Field Characterization of Partial Resistance to Gray Leaf Spot in Elite Maize Germplasm

Genetic dissection of resistance to gray leaf spot by genome-wide association study in a multi-parent maize population

BACKGROUND

Understanding the genetic mechanisms underlying gray leaf spot (GLS) resistance in maize is crucial for breeding GLS-resistant inbred lines and commercial hybrids. Genome-wide association studies (GWAS) and gene functional annotation are valuable methods for identifying potential SNPs (single nucleotide polymorphism) and candidate genes associated with GLS resistance in maize.

RESULTS

In this study, a total of 757 lines from five recombinant inbred line (RIL) populations of maize at the F7 generation were used to construct an association mapping panel. SNPs obtained through genotyping-by-sequencing (GBS) were used to perform GWAS for GLS resistance using a linear mixture model in GEMMA. Candidate gene screening was performed by analyzing the 10 kb region upstream and downstream of the significantly associated SNPs linked to GLS resistance. Through GWAS analysis of multi-location phenotypic data, we identified ten candidate genes that were consistently detected in two locations or from one location along with best linear unbiased estimates (BLUE). One of these candidate genes, Zm00001d003257 that might impact GLS resistance by regulating gibberellin content, was further identified through haplotype-based association analysis, candidate gene expression analysis, and previous reports.

CONCLUSIONS

The discovery of the novel candidate gene provides valuable genomic resources for elucidating the genetic mechanisms underlying GLS resistance in maize. Additionally, these findings will contribute to the development of new genetic resources by utilizing molecular markers to facilitate the genetic improvement and breeding of maize for GLS resistance.

Combination of linkage and association mapping with genomic prediction to infer QTL regions associated with gray leaf spot and northern corn leaf blight resistance in tropical maize

Among the diseases threatening maize production in Africa are gray leaf spot (GLS) caused by Cercospora zeina and northern corn leaf blight (NCLB) caused by Exserohilum turcicum. The two pathogens, which have high genetic diversity, reduce the photosynthesizing ability of susceptible genotypes and, hence, reduce the grain yield. To identify population-based quantitative trait loci (QTLs) for GLS and NCLB resistance, a biparental population of 230 lines derived from the tropical maize parents CML511 and CML546 and an association mapping panel of 239 tropical and sub-tropical inbred lines were phenotyped across multi-environments in western Kenya. Based on 1,264 high-quality polymorphic single-nucleotide polymorphisms (SNPs) in the biparental population, we identified 10 and 18 QTLs, which explained 64.2% and 64.9% of the total phenotypic variance for GLS and NCLB resistance, respectively. A major QTL for GLS, qGLS1_186 accounted for 15.2% of the phenotypic variance, while qNCLB3_50 explained the most phenotypic variance at 8.8% for NCLB resistance. Association mapping with 230,743 markers revealed 11 and 16 SNPs significantly associated with GLS and NCLB resistance, respectively. Several of the SNPs detected in the association panel were co-localized with QTLs identified in the biparental population, suggesting some consistent genomic regions across genetic backgrounds. These would be more relevant to use in field breeding to improve resistance to both diseases. Genomic prediction models trained on the biparental population data yielded average prediction accuracies of 0.66-0.75 for the disease traits when validated in the same population. Applying these prediction models to the association panel produced accuracies of 0.49 and 0.75 for GLS and NCLB, respectively. This research conducted in maize fields relevant to farmers in western Kenya has combined linkage and association mapping to identify new QTLs and confirm previous QTLs for GLS and NCLB resistance. Overall, our findings imply that genetic gain can be improved in maize breeding for resistance to multiple diseases including GLS and NCLB by using genomic selection.

Effects of Host and Weather Factors on the Growth Rate of Septoria nodorum Blotch Lesions on Winter Wheat

Septoria nodorum blotch (SNB), caused by Parastagonospora nodorum, is a major disease of winter wheat that occurs frequently in the central and southeastern United States. Quantitative resistance to SNB in wheat is determined by various disease resistance components and their interaction with environmental factors. A study was conducted in North Carolina from 2018 to 2020 to characterize SNB lesion size and growth rate and to quantify the effects of temperature and relative humidity on lesion expansion in winter wheat cultivars with different levels of resistance. Disease was initiated in the field by spreading P. nodorum-infected wheat straw in experimental plots. Cohorts (groups of foliar lesions arbitrarily selected and tagged as an observational unit) were sequentially selected and monitored throughout each season. Lesion area was measured at regular intervals, and weather data were collected using in-field data loggers and the nearest weather stations. Final mean lesion area was approximately seven times greater on susceptible than on moderately resistant cultivars, and lesion growth rate was approximately four times higher on susceptible than on moderately resistant cultivars. Across trials and cultivars, temperature had a strong effect of increasing lesion growth rates (P < 0.001), while relative humidity had no significant effect (P = 0.34). Lesion growth rate declined slightly and steadily over the duration of cohort assessment. Our results demonstrate that restricting lesion growth is an important component of SNB resistance in the field and suggest that the ability to limit lesion size may be a useful breeding goal.

Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding

Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection.

High-density mapping for gray leaf spot resistance using two related tropical maize recombinant inbred line populations

Gray leaf spot (GLS) caused by Cercospora zeae-maydis or Cercospora zeina is one of the devastating maize foliar diseases worldwide. Identification of GLS-resistant quantitative trait loci (QTL)/genes plays an urgent role in improving GLS resistance in maize breeding practice. Two groups of recombinant inbred line (RIL) populations derived from CML373 × Ye107 and Chang7-2 × Ye107 were generated and subjected to genotyping-by-sequencing (GBS). A total of 1,929,222,287 reads in CML373 × Ye107 (RIL-YCML) and 2,585,728,312 reads in Chang7-2 × Ye107 (RIL-YChang), with an average of 10,961,490 (RIL-YCML) and 13,609,096 (RIL-YChang) reads per individual, were got, which was roughly equal to 0.70-fold and 0.87-fold coverage of the maize B73 RefGen_V4 genome for each F₇ individual, respectively. 6418 and 5139 SNP markers were extracted to construct two high-density genetic maps. Comparative analysis using these physically mapped marker loci demonstrated a satisfactory colinear relationship with the reference genome. 11 GLS-resistant QTL have been detected. The individual QTL accounted for 1.53-24.00% of the phenotypic variance explained (PVE). The new consensus QTL (qYCM-DS3-3/qYCM-LT3-1/qYCM-LT3-2) with the largest effect was located in chromosome bin 3.05, with an interval of 2.7 Mb, representing 13.08 to 24.00% of the PVE. Further gene annotation indicated that there were four candidate genes (GRMZM2G032384, GRMZM2G041415, GRMZM2G041544, and GRMZM2G035992) for qYCM-LT3-1, which may be related to GLS resistance. Combining RIL populations and GBS-based high-density genetic maps, a new larger effect QTL was delimited to a narrow genomic interval, which will provide a new resistance source for maize breeding programs.