Multispectral and thermal infrared data, visual scores for severity of common rust symptoms, and genotypic single nucleotide polymorphism data of three F2-derived biparental doubled-haploid maize populations.

Three F2-derived biparental doubled haploid (DH) maize populations were generated for genetic mapping of resistance to common rust. Each of the three populations has the same susceptible parent, but a different resistance donor parent. Population 1 and 3 consist of 320 lines each, population 2 consists of 260 lines. The DH lines were evaluated for their susceptibility to common rust in two years and with two replications in each year. For phenotyping, a visual score (VS) for susceptibility was assigned. Additionally, unmanned aerial vehicle (UAV) derived multispectral and thermal infrared data was recorded and combined in different vegetation indices ("remote sensing", RS). The DH lines were genotyped with the DarTseq method, to obtain data on single nucleotide polymorphisms (SNPs). After quality control, 9051 markers remained. Missing values were "imputed" by the empirical mean of the marker scores of the respective locus. We used the data for comparison of genome-wide association studies and genomic prediction when based on different phenotyping methods, that is either VS or RS data. The data may be interesting for reuse for instance for benchmarking genomic prediction models, for phytopathological studies addressing common rust, or for specifications of vegetation indices.

Exploiting genomic tools for genetic dissection and improving the resistance to Fusarium stalk rot in tropical maize.

KEY MESSAGE: A stable genomic region conferring FSR resistance at ~250 Mb on chromosome 1 was identified by GWAS. Genomic prediction has the potential to improve FSR resistance. Fusarium stalk rot (FSR) is a global destructive disease in maize; the efficiency of phenotypic selection for improving FSR resistance was low. Novel genomic tools of genome-wide association study (GWAS) and genomic prediction (GP) provide an opportunity for genetic dissection and improving FSR resistance. In this study, GWAS and GP analyses were performed on 562 tropical maize inbred lines consisting of two populations. In total, 15 SNPs significantly associated with FSR resistance were identified across two populations and the combinedPOP consisting of all 562 inbred lines, with the P-values ranging from 1.99 × 10-7 to 8.27 × 10-13, and the phenotypic variance explained (PVE) values ranging from 0.94 to 8.30%. The genetic effects of the 15 favorable alleles ranged from -4.29 to -14.21% of the FSR severity. One stable genomic region at ~ 250 Mb on chromosome 1 was detected across all populations, and the PVE values of the SNPs detected in this region ranged from 2.16 to 5.18%. Prediction accuracies of FSR severity estimated with the genome-wide SNPs were moderate and ranged from 0.29 to 0.51. By incorporating genotype-by-environment interaction, prediction accuracies were improved between 0.36 and 0.55 in different breeding scenarios. Considering both the genome coverage and the threshold of the P-value of SNPs to select a subset of molecular markers further improved the prediction accuracies. These findings extend the knowledge of exploiting genomic tools for genetic dissection and improving FSR resistance in tropical maize.

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize.

Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher -logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.

Occurrence and Distribution of Physiological Races of Exserohilum turcicum in Maize-Growing Regions of Mexico.

Turcicum leaf blight (TLB) is a common foliar disease of maize in Mexico that is caused by the fungal pathogen Exserohilum turcicum. The most effective management strategy against TLB is monogenic race-specific resistance. Among the 140 E. turcicum isolates from symptomatic leaves collected from maize fields in Mexico, 100 were obtained from tropical (Veracruz) and temperate areas (Estado de México) between 2010 and 2019, and 40 isolates were obtained from tropical (Sinaloa, Tamaulipas, Veracruz, and Chiapas), subtropical (Nayarit, Jalisco, and Guanajuato), and temperate areas (Estado de Mexico, Hidalgo, and Puebla) collected in 2019. All the isolates caused TLB symptoms on the positive control (ht4), showing that they were all pathogenic. Six physiological races of E. turcicum (2, 3, 23, 3N, 23N, and 123N) were identified based on resistant or susceptible responses displayed by five maize differential genotypes (A619Ht1, A619Ht2, A619Ht3, B68HtN, and A619ht4). The most common was race 23, accounting for 68% of the isolates, followed by races 23N, 123N, 3, 2, and 3N at 15, 8, 6, 2, and 1%, respectively. Race 123N was able to infect the greatest number of maize differential genotypes used in the study. Race 123N was detected in Sinaloa and Estado de México. Race 3 was detected in Nayarit and Jalisco. Race 2 was detected in Jalisco, Estado de México, and Veracruz, and race 3N was detected in Tamaulipas. Race 23 was equally dominant in the tropical, subtropical, and temperate regions, while race 123N was more common in the tropical environment, and race 23N was more common in the tropical and temperate environments. There was no evidence for shifts in the races between 2010 and 2019.