Fine mapping of a quantitative trait locus for spikelet number per panicle in a new plant type rice and evaluation of a near-isogenic line for grain productivity

Genetic variation in ZmKW1 contributes to kernel weight and size in dent corn and popcorn

Kernel weight is a critical factor that essentially affects maize (Zea mays) yield. In natural inbred lines, popcorn kernels exhibit overtly smaller sizes compared to dent corn kernels, and kernel weight, which is controlled by multiple genetic loci, varies widely. Here, we characterized a major quantitative trait locus on chromosome 1, responsible for controlling kernel weight (qKW1) and size. The qKW1 locus encodes a protein containing a seven in absentia domain with E3 ubiquitin ligase activity, expressed prominently from the top to the middle region of the endosperm. The presence and function of qKW1 were confirmed through ZmKW1 gene editing, where the mutations in ZmKW1 within dent corn significantly increased kernel weight, consistent with alterations in kernel size, while overexpression of ZmKW1 had the opposite effect. ZmKW1 acts as a negative regulator of kernel weight and size by reducing both the number and size of the endosperm cells and impacting endosperm filling. Notably, the popcorn allele qKW1N and the dent corn allele qKW1D encode identical proteins; however, the differences in promoter activity arise due to the insertion of an Indel-1346 sequence in the qKW1N promoter, resulting in higher expression levels compared to qKW1D, thus contributing to the variation in kernel weight and size between popcorn and dent corn kernels. Linkage disequilibrium analysis of the 2.8 kb promoter region of ZmKW1 in a dataset comprising 111 maize association panels identified two distinct haplotypes. Our results provide insight into the mechanisms underlying kernel development and yield regulation in dent corn and popcorn, with a specific focus on the role of the ubiquitination system.

Identification of Genetic Factors Controlling the Formation of Multiple Flowers Per Node in Pepper (Capsicum spp.)

Flower production provides the foundation for crop yield and increased profits. Capsicum annuum is a pepper species with a sympodial shoot structure with solitary flowers. By contrast, C. chinense produces multiple flowers per node. C. annuum accounts for 80% of pepper production worldwide. The identification of C. chinense genes that control multiple flowers and their transfer into C. annuum may open the way to increasing fruit yield. In this study, we dissected the genetic factors were dissected controlling the multiple-flower-per-node trait in Capsicum. 85 recombinant inbred lines (RILs) between the contrasting C. annuum 'TF68' and C. chinense 'Habanero' accessions were phenotyped and genotyped. Quantitative Trait Loci (QTL) analysis identified four novel QTLs on chromosomes 1, 2, 7, and 11 that accounted for 65% of the total phenotypic variation. Genome-wide association study was also performed on a panel of 276 genotyped and phenotyped C. annuum accessions, which revealed 28 regions significantly associated with the multiple-flower trait, of which three overlapped the identified QTLs. Five candidate genes involved in the development of the shoot and flower meristems were identified and these genes could cause multiple flowers per node in pepper. These results contribute to our understanding of multiple flower formation in Capsicum and will be useful to develop high-yielding cultivars.

Next generation long-culm rice with superior lodging resistance and high grain yield, Monster Rice 1

During late 1960s Green Revolution, researchers utilized semidwarf 1 (sd1) to improve the yield and lodging resistance in rice (Oryza sativa L.). However, sd1 has a negative effect to culm strength and biomass production. To increase yield dramatically in 21^th century, development of next generation long-culm rice for non-lodging and high grain yield independent of sd1 has been needed. The present study developed Monster Rice 1, a long-culm and heavy-panicle type of rice line and compared it with Takanari, a high-yielding semidwarf rice variety about yield and lodging resistance associated traits. Brown rice yield and bending moment at breaking of the basal elongated internode were higher in Monster Rice 1 than those in Takanari due to a large number of spikelets per panicle and thicker culm. Furthermore, to identify QTLs with superior alleles for these traits, QTL and haplotype analyses were performed using F₂ population and recombinant inbred lines derived from a cross between Monster Rice 1 and Takanari. The results from this study suggest that long-culm and heavy-panicle type of rice with a superior lodging resistance by culm strength can perform its high yield potential by using these identified QTLs contributing yield and lodging resistance.

Detection of QTL for panicle architecture in F2 population of rice

Panicle traits are the most important agronomic characters which directly relate to yield in rice. Panicle length (PL) being one of the major components of rice panicle structure is controlled by quantitative trait loci (QTLs). In our research, conducted at Research Farm of SKUAST-J, crosses of parental lines K343 and DHMAS were made for generating F₂ mapping population, which were then transplanted into the field using augmented design-I. The F₂ population was used for phenotypic evaluation, development of linkage map and identification of QTLs on the chromosomes by using SSR markers. A total of 450 SSR markers were used for screening both the parents of which 53 highly polymorphic markers were selected and used for genotyping of 233 genotypes of F₂population. Linkage map was generated using MAPMAKER/EXP3.0 software, seven linkage groups were found distributed on 11 chromosomes of rice. QTLs were detected using QTL Cartographer (v2.5) software. Based on 1000 permutation tests, a logarithm of odds (LOD) threshold value 2.0 and 3.0 was set. Composite interval mapping was used to map QTLs in populations derived from bi-parental crosses. The phenotypic data, genotypic data and the genetic linkage map generated identified total three QTLs of which one was identified for PL qPL2, located at 85.01 cM position with 2.1 LOD value and in between the marker intervals RM324-RM208, this QTL explained the phenotype variation by 4.36%. The other two QTLs were identified for spikelet density (SD) qSD3.1 and qSD3.2, located at 28.91 and 39.51 cM, respectively, both with a flanking marker RM6832 on chromosome 3. The LOD value and phenotypic variation explained for qSD3.1 and qSD3.2 was 3.00 and 3.25; 9.70 and 12.34% respectively. The reported QTLs identified in the study suggested a less diversity in the parents used and also the rejection of not so useful markers from the used set of markers for PL and SD.

The different influences of drought stress at the flowering stage on rice physiological traits, grain yield, and quality

Seasonal drought is a major threat to rice production. However, the sensitivity of rice to drought stress (DS) at different growth periods remains unclear. The objective of this study was to reveal the different impacts of DS at the flowering stage on rice physiological traits, grain yield, and quality. Field experiments were conducted with two rice cultivars, Yangliangyou 6 (YLY6) and Hanyou 113 (HY113) under two water treatments (traditional flooding (CK) and DS at flowering stage) in 2013 and 2014. Compared with CK, grain yield (GY) under DS was significantly reduced by 23.2% for YLY6 and 24.0% for HY113 while instantaneous water use efficiency (IWUE) was significantly increased by 39% for YLY6 and 37% for HY113, respectively. All physiological traits were significantly decreased under DS and physiological activities did not revert to normal levels at grain filling stage. There was no significant effect on the appearance and nutritional quality except for the significant increase in chalky kernel and chalkiness under DS. Our data suggest that drought stress at flowering stage has a strong influence on rice physiological traits and yield. Stronger recovery capability contributes to maintaining relatively high grain production, which could be a great target for the breeder in developing drought-tolerant rice cultivars.

A novel Rice QTL qOPW11 Associated with Panicle Weight Affects Panicle and Plant Architecture

BACKGROUND

The improvement of rice yield is a crucial global issue, but evaluating yield requires substantial efforts. Rice yield comprises the following indices: panicle number (PN), grain number per panicle (GN), 1000-grain weight, and percentage of ripened grain. To simplify measurements, we analyzed one panicle weight (OPW) as a simplified yield index that integrates GN, grain weight, and percentage of ripened grain, and verified its suitability as a proxy for GN and grain weight in particular.

RESULTS

Quantitative trait locus (QTL) analysis using 190 recombinant inbred lines derived from Koshihikari (large panicle and small grain) and Yamadanishiki (small panicle and large grain), japonica cultivars detected three QTLs on chromosomes 5 (qOPW5), 7 (qOPW7) and 11 (qOPW11). Of these, qOPW5 and qOPW11 were detected over two years. qOPW5 and qOPW7 increased OPW, and qOPW11 decreased it at Yamadanishiki alleles. A chromosome segment substitution line (CSSL) with a genomic segment from Yamadanishiki substituted in the Koshihikari genetic background harboring qOPW5 increased grain weight. qOPW11 had the largest genetic effect of QTLs, which was validated using a CSSL. Substitution mapping using four CSSLs revealed that qOPW11 was located in the range of 1.46 Mb on chromosome 11. The CSSL harboring qOPW11 decreased primary and secondary branch numbers, culm length, and panicle length, and increased PN.

CONCLUSIONS

In this study, three QTLs associated with OPW were detected. The CSSL with the novel and largest QTL, qOPW11, differed in some traits associated with both panicle and plant architecture, indicating different functions for the meristem in the vegetative versus the reproductive stages. qOPW5 coincided with an identified QTL for grain width and grain weight, suggesting that qOPW5 was affected by rice grain size. OPW can be considered a useful trait for efficient detection of QTLs associated with rice yield.