Heterosis is a complex biological phenomenon regulated by genetic variations and epigenetic changes. However, the roles of small RNAs (sRNAs), an important epigenetic regulatory element, on plant heterosis are still poorly understood. Here, an integrative analysis was performed with sequencing data from multi-omics layers of maize hybrids and their two homologous parental lines to explore the potential underlying mechanisms of sRNAs in plant height (PH) heterosis. sRNAome analysis revealed that 59 (18.61%) microRNAs (miRNAs) and 64,534 (54.00%) 24-nt small interfering RNAs (siRNAs) clusters were non-additively expressed in hybrids. Transcriptome profiles showed that these non-additively expressed miRNAs regulated PH heterosis through activating genes involved in vegetative growth-related pathways while suppressing those related to reproductive and stress response pathways. DNA methylome profiles showed that non-additive methylation events were more likely to be induced by non-additively expressed siRNA clusters. Genes associated with low-parental expression (LPE) siRNAs and trans-chromosomal demethylation (TCdM) events were enriched in developmental processes as well as nutrients and energy metabolism, whereas genes associated with high-parental expression (HPE) siRNAs and trans-chromosomal methylation (TCM) events were gathered in stress response and organelle organization pathways. Our results provide insights into the expression and regulation patterns of sRNAs in hybrids and help to elucidate their potential targeting pathways contributing to PH heterosis.

Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59–0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.

Maize breeding is greatly affected by hybrid vigor, a phenomenon that hybrids exhibit superior performance than parental lines. The immortalized F₂ population (IMF₂) is ideal for the genetic dissection and prediction of hybrid performance. Here, in this study, we conducted the QTL mapping and genomic prediction of six traits related to plant architecture using an IMF₂ population. Broad-sense heritability of these traits ranged from 0.85 to 0.94. Analysis of genetic effects showed that additive variance was the main contributor to phenotypic variations. The mapping of quantitative trait loci (QTLs) revealed 10 to 16 QTLs (including pleiotropic loci and epistatic QTLs) for the six traits. Additionally, we identified 15 fine-tuning QTLs for plant height (PH). For genomic prediction (GP), the model of additive and dominance (AD) exhibited higher prediction accuracy than those fitting general combining ability (GCA) and its combination with special combining ability (SCA) effects for all tested traits. And adding the epistasis (E) effect into the AD model did not significantly increase its prediction accuracy. Moreover, the identified 15 fine-tuning QTLs of PH, which exerted large genomic prediction effects, were verified by the marker effect of GP. Our results not only provide an approach for the fine-mapping of fine-tuning QTLs but also serve as references for GP breeding in crops.

Although bulked segregant analysis (BSA) has been used extensively in genetic mapping, user-friendly tools which can integrate current algorithms for researchers with no background in bioinformatics are scarce. To address this issue, we developed an R package, PNGSeqR, which takes single-nucleotide polymorphism (SNP) markers from next-generation sequencing (NGS) data in variant call format (VCF) as the input file, provides four BSA algorithms to indicate the magnitude of genome-wide signals, and rapidly defines the candidate region through the permutation test and fractile quantile. Users can choose the analysis methods according to their data and experimental design. In addition, it also supports differential expression gene analysis (DEG) and gene ontology analysis (GO) to prioritize the target gene. Once the analysis is completed, the plots can conveniently be exported.