Consensus Quantitative Trait Loci Research Articles

Consensus genetic linkage map and QTL mapping allow to capture the genomic regions associated with agronomic traits in pearl millet.

A genetic linkage map representing the pearl millet genome was constructed with SNP markers. Major and stable QTL associated with flowering, number of productive tillers, ear head length, and test weight were mapped on chromosomes 1 and 3. Pearl millet (Pennisetum glaucum) is a major cereal and fodder crop in arid and semi-arid regions of Asia and Africa. Agronomic traits are important traits in pearl millet breeding and genetic and environmental factors highly influence them. In the present study, an F9 recombinant inbred line (RIL) population derived from a cross between PT6029 and PT6129 was evaluated for agronomic traits in three environments. Utilizing a genotyping by sequencing approach, a dense genetic map with 993 single nucleotide polymorphism markers covering a total genetic distance of 1035.4cM was constructed. The average interval between the markers was 1.04cM, and the seven chromosomes varied from 115.39 to 206.72cM. Quantitative trait loci (QTL) mapping revealed 35 QTL for seven agronomic traits, and they were distributed on all pearl millet chromosomes. These QTL individually explained 11.35 to 26.71% of the phenotypic variation, with LOD values ranging from 2.74 to 5.80.Notably, four QTL (qDFF1.1, qNPT3.1, qEHL3.1, and qTW1.1) associated with days to fifty percent flowering, the number of productive tillers, ear head length, and test weight were found to be major and stable QTL located on chromosomes 1 and 3. Collectively, our results provide an important base for understanding the genetic architecture of agronomic traits in pearl millet, which is useful for accelerating the genetic gain toward crop improvement.

Consensus linkage map construction and QTL mapping for eight yield-related traits in wheat using BAAFS 90K SNP array

Identification of stable quantitative trait loci (QTL) for yield-related traits across populations and environments is crucial for wheat breeding and genetic studies. Consensus maps also play important roles in wheat genetic and genomic researches. In the present study, a wheat consensus map was constructed using a doubled haploid (DH) population derived from Jinghua 1×Xiaobaidongmai (JX) and an F2 population derived from L43×Shanxibaimai (LS) using BAAFS AFFY wheat 90K single nucleotide polymorphism (SNP) array. A total of 44503 SNP markers were mapped on the constructed consensus map, covering 5437.92 cM across 21 chromosomes. The consensus map showed high collinearity with the individual maps and the wheat reference genome IWGSC RefSeq v2.1. Phenotypic data on eight yield-related traits were collected in the JX population and F2:3 and F2:4 populations of LS in six, two and two environments, respectively, and used for QTL analysis. Inclusive composite interval mapping (ICIM) identified 32 environmentally stable QTL for eight yield-related traits. Among them, four QTL, i.e., QPH.baafs-4B, QKNS.baafs-4B, QTGW.baafs-4B and QSL.baafs-5A.3, were detected across mapping populations and environments, and seven stable QTL i.e., QPH.baafs-2B, QKNS.baafs-3D, QSL.baafs-3D, QKW.baafs-4B, QPH.baafs-5D, QPH.baafs-6A.1 and QSL.baafs-6A, are likely to be new. The physical region of 17.25-44.91 Mb on chromosome 4B was associated with six yield-related traits, which is an important region for wheat yield. The physical region around the dwarfing gene Rht24 contained QTL for KL, KW, SL and TGW, which are either from a pleiotropic effect of Rht24 or closely linked loci. Two hundred and fifty-four promising candidate genes were identified for the stable QTL. Among them, TraesCS5A03G1264300, TraesCS1B03G0624000 and TraesCS6A03G0697000 were particularly noteworthy due to their homologous genes have similar functions for the corresponding traits. The constructed consensus map and the identified QTL along with their candidate genes will facilitate the genetic dissection of wheat yield-related traits and accelerate the development of wheat cultivars with desirable plant morphology and high yield.

QTL Mapping for Ovary- and Fruit-Related Traits in Cucumis sativus-C. hystrix Introgression Line IL52.

IL52 is a valuable introgression line obtained from interspecific hybridization between cultivated cucumber (Cucumis sativus L., 2n = 14) and the wild relative species C. hystrix Chakr. (2n = 24). IL52 exhibits high resistance to a number of diseases, including downy mildew, powdery mildew, and angular leaf spot. However, the ovary- and fruit-related traits of IL52 have not been thoroughly investigated. Here, we conducted quantitative trait loci (QTL) mapping for 11 traits related to ovary size, fruit size, and flowering time using a previously developed 155 F7:8 RIL population derived from a cross between CCMC and IL52. In total, 27 QTL associated with the 11 traits were detected, distributed on seven chromosomes. These QTL explained 3.61% to 43.98% of the phenotypic variance. Notably, we identified a major-effect QTL (qOHN4.1) on chromosome 4 associated with the ovary hypanthium neck width and further delimited it into a 114-kb candidate region harboring 13 candidate genes. Furthermore, the QTL qOHN4.1 is co-localized with the QTL detected for ovary length, mature fruit length, and fruit neck length, all residing within the consensus QTL FS4.1, suggesting a plausible pleiotropic effect.

QTL Mapping of Growth Traits in Yellow River Carp (Cyprinus carpio haematopterus) at 5–17 Months after Hatching

To screen the quantitative trait loci (QTL) and genes related to the growth of Yellow River carp (Cyprinus carpio haematopterus) after removing the maternal effect, we established a family of these carps. Four growth-related traits (body length, height, thickness, and weight) of the family at 5 and 17 months after hatching (MAH) were measured. Analysis of QTL mapping for the four growth-related traits was conducted using the genetic linkage map constructed in our laboratory. We identified 47 QTL that were related to the four growth traits and three consensus QTL (cQTL). A total of 10, 14, 10, and 13 QTL were associated with body length, height, thickness, and weight, respectively; cQTL-1, cQTL-2, and cQTL-3 contained 11, 2, and 2 QTL, respectively. We detected 17 growth-related candidate genes within 50 Kb upstream and downstream of the five main-effect QTL (phenotypic variation explained > 10%, logarithm of odds > 5.5). Two genes (cbfa2t2 and neca1) that may be affected by maternal effects were identified by comparing the main-effect QTL at 17 and 5–17 MAH. This study was the first attempt to eliminate growth-related QTL and genes affected by maternal effects in Yellow River carp. These results can be used in molecular marker-assisted breeding and provide valuable genomic resources for the genetic mechanisms underlying growth in Yellow River carp.

Mining synergistic genes for nutrient utilization and disease resistance in maize based on co-expression network and consensus QTLs.

Nutrient restrictions and large-scale emergence of diseases are threatening the maize production. Recent findings demonstrated that there is a certain synergistic interaction between nutrition and diseases pathways in model plants, however there are few studies on the synergistic genes of nutrients and diseases in maize. Thus, the transcriptome data of nitrogen (N) and phosphorus (P) nutrients and diseases treatments in maize, rice, wheat and Arabidopsis thaliana were collected in this study, and four and 22 weighted co-expression modules were obtained by using Weighted Gene Co-expression Network Analysis (WGCNA) in leaf and root tissues, respectively. With a total of 5252 genes, MFUZZ cluster analysis screened 26 clusters with the same expression trend under nutrition and disease treatments. In the meantime, 1427 genes and 22 specific consensus quantitative trait loci (scQTLs) loci were identified by meta-QTL analysis of nitrogen and phosphorus nutrition and disease stress in maize. Combined with the results of cluster analysis and scQTLs, a total of 195 consistent genes were screened, of which six genes were shown to synergistically respond to nutrition and disease both in roots and leaves. Moreover, the six candidate genes were found in scQTLs associated with gray leaf spot (GLS) and corn leaf blight (CLB). In addition, subcellular localization and bioinformatics analysis of the six candidate genes revealed that they were primarily expressed in endoplasmic reticulum, mitochondria, nucleus and plasma membrane, and were involved in defense and stress, MeJA and abscisic acid response pathways. The fluorescence quantitative PCR confirmed their responsiveness to nitrogen and phosphorus nutrition as well as GLS treatments. Taken together, findings of this study indicated that the nutrition and disease have a significant synergistic response in maize.

Mapping of Two Major QTLs Controlling Flowering Time in Brassica napus Using a High-Density Genetic Map.

Research on the flowering habit of rapeseed is important for the selection of varieties adapted to specific ecological environments. Here, quantitative trait loci (QTL) for the days-to-flowering trait were identified using a doubled haploid population of 178 lines derived from a cross between the winter type SGDH284 and the semi-winter type 158A. A linkage map encompassing 3268.01 cM was constructed using 2777 bin markers obtained from next-generation sequencing. The preliminary mapping results revealed 56 QTLs for the days to flowering in the six replicates in the three environments. Twelve consensus QTLs were identified by a QTL meta-analysis, two of which (cqDTF-C02 and cqDTF-C06) were designated as major QTLs. Based on the micro-collinearity of the target regions between B. napus and Arabidopsis, four genes possibly related to flowering time were identified in the cqDTF-C02 interval, and only one gene possibly related to flowering time was identified in the cqDTF-C06 interval. A tightly linked insertion–deletion marker for the cqFT-C02 locus was developed. These findings will aid the breeding of early maturing B. napus varieties.

Genetic mapping of QTLs for drought tolerance in chickpea (Cicer arietinum L.).

Chickpea yield is severely affected by drought stress, which is a complex quantitative trait regulated by multiple small-effect genes. Identifying genomic regions associated with drought tolerance component traits may increase our understanding of drought tolerance mechanisms and assist in the development of drought-tolerant varieties. Here, a total of 187 F8 recombinant inbred lines (RILs) developed from an interspecific cross between drought-tolerant genotype GPF 2 (Cicer arietinum) and drought-sensitive accession ILWC 292 (C. reticulatum) were evaluated to identify quantitative trait loci (QTLs) associated with drought tolerance component traits. A total of 21 traits, including 12 morpho-physiological traits and nine root-related traits, were studied under rainfed and irrigated conditions. Composite interval mapping identified 31 QTLs at Ludhiana and 23 QTLs at Faridkot locations for morphological and physiological traits, and seven QTLs were identified for root-related traits. QTL analysis identified eight consensus QTLs for six traits and five QTL clusters containing QTLs for multiple traits on linkage groups CaLG04 and CaLG06. The identified major QTLs and genomic regions associated with drought tolerance component traits can be introgressed into elite cultivars using genomics-assisted breeding to enhance drought tolerance in chickpea.

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.

Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population

BackgroundMaize kernel row number (KRN) is one of the most important yield traits and has changed greatly during maize domestication and selection. Elucidating the genetic basis of KRN will be helpful to improve grain yield in maize.ResultsHere, we measured KRN in four environments using a nested association mapping (NAM) population named HNAU-NAM1 with 1,617 recombinant inbred lines (RILs) that were derived from 12 maize inbred lines with a common parent, GEMS41. Then, five consensus quantitative trait loci (QTLs) distributing on four chromosomes were identified in at least three environments along with the best linear unbiased prediction (BLUP) values by the joint linkage mapping (JLM) method. These QTLs were further validated by the separate linkage mapping (SLM) and genome-wide association study (GWAS) methods. Three KRN genes cloned through the QTL assay were found in three of the five consensus QTLs, including qKRN1.1, qKRN2.1 and qKRN4.1. Two new QTLs of KRN, qKRN4.2 and qKRN9.1, were also identified. On the basis of public RNA-seq and genome annotation data, five genes highly expressed in ear tissue were considered candidate genes contributing to KRN.ConclusionsThis study carried out a comprehensive analysis of the genetic architecture of KRN by using a new NAM population under multiple environments. The present results provide solid information for understanding the genetic components underlying KRN and candidate genes in qKRN4.2 and qKRN9.1. Single-nucleotide polymorphisms (SNPs) closely linked to qKRN4.2 and qKRN9.1 could be used to improve inbred yield during molecular breeding in maize.

Mapping Growth-Related Quantitative Trait Locus (QTL) in Commercial Yellow River Carp (Cyprinus carpio haematopterus) during Overwintering

The common carp (Cyprinus carpio) is farmed globally; as a subspecies, the Yellow River carp (C. c. haematopterus) is widely farmed in northern China. Since the Yellow River carp grows slowly in winter, it would benefit breeders to implement a breeding program targeting growth in winter. In May 2017, we established a Yellow River carp family and measured the body length, height, thickness, and weight of individuals before (October 2017) and after winter (February 2018). Based on the genetic linkage map established in our laboratory, the quantitative trait locus (QTL) mapping of these four traits led to the detection of 29 growth-related QTLs, of which 9, 7, 3, and 10 QTLs were associated with body length, height, thickness, and body weight, respectively. One consensus QTL contained seven QTLs associated with four traits. Through the two major-effect QTLs (qBL-p-1 and qBL-p-2), we screened three candidate genes (fnd3b, ghsr, and pld1), which were associated with biological activities, such as fat formation, growth, and membrane transport. These results are helpful in exploring the growth regulation mechanism and providing a reference for genetic breeding with regard to the overwintering growth of Yellow River carp.

Identification of candidate genes related to soluble sugar contents in soybean seeds using multiple genetic analyses

Soluble sugar content in seeds is an important quality trait of soybean. In this study, 57 quantitative trait loci (QTLs) related to soluble sugar contents in soybean seeds were collected from databases and published papers. After meta-overview-collinearity integrated analysis to refine QTL intervals, eight consensus QTLs were identified. To further verify the consensus QTLs, a population of chromosome segment substitution lines (CSSLs) was analyzed. Two lines containing fragments covering the regions of consensus QTLs and the recurrent parent were selected: one line showed high soluble sugar contents associated with a consensus QTL fragment, and the other line showed low soluble sugar contents. Transcriptome sequencing was conducted for these two lines at the early, middle, and late stages of seed development, which identified 158, 109 and 329 differentially expressed genes, respectively. Based on the analyses of re-sequencing data of the CSSLs and the consensus QTL region, three candidate genes (Glyma.19G146800, Glyma.19G122500, and Glyma.19G128500) were identified in the genetic fragments introduced from wild soybean. Sequence comparisons between the two CSSL parents SN14 and ZYD00006 revealed a single nucleotide polymorphism (SNP) mutation in the coding sequence of Glyma.19G122500, causing a non-synonymous mutation in the amino acid sequence that affected the predicted protein structure. A Kompetitive allele-specific PCR (KASP) marker was developed based on this SNP and used to evaluate the CSSLs. These results lay the foundation for further research to identify genes related to soluble sugar contents in soybean seeds and for future soybean breeding.

Systematic trait dissection in oilseed rape provides a comprehensive view, further insight, and exact roadmap for yield determination

BackgroundYield is the most important and complex trait that is influenced by numerous relevant traits with very complicated interrelations. While there are a large number of studies on the phenotypic relationship and genetic basis of yield traits, systematic studies with further dissection focusing on yield are limited. Therefore, there is still lack of a comprehensive and in-depth understanding of the determination of yield.ResultsIn this study, yield was systematically dissected at the phenotypic, genetic to molecular levels in oilseed rape (Brassica napus L.). The analysis of correlation, network, and principal component for 21 traits in BnaZN-RIL population showed that yield was determined by a complex trait network with key contributors. The analysis of the constructed high-density single nucleotide polymorphism (SNP) linkage map revealed the concentrated distribution of distorted and heterozygous markers, likely due to selection on genes controlling the growth period and yield heterosis. A total of 134 consensus quantitative trait loci (QTL) were identified for 21 traits, of which all were incorporated into an interconnecting QTL network with dozens of hub-QTL. Four representative hub-QTL were further dissected to the target or candidate genes that governed the causal relationships between the relevant traits.ConclusionsThe highly consistent results at the phenotypic, genetic, and molecular dissecting demonstrated that yield was determined by a multilayer composite network that involved numerous traits and genes showing complex up/down-stream and positive/negative regulation. This provides a systematic view, further insight, and exact roadmap for yield determination, which represents a significant advance toward the understanding and dissection of complex traits.

Meta-QTL analysis associated with bacterial stalk rot resistance in maize (Zea mays L.) segregating populations

Bacterial stalk rot (BSR) caused by Dickeya zeae is one of the important diseases of maize that significantly affects maize yield performance. Resistance to D. zeae is influenced by high humidity and temperature. Affected tissues are described as soft, mushy, and emitting a foul odor. Yield losses can reach approximately 98.8% of the grower‟s potential. Quantitative trait locus (QTL) mapping experiments using seven biparental populations were conducted at Syngenta Philippines, Inc., from 2014–2020 to locate consistent QTL and markers involved in BSR resistance. The QTL detected in NMM033, NMM073, NMM089, NMM090, NMM091, NMX003, and NMX001 populations were used to estimate the numbers and positions of consensus QTL with BioMercator V4.2.3 software. Meta-analysis for BSR resistance was conducted by considering all QTL for BSR resistance traits identified in 2014–2020. Among the 49 distinct markers on chromosomes (chrs) 1 to 10, eight most significant loci were detected, i.e., MSRQTL1-1, MSRQTL2-1, MSRQTL3-1, MSRQTL3-2, MSRQTL5-1, MSRQTL5-2, MSRQTL6-1, and MSRQTL10-1. Meta-QTL were identified in chrs 1, 5, and 10 in four populations; in chr 2 in three populations; and in chrs 3 and 6 in two populations evaluated in this study. The regions identified in chrs 1, 2, 3, 5, 6, and 10 with high QTL colocalization across biparental populations were considered as important QTL for BSR resistance traits. Further implementation through fine-mapping is recommended for marker development. The impact of this discovery would strengthen downstream applications in marker-assisted backcrossing and is not only limited to maize BSR resistance but also to other native traits of different crops.

Genetic linkage map construction and identification of QTLs associated with agronomic traits in bambara groundnut (Vigna subterranea (L.) Verdc.) using DArTseq‐based SNP markers

AbstractBambara groundnut [Vigna subterranea (L.) Verdc.] is an underutilised, protein‐rich and self‐pollinating legume that can withstand high temperature and drought stress and is mainly grown in semi‐arid Africa. In order to dissect the complexity of drought resistance and to use genomic tools for yield enhancement of bambara groundnut in response to drought stress, yield‐related and morphological traits under drought‐stressed (DS) and well‐watered (WW) conditions were evaluated in the F3 and F4 segregating generations derived from a cross between two genotypes selected from landraces S19‐3 (originally from Namibia) and DodR (originally from Tanzania). Significant quantitative trait loci (QTLs) for shoot dry weight (SDW) were mapped on LG10 accounting for 15.5% of the phenotypic variation explanation (PVE) under well‐watered conditions and a putative quantitative trait locus (QTL) for the same trait mapped on LG10 with reduced PVE (10.10%) under drought‐stressed conditions in the F3 segregating population. Significant QTLs associated with the number of seeds per plant (NS), number of double‐seeded pods per plant (NDP), seed weight per plant (SW) and pod weight per plant (PW) were mapped on LG4 (nearest marker: 4181663 and 4175954) with overlapping confidence intervals and explained 21.9%, 21.8%, 23.5% and 19.9% of the PVE, respectively, under well‐watered conditions in the F4 population, which could be considered as the major QTL involved in the control of these traits. Seven consensus QTLs for yield‐related and morphological traits were mapped on LG2, LG3, LG4, LG7A and LG10. The study provides fundamental knowledge of QTLs associated with yield‐related and morphological traits under drought‐stressed and well‐watered conditions in bambara groundnut, which is also essential for yield improvement of bambara groundnut in response to drought stress.

Identification of Finely Mapped Quantitative Trait Locus and Candidate Gene Mining for the Three-Seeded Pod Trait in Soybean.

The three-seeded pod number is an important trait that positively influences soybean yield. Soybean variety with increased three-seeded pod number contributes to the seed number/plant and higher yield. The candidate genes of the three-seeded pod may be the key for improving soybean yield. In this study, identification and validation of candidate genes for three-seeded pod has been carried out. First, a total of 36 quantitative trait locus (QTL) were detected from the investigation of recombinant inbred lines including 147 individuals derived from a cross between Charleston and Dongning 594 cultivars. Five consensus QTLs were integrated. Second, an introgressed line CSSL-182 carrying the target segment for the trait from the donor parent was selected to verify the consensus QTL based on its phenotype. Third, a secondary group was constructed by backcrossing with CSSL-182, and two QTLs were confirmed. There were a total of 162 genes in the two QTLs. The mining of candidate genes resulted in the annotation of eight genes with functions related to pod and seed sets. Finally, haplotype analysis and quantitative reverse transcriptase real-time PCR were carried to verify the candidate genes. Four of these genes had different haplotypes in the resource group, and the differences in the phenotype were highly significant. Moreover, the differences in the expression of the four genes during pod and seed development were also significant. These four genes were probably related to the development process underlying the three-seeded pod in soybean. Herein, we discuss the past and present studies related to the three-seeded pod trait in soybean.

QTL Analysis of Five Silique-Related Traits in Brassica napus L. Across Multiple Environments.

As an important physiological and reproductive organ, the silique is a determining factor of seed yield and a breeding target trait in rapeseed (Brassica napus L.). Genetic studies of silique-related traits are helpful for rapeseed marker-assisted high-yield breeding. In this study, a recombinant inbred population containing 189 lines was used to perform a quantitative trait loci (QTLs) analysis for five silique-related traits in seven different environments. As a result, 120 consensus QTLs related to five silique-related traits were identified, including 23 for silique length, 25 for silique breadth, 29 for silique thickness, 22 for seed number per silique and 21 for silique volume, which covered all the chromosomes, except C5. Among them, 13 consensus QTLs, one, five, two, four and one for silique length, silique breadth, silique thickness, seed number per silique and silique volume, respectively, were repeatedly detected in multiple environments and explained 4.38–13.0% of the phenotypic variation. On the basis of the functional annotations of Arabidopsis homologous genes and previously reported silique-related genes, 12 potential candidate genes underlying these 13 QTLs were screened and found to be stable in multiple environments by analyzing the re-sequencing results of the two parental lines. These findings provide new insights into the gene networks affecting silique-related traits at the QTL level in rapeseed.

Identification of quantitative trait loci and candidate genes controlling seed pigments of rapeseed

Rapeseed (Brassica napus L.) is an important source of edible vegetable oil and feed protein; however, seed pigments affect the quality of rapeseed oil and the feed value of the residue from oil pressing. Here, we used a population of rapeseed recombinant inbred lines (RILs) derived from the black-seeded male parent cultivar Zhongyou 821 and the yellow-seeded female parent line GH06 to map candidate genes controlling seed pigments in embryos and the seed coat. We detected 94 quantitative trait loci (QTLs) for seed pigments (44 for embryos and 50 for seed coat), distributed over 15 of the 19 rapeseed chromosomes. These included 28 QTLs for anthocyanidin content, explaining 2.41–44.66% of phenotypic variation; 24 QTLs for flavonoid content, explaining 2.41–20.26% of phenotypic variation; 16 QTLs for total phenol content, accounting for 2.74–23.68% of phenotypic variation; and 26 QTLs for melanin content, accounting for 2.37–24.82% of phenotypic variation, indicating that these traits are under multigenic control. Consensus regions on chromosomes A06, A09 and C08 were associated with multiple seed pigment traits, including 15, 19 and 10 QTLs, respectively, most of which were major QTLs explaining >10% of the phenotypic variation. Based on the annotation of the B. napus “Darmor-bzh” reference genome, 67 candidate genes were predicted from these consensus QTLs regions, and 12 candidate genes were identified as potentially involved in pigment accumulation by RNA-seq and qRT-PCR analysis. These preliminary results provide insight into the genetic architecture of pigment biosynthesis and lay a foundation for exploring the molecular mechanisms underlying seed coat color in B. napus.

Dissecting the Genetic Basis of Flowering Time and Height Related-Traits Using Two Doubled Haploid Populations in Maize.

In the field, maize flowering time and height traits are closely linked with yield, planting density, lodging resistance, and grain fill. To explore the genetic basis of flowering time and height traits in maize, we investigated six related traits, namely, days to anthesis (AD), days to silking (SD), the anthesis–silking interval (ASI), plant height (PH), ear height (EH), and the EH/PH ratio (ER) in two locations for two years based on two doubled haploid (DH) populations. Based on the two high-density genetic linkage maps, 12 and 22 quantitative trait loci (QTL) were identified, respectively, for flowering time and height-related traits. Of these, ten QTLs had overlapping confidence intervals between the two populations and were integrated into three consensus QTLs (qFT_YZ1a, qHT_YZ5a, and qHT_YZ7a). Of these, qFT_YZ1a, conferring flowering time, is located at 221.1–277.0 Mb on chromosome 1 and explained 10.0–12.5% of the AD and SD variation, and qHT_YZ5a, conferring height traits, is located at 147.4–217.3 Mb on chromosome 5 and explained 11.6–15.3% of the PH and EH variation. These consensus QTLs, in addition to the other repeatedly detected QTLs, provide useful information for further genetic studies and variety improvements in flowering time and height-related traits.

QTL Mapping and Diurnal Transcriptome Analysis Identify Candidate Genes Regulating Brassica napus Flowering Time.

Timely flowering is important for seed formation and maximization of rapeseed (Brassica napus) yield. Here, we performed flowering-time quantitative trait loci (QTL) mapping using a double haploid (DH) population grown in three environments to study the genetic architecture. Brassica 60 K Illumina Infinium™ single nucleotide polymorphism (SNP) array and simple sequence repeat (SSR) markers were used for genotyping of the DH population, and a high-density genetic linkage map was constructed. QTL analysis of flowering time from the three environments revealed five consensus QTLs, including two major QTLs. A major QTL located on chromosome A03 was detected specifically in the semi-winter rapeseed growing region, and the one on chromosome C08 was detected in all environments. Ribonucleic acid sequencing (RNA-seq) was performed on the parents’ leaves at seven time-points in a day to determine differentially expressed genes (DEGs). The biological processes and pathways with significant enrichment of DEGs were obtained. The DEGs in the QTL intervals were analyzed, and four flowering time-related candidate genes were found. These results lay a foundation for the genetic regulation of rapeseed flowering time and create a rapeseed gene expression library for seven time-points in a day.

Fine mapping and candidate gene analysis of a major locus controlling ovule abortion and seed number per silique in Brassica napus L.

A major QTL controlling ovule abortion and SN was fine-mapped to a 80.1-kb region on A8 in rapeseed, and BnaA08g07940D and BnaA08g07950D are the most likely candidate genes. The seed number per silique (SN), an important yield determining trait of rapeseed, is the final consequence of a complex developmental process including ovule initiation and the subsequent ovule/seed development. To explore the genetic mechanism regulating the natural variation of SN and its related components, quantitative trait locus (QTL) mapping was conducted using a doubled haploid (DH) population derived from the cross between C4-146 and C4-58B, which showed significant differences in SN and aborted ovule number (AON), but no obvious differences in ovule number (ON). QTL analysis identified 19 consensus QTLs for six SN-related traits across three environments. A novel QTL on chromosome A8, un.A8, which associates with multiple traits, except for ON, was stably detected across the three environments. This QTL explained more than 50% of the SN, AON and percentage of aborted ovules (PAO) variations as well as a moderate contribution on silique length (SL) and thousand seed weight (TSW). The C4-146 allele at the locus increases SN and SL but decreases AON, PAO and TSW. Further fine mapping narrowed down this locus into an 80.1-kb interval flanked by markers BM1668 and BM1672, and six predicted genes were annotated in the delimited region. Expression analyses and DNA sequencing showed that two homologs of Arabidopsis photosystem I subunit F (BnaA08g07940D) and zinc transporter 10 precursor (BnaA08g07950D) were the most promising candidate genes underlying this locus. These results provide a solid basis for cloning un.A8 to reduce the ovule abortion and increase SN in the yield improvement of rapeseed.