Tar spot complex (TSC) is one of the most destructive foliar diseases of maize (Zea mays L.) in tropical and subtropical areas of Central and South America, causing significant grain yield losses when weather conditions are conducive. To dissect the genetic architecture of TSC resistance in maize, association mapping, in conjunction with linkage mapping, was conducted on an association-mapping panel and three biparental doubled-haploid (DH) populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Association mapping revealed four quantitative trait loci (QTL) on chromosome 2, 3, 7, and 8. All the QTL, except for the one on chromosome 3, were further validated by linkage mapping in different genetic backgrounds. Additional QTL were identified by linkage mapping alone. A major QTL located on bin 8.03 was consistently detected with the largest phenotypic explained variation: 13% in association-mapping analysis and 13.18 to 43.31% in linkage-mapping analysis. These results indicated that TSC resistance in maize was controlled by a major QTL located on bin 8.03 and several minor QTL with smaller effects on other chromosomes. Genomic prediction results showed moderate-to-high prediction accuracies in different populations using various training population sizes and marker densities. Prediction accuracy of TSC resistance was >0.50 when half of the population was included into the training set and 500 to 1,000 SNPs were used for prediction. Information obtained from this study can be used for developing functional molecular markers for marker-assisted selection (MAS) and for implementing genomic selection (GS) to improve TSC resistance in tropical maize. Abbreviations: BLUP, best linear unbiased prediction; DH, doubledhaploid; DTMA, Drought Tolerant Maize for Africa; FDR, false discovery rate; GBS, genotyping-by-sequencing; GS, genomic selection; LD, linkage disequilibrium; LOD, logarithm of odds; MAF, minor allele frequency; MAS, marker-assisted selection; PCA, principle component analysis; PVE, phenotypic variation explained; QTL, quantitative trait loci; r MG , genomic prediction accuracy; SNP, single-nucleotide polymorphism; TSC, tar spot complex. Core Ideas• Association and linkage mapping are effective for dissecting genetic architecture of complex traits in maize.• TSC resistance in maize is controlled by a major QTL and several minor QTL.• Major QTL on bin 8.03 confirmed by association and linkage mapping.• TSC resistance in tropical maize could be improved by MAS and GS individually or stepwise.

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Spring maize sowing occurs during a period of low temperature (LT) in Northeast China, and the LT suppresses nitrogen (N) metabolism and photosynthesis, further reducing dry matter accumulation. Diethyl aminoethyl hexanoate (DA-6) improves N metabolism; hence, we studied the effects of DA-6 on maize seedlings under LT conditions. The shoot and root fresh weight and dry weight decreased by 17.70%~20.82% in the LT treatment, and decreased by 5.81%~13.57% in the LT + DA-6 treatment on the 7 th day, respectively. Exogenous DA-6 suppressed the increases in ammonium (NH 4 +) content and glutamate dehydrogenase (GDH) activity, and suppressed the decreases in nitrate (NO 3-) and nitrite (NO 2-) contents, and activities of nitrate reductase (NR), nitrite reductase (NiR), glutamine synthetase (GS), glutamate synthase (GOGAT) and transaminase activities. NiR activity was most affected by DA-6 under LT conditions. Additionally, exogenous DA-6 suppressed the net photosynthetic rate (Pn) decrease, and the suppressed the increases of superoxide anion radical (O 2 � −) generation rate and hydrogen peroxide (H 2 O 2) content. Taken together, our results suggest that exogenous DA-6 mitigated the repressive effects of LT on N metabolism by improving photosynthesis and modulating oxygen metabolism, and subsequently enhanced the LT tolerance of maize seedlings.

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Maize (Zea mays L.) is sensitive to a minor decrease in temperature at early growth stages, resulting in deteriorated growth at later stages. Although there are significant variations in maize germplasm in response to cold stress, the metabolic responses as stress tolerance mechanisms are largely unknown. Therefore, this study aimed at providing insight into the metabolic responses under cold stress at the early growth stages of maize. Two inbred lines, tolerant (B144) and susceptible (Q319), were subjected to cold stress at the seedling stage, and their corresponding metabolic profiles were explored. The study identified differentially accumulated metabolites in both cultivars in response to induced cold stress with nine core conserved cold-responsive metabolites. Guanosine 3′,5′-cyclic monophosphate was detected as a potential biomarker metabolite to differentiate cold tolerant and sensitive maize genotypes. Furthermore, Quercetin-3-O-(2″′-p-coumaroyl)sophoroside-7-O-glucoside, Phloretin, Phloretin-2′-O-glucoside, Naringenin-7-O-Rutinoside, L-Lysine, L-phenylalanine, L-Glutamine, Sinapyl alcohol, and Feruloyltartaric acid were regulated explicitly in B144 and could be important cold-tolerance metabolites. These results increase our understanding of cold-mediated metabolic responses in maize that can be further utilized to enhance cold tolerance in this significant crop.

Weighted gene co-expression network analysis (WGCNA) is a research method in systematic biology. It is widely used to identify gene modules related to target traits in multi-sample transcriptome data. In order to further explore the molecular mechanism of maize response to low-temperature stress at the seedling stage, B144 (cold stress tolerant) and Q319 (cold stress sensitive) provided by the Maize Research Institute of Heilongjiang Academy of Agricultural Sciences were used as experimental materials, and both inbred lines were treated with 5 °C for 0 h, 12 h, and 24 h, with the untreated material as a control. Eighteen leaf samples were used for transcriptome sequencing, with three biological replicates. Based on the above transcriptome data, co-expression networks of weighted genes associated with low-temperature-tolerance traits were constructed by WGCNA. Twelve gene modules significantly related to low-temperature tolerance at the seedling stage were obtained, and a number of hub genes involved in low-temperature stress regulation pathways were discovered from the four modules with the highest correlation with target traits. These results provide clues for further study on the molecular genetic mechanisms of low-temperature tolerance in maize at the seedling stage.

Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.

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