A Major Quantitative Trait Loci Cluster Controlling Three Components of Yield and Plant Height Identified on Chromosome 4B of Common Wheat

Wheat yield is not only affected by three components of yield, but also affected by plant height (PH). Identification and utilization of the quantitative trait loci (QTL) controlling these four traits is vitally important for breeding high-yielding wheat varieties. In this work, we conducted a QTL analysis using the recombinant inbred lines (RILs) derived from a cross between two winter wheat varieties of China, “Nongda981” (ND981) and “Nongda3097” (ND3097), exhibiting significant differences in spike number per unit area (SN), grain number per spike (GNS), thousand grain weight (TGW), and PH. A total of 11 environmentally stable QTL for these four traits were detected. Among them, four major and stable QTLs (QSn.cau-4B-1.1, QGns.cau-4B-1, QTgw.cau-4B-1.1, and QPh.cau-4B-1.2) explaining the highest phenotypic variance for SN, GNS, TGW, and PH, respectively, were mapped on the same genomic region of chromosome 4B and were considered a QTL cluster. The QTL cluster spanned a genetic distance of about 12.3 cM, corresponding to a physical distance of about 8.7 Mb. Then, the residual heterozygous line (RHL) was used for fine mapping of the QTL cluster. Finally, QSn.cau-4B-1.1, QGns.cau-4B-1, and QPh.cau-4B-1.2 were colocated to the physical interval of about 1.4 Mb containing 31 annotated high confidence genes. QTgw.cau-4B-1.1 was divided into two linked QTL with opposite effects. The elite NILs of the QTL cluster increased SN and PH by 55.71–74.82% and 14.73–23.54%, respectively, and increased GNS and TGW by 29.72–37.26% and 5.81–11.24% in two environments. Collectively, the QTL cluster for SN, GNS, TGW, and PH provides a theoretical basis for improving wheat yield, and the fine-mapping result will be beneficial for marker-assisted selection and candidate genes cloning.

Identification and verification of grain shape QTLs by SNP array in rice

Grain shape strongly influences the economic value and grain yield of rice. Thus, identifying quantitative trait loci (QTLs) for grain shape has been a longstanding goal in rice genetic research and breeding programs. Single nucleotide polymorphism (SNP) markers are ubiquitous in the rice genome and are more abundant and evenly distributed on the 12 rice chromosomes than traditional markers. An F2 population was genotyped using the RICE6K SNP array to elucidate the mechanisms governing grain shape. Thirty-five QTLs for grain shape were detected on 11 of 12 chromosomes over 2 years. The major QTL cluster qGS7 was detected in both years and displayed strong genetic effects on grain length and width, showing consistency with GL7/GW7. Some minor QTLs were also detected, and the effects of four QTLs on seed size were then validated using BC1F6 populations with residual heterozygous lines in each QTL region. Our findings provide insights into the molecular basis of grain shape as well as additional resources and approaches for producing hybrid high-yield rice varieties.

Quantitative traits loci mapping and molecular marker development for total glutenin and glutenin fraction contents in wheat

Background Glutenin contents and compositions are crucial factors influencing the end-use quality of wheat. Although the composition of glutenin fractions is well known, there has been relatively little research on the genetic basis of glutenin fractions in wheat. Results To elucidate the genetic basis for the contents of glutenin and its fractions, a population comprising 196 recombinant inbred lines (RILs) was constructed from two parents, Luozhen No.1 and Zhengyumai 9987, which differ regarding their total glutenin and its fraction contents (except for the By fraction). Forty-one additive Quantitative Trait Loci (QTL) were detected in four environments over two years. These QTL explained 1.3% - 53.4% of the phenotypic variation in the examined traits. Forty-three pairs of epistatic QTL (E-QTL) were detected in the RIL population across four environments. The QTL controlling the content of total glutenin and its seven fractions were detected in clusters. Seven clusters enriched with QTL for more than three traits were identified, including a QTL cluster 6AS-3, which was revealed as a novel genetic locus for glutenin and related traits. Kompetitive Allele-Specific PCR (KASP) markers developed from the main QTL cluster 1DL-2 and the previously developed KASP marker for the QTL cluster 6AS-3 were validated as significantly associated with the target traits in the RIL population and in natural varieties. Conclusions This study identified novel genetic loci related to glutenin and its seven fractions. Additionally, the developed KASP markers may be useful for the marker-assisted selection of varieties with high glutenin fraction content and for identifying individuals in the early developmental stages without the need for phenotyping mature plants. On the basis of the results of this study and the KASP markers described herein, breeders will be able to efficiently select wheat lines with favorable glutenin properties and develop elite lines with high glutenin subunit contents.

Genetic Control and Phenotypic Characterization of Panicle Architecture and Grain Yield Related Traits in Foxtail Millet (Setaria Italica)

Panicle architecture and grain weight, both of which are influenced by genetic and environmental factors, have significant effects on grain yield potential. Here, we used a recombinant inbred line population (RIL) of 333 lines, which were grown in 13 trials with varying environmental conditions, to identify quantitative trait loci (QTL) that control differences in 9 agronomic traits related to panicle architecture and grain yield. We find that panicle weight, grain weight per panicle, panicle length, panicle diameter, and panicle exsertion length varied across different geographical locations. QTL mapping revealed 159 QTL for nine traits, of these, 34 QTL were identified in 2 to 12 environments, suggesting that the genetic control of panicle architecture in foxtail millet is sensitive to photoperiod or other environmental factors. 88 QTL controlling different traits formed 34 co-located QTL clusters, including the triple QTL cluster qPD9.2/qPL9.5/qPEL9.3, the genomic region of which was detected by 23 times in 13 environments. Several candidate genes were identified in the genomic intervals of multi-environmental QTL or co-located QTL clusters, including Seita.2G388700, Seita.3G136000, Seita.4G185300, Seita.5G241500, Seita.5G243100, Seita.9G281300, and Seita.9G342700. Among these, Seita.9G342700 was the candidate gene of qPD9.2/qPL9.5/qPEL9.3 QTL cluster, it is homologous to rice OsMADS56, which encodes a putative MADS-box transcription factor that determines inflorescence architecture in rice. These results not only provided a basis for further fine mapping, functional studies and marker-assisted selection of panicle architecture related traits in foxtail millet, but also benefited comparative genomics of cereal crops.

Candidate Genes and Quantitative Trait Loci for Grain Yield and Seed Size in Durum Wheat

Grain yield (YLD) is affected by thousand kernel weight (TKW) which reflects the combination of grain length (GL), grain width (GW) and grain area (AREA). Grain weight is also influenced by heading time (HT) and plant height (PH). To detect candidate genes and quantitative trait loci (QTL) of yield components, a durum wheat recombinant inbred line (RIL) population was evaluated in three field trials. The RIL was genotyped with a 90K single nucleotide polymorphism (SNP) array and a high-density genetic linkage map with 5134 markers was obtained. A total of 30 QTL were detected including 23 QTL grouped in clusters on 1B, 2A, 3A, 4B and 6B chromosomes. A QTL cluster on 2A chromosome included a major QTL for HT co-located with QTL for YLD, TKW, GL, GW and AREA, respectively. The photoperiod sensitivity (Ppd-A1) gene was found in the physical position of this cluster. Serine carboxypeptidase, Big grain 1 and β-fructofuranosidase candidate genes were mapped in clusters containing QTL for seed size. This study showed that yield components and phenological traits had higher inheritances than grain yield, allowing an accurate QTL cluster detection. This was a requisite to physically map QTL on durum genome and to identify candidate genes affecting grain yield.

Candidate Genes and Stable QTL for Grain Yield and Seed Size in Durum Wheat

Background: In wheat grain yield is expressed as the product of different components. Among these, thousand kernels weight (TKW) reflects the combination of several grain related traits including grain length (GL), grain width (GW) and area. Grain weight is also affected by phenological traits, such as heading time (HT) and plant height (PH). To detect stable QTL and candidate genes involved in phenotypic control of grain yield, a recombinant inbred line (RIL) population derived from two elite durum wheat cultivars (Liberdur and Anco Marzio) was evaluated for yield components and grain related traits for three growing seasons in southern Italy. The mapping population was genotyped with a 90K SNP array and a high-density genetic linkage map with 5134 markers was obtained.Results: A total of 30 QTL were detected on the durum RIL population including 9 stable QTL for TKW (2 QTL), GL, GW (2 QTL), AREA, HT and PH (2 QTL) distributed on 1B, 2A, 3A and 6B chromosomes. Interestingly, a QTL cluster mapped on 2A included a major QTL for HT explaining at least 70% of phenotypic variance and co-located with a QTL for YLD, TKW, GL and GW and AREA, respectively. In the physical position of this QTL cluster a photoperiod sensitivity gene (Ppd-A1) was found. Serine carboxypeptidase, Big Grain 1 and β-fructofuranosidase candidate genes were mapped in clusters containing stable QTL. Candidate genes involved in auxin metabolism were also found in QTL clusters in which a QTL for AREA was declared. Conclusions: This study showed that yield components and phenological traits had higher inheritances than grain yield, allowing an accurate stable QTL cluster detection. This was a powerful requisite to physically map QTL on the reference durum wheat genome and to identify candidate genes strongly affecting the genetic grain yield network.

Candidate Genes and Stable QTL for Grain Yield and Seed Size in Durum Wheat

Background: In wheat grain yield is expressed as the product of different components. Among these, thousand kernels weight (TKW) reflects the combination of several grain related traits including grain length (GL), grain width (GW) and area. Grain weight is also affected by phenological traits, such as heading time (HT) and plant height (PH). To detect stable QTL and candidate genes involved in phenotypic control of grain yield, a recombinant inbred line (RIL) population derived from two elite durum wheat cultivars (Liberdur and Anco Marzio) was evaluated for yield components and grain related traits for three growing seasons in southern Italy. The mapping population was genotyped with a 90K SNP array and a high-density genetic linkage map with 5134 markers was obtained.Results: A total of 30 QTL were detected on the durum RIL population including 9 stable QTL for TKW (2 QTL), GL, GW (2 QTL), AREA, HT and PH (2 QTL) distributed on 1B, 2A, 3A and 6B chromosomes. Interestingly, a QTL cluster mapped on 2A included a major QTL for HT explaining at least 70% of phenotypic variance and co-located with a QTL for YLD, TKW, GL and GW and AREA, respectively. In the physical position of this QTL cluster a photoperiod sensitivity gene (Ppd-A1) was found. Serine carboxypeptidase, Big Grain 1 and β-fructofuranosidase candidate genes were mapped in clusters containing stable QTL. Candidate genes involved in auxin metabolism were also found in QTL clusters in which a QTL for AREA was declared.Conclusions: This study showed that yield components and phenological traits had higher inheritances than grain yield, allowing an accurate stable QTL cluster detection. This was a powerful requisite to physically map QTL on the reference durum wheat genome and to identify candidate genes strongly affecting the genetic grain yield network.

Construction of A High-Density SNP Genetic Map by Genotyping-By-Sequencing and Location of Seed Quality traits in Flax (Linum usitatisimum L.)

Background Oil and five main fatty acid content(especially linolenic acid content (LNA) )are six main quality traits in flax, and also the important target traits in flax breeding. However, the reports on genetic map construction and QTL mapping of linolenic acid content in flax are limited, which results in low accuracy and efficiency of quality breeding in oil flax. Construction the genetic linkage map and location the QTLs to discover the stable genetic QTLs sites related to oil content and fatty acid content, so as to provide targeted targets for breeders and improve breeding efficiency. Results In this study, a final integrated map consisting of 2,239 single nucleotide polymorphism (SNP) markers on 15 linkage groups (LGs) with an average distance of 0.46 cM between adjacent markers was generated using genotyping-by-sequencing (GBS) technique. A total of 21 quantitative trait loci (QTL)s for the six traits, i.e., palmitic acid content (PAL), stearic acid content (STE), oleic acid content (OLE), linoleic acid content (LIN), linolenic acid content (LNA), and oil content (OIL) in the RIL population under three environments. One QTL cluster harbored two QTLs for LIN and LNA trait, respectively was also identified. Especially, QTL qLIN-Group12-2 for LIN and QTL qLNA-Group12-2 for LNA on chromosome 12 were detected in multi-environments. Conclusions A high-density SNP genetic map with total 2239 markers was constructed with GBS technique, The total genetic distance of the SNP map was 1032.90 cM, with the average genetic distance of 0.46 cM per marker. A total of 21 quantitative trait loci (QTL)s for the six traits under three environments were determined. One QTL cluster harbored two QTLs for LIN and LNA trait, respectively was also identified. QTL qLIN-Group12-2 for LIN and QTL qLNA-Group12-2 for LNA on chromosome 12 were detected in multi-environments. These results provide more information for determining the seed quality related candidate genes and contribute to the marker-assisted selection in flax breeding.

Stable Qtl And Candidate Genes Involved Into The Genetic Network Affecting Grain Yield And Seed Size In Durum Wheat

Background: In wheat grain yield is expressed as the product of different components. Among these, thousand kernels weight (TKW) reflects the combination of several grain related traits including grain length (GL), grain width (GW) and area. Grain weight is also affected by phenological traits, such as heading time (HT) and plant height (PH). To detect stable QTL and candidate genes involved in phenotypic control of grain yield, a recombinant inbred line (RIL) population derived from two elite durum wheat cultivars (Liberdur and Anco Marzio) was evaluated for yield components and grain related traits for three growing seasons in southern Italy. The mapping population was genotyped with a 90K SNP array and a high-density genetic linkage map with 5134 markers were obtained.Results: A total of 30 QTL were detected on the durum RIL population including 9 stable QTL for TKW (2 QTL), GL, GW (2 QTL), AREA, HT and PH (2 QTL) distributed on 1B, 2A, 3A and 6B chromosomes. Interestingly, a QTL cluster mapped on 2A included a major QTL for HT explaining at least 70% of phenotypic variance and co-located with a QTL for YLD, TKW, GL and GW and AREA, respectively. In the physical position of this QTL cluster a photoperiod sensitivity gene (Ppd-A1) was found. Serine carboxypeptidase, Big Grain 1 and β-fructofuranosidase candidate genes were mapped in clusters containing stable QTL. Candidate genes involved in auxin metabolism were also found in QTL clusters in which a QTL for AREA was declared. Conclusions: This study showed that yield components and phenological traits had higher inheritances than grain yield, allowing an accurate stable QTL cluster detection. This was a powerful requisite to physically map QTL on the reference durum wheat genome and to identify candidate genes strongly affecting the genetic grain yield network.