Stomatal density (D) and size (S) are an important adaptive mechanism for abiotic stress tolerance and photosynthesis capacity in rice. However, the genetic base of rice stomata-related traits still remains unclear. We identified quantitative trait loci (QTLs) associated with D and S on abaxial and adaxial leaf surfaces using genomewide association analysis with 451 diverse accessions in two environments. D and S showed significant differences between indica (xian) and japonica (geng) accessions and significantly negative phenotypic correlations. A total of 64 QTLs influencing eight stomata-related traits were identified using 2,936,762 high-quality single nucleotide polymorphism markers. Twelve QTLs were consistently detected for the same traits in nine chromosomal regions in both environments. In addition, 12 QTL clusters were simultaneously detected for the same stomata-related traits on abaxial and adaxial leaf surfaces in the same environment, probably explaining the genetic bases of significant correlations of the stomata-related traits. We screened 64 candidate genes for the nine consistent QTL regions using haplotype analysis. Among them, LOC_Os01g66120 for qD ada 1, OsSPCH2 (LOC_Os02g15760) for qD ada 2.1 and qD aba 2.1, LOC_Os02g34320 for qS ada 2.2, OsFLP (LOC_Os07g43420) or LOC_Os07g43530 for qS aba 7.1, and LOC_Os07g41200 for qW ada 7 and qW aba 7 were considered as the most likely candidate genes based on functional annotations. The results systematically dissected the genetic base of stomata-related traits and provide useful information for improving rice yield potential via increasing abiotic stress tolerance and photosynthesis capacity under stressed and non-stressed conditions through deploying the favorable alleles underlying stomata-related traits by marker-assisted selection.

In rice (Oryza sativa L.), vascular bundle phloem tissue in the panicle neck is vital for the transport of photosynthetic products from leaf to panicle and is positively associated with grain yield. However, genetic regulation of the single large vascular bundle phloem area (LVPA) in rice panicle neck tissue remains poorly understood. In this study, we carried out genome-wide association analysis of LVPA in the panicle neck using 386 rice accessions and isolated and characterized the gene LVPA4, which is allelic to NARROW LEAF1 (NAL1). Phenotypic analyses were carried out on the near-isogenic line (NIL) NIL-LVPA4LT in the high-yielding indica (xian) cultivar Teqing and on overexpression lines transformed with a vector carrying the Lemont alleles of LVPA4. Both NIL-LVPA4LT and LVPA4 overexpression lines exhibited significantly increased LVPA, enlarged flag leaf size, and improved panicle type. NIL-LVPA4LT had a 7.6–9.6% yield increase, mainly due to the significantly higher filled grain number per panicle, larger vascular system for transporting photoassimilates to spikelets, and more sufficient source supply that could service the increased sink capacity. Moreover, NIL-LVPA4LT had improved grain quality compared with Teqing, which was mainly attributed to substantial improvement in grain filling, especially for inferior spikelets in NIL-LVPA4LT. The single nucleotide variation in the third exon of LVPA4 was associated with LVPA, spikelet number, and leaf size throughout sequencing analysis in 386 panels. The results demonstrate that LVPA4 has synergistic effects on source capacity, sink size, and flow transport and plays crucial roles in rice productivity and grain quality, thus revealing the value of LVPA4 in rice breeding programs for improved varieties.

Grains at different positions on rice panicles differ greatly in weight and quality, but few studies have focused on the effect of dep1 that influences panicle morphology and grain number on grain weight and quality at different spikelet positions and under different backgrounds. To clarify this, we compared spikelet characteristics and grain quality in the superior spikelets (SS) and inferior spikelets (IS), as well as sink-, source-and flow-related traits between NIL-dep1 (AKI-dep1 and LG5-dep1) and NIL-DEP1 (AKI-DEP1 and LG5-DEP1) under the Akitakomachi (AKI) and Liaogeng5 (LG5) backgrounds. The grain weight and quality in SS did not significantly differ between NIL-dep1 and NIL-DEP1 under the two backgrounds. However, the effect of dep1 on grain weight and quality in IS varied with background. AKI-dep1 significantly decreased the grain weight, processing quality, and eating and cooking quality in IS compared with AKI-DEP1, possibly due to poor grain filling and the lack of an adequate source supply and vascular system to fully meet the increased sink size. With the exception of appearance quality, the grain weight and quality of IS of LG5-dep1 were similar to LG5-DEP1, mainly due to the enlarged vascular system and similar grain filling rate and source supply. We concluded that the varied effects of dep1 on grain weight and quality under the two backgrounds could be attributed to differences in grain filling and source−sink−flow relationships.

Rice yield is determined by source, sink, and flow and the coordination of these factors. However, the genetic base of the sink-flow is still unknown. We conducted on genome-wide association to detect quantitative trait loci (QTL) related to sink size (the number of rachis branches and spikelet number in rachis branches) and flow vascular bundle (large and small vascular bundles number in panicle neck and second internode) using 440 germplasm resources. The accessions exhibited marked variation in all traits and demonstrated complex phenotypic correlations. A total of 138 QTL affecting the 8 traits were detected using 3,188,500 high-quality single nucleotide polymorphism markers. Sixteen QTL clusters simultaneously affected flow and sink traits, which might explain the genetic base of significant correlations between flow and sink traits. The nine candidate genes in two consistent chromosomal regions simultaneously affecting multiple vascular bundle and sink size traits by performing gene-based association analysis and haplotype analysis. Among them, D2 (LOC_Os01g10040) and Gn1a (LOC_Os01g10110) for qPLVN1.1, qSLVN1.1, and qPRN1.2 and OsPIN5b (LOC_Os08g41720) for qPLVN8, qSLVN8, qSSVN8, and qSTSN8.2 were considered the most likely candidate genes based on functional annotations. The results provide useful information for improving rice yield potential via balancing sink–flow relationships by marker-assisted selection.