Genome‐Wide Analysis and Prediction of Resistance to Goss's Wilt in Maize

Abstract

Core Ideas Goss's wilt is a complex, polygenic trait with no resistance genes or large‐effect QTL. Genomic prediction accuracy of 0.69 achieved for Goss's wilt in panel of diverse inbred lines. Association mapping using a diverse panel of maize lines revealed no significant SNPs. Goss's bacterial wilt and leaf blight is one of the most important foliar diseases of maize (Zea mays L.). To date, neither large‐effect resistance genes, nor practical chemical controls exist to manage the disease. Thus, the importance of discovering durable host resistance necessitates additional genetic mapping for this disease. Unfortunately, because of the biology of the pathogen and the highly significant genotype‐by‐environment interaction effect observed with Goss's wilt, consistent phenotyping across multiple years poses a hurdle for genetic studies and conventional breeding methods. The objective of this study was to perform a genome‐wide association study (GWAS) to identify regions of the genome associated with Goss's wilt resistance as well as to use genomic prediction models to evaluate the utility of genomic selection (GS) in predicting Goss's wilt phenotypes in a panel of diverse maize lines. Using genome‐wide association mapping, we were unable to identify any variants significantly associated with Goss's wilt. However, using genomic prediction we were able to train a model with an accuracy of 0.69. Taken together, this suggests that resistance to Goss's wilt is highly polygenic. In addition, when evaluating the accuracy of our prediction model under reduced marker density, it was shown that only 10,000 single nucleotide polymorphisms (SNPs), or ∼20% of our total marker set, was necessary to achieve prediction accuracies similar to the full marker set. This is the first report of genomic prediction for a bacterial disease of maize, and these results highlight the potential of GS for disease resistance in maize.

Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize

“…Thus, if there is a pleiotropic gene underlying these regions, the mechanism is not obviously associated with pathogen kingdom or the growth of the pathogen in the vasculature. Resistance to all five of the diseases examined here is largely quantitative ( Qiu et al 2020 ; Cooper et al 2019 ; Wisser et al 2006 ), and thus it is conceivable that common quantitative disease resistance mechanisms could underlie the observed multiple disease resistance. Several mechanisms have been hypothesized to underlie quantitative disease resistance ( Poland et al 2009 ; Yang et al 2017a ) and some of these could have effects across pathogen kindgoms and pathogenesis strategies.…”

“…Disease resistance introgression line population DRIL78 is an ideal CSSL population for multiple disease evaluation, as the donor parent (NC344) is multiple disease resistant and the recurrent parent (Oh7B) is multiple disease susceptible ( Lopez-Zuniga et al 2019 ; Cooper et al 2019 ; Qiu et al 2020 ; Wisser et al 2011 ). The population was developed by a cross between NC344 and Oh7B, three generations of backcrosses, and four subsequent generations of self-pollinating via single-seed descent to obtain BC 3 F 4:5 lines ( Lopez-Zuniga et al 2019 ).…”

“…Data for BLS ( Qiu et al 2020 ), SCLB, NCLB, and GLS ( Lopez-Zuniga et al 2019 ) were combined with the GW data to examine MDR. We evaluated the DRIL78 population, an ideal population for this study, as the donor line NC344 is resistant and the recurrent parent Oh7B is susceptible for all the diseases studied ( Cooper et al 2019 ; Qiu et al 2020 ; Lopez-Zuniga et al 2019 ). Thus, we hypothesized that we could identify regions for resistance to fungal and bacterial pathogens in this population.…”

Genetic Diversity and Population Structure of Maize Inbred Lines with Varying Levels of Resistance to Striga Hermonthica Using Agronomic Trait-Based and SNP Markers

“…SNPs are mostly used because of their stability and abundance in the genome [ 30 ]. They have been widely used in plants to evaluate genetic diversity, construct linkage maps, and association analysis [ 31 , 32 ]. However, studies have shown that joint analysis of molecular and morphological data provides in-depth insight into population structure and genetic diversity, and this has been used in different crops [ 33 , 34 , 35 , 36 , 37 ].…”

Genetic Mapping And Genomic Prediction For Northern Corn Leaf Blight (Exserohilum Turcicum (Pass.) Leonard And Suggs) Resistance

“…Higher prediction accuracies of 0.706 (dent) and 0.690 ( int) in estimating the GEBVs were reported based on the training set size for NCLB resistance[48]. Average prediction accuracies based on genomic data were also high for a complex trait like grain yield (0.72-0.74)[49], grain moisture (0.90)[50,51], Goss's wilt (0.69)[52], Gray leaf spot (0.84)[53], Fusarium ear rot (0.46) and fumonisin contamination (0.67)[54], Fusarium ear rot (ranging from 0.34 to 0.4)[55], and maize chlorotic mottle virus (0.32, 0.78, 0.47 and 0.21) [56] in maize. Higher prediction accuracies of 0.72 and 0.80 were reported for different agronomic traits in sugar beet[57].…”