-wide association and gene co-expression network analyses reveal complex genetics of resistance to Goss’s Wilt of Maize

Amritpal Singh, Guangyong Li, Alex B. Brohammer, Diego Jarquin, Candice N. Hirsch, James R. Alfano, Aaron J. Lorenz

Research output: Contribution to journalArticlepeer-review

Abstract

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 1–5% 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 Cmn inoculations 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.

Original languageEnglish (US)
Pages (from-to)3139-3152
Number of pages14
JournalG3: Genes, Genomes, Genetics
Volume9
Issue number10
DOIs
StatePublished - Oct 7 2019

Bibliographical note

Funding Information:
We are thankful to Dow AgroSciences for funding this research. ABB was supported by the DuPont Pioneer Bill Kuhn Honorary Fellowship and the MnDRIVE Global Food Ventures Graduate Student Fellowship. The Minnesota Supercomputing Institute (MSI) at the University of Minnesota and Holland Computing Center at the University of Nebraska provided computational resources that contributed to this research. We appreciate the help received from Nonoy Bandillo, Dnyaneshwar C. Kadam, Jon Luetchens, M.D. Liakat Ali, and Collin Lamkey for planting and inoculating the Goss’s wilt experiments. We are also thankful to Jackson lab members: Tamra Jackson, Jae Brungardt, Brad Tharnish, and Cody Kaarstad for providing inoculum. The U.S. National Plant Germplasm System and North Central Regional Plant Introduction Station provided seeds of germplasm used in this study, for which we are very grateful.

Publisher Copyright:
Copyright © 2019 Singh et al.

Keywords

  • Clavibacter
  • Genome-wide association mapping
  • Genotyping-by-sequencing
  • Goss’s wilt
  • Maize (Zea mays)
  • Quantitative disease resistance
  • Quantitative trait loci
  • Single nucleotide polymorphism
  • Weighted gene co-expression network analysis

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