Genetic mapping of quantitative trait loci and a major locus for resistance to grey leaf spot in maize

Quantitative trait locus analysis of gray leaf spot resistance in the maize IBM Syn10 DH population

KEY MESSAGE

The exploration and dissection of a set of QTLs and candidate genes for gray leaf spot disease resistance using two fully assembled parental genomes may help expedite maize resistance breeding. The fungal disease of maize known as gray leaf spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is a significant concern in China, Southern Africa, and the USA. Resistance to GLS is governed by multiple genes with an additive effect and is influenced by both genotype and environment. The most effective way to reduce the cost of production is to develop resistant hybrids. In this study, we utilized the IBM Syn 10 Doubled Haploid (IBM Syn10 DH) population to identify quantitative trait loci (QTLs) associated with resistance to gray leaf spot (GLS) in multiple locations. Analysis of seven distinct environments revealed a total of 58 QTLs, 49 of which formed 12 discrete clusters distributed across chromosomes 1, 2, 3, 4, 8 and 10. By comparing these findings with published research, we identified colocalized QTLs or GWAS loci within eleven clustering intervals. By integrating transcriptome data with genomic structural variations between parental individuals, we identified a total of 110 genes that exhibit both robust disparities in gene expression and structural alterations. Further analysis revealed 19 potential candidate genes encoding conserved resistance gene domains, including putative leucine-rich repeat receptors, NLP transcription factors, fucosyltransferases, and putative xyloglucan galactosyltransferases. Our results provide a valuable resource and linked loci for GLS marker resistance selection breeding in maize.

ZmWAK02 encoding an RD-WAK protein confers maize resistance against gray leaf spot

Gray leaf spot (GLS) caused by Cercospora zeina or C. zeae-maydis is a major maize disease throughout the world. Although more than 100 QTLs resistant against GLS have been identified, very few of them have been cloned. Here, we identified a major resistance QTL against GLS, qRglsSB, explaining 58.42% phenotypic variation in SB12×SA101 BC1 F1 population. By fine-mapping, it was narrowed down into a 928 kb region. By using transgenic lines, mutants and complementation lines, it was confirmed that the ZmWAK02 gene, encoding an RD wall-associated kinase, is the responsible gene in qRglsSB resistant against GLS. The introgression of the ZmWAK02 gene into hybrid lines significantly improves their grain yield in the presence of GLS pressure and does not reduce their grain yield in the absence of GLS. In summary, we cloned a gene, ZmWAK02, conferring large effect of GLS resistance and confirmed its great value in maize breeding.

The ZmWAKL-ZmWIK-ZmBLK1-ZmRBOH4 module provides quantitative resistance to gray leaf spot in maize

Gray leaf spot (GLS), caused by the fungal pathogens Cercospora zeae-maydis and Cercospora zeina, is a major foliar disease of maize worldwide (Zea mays L.). Here we demonstrate that ZmWAKL encoding cell-wall-associated receptor kinase-like protein is the causative gene at the major quantitative disease resistance locus against GLS. The ZmWAKLY protein, encoded by the resistance allele, can self-associate and interact with a leucine-rich repeat immune-related kinase ZmWIK on the plasma membrane. The ZmWAKLY/ZmWIK receptor complex interacts with and phosphorylates the receptor-like cytoplasmic kinase (RLCK) ZmBLK1, which in turn phosphorylates its downstream NADPH oxidase ZmRBOH4. Upon pathogen infection, ZmWAKLY phosphorylation activity is transiently increased, initiating immune signaling from ZmWAKLY, ZmWIK, ZmBLK1 to ZmRBOH4, ultimately triggering a reactive oxygen species burst. Our study thus uncovers the role of the maize ZmWAKL-ZmWIK-ZmBLK1-ZmRBOH4 receptor/signaling/executor module in perceiving the pathogen invasion, transducing immune signals, activating defense responses and conferring increased resistance to GLS.

Transcriptome Analysis and QTL Mapping Identify Candidate Genes and Regulatory Mechanisms Related to Low-Temperature Germination Ability in Maize

Low-temperature germination ability (LTGA) is an important characteristic for spring sowing maize. However, few maize genes related to LTGA were confirmed, and the regulatory mechanism is less clear. Here, maize-inbred lines Ye478 and Q1 with different LTGA were used to perform transcriptome analysis at multiple low-temperature germination stages, and a co-expression network was constructed by weighted gene co-expression network analysis (WGCNA). Data analysis showed that 7964 up- and 5010 down-regulated differentially expressed genes (DEGs) of Ye478 were identified at low-temperature germination stages, while 6060 up- and 2653 down-regulated DEGs of Q1 were identified. Gene ontology (GO) enrichment analysis revealed that ribosome synthesis and hydrogen peroxide metabolism were enhanced and mRNA metabolism was weakened under low-temperature stress for Ye478, while hydrogen peroxide metabolism was enhanced and mRNA metabolism was weakened for Q1. DEGs pairwise comparisons between the two genotypes found that Ye478 performed more ribosome synthesis at low temperatures compared with Q1. WGCNA analysis based on 24 transcriptomes identified 16 co-expressed modules. Of these, the MEbrown module was highly correlated with Ye478 at low-temperature stages and catalase and superoxide dismutase activity, and the MEred, MEgreen, and MEblack modules were highly correlated with Ye478 across low-temperature stages, which revealed a significant association between LTGA and these modules. GO enrichment analysis showed the MEbrown and MEred modules mainly functioned in ribosome synthesis and cell cycle, respectively. In addition, we conducted quantitative trait loci (QTL) analysis based on a doubled haploid (DH) population constructed by Ye478 and Q1 and identified a major QTL explanting 20.6% of phenotype variance on chromosome 1. In this QTL interval, we found three, four, and three hub genes in the MEbrown, MEred, and MEgreen modules, of which two hub genes (Zm00001d031951, Zm00001d031953) related to glutathione metabolism and one hub gene (Zm00001d031617) related to oxidoreductase activity could be the candidate genes for LTGA. These biological functions and candidate genes will be helpful in understanding the regulatory mechanism of LTGA and the directional improvement of maize varieties for LTGA.

Genetic dissection of QTLs for oil content in four maize DH populations

Oil is one of the main components in maize kernels. Increasing the total oil content (TOC) is favorable to optimize feeding requirement by improving maize quality. To better understand the genetic basis of TOC, quantitative trait loci (QTL) in four double haploid (DH) populations were explored. TOC exhibited continuously and approximately normal distribution in the four populations. The moderate to high broad-sense heritability (67.00-86.60%) indicated that the majority of TOC variations are controlled by genetic factors. A total of 16 QTLs were identified across all chromosomes in a range of 3.49-30.84% in term of phenotypic variation explained. Among them, six QTLs were identified as the major QTLs that explained phenotypic variation larger than 10%. Especially, qOC-1-3 and qOC-2-3 on chromosome 9 were recognized as the largest effect QTLs with 30.84% and 21.74% of phenotypic variance, respectively. Seventeen well-known genes involved in fatty acid metabolic pathway located within QTL intervals. These QTLs will enhance our understanding of the genetic basis of TOC in maize and offer prospective routes to clone candidate genes regulating TOC for breeding program to cultivate maize varieties with the better grain quality.

GBS-Based SNP Map Pinpoints the QTL Associated With Sorghum Downy Mildew Resistance in Maize (Zea mays L.)

Sorghum downy mildew (SDM), caused by the biotrophic fungi Peronosclerospora sorghi, threatens maize production worldwide, including India. To identify quantitative trait loci (QTL) associated with resistance to SDM, we used a recombinant inbred line (RIL) population derived from a cross between resistant inbred line UMI936 (w) and susceptible inbred line UMI79. The RIL population was phenotyped for SDM resistance in three environments [E1-field (Coimbatore), E2-greenhouse (Coimbatore), and E3-field (Mandya)] and also utilized to construct the genetic linkage map by genotyping by sequencing (GBS) approach. The map comprises 1516 SNP markers in 10 linkage groups (LGs) with a total length of 6924.7 cM and an average marker distance of 4.57 cM. The QTL analysis with the phenotype and marker data detected nine QTL on chromosome 1, 2, 3, 5, 6, and 7 across three environments. Of these, QTL namely qDMR1.2, qDMR3.1, qDMR5.1, and qDMR6.1 were notable due to their high phenotypic variance. qDMR3.1 from chromosome 3 was detected in more than one environment (E1 and E2), explaining the 10.3% and 13.1% phenotypic variance. Three QTL, qDMR1.2, qDMR5.1, and qDMR6.1 from chromosomes 1, 5, and 6 were identified in either E1 or E3, explaining 15.2%–18% phenotypic variance. Moreover, genome mining on three QTL (qDMR3.1, qDMR5.1, and qDMR6.1) reveals the putative candidate genes related to SDM resistance. The information generated in this study will be helpful for map-based cloning and marker-assisted selection in maize breeding programs.

Transcriptomic Analysis Reveals Candidate Genes Responding Maize Gray Leaf Spot Caused by Cercospora zeina

Gray leaf spot (GLS), caused by the fungal pathogen Cercospora zeina (C. zeina), is one of the most destructive soil-borne diseases in maize (Zea mays L.), and severely reduces maize production in Southwest China. However, the mechanism of resistance to GLS is not clear and few resistant alleles have been identified. Two maize inbred lines, which were shown to be resistant (R6) and susceptible (S8) to GLS, were injected by C. zeina spore suspensions. Transcriptome analysis was carried out with leaf tissue at 0, 6, 24, 144, and 240 h after inoculation. Compared with 0 h of inoculation, a total of 667 and 419 stable common differentially expressed genes (DEGs) were found in the resistant and susceptible lines across the four timepoints, respectively. The DEGs were usually enriched in 'response to stimulus' and 'response to stress' in GO term analysis, and 'plant-pathogen interaction', 'MAPK signaling pathways', and 'plant hormone signal transduction' pathways, which were related to maize's response to GLS, were enriched in KEGG analysis. Weighted-Genes Co-expression Network Analysis (WGCNA) identified two modules, while twenty hub genes identified from these indicated that plant hormone signaling, calcium signaling pathways, and transcription factors played a central role in GLS sensing and response. Combing DEGs and QTL mapping, five genes were identified as the consensus genes for the resistance of GLS. Two genes, were both putative Leucine-rich repeat protein kinase family proteins, specifically expressed in R6. In summary, our results can provide resources for gene mining and exploring the mechanism of resistance to GLS in maize.

Fine Mapping of a New Major QTL-qGLS8 for Gray Leaf Spot Resistance in Maize

Gray leaf spot (GLS), caused by different species of Cercospora, is a fungal, non-soil-borne disease that causes serious reductions in maize yield worldwide. The identification of major quantitative trait loci (QTLs) for GLS resistance in maize is essential for developing marker-assisted selection strategies in maize breeding. Previous research found a significant difference (P < 0.01) in GLS resistance between T32 (highly resistant) and J51 (highly susceptible) genotypes of maize. Initial QTL analysis was conducted in an F_{2 : 3} population of 189 individuals utilizing genetic maps that were constructed using 181 simple sequence repeat (SSR) markers. One QTL (qGLS8) was detected, defined by the markers umc1130 and umc2354 in three environments. The qGLS8 QTL detected in the initial analysis was located in a 51.96-Mb genomic region of chromosome 8 and explained 7.89-14.71% of the phenotypic variation in GLS resistance in different environments. We also developed a near isogenic line (NIL) BC₃F₂ population with 1,468 individuals and a BC₃F₂-Micro population with 180 individuals for fine mapping. High-resolution genetic and physical maps were constructed using six newly developed SSRs. The QTL-qGLS8 was narrowed down to a 124-kb region flanked by the markers ym20 and ym51 and explained up to 17.46% of the phenotypic variation in GLS resistance. The QTL-qGLS8 contained seven candidate genes, such as an MYB-related transcription factor 24 and a C ₃ H transcription factor 347), and long intergenic non-coding RNAs (lincRNAs). The present study aimed to provide a foundation for the identification of candidate genes for GLS resistance in maize.

Dissecting the Genetic Basis of Flowering Time and Height Related-Traits Using Two Doubled Haploid Populations in Maize

In the field, maize flowering time and height traits are closely linked with yield, planting density, lodging resistance, and grain fill. To explore the genetic basis of flowering time and height traits in maize, we investigated six related traits, namely, days to anthesis (AD), days to silking (SD), the anthesis-silking interval (ASI), plant height (PH), ear height (EH), and the EH/PH ratio (ER) in two locations for two years based on two doubled haploid (DH) populations. Based on the two high-density genetic linkage maps, 12 and 22 quantitative trait loci (QTL) were identified, respectively, for flowering time and height-related traits. Of these, ten QTLs had overlapping confidence intervals between the two populations and were integrated into three consensus QTLs (qFT_YZ1a, qHT_YZ5a, and qHT_YZ7a). Of these, qFT_YZ1a, conferring flowering time, is located at 221.1-277.0 Mb on chromosome 1 and explained 10.0-12.5% of the AD and SD variation, and qHT_YZ5a, conferring height traits, is located at 147.4-217.3 Mb on chromosome 5 and explained 11.6-15.3% of the PH and EH variation. These consensus QTLs, in addition to the other repeatedly detected QTLs, provide useful information for further genetic studies and variety improvements in flowering time and height-related traits.

Genetic dissection of maize disease resistance and its applications in molecular breeding

Disease resistance is essential for reliable maize production. In a long-term tug-of-war between maize and its pathogenic microbes, naturally occurring resistance genes gradually accumulate and play a key role in protecting maize from various destructive diseases. Recently, significant progress has been made in deciphering the genetic basis of disease resistance in maize. Enhancing disease resistance can now be explored at the molecular level, from marker-assisted selection to genomic selection, transgenesis technique, and genome editing. In view of the continuing accumulation of cloned resistance genes and in-depth understanding of their resistance mechanisms, coupled with rapid progress of biotechnology, it is expected that the large-scale commercial application of molecular breeding of resistant maize varieties will soon become a reality.

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.

Genetic Dissection of Resistance to Gray Leaf Spot by Combining Genome-Wide Association, Linkage Mapping, and Genomic Prediction in Tropical Maize Germplasm

Gray leaf spot (GLS) is one of the major maize foliar diseases in sub-Saharan Africa. Resistance to GLS is controlled by multiple genes with additive effect and is influenced by both genotype and environment. The objectives of the study were to dissect the genetic architecture of GLS resistance through linkage mapping and genome-wide association study (GWAS) and assessing the potential of genomic prediction (GP). We used both biparental populations and an association mapping panel of 410 diverse tropical/subtropical inbred lines that were genotyped using genotype by sequencing. Phenotypic evaluation in two to four environments revealed significant genotypic variation and moderate to high heritability estimates ranging from 0.43 to 0.69. GLS was negatively and significantly correlated with grain yield, anthesis date, and plant height. Linkage mapping in five populations revealed 22 quantitative trait loci (QTLs) for GLS resistance. A QTL on chromosome 7 (qGLS7-105) is a major-effect QTL that explained 28.2% of phenotypic variance. Together, all the detected QTLs explained 10.50, 49.70, 23.67, 18.05, and 28.71% of phenotypic variance in doubled haploid (DH) populations 1, 2, 3, and F3 populations 4 and 5, respectively. Joint linkage association mapping across three DH populations detected 14 QTLs that individually explained 0.10-15.7% of phenotypic variance. GWAS revealed 10 significantly (p < 9.5 × 10-6) associated SNPs distributed on chromosomes 1, 2, 6, 7, and 8, which individually explained 6-8% of phenotypic variance. A set of nine candidate genes co-located or in physical proximity to the significant SNPs with roles in plant defense against pathogens were identified. GP revealed low to moderate prediction correlations of 0.39, 0.37, 0.56, 0.30, 0.29, and 0.38 for within IMAS association panel, DH pop1, DH pop2, DH pop3, F3 pop4, and F3 po5, respectively, and accuracy was increased substantially to 0.84 for prediction across three DH populations. When the diversity panel was used as training set to predict the accuracy of GLS resistance in biparental population, there was 20-50% reduction compared to prediction within populations. Overall, the study revealed that resistance to GLS is quantitative in nature and is controlled by many loci with a few major and many minor effects. The SNPs/QTLs identified by GWAS and linkage mapping can be potential targets in improving GLS resistance in breeding programs, while GP further consolidates the development of high GLS-resistant lines by incorporating most of the major- and minor-effect genes.