The DTH8-SD1 pair regulates panicle architecture in rice.

Rice panicle architecture displays remarkable diversity in branch number, branch length, and grain arrangement; however, much remains unknown about how such diversity in patterns is generated. Although several genes related to panicle branch number and panicle length have been identified, how panicle branch number and panicle length are coordinately regulated is unclear. Here, we show that panicle length and panicle branch number are independently regulated by the genes Prl5/OsGA20ox4, Pbl6/APO1, and Gn1a/OsCKX2. We produced near-isogenic lines (NILs) in the Koshihikari genetic background harboring the elite alleles for Prl5, regulating panicle rachis length; Pbl6, regulating primary branch length; and Gn1a, regulating panicle branching in various combinations. A pyramiding line carrying Prl5, Pbl6, and Gn1a showed increased panicle length and branching without any trade-off relationship between branch length or number. We successfully produced various arrangement patterns of grains by changing the combination of alleles at these three loci. Improvement of panicle architecture raised yield without associated negative effects on yield-related traits except for panicle number. Three-dimensional (3D) analyses by X-ray computed tomography (CT) of panicles revealed that differences in panicle architecture affect grain filling. Importantly, we determined that Prl5 improves grain filling without affecting grain number.

The study was undertaken to identify the quantitative trait loci (QTLs) governing yield and its related traits using a recombinant inbred line (RIL) population derived from the popular rice hybrid, KRH-2 (IR58025A/KMR3R). A genetic map spanning 294.2 cM was constructed with 126 simple sequence repeats (SSR) loci uniformly distributed across the rice genome. QTL analysis using phenotyping and genotyping information identified a total of 22 QTLs. Of these, five major effect QTLs were identified for the following traits: total grain yield/plant (qYLD3-1), panicle weight (qPW3-1), plant height (qPH12-1), flag leaf width (qFLW4-1) and panicle length (qPL3-1), explaining 20.23–22.76% of the phenotypic variance with LOD scores range of 6.5–10.59. Few genomic regions controlling several traits (QTL hotspot) were identified on chromosome 3 for total grain yield/plant (qYLD3-1) and panicle length (qPL3-1). Significant epistatic interactions were also observed for total grain yield per plant (YLD) and panicle length (PL). While most of these QTLs were observed to be co-localized with the previously reported QTL regions, a novel, major QTL associated with panicle length (qPL3-1) was also identified. SNP genotyping of selected high and low yielding RILs and their QTL mapping with 1,082 SNPs validated most of the QTLs identified through SSR genotyping. This facilitated the identification of novel major effect QTLs with much better resolution and precision. In-silico analysis of novel QTLs revealed the biological functions of the putative candidate gene (s) associated with selected traits. Most of the high-yielding RILs possessing the major yield related QTLs were identified to be complete restorers, indicating their possible utilization in development of superior rice hybrids.

Panicle exsertion (PE) is an important morphological trait that is closely associated with spikelet fertility and grain yield. To understand the genetic basis of PE and its relationships with yield and yield-related traits, a recombinant inbred population consisting of 240 lines derived from a cross between an Indica cultivar 'Kasalath' and a Japonica germplasm 'TD70', was studied over two years. PE was significantly correlated with plant height, heading date (HD), panicle length (PL), and panicle characteristics such as primary branch number, spikelet number per panicle, and spikelet density, but showed poor correlation with yield components. Based on linkage mapping of 141 SSR markers, a total of 38 quantitative trait loci (QTLs) were located for 12 investigated traits, with the contribution varying from 6.51 to 8.61%. Among these, four QTL clusters were identified on chromosomes 1, 2, 3, and 6, suggesting the existence of pleiotropic alleles. In some intervals, two loci for PE were collocated with several traits, which is consistent with the correlations observed with phenotypic variations. The PE QTLs with 'Kasalath' alleles and without pleiotropic effects would be valuable for the improvement of PE in 'TD70' and in other rice varieties.

Rice (Oryza sativa L.) is the staple food crop and it is cultivated under diverse ecological conditions. Understanding genetic effects and identifying superior hybrids are important steps in rice breeding to enhance yield. Six parents with varying numbers of grains per panicle were subjected to full diallel mating. Hayman’s graphical approach was applied to compute various genetic parameters and better parental, mid parental and standard heterosis for panicle and yield related traits. The results revealed that, grain yield per plant and panicle related traits were governed by both additive and non-additive effects but the additive component was predominant for the number of filled grains per panicle and non-additive for grain yield per plant. The number of spikelets per panicle, the number of filled grains per panicle and the number of primary branches per panicle were mainly governed by recessive alleles present in the parents. The overdominance effect was found in the number of productive tillers, flag leaf area, panicle length, the number of primary branches per panicle, length of primary branches, thousand grain weight and grain yield per plant. The Vr-Wr graph depicted that the number of spikelets per panicle, the number of filled grains per panicle and the number of secondary branches per panicle were governed by partial dominance and grain yield per plant had over dominance effect and the low grain number parents, IET 28835 and ADT (R) 48 possessed more number of dominant alleles than other parents for all these traits. The promising cross combination BPT 5204 × CB12132 and its reciprocal hybrid exhibited high mid parental and standard heterosis for important panicle traits were adjudged as promising hybrids

Oryza longistaminata (AA genome) is a wild rice species that is phenotypically inferior to cultivated rice but possesses useful alleles that can be used to improve agronomically important traits. Interspecific hybrids that are derived from cultivated rice and wild rice species with AA genome are important contributors of genetic diversity in rice. To illustrate the potential of wild rice relatives as a source of novel alleles for rice improvement, a total of 40 chromosome segment substitution lines (CSSLs) of O. longistaminata in the background of the elite japonica cultivar Taichung 65 were developed and evaluated for yield and various yield-related traits. A number of CSSLs carrying putative quantitative trait loci (QTLs) controlling different yield-related traits were identified during both dry and wet seasons. In particular, 10 major putative QTLs controlling early heading date, plant height, tiller number, panicle length, number of primary branches per panicle, grain number per panicle, grain width, and grain thickness were identified. Interestingly, one of the CSSL lines, LTSL26, with major putative QTLs on chromosomes 1 and 8 that increase grain number per panicle, showed pleiotropic effects on other traits such as plant height, days to flowering, tiller number, number of branches per panicle, and grain length. These results suggest that O. longistaminata is a good source of new alleles that can be used to improve yield-related traits in cultivated rice varieties.

BackgroundNumerous quantitative trait loci (QTLs) and candidate genes associated with yield-related traits have been identified in barley by genome-wide association study (GWAS) analysis. However, genetic bottlenecks in elite cultivars have reduced diversity, limiting further yield improvements. Grain yield is a complex, polygenic trait shaped by genetic and environmental factors, necessitating integrative breeding approaches. While genomic selection, marker-assisted selection, and GWAS have identified key loci for yield-related traits, functional validation remains a significant challenge.ResultsA total of 346 QTLs for seven barley yield-related traits were identified in GWAS, including 93 stable QTLs across multiple environments. Two major-effect QTLs for spike length and thousand kernel weight, along with several moderate-effect QTLs, show potential for breeding. Candidate gene analysis revealed 134 highly expressed genes linked to stress response, transport, and metabolism. Notable genes include HORVU.MOREX.r3.5HG0514790 (growth and stress adaptation) and HORVU.MOREX.r3.2HG0212810 (seed storage protein). A total of eight presumably novel QTLs were identified. One of the novel QTLs, Hv_TKW_3H.5, had the strongest effect on total barley grain yield. The integration of favorable alleles from eight moderate- and major-effect QTLs significantly influenced the weight of kernels per spike.ConclusionsThis study aimed to identify and characterize QTLs associated with barley yield-related traits through GWAS. Integrating genomic and transcriptomic methods suggests a promising strategy for genomic selection and marker-assisted breeding to enhance barley grain yield.

BackgroundMany large-effect quantitative trait loci (QTLs) for yield and disease resistance related traits have been identified in different mapping populations of peanut (Arachis hypogaea L.) under multiple environments. However, only a limited number of QTLs have been used in marker-assisted selection (MAS) because of unfavorable epistatic interactions between QTLs in different genetic backgrounds. Thus, it is essential to identify consensus QTLs across different environments and genetic backgrounds for use in MAS. Here, we used QTL meta-analysis to identify a set of consensus QTLs for yield and disease resistance related traits in peanut.ResultsA new integrated consensus genetic map with 5874 loci was constructed. The map comprised 20 linkage groups (LGs) and was up to a total length of 2918.62 cM with average marker density of 2.01 loci per centimorgan (cM). A total of 292 initial QTLs were projected on the new consensus map, and 40 meta-QTLs (MQTLs) for yield and disease resistance related traits were detected on four LGs. The genetic intervals of these consensus MQTLs varied from 0.20 cM to 7.4 cM, which is narrower than the genetic intervals of the initial QTLs, meaning they may be suitable for use in MAS. Importantly, a region of the map that previously co-localized multiple major QTLs for pod traits was narrowed from 3.7 cM to 0.7 cM using an overlap region of four MQTLs for yield related traits on LG A05, which corresponds to a physical region of about 630.3 kb on the A05 pseudomolecule of peanut, including 38 annotated candidate genes (54 transcripts) related to catalytic activity and metabolic process. Additionally, one major MQTL for late leaf spot (LLS) was identified in a region of about 0.38 cM. BLAST searches identified 26 candidate genes (30 different transcripts) in this region, some of which were annotated as related to regulation of disease resistance in different plant species.ConclusionsCombined with the high-density marker consensus map, all the detected MQTLs could be useful in MAS. The biological functions of the 64 candidate genes should be validated to unravel the molecular mechanisms of yield and disease resistance in peanut.

BackgroundYield-related traits including thousand grain weight (TGW), grain number per spike (GNS), grain width (GW), grain length (GL), plant height (PH), spike length (SL), and spikelet number per spike (SNS) are greatly associated with grain yield of wheat (Triticum aestivum L.). To detect quantitative trait loci (QTL) associated with them, 193 recombinant inbred lines derived from two elite winter wheat varieties Chuanmai42 and Chuanmai39 were employed to perform QTL mapping in six/eight environments.ResultsA total of 30 QTLs on chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 4A, 5A, 5B, 6A, 6D, 7A, 7B and 7D were identified. Among them, six major QTLs QTgw.cib-6A.1, QTgw.cib-6A.2, QGw.cib-6A, QGl.cib-3A, QGl.cib-6A, and QSl.cib-2D explaining 5.96-23.75% of the phenotypic variance were detected in multi-environments and showed strong and stable effects on corresponding traits. Three QTL clusters on chromosomes 2D and 6A containing 10 QTLs were also detected, which showed significant pleiotropic effects on multiple traits. Additionally, three Kompetitive Allele Specific PCR (KASP) markers linked with five of these major QTLs were developed. Candidate genes of QTgw.cib-6A.1/QGl.cib-6A and QGl.cib-3A were analyzed based on the spatiotemporal expression patterns, gene annotation, and orthologous search.ConclusionsSix major QTLs for TGW, GL, GW and SL were detected. Three KASP markers linked with five of these major QTLs were developed. These QTLs and KASP markers will be useful for elucidating the genetic architecture of grain yield and developing new wheat varieties with high and stable yield in wheat.

Ratoon rice plays a crucial role in sustainable rice production due to its potential for additional harvests; however, the genetic basis of its yield remains to be explored. In this study, we aimed to precisely dissect the genetic basis of yield in ratoon rice by selecting 302 eight-way MAGIC lines that achieved synchronized heading within a 10-day period through staggered sowing. The eight parental lines exhibited distinct yield performances across both main and ratoon crops. Significant correlations were observed between the main and ratoon crops concerning panicle length (R = 0.67) and spikelets per panicle (R = 0.36). Genome-wide association studies (GWAS) revealed a total of 17 quantitative trait loci (QTLs) associated with five yield-related traits in both main and ratoon crops. Specifically, seven QTLs were detected for yield components in the main crop, while six QTLs were identified in the ratoon crop, in addition to five QTLs associated with ratooning ability. Notably, only one QTL, qPL1, was commonly detected in both crops, exhibiting opposite effects on tiller number across crop types. Among the QTLs specifically identified in the ratoon crop, qGY10 demonstrated the largest effect on ratoon grain yield without compromising the performance of the main crop. The known gene, Ghd7.1, exhibited pleiotropic effects on both ratooning ability and ratoon grain yield. Candidate gene analysis prioritized likely causal genes and defined key haplotypes within these QTL intervals by leveraging the genomic diversity of the eight founders. These findings underscore the distinct genetic determinants for yields in main and ratoon crops, providing a genetic basis for breeding high-yielding varieties in both crop types.

BackgroundThousand grain weight is a key component of grain yield in rice, and a trait closely related to grain length (GL) and grain width (GW) that are important traits for grain quality. Causal genes for 16 quantitative trait loci (QTL) affecting these traits have been cloned, but more QTL remain to be characterized for establishing a genetic regulating network. A QTL controlling grain size in rice, qGS10, was previously mapped in the interval RM6100–RM228 on chromosome 10. This study aimed to delimitate this QTL to a more precise location.MethodA total of 12 populations were used. The ZC9 population comprised 203 S1:2 families derived from a residual heterozygous (RH) plant in the F9 generation of the indica rice cross Teqing (TQ)/IRBB52, segregating the upper region of RM6100–RM228 and three more regions on chromosomes 1, 9, and 11. The Ti52-1 population comprised 171 S1 plants derived from one RH plant in F7 of TQ/IRBB52, segregating a single interval that was in the lower portion of RM6100–RM228. The other ten populations were all derived from Ti52-1, including five S1 populations with sequential segregating regions covering the target region and five near isogenic line (NIL) populations maintaining the same segregating pattern. QTL analysis for 1,000-grain weight, GL, and GW was performed using QTL IciMapping and SAS procedure GLM.ResultThree QTL were separated in the original qGS10 region. The qGL10.1 was located in the upper region RM6704–RM3773, shown to affect GL only. The qGS10.1 was located within a 207.1-kb interval flanked by InDel markers Te20811 and Te21018, having a stable and relatively high effect on all the three traits analyzed. The qGS10.2 was located within a 1.2-Mb interval flanked by simple sequence repeat markers RM3123 and RM6673. This QTL also affected all the three traits but the effect was inconsistent across different experiments. QTL for grain size were also detected in all the other three segregating regions.ConclusionThree QTL for grain size that were tightly linked on the long arm of chromosome 10 of rice were separated using NIL populations with sequential segregating regions. One of them, qGS10.1, had a stable and relatively high effect on grain weight, GL, and GW, providing a good candidate for gene cloning. Another QTL, qGS10.2, had a significant effect on all the three traits but the effect was inconsistent across different experiments, providing an example of genotype-by-environmental interaction.

In order to identify quantitative trait loci (QTLs) controlling agronomic trait variation and their consistency under Mediterranean conditions in barley, a progeny of 167 recombinant inbred lines (RILs) and the parents Tadmor and Er/Apm, originating from the Mediterranean basin, were grown under Mediterranean conditions in 1995, 1996, 1997 and 1999. For the 2 first years (M95 and G96), one replicate was grown, but for the latter (M97 and M99) two rainfed (rain) and two irrigated (ir) replicates were produced. M95, G96, M97rain, M97ir, M99rain and M99ir were considered as six different environments and were compared in terms of their meteorological conditions and water supply. Grain yield and yield components were assessed, as well as heading date and plant height. Highly significant differences were noted between environments. QTLs were obtained from each environment separately and from a multiple environment analysis (simple interval mapping and simplified composite interval mapping). Despite heterogeneity between environments, numerous QTLs were common to several environments. This was particularly true for traits like plant height and thousand-grain weight. The most reliable QTLs which explained the largest part of the phenotypic variation were obtained for plant height on chromosomes 3 (3H) and 6 (6H). The multiple-environment analysis provided an opportunity to identify consistent QTLs for agronomic traits over six Mediterranean environments. A total of 24 consistent QTLs were detected. Out of these, 11 presented main effects, seven presented QTL×E interaction, and six presented both effects. In addition, 18 of the consistent QTLs were common to other published work and six seemed specific to this study. These latter QTLs could be involved in Mediterranean adaptive specificities or could be specific to the studied genetic background. Finally, when the rainfed and the irrigated environments of M97 were considered separately, a total of 16 QTLs presenting main effects over the two water conditions were identified, whereas five QTLs seemed dependent on the water conditions.

Grain number per panicle is a major component of rice yield that is typically controlled by many quantitative trait loci (QTLs). The identification of genes controlling grain number per panicle in rice would be valuable for the breeding of high-yielding rice. The Oryza glaberrima chromosome segment substitution line 9IL188 had significantly smaller panicles compared with the recurrent parent 9311. QTL analysis in an F2 population derived from a cross between 9IL188 and 9311 revealed that qgnp7(t), a major QTL located on the short arm of chromosome 7, was responsible for this phenotypic variation. Fine mapping was conducted using a large F3 population containing 2250 individuals that were derived from the F2 heterozygous plants. Additionally, plant height, panicle length, and grain number per panicle of the key F4 recombinant families were examined. Through two-step substitution mapping, qgnp7(t) was finally localized to a 41 kb interval in which eight annotated genes were identified according to available sequence annotation databases. Phenotypic evaluation of near isogenic lines (NIL-qgnp7 and NIL-qGNP7) indicated that qgnp7(t) has pleiotropic effects on rice plant architecture and panicle structure. In addition, yield estimation of NILs indicated that qGNP7(t) derived from 9311 is the favorable allele. Our results provide a foundation for isolating qgnp7(t). Markers flanking this QTL will be a useful tool for the marker-assisted selection of favorable alleles in O. glaberrima improvement programs.

BackgroundMost agronomical traits of crop species are complex traits controlled by multiple genes and affected by environmental factors. While considerable efforts have been made to fine-map and clone major quantitative trait loci (QTLs) for yield-related traits in rice, it is not until recently that the attention has been paid to minor QTLs. Following previous dissection of QTLs for grain weight and grain size in a 12-Mb interval on the long arm of chromosome 1 in rice, this study targeted at one putative QTL region for a more precise mapping and for analyzing the genotype-by-environment interaction of minor QTLs.ResultsFour BC2F10 plants of the indica rice cross ZS97///ZS97//ZS97/MY46 were selected. They carried overlapped heterozygous segments that jointly covered the entire putative region for qTGW1.1 detected previously. Four sets of near isogenic lines (NILs) were developed from selfing progenies of the four plants. Each NIL set consisted of 32 ZS97 homozygous lines and 32 MY46 homozygous lines that differed in the corresponding heterozygous region. They were grown in two locations having distinct ecological conditions and measured for 1000-grain weight, grain length and grain width. Two QTLs were separated in an 835.2-kb interval flanked by DNA markers Wn28447 and RM11569. They both showed consistent effects across the two environments. The qTGW1.1a located within the 120.4-kb interval Wn28447 − RM11543 significantly affect all the three traits with the enhancing allele derived from ZS97, showing a stronger influence on grain weight than on grain length and width. The qTGW1.1b located in the 521.8-kb interval RM11554 − RM11569 significantly affect grain weight and length with the enhancing allele derived from MY46, having a stronger influence on grain length than on grain weight. Consistent performance of the two QTLs was confirmed in a validation experiment using five NIL-F2 populations segregated for either qTGW1.1a or qTGW1.1b.ConclusionSeparation of closely-linked QTLs having small effects is achievable in the absence of major-QTL segregation. Minor QTLs for complex traits could act consistently in diverse environments, offering the potential of pyramiding beneficial alleles of multiple minor QTLs through marker-assisted selection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0410-5) contains supplementary material, which is available to authorized users.

Context Broadening the gene pool of staple food crops by incorporating desirable genes from wild/relative species is way to achieve higher genetic gains. The African rice species Oryza glaberrima Steud. is a source of genes for genetic improvement of Oryza sativa L. to various biotic and abiotic stresses. Aim The study was undertaken to identify genomic regions associated with yield and yield-related traits in interspecific backcross mapping populations derived from variety IR64 (O. sativa indica) × O. glaberrima. Methods The BC1F2 and BC1F2:3 populations were phenotyped for days to 50% flowering, plant height, number of tillers, number of productive tillers, panicle length, spikelet fertility and grain yield under irrigated conditions. The BC1F2 population was genotyped with 99 genome-wide polymorphic SSR markers. Quantitative trait loci (QTLs) associated with yield-enhancing traits were identified through single marker analysis. Key results Significant genetic variability was observed with moderate to high heritability in mapping populations. Genotyping revealed the presence of segregation distortion and skewed genome composition. Single marker analysis identified 20 QTLs linked to the studied traits. A QTL linked to grain yield, qGY-4.1, derived from O. glaberrima was found in both populations with high phenotypic variance. The chromosome region around marker RM510 was identified as a QTL hotspot and putative candidate genes were identified. Conclusions Our study demonstrates that O. glaberrima can serve as a valuable genetic resource for genetic improvement of yield-enhancing traits in O. sativa cultivars. Implications The identified QTLs regions can be employed in molecular breeding. The promising introgression lines identified can be used as pre-breeding lines in future breeding programs.

The upsurge in global temperatures and the increasing threat of salinity are adversely affecting rice productivity in the southern coastal lands of Bangladesh. A Bangladeshi rice landrace Horkuch, adapted to these saline conditions, has long been cultivated by farmers in this region. To understand the trend and heritability of its tolerance traits, a reciprocal population of Horkuch with the high-yielding sensitive rice IR29 was generated for mapping of quantitative trait loci (QTLs) at both seedling and reproductive stages in an F2:3 strategy using double-digest restriction associated DNA. We found 29 QTLs and some QTL clusters containing survival and yield-related QTLs showing pleiotropic effects over some phenotypes. We advanced the generation up to F5 to obtain homozygosity and our current work is aimed at confirming these mapped QTLs at F5 by salt stress physiological tests at both stages as well as DNA markers linked to these QTLs. We confirmed SES QTL (survival of seedling under stress) associated with SSR marker RM25789 and two QTL clusters at chromosome 10 and 11 associated with RM26964 and RM27027, respectively. One cluster harbored yield-related filled grain number, filled grain weight, total grain number, harvest index, ionic balance and transpiration-related total sodium, total potassium and stomatal conductance QTLs. The other cluster contained total grain weight, filled grain weight and seedling root relative water content QTLs. We identified some individuals based on their physiological performance under stress as well as the presence of QTLs associated with some SSR markers that can be used for breeding and production of highly salt-tolerant cultivars with high yield.