QTL mapping of grain weight in rice and the validation of the QTL qTGW3.2

Identification of Potential QTLs Related to Grain Size in Rice

Rice is a major crop, providing calories and food for most of the world's population. Currently, the global population is rapidly increasing, and securing a yield of rice that can satisfy everyone is an ongoing challenge. The yield of rice can be increased by controlling 1000-grain weight as one of the important determining factors. Grain length, grain width, grain thickness, and 1000-grain weight, which determine grain size, are controlled by QTLs. To identify QTLs related to grain size, we screened and then mapped 88 RIL individuals derived from a cross between JJ625LG, which has a long grain size, long spindle-shaped grains, and low 1000-grain weight, and Namchan, which has short grains with round shape and heavy 1000-grain weight. In 2021 and 2022, 511 SNP markers were used to map QTLs related to grain size to a physical map. The QTLs found to be related to grain size are evenly distributed on chromosomes 2, 3, 5, 10, and 11. The mapping results also show that the QTLs qGl3-2, qRlw3, and qRlw3-2 of chromosome 3, and qGt5 and qRlw5 of chromosome 5 are, respectively, associated with GS3 and qSW5, which are the major genes previously cloned and found to be related to grain size. In addition, qGw10 and qGw10-1, which were additionally detected in this study, were found to be associated with Os10g0525200 (OsCPq10), a potential candidate gene involved in controlling grain size. This gene codes for a cytochrome P450 family protein and is reported to have a positive effect on grain size by interacting with proteins related to mechanisms determining grain size. In particular, OsCPq10 was screened in the same identified QTL region for 2 consecutive years, which is expected to have a positive effect on grain size. These results will be helpful for breeding elite rice cultivars with high yields through additional fine mapping related to grain size.

A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.

Fine Mapping of qTGW7b, a Minor Effect QTL for Grain Weight in Rice (Oryza sativa L.)

Grain weight is a key trait that determines rice quality and yield, and it is primarily controlled by quantitative trait loci (QTL). Recently, attention has been paid to minor QTLs. A minor effect QTL qTGW7 that controls grain weight was previously identified in a set of chromosomal fragment substitution lines (CSSLs) derived from Nipponbare (NPB)/93-11. Compared to NPB, the single segment substitution line (SSSL) N83 carrying the qTGW7 introgression exhibited an increase in grain length and width and a 4.5% increase in grain weight. Meanwhile, N83 was backcrossed to NPB to create a separating population, qTGW7b, a QTL distinct from qTGW7, which was detected between markers G31 and G32. Twelve near-isogenic lines (NILs) from the BC₉F₃ population and progeny of five NILs from the BC₉F_3:4 population were genotyped and phenotyped, resulting in the fine mapping of the minor effect QTL qTGW7b to the approximately 86.2-kb region between markers G72 and G32. Further sequence comparisons and expression analysis confirmed that five genes, including Os07g39370, Os07g39430, Os07g39440, Os07g39450, and Os07g39480, were considered as the candidate genes underlying qTGW7b. These results provide a crucial foundation for further cloning of qTGW7b and molecular breeding design in rice.

Combined Analysis of BSA-Seq Based Mapping, RNA-Seq, and Metabolomic Unraveled Candidate Genes Associated with Panicle Grain Number in Rice (Oryza sativa L.)

Rice grain yield is a complex and highly variable quantitative trait consisting of several key components, including the grain weight, the effective panicles per unit area, and the grain number per panicle (GNPP). The GNPP is a significant contributor to grain yield controlled by multiple genes (QTL) and is crucial for improvement. Attempts have been made to find genes for this trait, which has always been a challenging and arduous task through conventional methods. We combined a BSA analysis, RNA profiling, and a metabolome analysis in the present study to identify new candidate genes involved in the GNPP. The F₂ population from crossing R4233 (high GNPP) and Ce679 (low GNPP) revealed a frequency distribution fitting two segregated genes. Three pools, including low, middle, and high GNPP, were constructed and a BSA analysis revealed six candidate regions spanning 5.38 Mb, containing 739 annotated genes. Further, a conjunctive analysis of BSA-Seq and RNA-Seq showed 31 differentially expressed genes (DEGs) in the candidate intervals. Subsequently, a metabolome analysis showed 1024 metabolites, with 71 significantly enriched, including 44 up and 27 downregulated in Ce679 vs. R4233. A KEGG enrichment analysis of these 31 DEGs and 71 differentially enriched metabolites (DEMs) showed two genes, Os12g0102100 and Os01g0580500, significantly enriched in the metabolic pathways’ biosynthesis of secondary metabolites, cysteine and methionine metabolism, and fatty acid biosynthesis. Os12g0102100, which encodes for the alcohol dehydrogenase superfamily and a zinc-containing protein, is a novel gene whose contribution to the GNPP is not yet elucidated. This gene coding for mitochondrial trans-2-enoyl-CoA reductase is involved in the biosynthesis of myristic acid, also known as tetradecanoic acid. The Os01g0580500 coding for the enzyme 1-aminoclopropane-1-carboxylate oxidase (OsACO7) is responsible for the final step of the ethylene biosynthesis pathway through the conversion of 1-aminocyclopropane-1-carboxylic acid (ACC) into ethylene. Unlike Os12g0102100, this gene was significantly upregulated in R4233, downregulated in Ce679, and significantly enriched in two of the three metabolite pathways. This result pointed out that these two genes are responsible for the difference in the GNPP in the two cultivars, which has never been identified. Further validation studies may disclose the physiological mechanisms through which they regulate the GNPP in rice.

Identifying Quantitative Trait Loci for Thousand Grain Weight in Eggplant by Genome Re-Sequencing Analysis

Eggplant (Solanum melongena L.; 2n = 24) is one of the most important Solanaceae vegetables and is primarily cultivated in China (approximately 42% of world production) and India (approximately 39%). Thousand-grain weight (TGW) is an important trait that affects eggplant breeding cost and variety promotion. This trait is controlled by quantitative trait loci (QTLs); however, no quantitative trait loci (QTL) has been reported for TGW in eggplant so far, and its potential genetic basis remain unclear. In this study, two eggplant lines, 17C01 (P1, wild resource, small seed) and 17C02 (P2, cultivar, large seed), were crossed to develop F1, F2 (308 lines), BC1P1 (44 lines), and BC1P2 (44 lines) populations for quantitative trait association analysis. The TGWs of P1, P2 and F1 were determined as 3.00, 3.98 and 3.77 g, respectively. The PG-ADI (polygene-controlled additive-dominance-epistasis) genetic model was identified as the optimal model for TGW and the polygene heritability value in the F2 generation was as high as 80.87%. A high-quality genetic linkage bin map was constructed with resequencing analysis. The map contained 3,918 recombination bins on 12 chromosomes, and the total length was 1,384.62 cM. A major QTL (named as TGW9.1) located on chromosome 9 was identified to be strongly associated with eggplant TGW, with a phenotypic variance explanation of 20.51%. A total of 45 annotated genes were identified in the genetic region of TGW9.1. Based on the annotation of Eggplant genome V3 and orthologous genes in Arabidopsis thaliana, one candidate gene SMEL_009g329850 (SmGTS1, encoding a putative ubiquitin ligase) contains 4 SNPs and 2 Indels consecutive intron mutations in the flank of the same exon in P1. SmGTS1 displayed significantly higher expression in P1 and was selected as a potential candidate gene controlling TGW in eggplant. The present results contribute to shed light on the genetic basis of the traits exploitable in future eggplant marker-assisted selection (MAS) breeding.

In Silico Analysis of Fatty Acid Desaturases Structures in Camelina sativa, and Functional Evaluation of Csafad7 and Csafad8 on Seed Oil Formation and Seed Morphology

Fatty acid desaturases add a second bond into a single bond of carbon atoms in fatty acid chains, resulting in an unsaturated bond between the two carbons. They are classified into soluble and membrane-bound desaturases, according to their structure, subcellular location, and function. The orthologous genes in Camelina sativa were identified and analyzed, and a total of 62 desaturase genes were identified. It was revealed that they had the common fatty acid desaturase domain, which has evolved separately, and the proteins of the same family also originated from the same ancestry. A mix of conserved, gained, or lost intron structure was obvious. Besides, conserved histidine motifs were found in each family, and transmembrane domains were exclusively revealed in the membrane-bound desaturases. The expression profile analysis of C. sativa desaturases revealed an increase in young leaves, seeds, and flowers. C. sativa ω3-fatty acid desaturases CsaFAD7 and CsaDAF8 were cloned and the subcellular localization analysis showed their location in the chloroplast. They were transferred into Arabidopsis thaliana to obtain transgenic lines. It was revealed that the ω3-fatty acid desaturase could increase the C18:3 level at the expense of C18:2, but decreases in oil content and seed weight, and wrinkled phenotypes were observed in transgenic CsaFAD7 lines, while no significant change was observed in transgenic CsaFAD8 lines in comparison to the wild-type. These findings gave insights into the characteristics of desaturase genes, which could provide an excellent basis for further investigation for C. sativa improvement, and overexpression of ω3-fatty acid desaturases in seeds could be useful in genetic engineering strategies, which are aimed at modifying the fatty acid composition of seed oil.

Identification of QTL TGW12 responsible for grain weight in rice based on recombinant inbred line population crossed by wild rice (Oryza minuta) introgression line K1561 and indica rice G1025

Background

Limited genetic resource in the cultivated rice may hinder further yield improvement. Some valuable genes that contribute to rice yield may be lost or lacked in the cultivated rice. Identification of the quantitative trait locus (QTL) for yield-related traits such as thousand-grain weight (TGW) from wild rice speices is desired for rice yield improvement.

Results

In this study, sixteen TGW QTL were identified from a recombinant inbred line (RIL) population derived from the cross between the introgression line K1561 of Oryza minuta and the rice cultivar G1025. TGW12, One of most effective QTL was mapped to the region of 204.12 kb between the marker 2,768,345 and marker 2,853,491 of the specific locus amplified fragment (SLAF). The origin of TGW12 was tested using three markers nearby or within the TGW12 region, but not clarified yet. Our data indicated thirty-two open reading fragments (ORFs) were present in the region. RT-PCR analysis and sequence alignment showed that the coding domain sequences of ORF12, one MADS-box gene, in G1025 and K1561 were different due to alternative slicing, which caused premature transcription termination. The MADS-box gene was considered as a candidate of TGW12.

Conclusion

The effective QTL, TGW12, was mapped to a segment of 204.12 kb using RILs population and a MADS-box gene was identified among several candidate genes in the segment. The region of TGW12 should be further narrowed and creation of transgenic lines will reveal the gene function. TGW12 could be applied for improvement of TGW in breeding program.

Genetic Mapping Identifies Consistent Quantitative Trait Loci for Yield Traits of Rice under Greenhouse Drought Conditions

Improving drought resistance in crops is imperative under the prevailing erratic rainfall patterns. Drought affects the growth and yield of most modern rice varieties. Recent breeding efforts aim to incorporate drought resistance traits in rice varieties that can be suitable under alternative irrigation schemes, such as in a (semi)aerobic system, as row (furrow-irrigated) rice. The identification of quantitative trait loci (QTLs) controlling grain yield, the most important trait with high selection efficiency, can lead to the identification of markers to facilitate marker-assisted breeding of drought-resistant rice. Here, we report grain yield QTLs under greenhouse drought using an F_2:3 population derived from Cocodrie (drought sensitive) × Nagina 22 (N22) (drought tolerant). Eight QTLs were identified for yield traits under drought. Grain yield QTL under drought on chromosome 1 (phenotypic variance explained (PVE) = 11.15%) co-localized with the only QTL for panicle number (PVE = 37.7%). The drought-tolerant parent N22 contributed the favorable alleles for all QTLs except qGN3.2 and qGN5.1 for grain number per panicle. Stress-responsive transcription factors, such as ethylene response factor, WD40 domain protein, zinc finger protein, and genes involved in lipid/sugar metabolism were linked to the QTLs, suggesting their possible role in drought tolerance mechanism of N22 in the background of Cocodrie, contributing to higher yield under drought.

Molecular insights into the regulation of rice kernel elongation

A large number of rice agronomic traits are complex, multi factorial and polygenic. As the mechanisms and genes determining grain size and yield are largely unknown, the identification of regulatory genes related to grain development remains a preeminent approach in rice genetic studies and breeding programs. Genes regulating cell proliferation and expansion in spikelet hulls and participating in endosperm development are the main controllers of rice kernel elongation and grain size. We review here and discuss recent findings on genes controlling rice grain size and the mechanisms, epialleles, epigenomic variation, and assessment of controlling genes using genome-editing tools relating to kernel elongation.

Updating the Genome of the Elite Rice Variety Kongyu131 to Expand Its Ecological Adaptation Region

As an elite rice variety cultivated in the third accumulative temperature belt in Heilongjiang province, China, Kongyu131 has many excellent traits, such as high quality, high stability, early maturation and cold resistance. However, as with other crop varieties, Kongyu131 has regional restrictions, exhibiting decreased yields when grown at low latitudes. To address these problems, two populations were constructed from cross between japonica and indica varieties. QTL analyses were performed with these two populations to detect regional adaptation related quantitative trait locus. Results in a BC₁F₆ backcross inbred line population with 168 lines derived from cross between Kongyu131 and GKMP showed a large pleiotropic QTL near 9 Mb on chromosome 7, which significantly delayed the HD of Kongyu131 and increased the plant height (PH), length of main panicle (LMP), number of primary branches (NPB) and grain number of main panicles (GNP). We also found a similar QTL in the population BC₃F₂ derived from Kongyu131 and GKLPL. Based on the QTL, we developed a gene module named mRA7 with 5 single-nucleotide polymorphism (SNP) markers around the QTL. Through a foreground and background selection based on 197 SNP markers evenly distributed over the 12 chromosomes, we obtained a new plant (a single point substitution line, SPSL) with a new Kongyu131 genome, carrying only a small chromosomal fragment less than 800 kb from GKLPL. The background recovery ratio of the SPSL was 99.8%. Compared with Kongyu131, the SPSL exhibited a significant HD delay of approximately 31 days and increased PH, LMP and GNP values when planted in Heilongjiang province. When cultivated in Guangdong province, HD of SPSL showed only 16 days delay, and less increase in PH, LMP and GNP than in Heilongjiang province. Phenotypic evaluation showed that the SPSL could be moved to south by more than 3 latitude units and cultivated in low-latitude regions. This study exemplifies the feasibility of expanding the regions of cultivation of elite rice varieties via similar methods.

Comparison of physicochemical properties and cooking edibility of waxy and non-waxy proso millet (Panicum miliaceum L.)

The quality characteristics of waxy and non-waxy proso millet (Panicum miliaceum L.) are different because of their varying amylose content. Physical appearance, pasting properties, cooking and edibility were investigated in five waxy and five non-waxy proso millet varieties. The results showed that the amylose content of proso millet flour was positively correlated with peak viscosity, trough viscosity, breakdown viscosity, final viscosity, setback viscosity, peak time, and pasting temperature. The porridge made with non-waxy proso millet was thicker as compared with that of made with waxy proso millet. Cooked non-waxy proso millet was hard whereas waxy proso millet was sticky. The non-waxy proso millet contained higher resistant starch and lower rapidly digestible starch than waxy proso millet. From this study, we can conclude that quality characteristics of waxy and non-waxy proso millet are different, and this may provide an insight in food processing and commercial production of proso millet.

Fine-mapping and identifying candidate genes conferring resistance to Soybean mosaic virus strain SC20 in soybean

KEY MESSAGE

The Mendelian gene conferring resistance to Soybean mosaic virus Strain SC20 in soybean was fine-mapped onto a 79-kb segment on Chr.13 where two closely linked candidate genes were identified and qRT-PCR verified. Soybean mosaic virus (SMV) threatens the world soybean production, particularly in China. A country-wide SMV strain system composed of 22 strains was established in China, among which SC20 is a dominant strain in five provinces in Southern China. Resistance to SC20 was evaluated in parents, F1, F2 and the F2:7 RIL (recombinant inbred line) population derived from a cross between Qihuang-1 (resistant) and NN1138-2 (susceptible). The segregation ratio of resistant to susceptible in the populations suggested a single dominant gene involved in the resistance to SC20 in Qihuang-1. A "partial genome mapping strategy" was used to map the resistance gene on Chromosome 13. Linkage analysis between 178 RILs and genetic markers showed that the SC20-resistance gene located at 3.9 and 3.8 cM to the flanking markers BARCSOYSSR_13_1099 and BARCSOYSSR_13_1185 on Chromosome 13. Subsequently, a residual heterozygote segregating population with 346 individuals was developed by selfing four plants heterozygous at markers adjacent to the tentative SC20-resistance gene; then, the candidate region was delimited to a genomic interval of approximately 79 kb flanked by the new markers gm-ssr_13-14 and gm-indel_13-3. Among the seven annotated candidate genes in this region, two genes, Glyma.13G194700 and Glyma.13G195100, encoding Toll Interleukin Receptor-nucleotide-binding-leucine-rich repeat resistance proteins were identified as candidate resistance genes by quantitative real-time polymerase chain reaction and sequence analysis. The two closely linked genes work together to cause the phenotypic segregation as a single Mendelian gene. These results will facilitate marker-assisted selection, gene cloning and breeding for the resistance to SC20.

Genetic Mapping of Quantitative Trait Loci for Grain Yield under Drought in Rice under Controlled Greenhouse Conditions

Drought stress is a constant threat to rice production worldwide. Most modern rice cultivars are sensitive to drought, and the effect is severe at the reproductive stage. Conventional breeding for drought resistant (DR) rice varieties is slow and limited due to the quantitative nature of the DR traits. Identification of genes (QTLs)/markers associated with DR traits is a prerequisite for marker-assisted breeding. Grain yield is the most important trait and to this end drought yield QTLs have been identified under field conditions. The present study reports identification of drought yield QTLs under controlled conditions without confounding effects of other factors prevalent under natural conditions. A linkage map covering 1,781.5 cM with an average resolution of 9.76 cM was constructed using an F₂ population from a cross between two Japonica cultivars, Cocodrie (drought sensitive) and Vandana (drought tolerant) with 213 markers distributed over 12 rice chromosomes. A subset of 59 markers (22 genic SSRs and 37 SNPs) derived from the transcriptome of the parents were also placed in the map. Single marker analysis using 187 F_{2 : 3} progeny identified 6 markers distributed on chromosomes 1, 5, and 8 to be associated with grain yield under drought (GYD). Composite interval mapping identified six genomic regions/quantitative trait loci (QTL) on chromosome 1, 5, 8, and 9 to be associated with GYD. QTLs located on chromosome 1 (qGYD1.2, qGYD1.3), chromosome 5 (qGYD5.1) and chromosome 8 (qGYD8.1) were contributed by Vandana alleles, whereas the QTLs, qGYD1.1 and qQYD9.1 were contributed by Cocodrie alelles. The additive positive phenotypic variance explained by the QTLs ranged from 30.0 to 34.0%. Candidate genes annotation within QTLs suggested the role of transcription factors and genes involved in osmotic potential regulation through catalytic/metabolic pathways in drought tolerance mechanism contributing to yield.

Molecular mapping and genetic analysis of a QTL controlling spike formation rate and tiller number in wheat

Spike formation rate (SR), which is based on maximum tiller number per unit area and spike number per unit area, is an important yield-related trait in wheat. Increasing the spike formation rate reduces growth competition and wastage of photosynthate from ineffective tillers. Unfortunately, research studies involving quantitative trait locus (QTL) mapping for wheat spike formation rate are limited. In the present study, a set of 371 recombinant inbreed line (RIL) population, which were derived from 1BL/LRS wheat-rye translocation lines CN18 and T1208, was analysed by simple sequence repeat (SSR) markers. Genetic analysis showed that a stable and major QTL (QSR.sicau-4D) for spike formation rate was localized to chromosome 4D and explained 18.24% and 24.48% of the observed phenotypic variance in 2015 and 2016, respectively. This QTL was closely linked to SSR marker Xcfd23, and the genetic distance between the flank markers was 3.28cM. Furthermore, QSR.sicau-4D might be a novel pleiotropic QTL, which also controlled maximum tiller number per unit area (QMTN.sicau-4D) and tiller number during pre-winter per unit area (QTNW.sicau-4D). The marker Xcfd23 associated with SR may be utilized in marker-assisted selection in wheat breeding.

Combination of twelve alleles at six quantitative trait loci determines grain weight in rice

Grain weight, which is controlled by quantitative trait loci (QTLs), is one of the most important determinants of rice yield. Although many QTLs for grain weight have been identified, little is known about how different alleles in different QTLs coordinate to determine grain weight. In the present study, six grain-weight-QTLs were detected in seven mapping populations (two F₂, one F₃, and four recombinant inbred lines) developed by crossing ‘Lemont’, a United States japonica variety, with ‘Yangdao 4’, a Chinese indica variety. In each of the six loci, one allele from one parent increased grain weight and one allele from another parent decreased it. Thus, the 12 alleles at the six QTLs were subjected to regression analysis to examine whether they acted additively across loci leading to a linear relationship between the predicted breeding value of QTL and phenotype. Results suggested that a combination of the 12 alleles determined grain weight. In addition, plants carrying more grain-weight-increasing alleles had heavier grains than those carrying more grain-weight-decreasing alleles. This trend was consistent in the seven mapping populations. Thus, these six QTLs might be used in marker-assisted selection of grain weight, by stacking different grain-weight-increasing or -decreasing alleles.

Genetic Dissection of the Major Quantitative Trait Locus (qSE11), and Its Validation As the Major Influence on the Rate of Stigma Exsertion in Rice (Oryza sativa L.)

The rate of stigma exsertion (SE) is an important trait in rice breeding because the efficiency of hybrid rice seed production can be improved by increasing the percentage of stigmas that exsert. In this study, we developed a near isogenic line (NIL) from two parents, XieqingzaoB (XQZB) and Zhonghoi9308 (ZH9308), which have high and low SE rates in that order. In our previous study, we employed 75 chromosome segment substitution lines (CSSLs) and analyzed quantitative trait loci (QTLs) for their influence on SE rate. The single gene QTL (qSE11), which is located on chromosome 11, was responsible for this trait. In this study, we focused on one of the CSSLs (C65), namely, the NIL (qSE11XB). It contains an introgression segment of XQZB in the genetic background of ZH9308, and exhibits a significantly higher SE rate than that of the parents. We demonstrated that qSE11 regulated both the single and the dual SE rates. They both contribute to the total SE rate. Genetic analysis revealed that qSE11 acted as a single Mendelian factor and that the allele from XQZB increased the SE rate. The validity of our conclusions was established when C65 was used to develop secondary F2 (BC5F2) and F2:3 (BC5F2:3) populations by backcrossing to ZH9308, with subsequent selfing. We entered 3600 plants from the F₂ population and 3200 from the F_2:3 populations into a genetic dissection program and dissected the major QTL qSE11 to a 350.7-kb region located on chromosome 11. This study will contribute to the future isolation of candidate genes of SE and will play a vital role in future hybrid rice seed production programs.

Combination of Eight Alleles at Four Quantitative Trait Loci Determines Grain Length in Rice

Grain length is an important quantitative trait in rice (Oryza sativa L.) that influences both grain yield and exterior quality. Although many quantitative trait loci (QTLs) for grain length have been identified, it is still unclear how different alleles from different QTLs regulate grain length coordinately. To explore the mechanisms of QTL combination in the determination of grain length, five mapping populations, including two F₂ populations, an F₃ population, an F₇ recombinant inbred line (RIL) population, and an F₈ RIL population, were developed from the cross between the U.S. tropical japonica variety ‘Lemont’ and the Chinese indica variety ‘Yangdao 4’ and grown under different environmental conditions. Four QTLs (qGL-3-1, qGL-3-2, qGL-4, and qGL-7) for grain length were detected using both composite interval mapping and multiple interval mapping methods in the mapping populations. In each locus, there was an allele from one parent that increased grain length and another allele from another parent that decreased it. The eight alleles in the four QTLs were analyzed to determine whether these alleles act additively across loci, and lead to a linear relationship between the predicted breeding value of QTLs and phenotype. Linear regression analysis suggested that the combination of eight alleles determined grain length. Plants carrying more grain length-increasing alleles had longer grain length than those carrying more grain length-decreasing alleles. This trend was consistent in all five mapping populations and demonstrated the regulation of grain length by the four QTLs. Thus, these QTLs are ideal resources for modifying grain length in rice.

Development of gene-based markers for use in construction of the chickpea (Cicer arietinum L.) genetic linkage map and identification of QTLs associated with seed weight and plant height

Seed weight and plant height are important agronomic traits and contribute to seed yield. The objective of this study was to identify QTLs underlying these traits using an intra-specific mapping population of chickpea. A F11 population of 177 recombinant inbred lines derived from a cross between SBD377 (100-seed weight--48 g and plant height--53 cm) and BGD112 (100-seed weight--15 g and plant height--65 cm) was used. A total of 367 novel EST-derived functional markers were developed which included 187 EST-SSRs, 130 potential intron polymorphisms (PIPs) and 50 expressed sequence tag polymorphisms (ESTPs). Along with these, 590 previously published markers including 385 EST-based markers and 205 genomic SSRs were utilized. Of the 957 markers tested for analysis of parental polymorphism between the two parents of the mapping population, 135 (14.64%) were found to be polymorphic. Of these, 131 polymorphic markers could be mapped to the 8 linkage groups. The linkage map had a total length of 1140.54 cM with an average marker density of 8.7 cM. The map was further used for QTL identification using composite interval mapping method (CIM). Two QTLs each for seed weight, qSW-1 and qSW-2 (explaining 11.54 and 19.24% of phenotypic variance, respectively) and plant height, qPH-1 and qPH-2 (explaining 13.98 and 12.17% of phenotypic variance, respectively) were detected. The novel set of genic markers, the intra-specific linkage map and the QTLs identified in the present study will serve as valuable genomic resources in improving the chickpea seed yield using marker-assisted selection (MAS) strategies.

Rapid identification of major QTLs associated with rice grain weight and their utilization

To uncover the genetics of rice grain weight, we constructed an RIL population derived from a cross between a large grain accession M201 and a small size variety JY293. Specific Locus Amplified Fragment Sequencing (SLAF-Seq) technology was used to genotype two bulked DNA pools made from individual DNA of the heaviest 30 lines and the lightest 30 lines according to the 1000 grain weight (TGW). Bulked segregant analysis (BSA) was used to identify SLAFs strongly associated with TGW. Two marker-intensive regions at 24,600,000–24,850,000 bp and 25,000,000–25,350,000 bp on chromosome 3 were identified tightly related to the TGW. Then a linkage map of chromosome 3 was constructed with SSR markers and some SLAF derived single nucleotide polymorphisms (SNPs). Quantitative trait locus (QTL) mapping for TGW, grain length, grain width, and grain thickness revealed one major QTL in the second hot-region and two other minor QTLs for grain weight. These three QTLs displayed hierarchical effects on grain length and grain weight in order of qTGW3.2 (qGL3) qTGW3.1 (GS3) qTGW3.3. Multiple comparisons of means among the eight combinations of 3 QTLs revealed that the lines with two of three QTLs deriving from M201 displayed a large grain weight phenotype (TGW 40.2g, average data of three years) and lines with both qTGW3.1 and qTGW3.3 alleles from M201 (42.5g) had similar grain weight to the qTGW3.2 (40.8g) alone. Two strategies with similar effectiveness were proposed to improve grain weight by marker-assisted selection (MAS). One is to introduce the novel qTGW3.2 allele alone, and the other is to pyramid qTGW3.1 and qTGW3.3 alleles together. One new allele of GS3 (39 bp deletion in intron 1) and two SNPs in coding sequence of qGL3 identified in this study from M201 are useful in pyramiding elite alleles for molecular breeding for improvement of rice yield.