Consensus Quantitative Trait Loci Research Articles

Identification and validation of number of pod ‐ and seed ‐ related traits QTLs in soybean

AbstractTraits related to the number of pods and seeds are important yield factors on soybean. The relationships between phenotype and quantitative trait loci (QTLs) of these traits may reveal the mechanisms underlying productivity. Our study objectives were to analyse phenotypic correlations, detect stable QTLs and identify candidate genes useful for marker‐assisted selection. Phenotypic analyses revealed that NThSP (number of three‐seeded pods) was positively correlated with NPPP (number of pods per plant) and SNPP (number of seeds per plant). Seventy‐five QTLs were identified based on the mean phenotypic data for at least 2 years. We detected two to 15 and one to three significant QTLs identified at the same location, respectively. Six consensus QTLs associated with at least two NPS‐related (number of pods and seeds related) traits were identified. Two of these were verified in another population. The QTLs for NPPP, SNPP and NThSP formed a consensus QTL cluster on GM02. Another 27 QTLs also formed clusters in five regions. Fifteen candidate genes were mined and discussed. The results will provide more information to soybean breeding.

Consensus genetic linkage map construction and QTL mapping for plant height-related traits in linseed flax (Linum usitatissimum L.)

BackgroundFlax is an important field crop that can be used for either oilseed or fiber production. Plant height and technical length are important characters for flax. For linseed flax, plants usually have a short technical length and plant height than those for fiber flax. As an important agronomical character for fiber and linseed flax, plant height is usually a selection target for breeding. However, because of limited technologies and methods available, there has been little research focused on discovering the molecular mechanism controlling plant height.ResultsIn this study, two related recombinant inbred line (RIL) populations developed from crosses of linseed and fiber parents were developed and phenotyped for plant height and technical length in four environments. A consensus linkage map based on two RIL populations was constructed using SNP markers generated by genotyping by sequencing (GBS) technology. A total of 4497 single nucleotide polymorphism (SNP) markers were included on 15 linkage groups with an average marker density of one marker every 2.71 cM. Quantitative trait locus (QTL) mapping analysis was performed for plant height and technical length using the two populations. A total of 19 QTLs were identified for plant height and technical length. For the MH population, eight plant height QTLs and seven technical length QTLs were identified, five of which were common QTLs for both traits. For the PH population, six plant height and three technical length QTLs were identified. By comparing the QTLs and candidate gene information in the two population, two common QTLs and three candidate genes were discovered.ConclusionsThis study provides a foundation for map-based cloning of QTLs and marker-assisted selection for plant height-related traits in linseed and fiber flax.

Stable QTLs for Plant Height on Chromosome A09 Identified From Two Mapping Populations in Peanut (Arachis hypogaea L.).

The peanut (Arachis hypogaea L.) is an important grain legume extensively cultivated worldwide, supplying edible oil and protein for human consumption. As in many other crops, plant height is a crucial factor in determining peanut architecture traits and has a unique effect on resistance to lodging and efficiency of mechanized harvesting as well as yield. Currently, the genetic basis underlying plant height remains unclear in peanut, which have hampered marker-assisted selection in breeding. In this study, we conducted a quantitative trait locus (QTL) analysis for peanut plant height by using two recombinant inbred line (RIL) populations including “Yuanza 9102 × Xuzhou 68-4 (YX)” and “Xuhua 13 × Zhonghua 6 (XZ)”. In the YX population, 38 QTLs including 10 major QTLs from 9 chromosomes were detected in 4 environments, and 8 consensus QTLs integrated by meta-analysis expressed stably across multiple environments. In the XZ population, 3 major QTLs and seven minor QTLs from 6 chromosomes were detected across 3 environments. Generally, most major QTLs from the two populations were located on pseudomolecule chromosome 9 of Arachis duranesis (A09), indicating there would be key genes on A09 controlling plant height. Further analysis revealed that qPHA09.1a from the XZ population and one consensus QTL, cqPHA09.d from the YX population were co-localized in a reliable 3.4 Mb physical interval on A09, which harbored 161 genes including transcription factors and enzymes related to signaling transduction and cell wall formation. The major and stable QTLs identified in this study may be useful for further gene cloning and identification of molecular markers applicable for breeding.

Major Co-localized QTL for Plant Height, Branch Initiation Height, Stem Diameter, and Flowering Time in an Alien Introgression Derived Brassica napus DH Population.

Plant height (PH), branch initiation height (BIH), and stem diameter (SD) are three stem-related traits that play crucial roles in plant architecture and lodging resistance. Herein, we show one doubled haploid (DH) population obtained from a cross between Y689 (one Capsella bursa-pastoris derived Brassica napus intertribal introgression) and Westar (B. napus cultivar) that these traits were significantly positively correlated with one another and with flowering time (FT). Based on a high-density SNP map, a total of 102 additive quantitative trait loci (QTL) were identified across six environments. Seventy-two consensus QTL and 49 unique QTL were identified using a two-round strategy of QTL meta-analysis. Notably, a total of 19 major QTL, including 11 novel ones, were detected for these traits, which comprised two QTL clusters on chromosomes A02 and A07. Conditional QTL mapping was performed to preliminarily evaluate the genetic basis (pleiotropy or tight linkage) of the co-localized QTL. In addition, QTL by environment interactions (QEI) mapping was performed to verify the additive QTL and estimate the QEI effect. In the genomic regions of all major QTL, orthologs of the genes involved in phytohormone biosynthesis, phytohormone signaling, flower development, and cell differentiation in Arabidopsis were proposed as candidate genes. Of these, BnaA02g02560, an ortholog of Arabidopsis GASA4, was suggested as a candidate gene for PH, SD, and FT; and BnaA02g08490, an ortholog of Arabidopsis GNL, was associated with PH, BIH and FT. These results provide useful information for further genetic studies on stem-related traits and plant growth adaptation.

Meta‐analysis and overview analysis of quantitative trait locis associated with fatty acid content in soybean for candidate gene mining

AbstractAs soybean seed fatty acid content is valued in food, animal feed and some industrial applications, plant breeders continually aim to improve seed fatty acid constituent value. This study analysed 163 original quantitative trait loci (QTLs) related to soybean fatty acid content from databases and references and revealed 43 consensus QTLs. Meta‐analysis using BioMercator ver.2.1 indicated that these were located across 16 linkage groups (LGs) excluding LG D1a, LG C1, LG M and LG H. Moreover, the overview method was used to optimize these QTLs based on statistical analysis. Some valid QTL regions were narrowed down to 0.5 Mb and mapped on the same LGs as the meta‐analysis result. Furthermore, the functions of all genes located in these consensus QTL intervals were predicted and eight candidate genes were identified. KEGG pathway analysis indicated that Glyma.13G127900 and Glyma.18G232000 were involved in the fatty acid synthesis metabolic (pathway ID ko00071, ko00062, ko01040). These results lay a foundation for fine mapping of QTLs related to fatty acid content and marker‐assisted breeding in soybean.

Dynamic and epistatic QTL mapping reveals the complex genetic architecture of waterlogging tolerance in chrysanthemum.

37 unconditional QTLs, 51 conditional QTLs and considerable epistatic QTLs were detected for waterlogging tolerance, and six favourable combinations were selected accelerating the possible application of MAS in chrysanthemum breeding. Chrysanthemum is seriously impacted by soil waterlogging. To determine the genetic characteristics of waterlogging tolerance (WAT) in chrysanthemum, a population of 162 F1 lines was used to construct a genetic map to identify the dynamic and epistatic quantitative trait loci (QTLs) for four WAT traits: wilting index (WI), dead leaf ratio (DLR), chlorosis score (Score) and membership function value of waterlogging (MFVW). The h B2 for the WAT traits ranged from 0.49 to 0.64, and transgressive segregation was observed in both directions. A total of 37 unconditional consensus QTLs with 5.81-18.21% phenotypic variation explanation (PVE) and 51 conditional consensus QTLs with 5.90-24.56% PVE were detected. Interestingly, three unconditional consensus QTLs were consistently identified across different stages, whereas no conditional consensus QTLs were consistently expressed. In addition, considerable epistatic QTLs, all with PVE values ranging from 0.01 to 8.87%, were detected by a joint analysis of WAT phenotypes. These results illustrated that the QTLs (genes) controlling WAT were environmentally dependent and selectively expressed at different times and indicated that both additive and epistatic effects underlie the inheritance of WAT in chrysanthemum. The findings of the current study provide insights into the complex genetic architecture of WAT, and the identification of favourable alleles represents an important step towards the application of molecular marker-assisted selection (MAS) and QTL pyramiding in chrysanthemum WAT breeding programmes.

High Density Linkage Map Construction and QTL Detection for Three Silique-Related Traits in Orychophragmus violaceus Derived Brassica napus Population.

Seeds per silique (SS), seed weight (SW), and silique length (SL) are important determinant traits of seed yield potential in rapeseed (Brassica napus L.), and are controlled by naturally occurring quantitative trait loci (QTLs). Mapping QTLs to narrow chromosomal regions provides an effective means of characterizing the genetic basis of these complex traits. Orychophragmus violaceus is a crucifer with long siliques, many SS, and heavy seeds. A novel B. napus introgression line with many SS was previously selected from multiple crosses (B. rapa ssp. chinesis × O. violaceus) × B. napus. In present study, a doubled haploid (DH) population with 167 lines was established from a cross between the introgression line and a line with far fewer SS, in order to detect QTLs for silique-related traits. By screening with a Brassica 60K single nucleotide polymorphism (SNP) array, a high-density linkage map consisting of 1,153 bins and spanning a cumulative length of 2,209.1 cM was constructed, using 12,602 high-quality polymorphic SNPs in the DH population. The average recombination bin densities of the A and C subgenomes were 1.7 and 2.4 cM, respectively. 45 QTLs were identified for the three traits in all, which explained 4.0–34.4% of the total phenotypic variation; 20 of them were integrated into three unique QTLs by meta-analysis. These unique QTLs revealed a significant positive correlation between SS and SL and a significant negative correlation between SW and SS, and were mapped onto the linkage groups A05, C08, and C09. A trait-by-trait meta-analysis revealed eight, four, and seven consensus QTLs for SS, SW, and SL, respectively, and five major QTLs (cqSS.A09b, cqSS.C09, cqSW.A05, cqSW.C09, and cqSL.C09) were identified. Five, three, and four QTLs for SS, SW, and SL, respectively, might be novel QTLs because of the existence of alien genetic loci for these traits in the alien introgression. Thirty-eight candidate genes underlying nine QTLs for silique-related traits were identified.

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

Key messageA comprehensive linkage atlas for seed yield in rapeseed.Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.

Investigation of the Genetic Diversity and Quantitative Trait Loci Accounting for Important Agronomic and Seed Quality Traits in Brassica carinata.

Brassica carinata (BBCC) is an allotetraploid in Brassicas with unique alleles for agronomic traits and has huge potential as source for biodiesel production. To investigate the genome-wide molecular diversity, population structure and linkage disequilibrium (LD) pattern in this species, we genotyped a panel of 81 accessions of B. carinata with genotyping by sequencing approach DArTseq, generating a total of 54,510 polymorphic markers. Two subpopulations were exhibited in the B. carinata accessions. The average distance of LD decay (r2 = 0.1) in B subgenome (0.25 Mb) was shorter than that of C subgenome (0.40 Mb). Genome-wide association analysis (GWAS) identified a total of seven markers significantly associated with five seed quality traits in two experiments. To further identify the quantitative trait loci (QTL) for important agronomic and seed quality traits, we phenotyped a doubled haploid (DH) mapping population derived from the “YW” cross between two parents (Y-BcDH64 and W-BcDH76) representing from the two subpopulations. The YW DH population and its parents were grown in three contrasting environments; spring (Hezheng and Xining, China), semi-winter (Wuhan, China), and spring (Wagga Wagga, Australia) across 5 years for QTL mapping. Genetic bases of phenotypic variation in seed yield and its seven related traits, and six seed quality traits were determined. A total of 282 consensus QTL accounting for these traits were identified including nine major QTL for flowering time, oleic acid, linolenic acid, pod number of main inflorescence, and seed weight. Of these, 109 and 134 QTL were specific to spring and semi-winter environment, respectively, while 39 consensus QTL were identified in both contrasting environments. Two QTL identified for linolenic acid (B3) and erucic acid (C7) were validated in the diverse lines used for GWAS. A total of 25 QTL accounting for flowering time, erucic acid, and oleic acid were aligned to the homologous QTL or candidate gene regions in the C genome of B. napus. These results would not only provide insights for genetic improvement of this species, but will also identify useful genetic variation hidden in the Cc subgenome of B. carinata to improve canola cultivars.

Combining QTL and candidate gene analysis with phenotypic model to unravel the relationship between lodging and related traits in soybean

Lodging is one of the major influencing factors of yield and quality in soybean [Glycine max (L.) Merr.] and other crops. To dissect the genetic basis of lodging in soybean, a recombinant inbred line population consisting of 165 lines was used to evaluate lodging percentage and eight related traits (branch number, internode length, number of nodes, plant height, stem diameter, stem strength, root length, and root weight) in three environments. Regression analysis indicated that plant height and root weight, which explain more than 55% of the variation in lodging percentage, might be the key factors influencing lodging in soybean. Nine consensus quantitative trait locus (QTLs) of lodging percentage were detected in one to three environments. Of which, eight consensus QTLs were colocated with 16 consensus QTLs of lodging-related traits by meta-analysis. In addition, seven candidate genes with the biological functions of shoot branching, root development, internode elongation, and lignin biosynthesis were identified on four pleiotropic QTL regions (oq.13-1, oq.13-2, oq.19-2, and oq.19-3) for lodging percentage and related traits. These findings showed that the consensus QTLs of lodging percentage might result from the pleiotropic QTLs affecting the lodging-related traits. Soybean lodging is determined by the cumulative effect of many traits/processes of growth and development. The combination of MAS, statistical model, and phenotypic selection will provide a powerful breeding strategy for lodging resistance in soybean.

Genetic mapping of yield traits using RIL population derived from Fuchuan Dahuasheng and ICG6375 of peanut (Arachis hypogaea L.)

The genetic architecture determinants of yield traits in peanut (Arachis hypogaea L.) are poorly understood. In the present study, an effort was made to map quantitative trait loci (QTLs) for yield traits using recombinant inbred lines (RIL). A genetic linkage map was constructed containing 609 loci, covering a total of 1557.48 cM with an average distance of 2.56 cM between adjacent markers. The present map exhibited good collinearity with the physical map of diploid species of Arachis. Ninety-two repeatable QTLs were identified for 11 traits including height of main stem, total branching number, and nine pod- and seed-related traits. Of the 92 QTLs, 15 QTLs were expressed across three environments and 65 QTLs were newly identified. Twelve QTLs for the height of main stem and the pod- and seed-related traits explaining more than 10 % of phenotypic variation showed a great potential for marker-assisted selection in improving these traits. The trait-by-trait meta-analysis revealed 33 consensus QTLs. The consensus QTLs and other QTLs were further integrated into 29 pleiotropic unique QTLs with the confidence interval of 1.86 cM on average. The significant co-localization of QTLs was consistent with the significant phenotypic correlations among these traits. The complexity of the genetic architecture of yield traits was demonstrated. The present QTLs for pod- and seed-related traits could be the most fundamental genetic factors contributing to the yield traits in peanut. The results provide a good foundation for fine mapping, cloning and designing molecular breeding of favorable genes in peanut.

High-density ddRAD linkage and yield-related QTL mapping delimits a chromosomal region responsible for oil content in rapeseed (Brassica napus L.).

Rapeseed (Brassica napus L.) is one of the most important oil crops almost all over the world. Seed-related traits, including oil content (OC), silique length (SL), seeds per silique (SS), and seed weight (SW), are primary targets for oil yield improvement. To dissect the genetic basis of these traits, 192 recombinant inbred lines (RILs) were derived from two parents with distinct oil content and silique length. High-density linkage map with a total length of 1610.4 cM were constructed using 1,329 double-digestion restriction site associated DNA (ddRAD) markers, 107 insertion/deletions (INDELs), and 90 well-distributed simple sequence repeats (SSRs) markers. A total of 37 consensus quantitative trait loci (QTLs) were detected for the four traits, with individual QTL explained 3.1–12.8% of the phenotypic variations. Interestingly, one OC consensus QTL (cqOCA10b) on chromosome A10 was consistently detected in all three environments, and explained 9.8% to 12.8% of the OC variation. The locus was further delimited into an approximately 614 kb genomic region, in which the flanking markers could be further evaluated for marker-assisted selection in rapeseed OC improvement and the candidate genes targeted for map-based cloning and genetic manipulation.

Development and deployment of a high-density linkage map identified quantitative trait loci for plant height in peanut (Arachis hypogaea L.).

Plant height is one of the most important architecture traits in crop plants. In peanut, the genetic basis of plant height remains ambiguous. In this context, we genotyped a recombinant inbred line (RIL) population with 140 individuals developed from a cross between two peanut varieties varying in plant height, Zhonghua 10 and ICG 12625. Genotyping data was generated for 1,175 SSR and 42 transposon polymorphic markers and a high-density genetic linkage map was constructed with 1,219 mapped loci covering total map length of 2,038.75 cM i.e., accounted for nearly 80% of the peanut genome. Quantitative trait locus (QTL) analysis using genotyping and phenotyping data for three environments identified 8 negative-effect QTLs and 10 positive-effect QTLs for plant height. Among these QTLs, 8 QTLs had a large contribution to plant height that explained ≥10% phenotypic variation. Two major-effect consensus QTLs namely cqPHA4a and cqPHA4b were identified with stable performance across three environments. Further, the allelic recombination of detected QTLs proved the existence of the phenomenon of transgressive segregation for plant height in the RIL population. Therefore, this study not only successfully reported a high-density genetic linkage map of peanut and identified genomic region controlling plant height but also opens opportunities for further gene discovery and molecular breeding for plant height in peanut.

Quantitative Trait Locus Analysis of Late Leaf Spot Resistance and Plant-Type-Related Traits in Cultivated Peanut (Arachis hypogaea L.) under Multi-Environments.

Late leaf spot (LLS) is one of the most serious foliar diseases affecting peanut worldwide leading to huge yield loss. To understand the genetic basis of LLS and assist breeding in the future, we conducted quantitative trait locus (QTL) analysis for LLS and three plant-type-related traits including height of main stem (HMS), length of the longest branch (LLB) and total number of branches (TNB). Significant negative correlations were observed between LLS and the plant-type-related traits in multi-environments of a RIL population from the cross Zhonghua 5 and ICGV 86699. A total of 20 QTLs were identified for LLS, of which two QTLs were identified in multi-environments and six QTLs with phenotypic variation explained (PVE) more than 10%. Ten, seven, fifteen QTLs were identified for HMS, LLB and TNB, respectively. Of these, one, one, two consensus QTLs and three, two, three major QTLs were detected for HMS, LLB and TNB, respectively. Of all 52 unconditional QTLs for LLS and plant-type-related traits, 10 QTLs were clustered in five genetic regions, of which three clusters including five robust major QTLs overlapped between LLS and one of the plant-type-related traits, providing evidence that the correlation could be genetically constrained. On the other hand, conditional mapping revealed different numbers and different extent of additive effects of QTLs for LLS conditioned on three plant-type-related traits (HMS, LLB and TNB), which improved our understanding of interrelationship between LLS and plant-type-related traits at the QTL level. Furthermore, two QTLs, qLLSB6-7 and qLLSB1 for LLS resistance, were identified residing in two clusters of NB-LRR—encoding genes. This study not only provided new favorable QTLs for fine-mapping, but also suggested that the relationship between LLS and plant-type-related traits of HMS, LLB and TNB should be considered while breeding for improved LLS resistance in peanut.

Quantitative trait loci analysis of Verticillium wilt resistance in interspecific backcross populations of Gossypium hirsutum × Gossypium barbadense.

Background Verticillium wilt (VW) caused by Verticillium dahliae (Kleb) is one of the most destructive diseases of cotton. The identification of highly resistant QTLs or genes in the whole cotton genome is quite important for developing a VW-resistant variety and for further molecular design breeding.ResultsIn the present study, BC1F1, BC1S1, and BC2F1 populations derived from an interspecific backcross between the highly resistant line Hai1 (Gossypium barbadense L.) and the susceptible variety CCRI36 (G. hirsutum L.) as the recurrent parent were constructed. Quantitative trait loci (QTL) related to VW resistance were detected in the whole cotton genome using a high-density simple sequence repeat (SSR) genetic linkage map from the BC1F1 population, with 2292 loci covering 5115.16 centiMorgan (cM) of the cotton (AD) genome, and the data concerning VW resistance that were obtained from four dates of BC2F1 in the artificial disease nursery and one date of BC1S1 and BC2F1 in the field. A total of 48 QTLs for VW resistance were identified, and 37 of these QTLs had positive additive effects, which indicated that the G. barbadense alleles increased resistance to VW and decreased the disease index (DI) by about 2.2–10.7. These QTLs were located on 19 chromosomes, in which 33 in the A subgenome and 15 QTLs in the D subgenome. The 6 QTLs were found to be stable. The 6 QTLs were consistent with those identified previously, and another 42 were new, unreported QTLs, of which 31 QTLs were from G. barbadense. By meta-analysis, 17 QTL hotspot regions were identified and 10 of them were new, unreported hotspot regions. 29 QTLs in this paper were in 12 hotspot regions and were all from G. barbadense.ConclusionsThese stable or consensus QTL regions warrant further investigation to better understand the genetics and molecular mechanisms underlying VW resistance. This study provides useful information for further comparative analysis and marker-assisted selection in the breeding of disease-resistant cotton. It may also lay an important foundation for gene cloning and further molecular design breeding for the entire cotton genome.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3128-x) contains supplementary material, which is available to authorized users.

Identification of stable QTLs for seed oil content by combined linkage and association mapping in Brassica napus

Seed oil content is an important agricultural trait in rapeseed breeding. Although numerous quantitative trait locus (QTL) have been identified, most of them cannot be applied in practical breeding mainly due to environmental instability or large confidence intervals. The purpose of this study was to identify and validate high quality and more stable QTLs by combining linkage mapping and genome-wide association study (GWAS). For linkage mapping, we constructed two F2 populations from crosses of high-oil content (∼50%) lines 6F313 and 61616 with a low-oil content (∼40%) line 51070. Two high density linkage maps spanned 1987cM (1659 bins) and 1856cM (1746 bins), respectively. For GWAS, we developed more than 34,000 high-quality SNP markers based on 227 accessions. Finally, 40 QTLs and 29 associations were established by linkage and association mapping in different environments. After merging the results, 32 consensus QTLs were obtained and 7 of them were identified by both mapping methods. Seven overlapping QTLs covered an average confidence interval of 183kb and explained the phenotypic variation of 10.23 to 24.45%. We further developed allele-specific PCR primers to identify each of the seven QTLs. These stable QTLs should be useful in gene cloning and practical breeding application.

Rapid identification of fruit length loci in cucumber (Cucumis sativus L.) using next-generation sequencing (NGS)-based QTL analysis.

The cucumber (Cucumis sativus L.) exhibits extensive variations in fruit size and shape. Fruit length is an important agronomic and domesticated trait controlled by quantitative trait loci (QTLs). Nonetheless, the underlying molecular and genetic mechanisms that determine cucumber fruit length remain unclear. QTL-seq is an efficient strategy for QTL identification that takes advantage of bulked-segregant analysis (BSA) and next-generation sequencing (NGS). In the present study, we conducted QTL mapping and QTL-seq of cucumber fruit length. QTL mapping identified 8 QTLs for immature and mature fruit length. A major-effect QTL fl3.2, which explained a maximum of 38.87% of the phenotypic variation, was detected. A genome-wide comparison of SNP profiles between two DNA bulks identified 6 QTLs for ovary length. QTLs ovl3.1 and ovl3.2 both had major effects on ovary length with a △ (SNP-index) of 0.80 (P < 0.01) and 0.74 (P < 0.01), respectively. Quantitative RT-PCR of fruit size-related homologous genes localized in the consensus QTL FL3.2 was conducted. Four candidate genes exhibited increased expression levels in long fruit genotypes. Our results demonstrated the power of the QTL-seq method in rapid QTL detection and provided reliable QTL regions for fine mapping of fruit length-related loci and for identifying candidate genes.

Genome-Wide Identification of QTL for Seed Yield and Yield-Related Traits and Construction of a High-Density Consensus Map for QTL Comparison in Brassica napus.

Seed yield (SY) is the most important trait in rapeseed, is determined by multiple seed yield-related traits (SYRTs) and is also easily subject to environmental influence. Many quantitative trait loci (QTLs) for SY and SYRTs have been reported in Brassica napus; however, no studies have focused on seven agronomic traits simultaneously affecting SY. Genome-wide QTL analysis for SY and seven SYRTs in eight environments was conducted in a doubled haploid population containing 348 lines. Totally, 18 and 208 QTLs for SY and SYRTs were observed, respectively, and then these QTLs were integrated into 144 consensus QTLs using a meta-analysis. Three major QTLs for SY were observed, including cqSY-C6-2 and cqSY-C6-3 that were expressed stably in winter cultivation area for 3 years and cqSY-A2-2 only expressed in spring rapeseed area. Trait-by-trait meta-analysis revealed that the 144 consensus QTLs were integrated into 72 pleiotropic unique QTLs. Among them, all the unique QTLs affected SY, except for uq.A6-1, including uq.A2-3, uq.C1-2, uq.C1-3, uq.C6-1, uq.C6-5, and uq.C6-6 could also affect more than two SYRTs. According to the constructed high-density consensus map and QTL comparison from literatures, 36 QTLs from five populations were co-localized with QTLs identified in this study. In addition, 13 orthologous genes were observed, including five each gene for SY and thousand seed weight, and one gene each for biomass yield, branch height, and plant height. The genomic information of these QTLs will be valuable in hybrid cultivar breeding and in analyzing QTL expression in different environments.

Organ-Specific Quantitative Genetics and Candidate Genes of Phenylpropanoid Metabolism in Brassica oleracea.

Phenolic compounds are proving to be increasingly important for human health and in crop development, defense and adaptation. In spite of the economical importance of Brassica crops in agriculture, the mechanisms involved in the biosynthesis of phenolic compounds presents in these species remain unknown. The genetic and metabolic basis of phenolics accumulation was dissected through analysis of total phenolics concentration and its individual components in leaves, flower buds, and seeds of a double haploid (DH) mapping population of Brassica oleracea. The quantitative trait loci (QTL) that had an effect on phenolics concentration in each organ were integrated, resulting in 33 consensus QTLs controlling phenolics traits. Most of the studied compounds had organ-specific genomic regulation. Moreover, this information allowed us to propose candidate genes and to predict the function of genes underlying the QTL. A number of previously unknown potential regulatory regions involved in phenylpropanoid metabolism were identified and this study illustrates how plant ontogeny can affect a biochemical pathway.

Mapping of QTL conferring resistance to northern corn leaf blight using high-density SNPs in maize

Northern corn leaf blight (NCLB) is a prevalent foliar disease in maize. Deployment of resistant cultivars is an effective way to control NCLB. In this study, 207 recombinant inbred lines derived from a K22 × By815 cross were planted in Yangling, China, in 2012 and 2013. NCLB score and lesion size were investigated after artificial inoculation. Significant phenotypic variation in NCLB resistance was observed in both years. Using a genetic map containing high-density single-nucleotide polymorphisms with average genetic distance of 0.74 cM, quantitative trait loci (QTL) for NCLB score and lesion size were analyzed. For NCLB score, four and three QTL were identified in 2012 and 2013, respectively. Two stable QTL were identified in both years. Of these, qNCLB5.04, located on chromosome 5 (bin 5.04), had the largest resistance effect, accounting for 19 and 20 % of the phenotypic variation in 2012 and 2013, respectively. For lesion size, six QTL were identified. Of these, one consensus QTL was associated with both lesion length and width, and the other five were associated only with lesion width. Among all QTL identified, only qNCLB5.04 was associated with both NCLB score and lesion size. Thus, our mapping results suggest that qNCLB5.04 could be a desirable target for marker-assisted selection for NCLB resistance in maize breeding programs.