In a study of DNA methylation changes in melatonin-deficient rice mutants, mutant plants showed premature leaf senescence during grain-filling and reduced grain yield. Melatonin deficiency led to transcriptional reprogramming, especially of genes involved in chlorophyll and carbon metabolism, redox regulation, and transcriptional regulation, during dark-induced leaf senescence. Hypomethylation of mCG and mCHG in the melatonin-deficient rice mutants was associated with the expression change of both protein-coding genes and transposable element-related genes. Changes in gene expression and DNA methylation in the melatonin-deficient mutants were compensated by exogenous application of melatonin. A decreased S-adenosyl-L-methionine level may have contributed to the DNA methylation variations in rice mutants of melatonin deficiency under dark conditions.

Genomic selection (GS) is a powerful tool for improving genetic gain in maize breeding. However, its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms. Although sequencing-based and array-based genotyping platforms have been used for GS, few studies have compared prediction performance among platforms. In this study, we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing (GBS), a 40K SNP array, and target sequence capture (TSC) using eight GS models. The GBS marker dataset yielded the highest predictabilities for all traits, followed by TSC and SNP array datasets. We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs, and BayesB, GBLUP, and RKHS performed well, while XGBoost performed poorly in most cases. We also selected significant SNP subsets using genome-wide association study (GWAS) analyses in three panels to predict hybrid performance. GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost, but depended heavily on the GWAS panel. We conclude that there is still room for optimization of the existing SNP array, and using genotyping by target sequencing (GBTS) techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.

Rice (Oryza sativa) provides a staple food source for more than half the world population. However, the current pace of rice breeding in yield growth is insufficient to meet the food demand of the ever-increasing global population. Genomic selection (GS) holds a great potential to accelerate breeding progress and is cost-effective via early selection before phenotypes are measured. Previous simulation and experimental studies have demonstrated the usefulness of GS in rice breeding. However, several affecting factors and limitations require careful consideration when performing GS. In this review, we summarize the major genetics and statistical factors affecting predictive performance as well as current progress in the application of GS to rice breeding. We also highlight effective strategies to increase the predictive ability of various models, including GS models incorporating functional markers, genotype by environment interactions, multiple traits, selection index, and multiple omic data. Finally, we envision that integrating GS with other advanced breeding technologies such as unmanned aerial vehicles and open-source breeding platforms will further improve the efficiency and reduce the cost of breeding.

As the end products of cellular regulatory processes, metabolites provide the link between genotypes and phenotypes. Although metabolites have been widely applied for functional gene detection and phenotype prediction in maize, there is little research focusing on the genetic information of metabolites per se. Here, we performed genetic analyses for the kernel metabolites of 11 parental inbred lines of six representative maize varieties, including Zhongdan 2, Danyu 13, Yedan 13, Zhengdan 958, Xianyu 355, and Suyu 16, as well as their 26 reciprocal hybrids. We identified a total of 208 metabolites in maize kernels using untargeted metabolite profiling technology. Both cluster analysis and principal component analysis indicated that kernel metabolites could distinguish hybrids from their parents. Analysis of variance further revealed that 163 metabolites exhibited significant differences between parents and hybrids, and 40 metabolites showed significant differences between reciprocal crosses. We also investigated the genetic effects and heterosis for each metabolite. By taking all hybrids into consideration, about two-thirds of all metabolites displayed overdominant with 36.8% and 31% of them displaying positive overdominant and negative overdominant, respectively. Besides, 27.5% and 20.4% of all hybrid combinations showed significant mid-parent heterosis and over-parent heterosis, respectively. Our findings revealed that kernel metabolites exhibited the diversity of relationship between maize hybrids and their parental lines. Additionally, we identified 25 significant metabolic markers related to 11 agronomic traits using the LASSO method. Seven metabolic markers were associated with more than one trait simultaneously. These results provide a genetic basis for further utilization of metabolites in the genetic improvement of maize.

Evaluation of general combining ability (GCA) is crucial to hybrid breeding in maize. Although the complete diallel cross design can provide an efficient estimation, sparse partial diallel cross (SPDC) is more flexible in breeding practice. Using real and simulated data sets of partial diallel crosses between 266 maize inbred lines, this study investigated the performance of SPDC designs for estimating the GCA. With different distributions of parental lines involved in crossing (called random, balanced and unbalanced samplings), different numbers of hybrids were sampled as the training sets to estimate the GCA of the 266 inbred lines. In this process, three statistical approaches were applied. One obtained estimations through the ordinary least square (OLS) method, and the other two utilized genomic prediction (GP) to estimate the GCA. It was found that the coefficient of determination of each approach was always higher than the heritability of a target trait, showing that the GCA for maize inbred lines could be accurately predicted with SPDC designs. Both the GP approaches were more accurate than the OLS, particularly in the scenario for a low-heritability trait with a small sample size. Additionally, prediction results demonstrated that a big sample of hybrids could greatly help improve the accuracy. The random sampling of parental lines had little influence on the average accuracy. However, the prediction for lines that never or seldom involved in crossing might suffer from much lower accuracy.

Crown root traits, including crown root angle (CRA), diameter (CRD), and number (CRN), are major determining factors of root system architecture, which influences crop production. In maize, the genetic mechanisms determining crown root traits in the field are largely unknown. CRA, CRD, and CRN were evaluated in a recombinant inbred line population in three field trials. High phenotypic variation was observed for crown root traits, and all measured traits showed significant genotype–environment interactions. Single-environment (SEA) and multi-environment (MEA) quantitative trait locus (QTL) analyses were conducted for CRA, CRD, and CRN. Of 46 QTL detected by SEA, most explained less than 10% of the phenotypic variation, indicating that a large number of minor-effect QTL contributed to the genetic component of these traits. MEA detected 25 QTL associated with CRA, CRD, and CRN, and 2 and 1 QTL were identified with significant QTL-by-environment interaction effects for CRA and CRD, respectively. A total of 26.1% (12/46) of the QTL identified by SEA were also detected by MEA, with many being detected in more than one environment. These findings contribute to our understanding of the phenotypic and genotypic patterns of crown root traits in different environments. The identified environment-specific QTL and stable QTL may be used to improve root traits in maize breeding.

Seed moisture at harvest is a critical trait affecting maize quality and mechanized production, and is directly determined by the dehydration process after physiological maturity. However, the dynamic nature of seed dehydration leads to inaccurate evaluation of the dehydration process by conventional determination methods. Seed dry weight and fresh weight were recorded at 14 time points after pollination in a recombinant inbred line (RIL) population derived from two inbred lines with contrasting seed dehydration dynamics. The dehydration curves of RILs were determined by fitting trajectories of dry weight accumulation and dry weight/fresh weight ratio change based on a logistic model, allowing the estimation of eight characteristic parameters that can be used to describe dehydration features. Quantitative trait locus (QTL) mapping, taking these parameters as traits, was performed using multiple methods. Single-trait QTL mapping revealed 76 QTL associated with dehydration characteristic parameters, of which the phenotypic variation explained (PVE) was 1.03% to 15.24%. Multiple-environment QTL analysis revealed 21 related QTL with PVE ranging from 4.23% to 11.83%. Multiple-trait QTL analysis revealed 58 QTL, including 51 pleiotropic QTL. Combining these mapping results revealed 12 co-located QTL and the dehydration process of RILs was divided into three patterns with clear differences in dehydration features. These results not only deepen general understanding of the genetic characteristics of seed dehydration but also suggest that this approach can efficiently identify associated genetic loci in maize.

With marker and phenotype information from observed populations, genomic selection (GS) can be used to establish associations between markers and phenotypes. It aims to use genome-wide markers to estimate the effects of all loci and thereby predict the genetic values of untested populations, so as to achieve more comprehensive and reliable selection and to accelerate genetic progress in crop breeding. GS models usually face the problem that the number of markers is much higher than the number of phenotypic observations. To overcome this issue and improve prediction accuracy, many models and algorithms, including GBLUP, Bayes, and machine learning have been employed for GS. As hot issues in GS research, the estimation of non-additive genetic effects and the combined analysis of multiple traits or multiple environments are also important for improving the accuracy of prediction. In recent years, crop breeding has taken advantage of the development of GS. The principles and characteristics of current popular GS methods and research progress in these methods for crop improvement are reviewed in this paper.

Dissecting the genetic architecture of complex traits is an ongoing challenge for geneticists. Two complementary approaches for genetic mapping, linkage mapping and association mapping have led to successful dissection of complex traits in many crop species. Both of these methods detect quantitative trait loci (QTL) by identifying marker–trait associations, and the only fundamental difference between them is that between mapping populations, which directly determine mapping resolution and power. Based on this difference, we first summarize in this review the advances and limitations of family-based mapping and natural population-based mapping instead of linkage mapping and association mapping. We then describe statistical methods used for improving detection power and computational speed and outline emerging areas such as large-scale meta-analysis for genetic mapping in crops. In the era of next-generation sequencing, there has arisen an urgent need for proper population design, advanced statistical strategies, and precision phenotyping to fully exploit high-throughput genotyping.