Genome-Wide Association Analysis of Effective Tillers in Rice under Different Nitrogen Gradients.

Nitrogen is a crucial element that impacts rice yields, and effective tillering is a significant agronomic characteristic that can influence rice yields. The way that reduced nitrogen affects effective tillering is a complex quantitative trait that is controlled by multiple genes, and its genetic basis requires further exploration. In this study, 469 germplasm varieties were used for a genome-wide association analysis aiming to detect quantitative trait loci (QTL) associated with effective tillering at low (60 kg/hm2) and high (180 kg/hm2) nitrogen levels. QTLs detected over multiple years or under different treatments were scrutinized in this study, and candidate genes were identified through haplotype analysis and spatio-temporal expression patterns. A total of seven genes (NAL1, OsCKX9, Os01g0690800, Os02g0550300, Os02g0550700, Os04g0615700, and Os04g06163000) were pinpointed in these QTL regions, and were considered the most likely candidate genes. These results provide favorable information for the use of auxiliary marker selection in controlling effective tillering in rice for improved yields.

Large Vascular Bundle Phloem Area 4 enhances grain yield and quality in rice via source-sink-flow.

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.

Genetic background- and environment-independent QTL and candidate gene identification of appearance quality in three MAGIC populations of rice.

Many QTL have been identified for grain appearance quality by linkage analysis (LA) in bi-parental mapping populations and by genome-wide association study (GWAS) in natural populations in rice. However, few of the well characterized genes/QTL have been successfully applied in molecular rice breeding due to genetic background (GB) and environment effects on QTL expression and deficiency of favorable alleles. In this study, GWAS and LA were performed to identify QTL for five grain appearance quality-related traits using three multi-parent advanced generation inter-cross (MAGIC) populations. A total of 22 QTL on chromosomes 1-3, 5-8 were identified by GWAS for five traits in DC1, DC2 and 8way, and four combined populations DC12 (DC1+DC2), DC18 (DC1+8way), DC28 (DC2+8way) and DC128 (DC1+DC2+8way). And a total of 42 QTL were identified on all 12 chromosomes except 10 by LA in the three single populations. Among 20 QTL identified by GWAS in DC1, DC2 and 8way, 10, four and three QTL were commonly detected in DC18, DC28, and DC128, respectively. Similarly, among 42 QTL detected by LA in the three populations, four, one and two QTL were commonly detected in DC18, DC28, and DC128, respectively. There was no QTL mapped together in DC12 by both two mapping methods, indicating that GB could greatly affect the mapping results, and it was easier to map the common QTL among populations with similar GB. The 8way population was more powerful for QTL mapping than the DC1, DC2 and various combined populations. Compared with GWAS, LA can not only identify large-effect QTL, but also identify minor-effect ones. Among 11 QTL simultaneously detected by the two methods in different GBs and environments, eight QTL corresponded to known genes, including AqGL3b and AqGLWR3a for GL and GLWR, AqGW5a, AqGLWR5, AqDEC5 and AqPGWC5 for GW, GLWR, DEC and PGWC, and AqDEC6b and AqPGWC6b for DEC and PGWC, respectively. AqGL7, AqGL3c/AqGLWR3b, AqDEC6a/AqPGWC6a, and AqPGWC7 were newly identified and their candidate genes were analyzed and inferred. It was discussed to further improve grain appearance quality through designed QTL pyramiding strategy based on the stable QTL identified in the MAGIC populations.

Identify QTLs and candidate genes underlying source-, sink-, and grain yield-related traits in rice by integrated analysis of bi-parental and natural populations.

The source-sink relationship determines the ultimate grain yield of rice. In this study, we used a set of reciprocal introgression lines (ILs) derived from Xuishui09 × IR2061 to map quantitative trait loci (QTLs) that were associated with sink-, source-, and grain yield-related traits. A total of 95 QTLs influencing eight measured traits were identified using 6181 high-quality single nucleotide polymorphism markers. Nine background-independent QTLs were consistently detected in seven chromosomal regions in different genetic backgrounds. Seven QTLs clusters simultaneously affected sink-, source-, and grain yield-related traits, probably due to the genetic basis of significant correlations of grain yield with source and sink traits. We selected 15 candidate genes in the four QTLs consistently identified in the two populations by performing gene-based association and haplotype analyses using 2288 accessions from the 3K project. Among these, LOC_Os03g48970 for qTSN3b, LOC_Os06g04710 for qFLL6a, and LOC_Os07g32510 for qTGW7 were considered as the most likely candidate genes based on functional annotations. These results provide a basis for further study of candidate genes and for the development of high-yield rice varieties by balancing source-sink relationships using marker-assisted selection.

Pleiotropic Effect of GNP1 Underlying Grain Number per Panicle on Sink, Source and Flow in Rice.

Rice yield potential is largely determined by the balance among source capacity, sink strength, and flow fluency. Our previous study indicated that the gene GNP1 encoding gibberellin biosynthesis gene GA20ox1 affects grain number per panicle (GNP) in rice, thus resulting in increase of grain yield. To clarify GNP1 effect on sink, source and flow in regulating rice grain yield, we compared Lemont, a japonica (geng) cultivar, with its near-isogenic line (NIL-GNP1 TQ) in Lemont background with introgression of the allele at GNP1 from Teqing, a high-yielding indica (xian) cultivar. NIL-GNP1 TQ exhibited averagely 32.8% more GNP than Lemont with the compensation by reduced seed setting rate, panicle number and single-grain weight. However, NIL-GNP1 TQ still produced averagely 7.2% higher grain yield than Lemont in two years, mainly attributed to significantly more filled grain number per panicle, and greater vascular system contributing to photoassimilates transport to spikelets. The significantly decreased grain weight of superior spikelets (SS) in NIL-GNP1 TQ was ascribed to a significant decrease of grain size while the significantly decreased grain weight of inferior spikelets (IS) ascribed to both grain size and poor grain-filling as compared with Lemont. The low activities of key enzymes of carbon metabolism might account for the poor grain-filling in IS, which resulted in more unfilled grains or small grain bulk density in NIL-GNP1 TQ. In addition, low seed setting rate and grain weight of IS in NIL-GNP1 TQ might be partially resulted from significantly lower carbohydrate accumulation in culms and leaf sheath before heading compared with Lemont. Our results indicated that significantly increased GNP from introgression of GNP1 TQ into Lemont did not highly significantly improve grain yield of NIL-GNP1 TQ as expected, due primarily to significant low sink activities in IS and possible insufficient source supply which didn't fully meet the increased sink capacity. The results provided useful information for improving rice yield potential through reasonably introgressing or pyramiding the favorable alleles underlying source-related or panicle number traits by marker-assisted selection.

Genetic Bases of the Stomata-Related Traits Revealed by a Genome-Wide Association Analysis in Rice (Oryza sativa L.).

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 genome-wide 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.

Natural Sequence Variations and Combinations of GNP1 and NAL1 Determine the Grain Number per Panicle in Rice.

BACKGROUND: The grain number per panicle (GNP), which is one of three grain yield components, is an important trait for the genetic improvement of rice. Although the NAL1 and GNP1 genes regulating the rice GNP and grain yield have been cloned, their allelic diversity, functional differences in rice germplasms, and effects of their combination on GNP and grain yield remain unclear. RESULTS: Based on DNA sequences of these two genes in 198 cultivated rice (Oryza sativa) and 8-10 wild rice (Oryza rufipogon) germplasms, 16 and 14 haplotypes were identified for NAL1 and GNP1, respectively. The NAL1 gene had the strongest effects on GNP in indica (xian) and japonica (geng) subpopulations. In contrast, GNP1 had no significant effects in the geng subpopulation and was rare in the xian background, in which the superior GNP1 allele (GNP1-6) was detected in only 4.0% of the 198 germplasms. Compared with the transgenic lines with GNP1 or NAL1, the transgenic lines with both genes had a higher GNP (15.5%-25.4% and 11.6%-15.9% higher, respectively) and grain yield (5.7%-9.0% and 8.3%-12.3% higher, respectively) across 3 years. The two genes combined in the introgression lines in Lemont background resulted in especially favorable effects on the GNP. CONCLUSIONS: Our results indicated that the GNP1 and NAL1 exhibited obvious differentiation and their combinations can significantly increase the grain yield in geng rice cultivars. These observations provide insights into the molecular basis of the GNP and may be useful for rice breeding of high yield potential by pyramiding GNP1 and NAL1.

Genetic dissection of seedling vigour in a diverse panel from the 3,000 Rice (Oryza sativa L.) Genome Project.

Seedling vigour (SV) is important for direct seeding rice (Oryza sativa L.), especially in a paddy-direct seeding system, but the genetic mechanisms behind the related traits remain largely unknown. Here, we used 744 germplasms, having at least two subsets, for the detection of quantitative trait loci (QTLs) affecting the SV-related traits tiller number, plant height, and aboveground dry weight at three sampling stages, 27, 34, and 41 d after sowing. A joint map based on GAPIT and mrMLM produced a satisfying balance between type I and II errors. In total, 42 QTL regions, containing 18 (42.9%) previously reported overlapping QTL regions and 24 new ones, responsible for SV were detected throughout the genome. Four QTL regions, qSV1a, qSV3e, qSV4c, and qSV7c, were delimited and harboured quantitative trait nucleotides that are responsible for SV-related traits. Favourable haplotype mining for the candidate genes within these four regions, as well as the early SV gene OsGA20ox1, was performed, and the favourable haplotypes were presented with donors from the 3,000 Rice Genome Project. This work provides new information and materials for the future molecular breeding of direct seeding rice, especially in paddy-direct seeding cultivation systems.

QTL mapping and candidate gene analysis of peduncle vascular bundle related traits in rice by genome-wide association study.

BACKGROUND: The vascular bundle especially in the peduncle is one of crucial limiting factors of rice yield, and it determines how plants efficiently transport photosynthetic products, mineral nutrients and water from leaf and root to panicle. However, the genetic base of rice vascular bundle related traits in the peduncle still remains unknown. RESULTS: The 423 panel showed substantial natural variations of peduncle vascular bundle. In total, 48 quantitative trait loci/locus (QTL) affecting the eight traits were identified throughout the genome by applying a significance threshold of P < 1.0 × 10- 4. Combined determining linkage disequilibrium (LD) blocks associated with significant SNPs and haplotype analyses allowed us to shortlist six candidate genes for four important QTL regions affecting the peduncle vascular bundle traits, including one cloned gene (NAL1) and three newly identified QTL (qLVN6, qSVN7, and qSVA8.1). Further the most likely candidate genes for each important QTL were also discussed based on functional annotation. CONCLUSIONS: Genetic base on peduncle vascular bundle related traits in rice was systematically dissected, and most likely candidate genes of the known gene NAL1 and the three newly identified QTL (qLVN6, qSVN7, and qSVA8.1) were analyzed. The results provided valuable information for future functional characterization and rice breeding for high yield through optimizing transportation efficiency of photosynthetic products by marker-assisted selection.