Understanding how numerous quantitative trait loci (QTL) shape phenotypic variation is an important question in genetics. To address this, we established a permanent population of 18,421 (18K) rice lines with reduced population structure. We generated reference-level genome assemblies of the founders and genotyped all 18K-rice lines through whole-genome sequencing. Through high-resolution mapping, 96 high-quality candidate genes contributing to variation in 16 traits were identified, including OsMADS22 and OsFTL1 verified as causal genes for panicle number and heading date, respectively. We identified epistatic QTL pairs and constructed a genetic interaction network with 19 genes serving as hubs. Overall, 170 masking epistasis pairs were characterized, serving as an important factor contributing to genetic background effects across diverse varieties. The work provides a basis to guide grain yield and quality improvements in rice.

Background: Genetic populations provide the basis for a wide range of genetic and genomic studies and have been widely used in genetic mapping, gene discovery and genomics-assisted breeding. Chromosome segment substitution lines (CSSLs) are the most powerful tools for the detection and precise mapping of quantitative trait loci (QTLs), for the analysis of complex traits in plant molecular genetics. Results: In this study, a wide population consisting of 128 CSSLs was developed, derived from the crossing and back-crossing of two sequenced rice cultivars: 9311, an elite indica cultivar as the recipient and Nipponbare, a japonica cultivar as the donor. First, a physical map of the 128 CSSLs was constructed on the basis of estimates of the lengths and locations of the substituted chromosome segments using 254 PCR-based molecular markers. From this map, the total size of the 142 substituted segments in the population was 882.2 Mb, was 2.37 times that of the rice genome. Second, every CSSL underwent high-throughput genotyping by whole-genome re-sequencing with a 0.13× genome sequence, and an ultrahigh-quality physical map was constructed. This sequencing-based physical map indicated that 117 new segments were detected; almost all were shorter than 3 Mb and were not apparent in the molecular marker map. Furthermore, relative to the molecular marker-based map, the sequencing-based map yielded more precise recombination breakpoint determination and greater accuracy of the lengths of the substituted segments, and provided more accurate background information. Third, using the 128 CSSLs combined with the bin-map converted from the sequencing-based physical map, a multiple linear regression QTL analysis mapped nine QTLs, which explained 89.50% of the phenotypic variance for culm length. A large-effect QTL was located in a 791,655 bp region that contained the rice 'green revolution' gene. Conclusions: The present results demonstrated that high throughput genotyped CSSLs combine the advantages of an ultrahigh-quality physical map with high mapping accuracy, thus being of great potential value for gene discovery and genetic mapping. These CSSLs may provide powerful tools for future whole genome large-scale gene discovery in rice and offer foundations enabling the development of superior rice varieties.

Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance. From the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115 × 5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115 + 5) rice varieties each mating with 3023 rice accessions from the 3000 rice genomes project (3 K RGP). Of the 362,760 potential hybrids, selection of the top 100 predicted hybrids would lead to 35.5%, 23.25%, 30.21%, 42.87%, 61.80%, 75.83%, 19.24% and 36.12% increase in grain yield per plant, thousand-grain weight, panicle number per plant, plant height, secondary branch number, grain number per panicle, panicle length and primary branch number, respectively. This study evaluated the factors affecting predictabilities for hybrid prediction and demonstrated the implementation of GS to predict hybrid performance of rice. Our results suggest that GS could enable the rapid selection of superior hybrids, thus increasing the efficiency of rice hybrid breeding.

Abiotic stresses, including drought, salinity, heat and cold, negatively affect maize (Zea mays L.) development and productivity. Additionally, these abiotic stresses are the major limiting factors of maize yield. To elucidate the molecular mechanisms of resistance to abiotic stresses in maize, RNA-seq was used for global transcriptome profiling of B73 seedling leaves exposed to drought, salinity, heat and cold stress. A total of 5,330 differentially expressed genes (DEGs) were detected in differential comparisons between the control and each stressed sample, with 1,661, 2,019, 2,346 and 1,841 DEGs being identified in comparisons of the control with salinity, drought, heat and cold stress, respectively. Functional annotations of DEGs suggested that the stress response was mediated by pathways involving hormone metabolism and signaling, transcription factors (TFs), very-long-chain fatty acid biosynthesis and lipid signaling, among others. Of the obtained DEGs (5,330), 167 genes are common to these four abiotic stresses, including 10 up-regulated TFs (5 ERFs, 2 NACs, 1 ARF, 1 MYB and 1 HD-ZIP) and two down-regulated TFs (1 b-ZIP and 1 MYB-related), which suggested that common mechanisms may be initiated in response to different abiotic stresses in maize. This study contributes to a better understanding of the molecular mechanisms of maize leaf responses to abiotic stresses and could be useful for developing maize cultivars resistant to abiotic stresses.

Maize starch plays a critical role in food processing and industrial application. The pasting properties, the most important starch characteristics, have enormous influence on fabrication property, flavor characteristics, storage, cooking, and baking. Understanding the genetic basis of starch pasting properties will be beneficial for manipulation of starch properties for a given purpose. Genome-wide association studies (GWAS) are becoming a powerful tool for dissecting the complex traits. Here, we carried out GWAS for seven pasting properties of maize starch with a panel of 230 inbred lines and 145,232 SNPs using one single-locus method, genome-wide efficient mixed model association (GEMMA), and three multi-locus methods, FASTmrEMMA, FarmCPU, and LASSO. We totally identified 60 quantitative trait nucleotides (QTNs) for starch pasting properties with these four GWAS methods. FASTmrEMMA detected the most QTNs (29), followed by FarmCPU (19) and LASSO (12), GEMMA detected the least QTNs (7). Of these QTNs, seven QTNs were identified by more than one method simultaneously. We further investigated locations of these significantly associated QTNs for possible candidate genes. These candidate genes and significant QTNs provide the guidance for further understanding of molecular mechanisms of starch pasting properties. We also compared the statistical powers and Type I errors of the four GWAS methods using Monte Carlo simulations. The results suggest that the multi-locus method is more powerful than the single-locus method and a combination of these multi-locus methods could help improve the detection power of GWAS.