Identification and characterization of stable QTLs for vascular bundle number at the panicle neck in rice (Oryza sativa L.).

A large vascular bundle number (VBN) in the panicle neck in rice (Oryza sativa L.) is related to the ability to transport assimilates from stem and leaf to reproductive organs during seed maturation. Several quantitative trait loci (QTLs) for VBN have been identified by using segregating populations derived from a cross between indica and japonica rice cultivars. However, the detailed location, effect, and interaction of QTLs for VBN were not understood well. Here, to elucidate the genetic basis of VBN, we identified three stable QTLs for VBN-qVBN5, qVBN6 and qVBN11-by using 71 recombinant inbred lines derived from a cross between indica 'IR24' and japonica 'Asominori'. We confirmed their positions and characterized their effects by using chromosome segment substitution lines (CSSLs) with an 'IR24' genetic background. qVBN6 had the most substantial effect on VBN, followed by qVBN11 and qVBN5. We developed pyramided lines carrying two QTLs for VBN to estimate their interaction. The combination of qVBN6 and qVBN11 accumulated VBN negatively in the pyramided lines owing to the independent actions of each QTL. The QTLs detected for VBN will enhance our understanding of genetic mechanisms of VBN and can be used in rice breeding.

Identification of candidate genes for key fibre-related QTLs and derivation of favourable alleles in Gossypium hirsutum recombinant inbred lines with G. barbadense introgressions.

Fine mapping QTLs and identifying candidate genes for cotton fibre-quality and yield traits would be beneficial to cotton breeding. Here, we constructed a high-density genetic map by specific-locus amplified fragment sequencing (SLAF-seq) to identify QTLs associated with fibre-quality and yield traits using 239 recombinant inbred lines (RILs), which was developed from LMY22 (a high-yield Gossypium hirsutumL. cultivar) × LY343 (a superior fibre-quality germplasm with G. barbadenseL. introgressions). The genetic map spanned 3426.57 cM, including 3556 SLAF-based SNPs and 199 SSR marker loci. A total of 104 QTLs, including 67 QTLs for fibre quality and 37 QTLs for yield traits, were identified with phenotypic data collected from 7 environments. Among these, 66 QTLs were co-located in 19 QTL clusters on 12 chromosomes, and 24 QTLs were detected in three or more environments and determined to be stable. We also investigated the genomic components of LY343 and their contributions to fibre-related traits by deep sequencing the whole genome of LY343, and we found that genomic components from G. hirsutum races (which entered LY343 via its G. barbadense parent) contributed more favourable alleles than those from G. barbadense. We further identified six putative candidate genes for stable QTLs, including Gh_A03G1147 (GhPEL6), Gh_D07G1598 (GhCSLC6) and Gh_D13G1921 (GhTBL5) for fibre-length QTLs and Gh_D03G0919 (GhCOBL4), Gh_D09G1659 (GhMYB4) and Gh_D09G1690 (GhMYB85) for lint-percentage QTLs. Our results provide comprehensive insight into the genetic basis of the formation of fibre-related traits and would be helpful for cloning fibre-development-related genes as well as for marker-assisted genetic improvement in cotton.

Identification of major QTLs for soybean seed size and seed weight traits using a RIL population in different environments.

Introduction: The seed weight of soybean [Glycine max (L.) Merr.] is one of the major traits that determine soybean yield and is closely related to seed size. However, the genetic basis of the synergistic regulation of traits related to soybean yield is unclear. Methods: To understand the molecular genetic basis for the formation of soybean yield traits, the present study focused on QTLs mapping for seed size and weight traits in different environments and target genes mining. Results: A total of 85 QTLs associated with seed size and weight traits were identified using a recombinant inbred line (RIL) population developed from Guizao1×B13 (GB13). We also detected 18 environmentally stable QTLs. Of these, qSL-3-1 was a novel QTL with a stable main effect associated with seed length. It was detected in all environments, three of which explained more than 10% of phenotypic variance (PV), with a maximum of 15.91%. In addition, qSW-20-3 was a novel QTL with a stable main effect associated with seed width, which was identified in four environments. And the amount of phenotypic variance explained (PVE) varied from 9.22 to 21.93%. Five QTL clusters associated with both seed size and seed weight were summarized by QTL cluster identification. Fifteen candidate genes that may be involved in regulating soybean seed size and weight were also screened based on gene function annotation and GO enrichment analysis. Discussion: The results provide a biologically basic reference for understanding the formation of soybean seed size and weight traits.

A Major and Stable Quantitative Trait Locus qSS2 for Seed Size and Shape Traits in a Soybean RIL Population.

Seed size and shape traits are important determinants of seed yield and appearance quality in soybean [Glycine max (L.) Merr.]. Understanding the genetic architecture of these traits is important to enable their genetic improvement through efficient and targeted selection in soybean breeding, and for the identification of underlying causal genes. To map seed size and shape traits in soybean, a recombinant inbred line (RIL) population developed from K099 (small seed size) × Fendou 16 (large seed size), was phenotyped in three growing seasons. A genetic map of the RIL population was developed using 1,485 genotyping by random amplicon sequencing-direct (GRAS-Di) and 177 SSR markers. Quantitative trait locus (QTL) mapping was conducted by inclusive composite interval mapping. As a result, 53 significant QTLs for seed size traits and 27 significant QTLs for seed shape traits were identified. Six of these QTLs (qSW8.1, qSW16.1, qSLW2.1, qSLT2.1, qSWT1.2, and qSWT4.3) were identified with LOD scores of 3.80-14.0 and R 2 of 2.36%-39.49% in at least two growing seasons. Among the above significant QTLs, 24 QTLs were grouped into 11 QTL clusters, such as, three major QTLs (qSL2.3, qSLW2.1, and qSLT2.1) were clustered into a major QTL on Chr.02, named as qSS2. The effect of qSS2 was validated in a pair of near isogenic lines, and its candidate genes (Glyma.02G269400, Glyma.02G272100, Glyma.02G274900, Glyma.02G277200, and Glyma.02G277600) were mined. The results of this study will assist in the breeding programs aiming at improvement of seed size and shape traits in soybean.

The Pivotal Role of Major Chromosomes of Sub-Genomes A and D in Fiber Quality Traits of Cotton.

Lack of precise information about the candidate genes involved in a complex quantitative trait is a major obstacle in the cotton fiber quality improvement, and thus, overall genetic gain in conventional phenotypic selection is low. Recent molecular interventions and advancements in genome sequencing have led to the development of high-throughput molecular markers, quantitative trait locus (QTL) fine mapping, and single nucleotide polymorphisms (SNPs). These advanced tools have resolved the existing bottlenecks in trait-specific breeding. This review demonstrates the significance of chromosomes 3, 7, 9, 11, and 12 of sub-genomes A and D carrying candidate genes for fiber quality. However, chromosome 7 carrying SNPs for stable and potent QTLs related to fiber quality provides great insights for fiber quality-targeted research. This information can be validated by marker-assisted selection (MAS) and transgene in Arabidopsis and subsequently in cotton.

QTL Mapping of Six Spike and Stem Traits in Hybrid Population of Agropyron Gaertn. in Multiple Environments.

Most Agropyron Gaertn. species are excellent sources of forage. The derivative lines of wheat-Agropyron cristatum show elite agronomic traits, and some are valuable for wheat breeding. The species of Agropyron Gaertn. was mainly recognized by the spike morphology in traditional taxon. Six traits, including spike length (SL), ear stem length (ESL), the second internodes length (SIL), spikelet number per spike (SNS), floret number per spikelet (FNS), and grain number per spikelet (GNS), are vital to morphology studies and also influences the forage crop yield. To elucidate the genetic basis of spike and stem traits, a quantitative trait locus (QTL) analysis was conducted in a cross-pollinated (CP) hybrid population derived from a cross between two diverse parents, Agropyron mongolicum Keng Z2098 and A. cristatum (L.) Gaertn. Z1842, evaluated across three ecotopes (Langfang, Changli, and Guyuan of Hebei, China) over 3 years (from 2014 to 2016). Construction of a high-density linkage map was based on 1,023 single-nucleotide polymorphism (SNP) markers, covering 907.8 cM of the whole Agropyron genome. A total of 306 QTLs with single QTL in different environments explaining 0.07-33.21% of the phenotypic variation were detected for study traits. Seven major-effect QTLs were identified, including one for ESL on chromosome 3, one for SIL on chromosome 5, three for SL (two on chromosome 2 and one on chromosome 4), and two for SNS on chromosomes 3 and 7. Also, seven stable QTLs, including four for ESL, one for SL, one for GNS, and one for FNS, were mainly mapped on chromosomes 2, 3, 4, 5, and 7, respectively, elucidating 0.25-14.98% of the phenotypic variations. On the use of Agropyron CP hybrid population to identify QTL determining spike and stem traits for the first time, these QTLs for six traits would provide a theoretical reference for the molecular marker-assisted selection in the improvement of forage and cereal crop species.