Heterosis is a well-known phenomenon widely applied in agriculture. Recent studies have suggested that differential gene and protein expression between hybrids and their parents play important roles in heterosis. Alternative splicing (AS) is an essential posttranscriptional mechanism that can greatly affect the transcriptome and proteome diversity in plants. However, genome-wide AS divergence in hybrids compared to their parents and its potential contribution to heterosis have not been comprehensively investigated. We report the direct profiling of the AS landscape using RNA sequencing data from immature ears of the maize hybrid ZD808 and its parents NG5 and CL11. Our results revealed a large number of significant differential AS (DAS) events in ZD808 relative to its parents, which can be further classified into parental-dominant and novel DAS patterns. Parental-dominant, especially NG5-dominant, events were prevalent in the hybrid, accounting for 42% of all analyzed DAS events. Functional enrichment analysis revealed that the NG5-dominant AS events were involved mainly in regulating the expression of genes associated with carbon/nitrogen metabolism and cell division processes and contributed greatly to maize ear heterosis. Among ZD808, CL11, and NG5, 32.5% of DAS contained or lacked binding sites of at least one annotated maize microRNA (miRNA) and may be involved in miRNA-mediated posttranscriptional regulation. Cis regulation was the predominant contributor to AS variation and participates in many important biological processes associated with immature ear development. This study provides a comprehensive view of genome-wide alternative splicing variation in a maize hybrid.

The cell wall composition and structure of the maize stalk directly affects its digestibility and in turn its feed value. Previous studies of stem quality have focused mostly on common maize germplasm, and few studies have focused on high-oil cultivars with high grain and straw quality. Investigation of the genetic basis of cell wall composition and digestibility of maize stalk using high-oil maize is desirable for improving maize forage quality. In the present study, a high-oil inbred line (By804) was crossed as male parent with the maize inbred line B73 to construct a population of 188 recombinant inbred lines (RILs). The phenotypes of six cell-wall-related traits were recorded, and QTL analysis was performed with a genetic map constructed with SNP markers. All traits were significantly correlated with one another and showed high broad-sense heritability. Of 20 QTLs mapped, the QTL associated with each trait explained 10.0%–41.1% of phenotypic variation. Approximately half of the QTL each explained over 10% of the phenotypic variation. These results provide a theoretical basis for improving maize forage quality by marker-assisted selection.

Genomic selection (GS) as a promising molecular breeding strategy has been widely implemented and evaluated for plant breeding, because it has remarkable superiority in enhancing genetic gain, reducing breeding time and expenditure, and accelerating the breeding process. In this study the factors affecting prediction accuracy (r_MG) in GS were evaluated systematically, using six agronomic traits (plant height, ear height, ear length, ear diameter, grain yield per plant and hundred-kernel weight) evaluated in one natural and two biparental populations. The factors examined included marker density, population size, heritability, statistical model, population relationships and the ratio of population size between the training and testing sets, the last being revealed by resampling individuals in different proportions from a population. Prediction accuracy continuously increased as marker density and population size increased and was positively correlated with heritability; r_MG showed a slight gain when the training set increased to three times as large as the testing set. Low predictive performance between unrelated populations could be attributed to different allele frequencies, and predictive ability and prediction accuracy could be improved by including more related lines in the training population. Among the seven statistical models examined, including ridge regression best linear unbiased prediction (RR-BLUP), genomic BLUP (GBLUP), BayesA, BayesB, BayesC, Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), and reproducing kernel Hilbert space (RKHS), the RKHS and additive-dominance model (Add + Dom model) showed credible ability for capturing non-additive effects, particularly for complex traits with low heritability. Empirical evidence generated in this study for GS-relevant factors will help plant breeders to develop GS-assisted breeding strategies for more efficient development of varieties.

Modern maize varieties have become more productive than ever, owing largely to increased tolerance of high plant density. However, the genetics of ear leaf traits under different densities remains poorly understood. In this study, Zhongdan 909 recombinant inbred lines (RILs) derived from a cross between Z58 and HD568 were genotyped for 3072 single-nucleotide polymorphisms (SNPs), and phenotyped for leaf length (LL), leaf width (LW), and leaf angle (LA) of the uppermost ear leaf under three planting densities (52,500, 67,500, and 82,500 plants ha^-1, respectively). A genetic map was then constructed using 1358 high-quality SNPs. The total length of the linkage map was 1985.2 cM and the average interval between adjacent markers 1.46 cM. With increasing density, LL and LW decreased from 63.68 to 63.02 cm and from 8.56 to 8.21 cm, respectively, while LA increased from 19.42° to 19.66°. All three traits had high heritabilities, of 0.75, 0.78, and 0.84, respectively. Using inclusive composite interval mapping, 23, 25, and 17 quantitative trait loci (QTL) were detected for LL, LW, and LA, respectively. Of these, 35 were simultaneously detected under two or three plant densities, while 30 were detected under only one. Sixty-five individual QTL explained 2.41% to 16.53% of phenotypic variation, while eight accounted for >10%. These findings will help us understand the genetic basis of leaf traits in maize as well as the response of maize to increased plant density.