Quantitative Trait Loci For Grain Weight Research Articles

Genetic dissection and validation of a major QTL for grain weight on chromosome 3B in bread wheat (Triticum aestivum L.)

Grain weight is one of the key components of wheat (Triticum aestivum L.) yield. Genetic manipulation of grain weight is an efficient approach for improving yield potential in breeding programs. A recombinant inbred line (RIL) population derived from a cross between W7268 and Chuanyu 12 (CY12) was employed to detect quantitative trait loci (QTLs) for thousand-grain weight (TGW), grain length (GL), grain width (GW), and the ratio of grain length to width (GLW) in six environments. Seven major QTLs, QGl.cib-2D, QGw.cib-2D, QGw.cib-3B, QGw.cib-4B.1, QGlw.cib-2D.1, QTgw.cib-2D.1 and QTgw.cib-3B.1, were consistently identified in at least four environments and the best linear unbiased estimation (BLUE) datasets, and they explained 2.61 to 34.85% of the phenotypic variance. Significant interactions were detected between the two major TGW QTLs and three major GW loci. In addition, QTgw.cib-3B.1 and QGw.cib-3B were co-located, and the improved TGW at this locus was contributed by GW. Unlike other loci, QTgw.cib-3B.1/QGw.cib-3B had no effect on grain number per spike (GNS). They were further validated in advanced lines using Kompetitive Allele Specific PCR (KASP) markers, and a comparison analysis indicated that QTgw.cib-3B.1/QGw.cib-3B is likely a novel locus. Six haplotypes were identified in the region of this QTL and their distribution frequencies varied between the landraces and cultivars. According to gene annotation, spatial expression patterns, ortholog analysis and sequence variation, the candidate gene of QTgw.cib-3B.1/QGw.cib-3B was predicted. Collectively, the major QTLs and KASP markers reported here provide valuable information for elucidating the genetic architecture of grain weight and for molecular marker-assisted breeding in grain yield improvement.

Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification

Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1, PDIL, SAUR, and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight.

Fine mapping and characterization of a major QTL for grain weight on wheat chromosome arm 5DL.

We fine mapped QTL QTKW.caas-5DL for thousand kernel weight in wheat, predicted candidate genes and developed a breeding-applicable marker. Thousand kernel weight (TKW) is an important yield component trait in wheat, and identification of the underlying genetic loci is helpful for yield improvement. We previously identified a stable quantitative trait locus (QTL) QTKW.caas-5DL for TKW in a Doumai/Shi4185 recombinant inbred line (RIL) population. Here we performed fine mapping of QTKW.caas-5DL using secondary populations derived from 15 heterozygous recombinants and delimited the QTL to an approximate 3.9Mb physical interval from 409.9 to 413.8Mb according to the Chinese Spring (CS) reference genome. Analysis of genomic synteny showed that annotated genes in the physical interval had high collinearity among CS and eight other wheat genomes. Seven genes with sequence variation and/or differential expression between parents were predicted as candidates for QTKW.caas-5DL based on whole-genome resequencing and transcriptome assays. A kompetitive allele-specific PCR (KASP) marker for QTKW.caas-5DL was developed, and genotyping confirmed a significant association with TKW but not with other yield component traits in a panel of elite wheat cultivars. The superior allele of QTKW.caas-5DL was frequent in a panel of cultivars, suggesting that it had undergone positive selection. These findings not only lay a foundation for map-based cloning of QTKW.caas-5DL but also provide an efficient tool for marker-assisted selection.

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 BC9F3:4 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.

Mapping of QTLs for Yield Traits Using F2:3:4 Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara

Wild introgressions play a crucial role in crop improvement by transferring important novel alleles and broadening allelic diversity of cultivated germplasm. In this study, two stable backcross alien introgression lines 166s and 14s derived from Swarn/Oryza nivara IRGC81848 were used as parents to generate populations to map quantitative trait loci (QTLs) for yield-related traits. Field evaluation of yield-related traits in F2, F3, and F4 population was carried out in normal irrigated conditions during the wet season of 2015 and dry seasons of 2016 and 2018, respectively. Plant height, tiller number, productive tiller number, total dry matter, and harvest index showed a highly significant association to single plant yield in F2, F3, and F4. In all, 21, 30, and 17 QTLs were identified in F2, F2:3, and F2:4, respectively, for yield-related traits. QTLs qPH6.1 with 12.54% phenotypic variance (PV) in F2, qPH1.1 with 13.01% PV, qTN6.1 with 10.08% PV in F2:3, and qTGW6.1 with 15.19% PV in F2:4 were identified as major effect QTLs. QTLs qSPY4.1 and qSPY6.1 were detected for grain yield in F2 and F2:3 with PV 8.5 and 6.7%, respectively. The trait enhancing alleles of QTLs qSPY4.1, qSPY6.1, qPH1.1, qTGW6.1, qTGW8.1, qGN4.1, and qTDM5.1 were from O. nivara. QTLs of the yield contributing traits were found clustered in the same chromosomal region. qTGW8.1 was identified in a 2.6 Mb region between RM3480 and RM3452 in all three generations with PV 6.1 to 9.8%. This stable and consistent qTGW8.1 allele from O. nivara can be fine mapped for identification of causal genes. From this population, lines C212, C2124, C2128, and C2143 were identified with significantly higher SPY and C2103, C2116, and C2117 had consistently higher thousand-grain weight values than both the parents and Swarna across the generations and are useful in gene discovery for target traits and further crop improvement.

Dissection of two quantitative trait loci for grain length linked on the long arm of chromosome 5 in rice

AbstractGrain size is the major determinant of grain weight and has an important role in grain quality. It is controlled by several major quantitative trait loci (QTL) and many minor QTL. Identification of QTL for grain size is important for understanding the genetic and molecular network regulating grain size in rice (Oryza sativa L.). Following previous identification of QTL for grain weight and size using an F2:3 family derived from the indica rice cross Teqing/IRBB52, one QTL, qTGW5, having significant effects on grain length and weight, was targeted for validation, dissection, and fine‐mapping. First, the effect of qTGW5 was validated using two near‐isogenic line (NIL)‐F2 populations. Then, qTGW5 was dissected into two closely linked QTL using four NIL populations. One of them, qGL5.1, having significant effects on grain length, width, and weight, was located within an 1,896.4‐kb region. The other one, qGL5.2, controlling grain length, was further delimited into a 68.8‐kb region using seven NIL‐F2 populations. Six annotated genes were found in the qGL5.2 region, of which five showed nucleotide polymorphisms between the two parental lines. Three of the six annotated genes showed significant expression differences between NILTeqing and NILIRBB52 in young panicles using quantitative real‐time polymerase chain reaction. The results will facilitate cloning the minor QTL and understanding the genetic architecture for grain size in rice.

Discovery of genomic regions and candidate genes for grain weight employing next generation sequencing based QTL-seq approach in rice (Oryza sativa L.).

Rice (Oryza sativa L.) yield enhancement is one of the prime objectives of plant breeders. Elucidation of the inheritance of grain weight, a key yield component trait, is of paramount importance for raising the yield thresholds in rice. In the present investigation, we employed Next-Generation Sequencing based QTL-seq approach to identify major genomic regions associated with grain weight using mapping populations derived from a cross between BPT5204 and MTU3626. QTL-seq analysis identified three grain weight quantitative trait loci (QTL) viz., qGW1 (35-40Mb), qGW7 (10-18Mb), and qGW8 (2-5Mb) on chromosomes 1, 7 and 8, respectively and all are found to be novel. Further, qGW8 was confirmed through conventional QTL mapping in F2, F3 and BC1F2 populations and found to explain the phenotypic variance of 17.88%, 16.70% and 15.00%, respectively, indicating a major QTL for grain weight. Based on previous reports, two candidate genes in the qGW8 QTL were predicted i.e., LOC_Os08g01490 (Cytochrome P450), and LOC_Os08g01680 (WD domain, G-beta repeat domain containing protein) and through in silico analysis they were found to be highly expressed in reproductive organs during different stages of grain development. Here, we have demonstrated that QTL-seq is one of the rapid approaches to uncover novel QTLs controlling complex traits. The candidate genes identified in the present study undoubtedly enhance our understanding of the mechanism and inheritance of the grain weight. These candidate genes can be exploited for yield enhancement after confirmation through complementary studies.

Fine-mapping of qTGW2, a quantitative trait locus for grain weight in rice (Oryza sativa L.).

BackgroundGrain weight is a grain yield component, which is an integrated index of grain length, width and thickness. They are controlled by a large number of quantitative trait loci (QTLs). Besides major QTLs, minor QTLs play an essential role. In our previous studies, QTL analysis for grain length and width was performed using a recombinant inbred line population derived from rice cross TQ/IRBB lines. Two major QTLs were detected, which were located in proximity to GS3 and GW5 that have been cloned. In the present study, QTLs for grain weight and shape were identified using rice populations that were homozygous at GS3 and GW5.MethodNine populations derived from the indica rice cross TQ/IRBB52 were used. An F10:11population named W1, consisting of 250 families and covering 16 segregating regions, was developed from one residual heterozygote (RH) in the F7generation of Teqing/IRBB52. Three near isogenic line (NIL)-F2 populations, ZH1, ZH2 and ZH3 that comprised 205, 239 and 234 plants, respectively, were derived from three RHs in F10:11. They segregated the target QTL region in an isogenic background. Two NIL populations, HY2 and HY3, were respectively produced from homozygous progeny of the ZH2 and ZH3 populations. Three other NIL-F2 populations, Z1, Z2 and Z3, were established using three RHs having smaller heterozygous segments. QTL analysis for 1000-grain weight (TGW), grain length (GL), grain width (GW), and length/width ratio (LWR) was conducted using QTL IciMapping and SAS procedure with GLM model.ResultA total of 27 QTLs distributed on 12 chromosomes were identified. One QTL cluster, qTGW2/qGL2/qGW2 located in the terminal region of chromosome 2, were selected for further analysis. Two linked QTLs were separated in region Tw31911−RM266. qGL2 was located in Tw31911−Tw32437 and mainly controlled GL and GW. The effects were larger on GL than on GW and the allelic directions were opposite. qTGW2 was located in Tw35293−RM266 and affected TGW, GL and GW with the same allelic direction. Finally, qTGW2 was delimited within a 103-kb region flanked by Tw35293 and Tw35395.ConclusionqTGW2 with significant effects on TGW, GL and GW was validated and fine-mapped using NIL and NIL-F2 populations. These results provide a basis for map-based cloning of qTGW2 and utilization of qTGW2 in the breeding of high-yielding rice varieties.

Use of near-isogenic lines to precisely map and validate a major QTL for grain weight on chromosome 4AL in bread wheat (Triticum aestivum L.).

This study precisely mapped and validated a major quantitative trait locus (QTL) on chromosome 4AL for thousand-grain weight in wheat using multiple near-isogenic lines. Thousand-grain weight (TGW) is an essential yield component. Following the previous identification of a major QTL for TGW within the interval of 15.7cM (92.7-108.4cM) on chromosome 4AL using the Nongda3338 (ND3338)/Jingdong6 (JD6) doubled haploid population, the aim of this study was to perform more precise mapping and validate the genetic effect of the QTL. Multiple near-isogenic lines (NILs) were developed using ND3338 as the recurrent parent through marker-assisted selection. Based on five independent BC3F3:4 segregating populations derived from BC3F3 plants with different heterozygous segments for the target QTL site and the results of genotyping analysis performed using the Wheat660K SNP array, it was possible to delimit the QTL region to a physical interval of approximately 6.5Mb (677.11-683.61Mb, IWGSC Ref Seq v1.0). Field trials across multiple environments showed that NILsJD6 had a consistent effect on increasing the TGW by 5.16-27.48% and decreasing the grain number per spike (GNS) by 3.98-32.91% compared to the corresponding NILsND3338, which exhibited locus-specific TGW-GNS trade-offs. Moreover, by using RNA sequencing (RNA-Seq) of whole grains at 10days after pollination stage of multiple NILs, we found that differentially expressed genes between the NIL pairs were significantly enriched for cell cycle and the replication of chromosome-related genes, hence affecting cell division and cell proliferation. Overall, our results provide a basis for map-based cloning of the major QTL and determining the mechanisms underlying TGW-GNS trade-offs in wheat, which would help to fine-tune these two components and maximize the grain yield for breeders.

Genome Based Meta-QTL Analysis of Grain Weight in Tetraploid Wheat Identifies Rare Alleles of GRF4 Associated with Larger Grains.

The domestication and subsequent genetic improvement of wheat led to the development of large-seeded cultivated wheat species relative to their smaller-seeded wild progenitors. While increased grain weight (GW) continues to be an important goal of many wheat breeding programs, few genes underlying this trait have been identified despite an abundance of studies reporting quantitative trait loci (QTL) for GW. Here we perform a QTL analysis for GW using a population of recombinant inbred lines (RILs) derived from the cross between wild emmer wheat accession ‘Zavitan’ and durum wheat variety ‘Svevo’. Identified QTLs in this population were anchored to the recent Zavitan reference genome, along with previously published QTLs for GW in tetraploid wheat. This genome-based, meta-QTL analysis enabled the identification of a locus on chromosome 6A whose introgression from wild wheat positively affects GW. The locus was validated using an introgression line carrying the 6A GW QTL region from Zavitan in a Svevo background, resulting in >8% increase in GW compared to Svevo. Using the reference sequence for the 6A QTL region, we identified a wheat ortholog to OsGRF4, a rice gene previously associated with GW. The coding sequence of this gene (TtGRF4-A) contains four single nucleotide polymorphisms (SNPs) between Zavitan and Svevo, one of which reveals the Zavitan allele to be rare in a core collection of wild emmer and completely absent from the domesticated emmer genepool. Similarly, another wild emmer accession (G18-16) was found to carry a rare allele of TtGRF4-A that also positively affects GW and is characterized by a unique SNP absent from the entire core collection. These results exemplify the rich genetic diversity of wild wheat, posit TtGRF4-A as a candidate gene underlying the 6A GW QTL, and suggest that the natural Zavitan and G18-16 alleles of TtGRF4-A have potential to increase wheat yields in breeding programs.

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 analysis of grain and malt quality in an elite barley population

Quantitative trait loci (QTLs) associated with grain weight, grain width, kernel hardness and malting quality were mapped in a doubled haploid population derived from two elite Australian malting barley varieties, Navigator and Admiral. A total of 30 QTLs for grain weight, grain width and kernel hardness were identified in three environments, and 63 QTLs were identified for ten malting quality traits in two environments. Three malting quality traits, namely β-amylase, diastatic power and apparent attenuation limit, were mainly controlled by a QTL linked to the Bmy1 gene at the distal end of chromosome 4H encoding a β-amylase enzyme. Six other malting quality traits, namely α-amylase, soluble protein, Kolbach index, free amino-acid nitrogen, wort β-glucan and viscosity, had coincident QTL clustered on chromosomes 1HS, 4HS, 7HS and 7HL, which demonstrated the interdependence of these traits. There was a strong association between these malt quality QTL clusters on chromosomes 1HS and 7HL and the major QTL for kernel hardness, suggesting that the use of this trait to enable early selection for malting quality in breeding programs would be feasible. In contrast, the majority of QTLs for hot-water extract were not coincident with those identified for other malt quality traits, which suggested differences in the mechanism controlling this trait. Novel QTLs have been identified for kernel hardness on chromosomes 2HL and 7HL, hot-water extract on 7HL and wort β-glucan on 6HL, and the resulting markers may be useful for marker-assisted selection in breeding programs.

Dissection of the qTGW1.1 region into two tightly-linked minor QTLs having stable effects for grain weight in rice.

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.

Quantitative Trait Loci for Yield and Yield-Related Traits in Spring Barley Populations Derived from Crosses between European and Syrian Cultivars.

In response to climatic changes, breeding programmes should be aimed at creating new cultivars with improved resistance to water scarcity. The objective of this study was to examine the yield potential of barley recombinant inbred lines (RILs) derived from three cross-combinations of European and Syrian spring cultivars, and to identify quantitative trait loci (QTLs) for yield-related traits in these populations. RILs were evaluated in field experiments over a period of three years (2011 to 2013) and genotyped with simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers; a genetic map for each population was constructed and then one consensus map was developed. Biological interpretation of identified QTLs was achieved by reference to Ensembl Plants barley gene space. Twelve regions in the genomes of studied RILs were distinguished after QTL analysis. Most of the QTLs were identified on the 2H chromosome, which was the hotspot region in all three populations. Syrian parental cultivars contributed alleles decreasing traits' values at majority of QTLs for grain weight, grain number, spike length and time to heading, and numerous alleles increasing stem length. The phenomic and molecular approaches distinguished the lines with an acceptable grain yield potential combining desirable features or alleles from their parents, that is, early heading from the Syrian breeding line (Cam/B1/CI08887//CI05761) and short plant stature from the European semidwarf cultivar (Maresi).

Prioritizing quantitative trait loci for root system architecture in tetraploid wheat.

Optimization of root system architecture (RSA) traits is an important objective for modern wheat breeding. Linkage and association mapping for RSA in two recombinant inbred line populations and one association mapping panel of 183 elite durum wheat (Triticum turgidum L. var. durum Desf.) accessions evaluated as seedlings grown on filter paper/polycarbonate screening plates revealed 20 clusters of quantitative trait loci (QTLs) for root length and number, as well as 30 QTLs for root growth angle (RGA). Divergent RGA phenotypes observed by seminal root screening were validated by root phenotyping of field-grown adult plants. QTLs were mapped on a high-density tetraploid consensus map based on transcript-associated Illumina 90K single nucleotide polymorphisms (SNPs) developed for bread and durum wheat, thus allowing for an accurate cross-referencing of RSA QTLs between durum and bread wheat. Among the main QTL clusters for root length and number highlighted in this study, 15 overlapped with QTLs for multiple RSA traits reported in bread wheat, while out of 30 QTLs for RGA, only six showed co-location with previously reported QTLs in wheat. Based on their relative additive effects/significance, allelic distribution in the association mapping panel, and co-location with QTLs for grain weight and grain yield, the RSA QTLs have been prioritized in terms of breeding value. Three major QTL clusters for root length and number (RSA_QTL_cluster_5#, RSA_QTL_cluster_6#, and RSA_QTL_cluster_12#) and nine RGA QTL clusters (QRGA.ubo-2A.1, QRGA.ubo-2A.3, QRGA.ubo-2B.2/2B.3, QRGA.ubo-4B.4, QRGA.ubo-6A.1, QRGA.ubo-6A.2, QRGA.ubo-7A.1, QRGA.ubo-7A.2, and QRGA.ubo-7B) appear particularly valuable for further characterization towards a possible implementation of breeding applications in marker-assisted selection and/or cloning of the causal genes underlying the QTLs.

Characterization of three wheat grain weight QTLs that differentially affect kernel dimensions

The QGw.nau - 2D, QGw.nau - 4B and QGw.nau - 5A intervals were investigated for their effects on weight, length, width, and thickness of kernels and their differential roles in determining kernel size and shape were demonstrated. Grain weight (GW) contributes greatly to wheat yield and is directly related to kernel size and shape. Although over 100 quantitative trait loci (QTLs) for GW have been reported in the literatures, few have been well characterized for their association with kernel traits. In this study, three GW QTLs identified in elite cultivar 'Nanda2419' ('Mentana'), including QGw.nau-2D, QGw.nau-4B and QGw.nau-5A, were investigated through near isogenic line (NIL) development and evaluation. NILs for all three QTLs and one NIL with both QGw.nau-4B and QGw.nau-5A were developed with the help of marker-assisted selection after two to three generations of backcross using cultivar 'Wangshuibai' as the recurrent parent. One NIL with QGw.nau-4B in the background of cultivar 'Wenmai6' was also obtained. In four different field trials, these NILs consistently produced heavier kernels than the recurrent parents. QGw.nau-4B showed the largest effect on GW; its presence resulted in 0.4-0.5g increase of hundred-grain weight, depending on genetic backgrounds. QGw.nau-4B and QGw.nau-5A functioned additively in conditioning GW. These three QTL intervals showed pleiotropic effects on, or close linkage with genes for, spike length, plant height and flag leaf width, respectively, and acted differentially in determining the kernel dimensions that are the major GW determinants. They all conditioned wider kernels with QGw.nau-5A displaying the largest effect. QGw.nau-4B and QGw.nau-5A also conditioned thicker kernels but had opposite effects on kernel length. This study demonstrated that marker-assisted selection is effective for GW improvement. The availability of GW NILs could facilitate cloning of GW genes and unraveling of kernel development mechanisms.

Fine mapping of qGW1, a major QTL for grain weight in sorghum.

We detected seven QTLs for 100-grain weight in sorghum using an F 2 population, and delimited qGW1 to a 101-kb region on the short arm of chromosome 1, which contained 13 putative genes. Sorghum is one of the most important cereal crops. Breeding high-yielding sorghum varieties will have a profound impact on global food security. Grain weight is an important component of grain yield. It is a quantitative trait controlled by multiple quantitative trait loci (QTLs); however, the genetic basis of grain weight in sorghum is not well understood. In the present study, using an F2 population derived from a cross between the grain sorghum variety SA2313 (Sorghum bicolor) and the Sudan-grass variety Hiro-1 (S. bicolor), we detected seven QTLs for 100-grain weight. One of them, qGW1, was detected consistently over 2 years and contributed between 20 and 40 % of the phenotypic variation across multiple genetic backgrounds. Using extreme recombinants from a fine-mapping F3 population, we delimited qGW1 to a 101-kb region on the short arm of chromosome 1, containing 13 predicted gene models, one of which was found to be under purifying selection during domestication. However, none of the grain size candidate genes shared sequence similarity with previously cloned grain weight-related genes from rice. This study will facilitate isolation of the gene underlying qGW1 and advance our understanding of the regulatory mechanisms of grain weight. SSR markers linked to the qGW1 locus can be used for improving sorghum grain yield through marker-assisted selection.

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.

Dissection of qTGW1.2 to three QTLs for grain weight and grain size in rice (Oryza sativa L.)

Dissection of qTGW1.2, a minor quantitative trait locus (QTL) for 1,000-grain weight located on the long arm of rice chromosome 1, was reported here. Six sets of near isogenic lines (NILs) were developed from six BC2F9 plants of the indica rice cross Zhenshan 973/Milyang 46, respectively, segregated in a sequential order covering the region for qTGW1.2. They were tested for 1,000-grain weight, grain length, grain width and heading date. The SAS procedure GLM was used to analyze the phenotype differences among three genotypic groups in each NIL set. The qTGW1.2 was separated into three QTLs, including qTGW1.2a located in the 933.6 kb interval RM11730–RM11762, qTGW1.2b in the 418.8 kb interval RM11781–RM11800, and qTGW1.2c in the 2.1 Mb interval RM11800–RM11885. While qTGW1.2a and qTGW1.2c displayed the genetic action mode of negatively partial dominance, qTGW1.2b appeared to have a positively dominance effect as large as its additive effect. In addition, the effects of the three QTLs were consistently detected in different NIL sets. It was also shown that the effects of qTGW1.2a and qTGW1.2b on grain weight were related more to grain length than grain width, and qTGW1.2c related more to grain width than grain length. Our results indicate that the effects of minor QTLs could be steadily detected in a highly isogenic background and suggest that dominance effects of minor QTLs might play an important role in the genetic control of heterosis in rice.

High-density mapping of quantitative trait loci for grain-weight and spikelet number in rice.

BackgroundHigh grain yield is one of the most important traits requiring improvement in rice breeding programs. Consequently, the genetic basis of spikelets per panicle (SPP) and grain weight (TGW) have received much research focus because of their importance in rice yield.ResultsIn this study, IL28, which is a near isogenic line (NIL) developed by introgressing chromosomal segments of the cultivar ‘Moroberekan’ into the cultivar ‘Ilpumbyeo’, showed a significant increase in the number of spikelets per panicle (SPP) and 1,000-grain weight (TGW) compared to the recurrent parent, Ilpumbyeo. Quantitative trait locus (QTL) analysis in 243 F2 plants derived from a cross between IL28 and Ilpumbyeo indicated that both qSPP6 and qTGW6 are located in the interval RM3430–RM20580. Following substitution mapping with 50 F3:4:5 lines, qSPP6 was mapped to a 429-kb interval between RM20521 and InDel-1, while qTGW6 was mapped to a 37.85-kb interval between InDel-1 and SNP--3 based on the japonica genome sequence. This result indicates that qSPP6 and qTGW6 are different genes. Yield trials with substitution lines indicated that lines harboring the homozygous Moroberekan segment at both the qSPP6 and qTGW6 region showed significantly higher grain yield than Ilpumbyeo.ConclusionBecause the Moroberekan alleles for SPP and TGW have been shown to be beneficial in the genetic background of Ilpumbyeo, both the qSPP6 and qTGW6 alleles might prove valuable in improving rice yields. Closely linked SSR markers are expected to facilitate the cloning of genes that underlie these QTLs, as well as with marker-assisted selection for variation in SPP and TGW in rice breeding programs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12284-014-0014-5) contains supplementary material, which is available to authorized users.