Viruses are significant pathogens causing severe plant infections and crop losses globally. The resistance mechanisms of rice to viral diseases, particularly Southern rice black-streaked dwarf virus (SRBSDV), remain poorly understood. In this study, we assessed SRBSDV susceptibility in 20 Xian/indica (XI) and 20 Geng/japonica (GJ) rice varieties. XI-1B accessions in the Xian subgroup displayed higher resistance than GJ accessions. Comparative transcriptome analysis revealed changes in processes like oxido-reductase activity, jasmonic acid (JA) metabolism, and stress response. JA sensitivity assays further linked antiviral defense to the JA pathway. These findings highlight a JA-mediated resistance mechanism in rice and offer insights for breeding SRBSDV-resistant varieties.

Increasing effective panicle number per plant (EPN) is one approach to increase yield potential in rice. However, molecular mechanisms underlying EPN remain unclear. In this study, we integrated map-based cloning and genome-wide association analysis to identify the EPN4 gene, which is allelic to NARROW LEAF1 (NAL1). Overexpression lines containing the Teqing allele (TQ) of EPN4 had significantly increased EPN. NIL-EPN4^TQ in japonica (geng) cultivar Lemont (LT) exhibited significantly improved EPN but decreased grain number and flag leaf size relative to LT. Haplotype analysis indicated that accessions with EPN4-1 had medium EPN, medium grain number, and medium grain weight, but had the highest grain yield among seven haplotypes, indicating that EPN4-1 is an elite haplotype of EPN4 for positive coordination of the three components of grain yield. Furthermore, accessions carrying the combination of EPN4-1 and haplotype GNP1-6 of GNP1 for grain number per panicle showed higher grain yield than those with other allele combinations. Therefore, pyramiding of EPN4-1 and GNP1-6 could be a preferred approach to obtain high yield potential in breeding.

Future demands for increased productivity and resilience to abiotic/biotic stresses of major crops require new technologies of breeding by design (BBD) built on massive information from functional and population genomics research. A novel strategy of breeding by selective introgression (BBSI) has been proposed and practiced for simultaneous improvement, genetic dissection and allele mining of complex traits to realize BBD. BBSI has three phases: a) developing large numbers of trait-specific introgression lines (ILs) using backcross breeding in elite genetic backgrounds as the material platform of BBD; b) efficiently identifying genes or quantitative trait loci (QTL) and mining desirable alleles affecting different target traits from diverse donors as the information platform of BBD; and c) developing superior cultivars by BBD using designed QTL pyramiding or marker-assisted recurrent selection. Phase (a) has been implemented massively in rice by many Chinese research institutions and IRRI, resulting in the development of many new green super rice cultivars plus large numbers of ILs in 30 + elite genetic backgrounds. Phase (b) has been demonstrated in a series of proof-of-concept studies of high-efficiency genetic dissection of rice yield and tolerance to abiotic stresses using ILs and DNA markers. Phase (c) has also been implemented by designed QTL pyramiding, resulting in a prototype of BBD in several successful cases. The BBSI strategy can be easily extended for simultaneous trait improvement, efficient gene and QTL discovery and allele mining of complex traits using advanced breeding lines from crosses between a common “backbone” parent and a set of elite parents in conventional pedigree breeding programs. BBSI can be relatively easily adopted by breeding programs with small budgets, but the BBSI-based BBD strategy can be fully and more efficiently implemented by large seed companies with sufficient capacity.

Appearance and cooked rice elongation are key quality traits of rice. Although some QTL for these traits have been identified, understanding of the genetic relationship between them remains limited. In the present study, large phenotypic variation was observed in 760 accessions from the 3K Rice Genomes Project for both appearance quality and cooked rice elongation. Most component traits of appearance quality and cooked rice elongation showed significant pairwise correlations, but a low correlation was found between appearance quality and cooked rice elongation. A genome-wide association study identified 74 QTL distributed on all 12 chromosomes for grain length, grain width, length to width ratio, degree of endosperm with chalkiness, rice elongation difference, and elongation index. Thirteen regions containing QTL stably expressed in multiple environments and/or exerting pleiotropic effects on multiple traits were detected. By gene-based association analysis and haplotype analysis, 46 candidate genes, including five cloned genes, and 49 favorable alleles were identified for these 13 QTL. The effect of the candidate gene Wx on rice elongation difference was validated by a transgenic strategy. These results shed light on the genetic bases of appearance quality and cooked rice elongation and provide gene resources for improving rice quality by molecular breeding.

The source-sink relationship determines the ultimate grain yield. We investigated the genetic basis of the relationship between source and sink and yield potential in rice. In two environments, we identified quantitative trait loci (QTL) associated with sink capacity (total spikelet number per panicle and thousand-grain weight), source leaf (flag leaf length, flag leaf width and flag leaf area), source-sink relationship (total spikelet number to flag leaf area ratio) and yield-related traits (filled grain number per panicle, panicle number per plant, grain yield per plant, biomass per plant, and harvest index) by genome-wide association analysis using 272 Xian (indica) accessions. The panel showed substantial variation for all traits in the two environments and revealed complex phenotypic correlations. A total of 70 QTL influencing the 11 traits were identified using 469,377 high-quality SNP markers. Five QTL were detected consistently in four chromosomal regions in both environments. Five QTL clusters simultaneously affected source, sink, source–sink relationship, and grain yield traits, probably explaining the genetic basis of significant correlations of grain yield with source and sink traits. We selected 24 candidate genes in the four consistent QTL regions by identifying linkage disequilibrium (LD) blocks associated with significant SNPs and performing haplotype analysis. The genes included one cloned gene (NOG1) and three newly identified QTL (qHI6, qTGW7, and qFLA8). These results provide a theoretical basis for high-yield rice breeding by increasing and balancing source–sink relationships using marker-assisted selection.

QTLs for quantitative traits are influenced by genetic background (GB) and environment. Identification of QTL with GB independency and environmental stability is prerequisite for effective marker-assisted selection (MAS). In this study, QTLs and QTL × environment interactions affecting grain yield per plant (GY) and its component traits, filled grain number per panicle (FGN), panicle number per plant (PN) and 1000-grain weight (TGW) across six environments were dissected using two sets of reciprocal introgression lines (ILs) derived from the cross Lemont × Teqing and SNP genotypic data. ANOVA indicated that the differences among genotypes and environments within each set of ILs were highly significant for all traits. A total of 72 distinct QTLs for GY and its component traits including 15 for GY, 25 for FGN, 18 for PN, and 29 for TGW were detected over the six environments. Most QTLs (87.4%) showed significant QTL × environment interactions (QEIs) and appeared to be more or less environment-specific. Among 72 QTLs, 15 (20.8%) QTLs and 12 (16.7%) QEIs were commonly identified in both backgrounds, indicating QTL especially QEI for yield and its component traits had strong GB effects. Four QTL regions affecting GY and its component traits, including S1269707–S4288071, S16661497–S17511092, and S35861863–S36341768 on chromosome 3, and S4134205–S7643153 on chromosome 5, were detected in both backgrounds and coincided with cloned genes for yield-related traits. These regions can be the targeted in rice breeding for high yield potential through MAS. Application of QTL main effects and their environmental interaction effects in MAS was discussed in detail.