Comparative Analysis of Multiple GWAS Results Identifies Metabolic Pathways Associated with Resistance to A. flavus Infection and Aflatoxin Accumulation in Maize.

Aflatoxins are carcinogenic secondary metabolites produced by several species of Aspergillus, including Aspergillus flavus, an important ear rot pathogen in maize. Most commercial corn hybrids are susceptible to infection by A. flavus, and aflatoxin contaminated grain causes economic damage to farmers. The creation of inbred lines resistant to Aspergillus fungal infection or the accumulation of aflatoxins would be aided by knowing the pertinent alleles and metabolites associated with resistance in corn lines. Multiple Quantitative Trait Loci (QTL) and association mapping studies have uncovered several dozen potential genes, but each with a small effect on resistance. Metabolic pathway analysis, using the Pathway Association Study Tool (PAST), was performed on aflatoxin accumulation resistance using data from four Genome-wide Association Studies (GWAS). The present research compares the outputs of these pathway analyses and seeks common metabolic mechanisms underlying each. Genes, pathways, metabolites, and mechanisms highlighted here can contribute to improving phenotypic selection of resistant lines via measurement of more specific and highly heritable resistance-related traits and genetic gain via marker assisted or genomic selection with multiple SNPs linked to resistance-related pathways.

Complex Genetic System Involved in Fusarium Ear Rot Resistance in Maize as Revealed by GWAS, Bulked Sample Analysis, and Genomic Prediction.

Fusarium ear rot (FER) caused by Fusarium verticillioides is one of the most prevalent maize diseases in China and worldwide. Resistance to FER is a complex trait controlled by multiple genes highly affected by environment. In this paper, genome-wide association study (GWAS), bulked sample analysis (BSA), and genomic prediction were performed for understanding FER resistance using 509 diverse inbred lines, which were genotyped by 37,801 high-quality single-nucleotide polymorphisms (SNPs). Ear rot evaluation was performed using artificial inoculation in four environments in China: Xinxiang, Henan, and Shunyi, Beijing, during 2017 and 2018. Significant phenotypic and genetic variation for FER severity was observed, and FER resistance was significantly correlated among the four environments with a generalized heritability of 0.78. GWAS identified 23 SNPs that were associated with FER resistance, 2 of which (1_226233417 on chromosome 1 and 10_14501044 on chromosome 10) were associated at threshold of 2.65 × 10-7 [-log(0.01/37,801)]. Using BSA, resistance quantitative trait loci were identified on chromosomes 3, 4, 7, 9, and 10 at the 90% confidence level and on chromosomes 3 and 10 at the 95% confidence level. A key region, bin 10.03, was detected by both GWAS and BSA. Genomic prediction for FER resistance showed that the prediction accuracy by trait-related markers was higher than that by randomly selected markers under different levels of marker density. Marker-assisted selection using genomic prediction could be an efficient strategy for genetic improvement for complex traits like FER resistance.

A region of maize chromosome 2 affects response to downy mildew pathogens.

Quantitative trait loci (QTLs) for downy mildew resistance in maize were identified based on co-segregation with linked restriction fragment length polymorphisms or simple sequence repeats in 220 F2 progeny from a cross between susceptible and resistant parents. Disease response was assessed on F3 families in nurseries in Egypt, Thailand, and South Texas and after inoculation in a controlled greenhouse test. Heritability of the disease reaction was high (around 93% in Thailand). One hundred and thirty polymorphic markers were assigned to the ten chromosomes of maize with LOD scores exceeding 4.9 and covering about 1,265 cM with an average interval length between markers of 9.5 cM. About 90% of the genome is located within 10 cM of the nearest marker. Three putative QTLs were detected in association with resistance to downy mildew in different environments using composite interval mapping. Despite environmental and symptom differences, one locus on chromosome 2 had a major effect and explained up to 70% of the phenotypic variation in Thailand where disease pressure was the highest. The other two QTLs on chromosome 3 and chromosome 9 had minor effects; each explained no more than 4% of the phenotypic variation. The three QTLs appeared to have additive effects on resistance, identifying one major gene and two minor genes that contribute to downy mildew resistance.