Dissection Of Quantitative Trait Loci Research Articles

Genetic dissection of major QTL for grain number per spike on chromosomes 5A and 6A in bread wheat (Triticum aestivum L.).

Grain number per spike (GNS) is a crucial component of grain yield and plays a significant role in improving wheat yield. To identify quantitative trait loci (QTL) associated with GNS, a recombinant inbred line (RIL) population derived from the cross of Zhongkemai 13F10 and Chuanmai 42 was employed to conduct QTL mapping across eight environments. Based on the bulked segregant exome sequencing (BSE-Seq), genomic regions associated with GNS were detected on chromosomes 5A and 6A. According to the constructed genetic maps, two major QTL QGns.cib-5A (LOD = 4.35-8.16, PVE = 8.46-14.43%) and QGns.cib-6A (LOD = 3.82-30.80, PVE = 5.44-12.38%) were detected in five and four environments, respectively. QGns.cib-6A is a QTL cluster for other seven yield-related traits. QGns.cib-5A and QGns.cib-6A were further validated using linked Kompetitive Allele Specific PCR (KASP) markers in different genetic backgrounds. QGns.cib-5A exhibited pleiotropic effects on productive tiller number (PTN), spike length (SL), fertile spikelet number per spike (FSN), and ratio of grain length to grain width (GL/GW) but did not significantly affect thousand grain weight (TGW). Haplotype analysis revealed that QGns.cib-5A and QGns.cib-6A were the targets of artificial selection during wheat improvement. Candidate genes for QGns.cib-5A and QGns.cib-6A were predicted by analyzing gene annotation, spatiotemporal expression patterns, and orthologous and sequence differences. These findings will be valuable for fine mapping and map-based cloning of genes underlying QGns.cib-5A and QGns.cib-6A.

Dissection of QTLs conferring drought tolerance in B. carinata derived B. juncea introgression lines

BackgroundDrought is one of the important abiotic stresses that can significantly reduce crop yields. In India, about 24% of Brassica juncea (Indian mustard) cultivation is taken up under rainfed conditions, leading to low yields due to moisture deficit stress. Hence, there is an urgent need to improve the productivity of mustard under drought conditions. In the present study, a set of 87 B. carinata-derived B. juncea introgression lines (ILs) was developed with the goal of creating drought-tolerant genotypes.MethodThe experiment followed the augmented randomized complete block design with four blocks and three checks. ILs were evaluated for seed yield and its contributing traits under both rainfed and irrigated conditions in three different environments created by manipulating locations and years. To identify novel genes and alleles imparting drought tolerance, Quantitative Trait Loci (QTL) analysis was carried out. Genotyping-by-Sequencing (GBS) approach was used to construct the linkage map.ResultsThe linkage map consisted of 5,165 SNP markers distributed across 18 chromosomes and spanning a distance of 1,671.87 cM. On average, there was a 3.09 cM gap between adjoining markers. A total of 29 additive QTLs were identified for drought tolerance; among these, 17 (58.6% of total QTLs detected) were contributed by B. carinata (BC 4), suggesting a greater contribution of B. carinata towards improving drought tolerance in the ILs. Out of 17 QTLs, 11 (64.7%) were located on the B genome, indicating more introgression segments on the B genome of B. juncea. Eight QTL hotspots, containing two or more QTLs, governing seed yield contributing traits, water use efficiency, and drought tolerance under moisture deficit stress conditions were identified. Seventeen candidate genes related to biotic and abiotic stresses, viz., SOS2, SOS2 like, NPR1, FAE1-KCS, HOT5, DNAJA1, NIA1, BRI1, RF21, ycf2, WRKY33, PAL, SAMS2, orf147, MAPK3, WRR1 and SUS, were reported in the genomic regions of identified QTLs.ConclusionsThe significance of B. carinata in improving drought tolerance and WUE by introducing genomic segments in Indian mustard is well demonstrated. The findings also provide valuable insights into the genetic basis of drought tolerance in mustard and pave the way for the development of drought-tolerant varieties.

Meta-Analysis of Microarray Data and Their Utility in Dissecting the Mapped QTLs for Heat Acclimation in Rice.

In the current global warming scenario, it is imperative to develop crops with improved heat tolerance or acclimation, for which knowledge of major heat stress-tolerant genes or genomic regions is a prerequisite. Though several quantitative trait loci (QTLs) for heat tolerance have been mapped in rice, candidate genes from these QTLs have not been reported yet. The meta-analysis of microarray datasets for heat stress in rice can give us a better genomic resource for the dissection of QTLs and the identification of major candidate genes for heat stress tolerance. In the present study, a database, RiceMetaSys-H, comprising 4227 heat stress-responsive genes (HRGs), was created using seven publicly available microarray datasets. This included in-house-generated microarray datasets of Nagina 22 (N22) and IR64 subjected to 8 days of heat stress. The database has provisions for searching the HRGs through genotypes, growth stages, tissues, and physical intervals in the genome, as well as Locus IDs, which provide complete information on the HRGs with their annotations and fold changes, along with the experimental material used for the analysis. The up-regulation of genes involved in hormone biosynthesis and signalling, sugar metabolism, carbon fixation, and the ROS pathway were found to be the key mechanisms of enhanced heat tolerance. Integrating variant and expression analysis, the database was used for the dissection of the major effect of QTLs on chromosomes 4, 5, and 9 from the IR64/N22 mapping population. Out of the 18, 54, and 62 genes in these three QTLs, 5, 15, and 12 genes harboured non-synonymous substitutions. Fifty-seven interacting genes of the selected QTLs were identified by a network analysis of the HRGs in the QTL regions. Variant analysis revealed that the proportion of unique amino acid substitutions (between N22/IR64) in the QTL-specific genes was much higher than the common substitutions, i.e., 2.58:0.88 (2.93-fold), compared to the network genes at a 0.88:0.67 (1.313-fold) ratio. An expression analysis of these 89 genes showed 43 DEGs between IR64/N22. By integrating the expression profiles, allelic variations, and the database, four robust candidates (LOC_Os05g43870, LOC_Os09g27830, LOC_Os09g27650, andLOC_Os09g28000) for enhanced heat stress tolerance were identified. The database thus developed in rice can be used in breeding to combat high-temperature stress.

Genetic dissection of QTLs for oil content in four maize DH populations.

Oil is one of the main components in maize kernels. Increasing the total oil content (TOC) is favorable to optimize feeding requirement by improving maize quality. To better understand the genetic basis of TOC, quantitative trait loci (QTL) in four double haploid (DH) populations were explored. TOC exhibited continuously and approximately normal distribution in the four populations. The moderate to high broad-sense heritability (67.00-86.60%) indicated that the majority of TOC variations are controlled by genetic factors. A total of 16 QTLs were identified across all chromosomes in a range of 3.49-30.84% in term of phenotypic variation explained. Among them, six QTLs were identified as the major QTLs that explained phenotypic variation larger than 10%. Especially, qOC-1-3 and qOC-2-3 on chromosome 9 were recognized as the largest effect QTLs with 30.84% and 21.74% of phenotypic variance, respectively. Seventeen well-known genes involved in fatty acid metabolic pathway located within QTL intervals. These QTLs will enhance our understanding of the genetic basis of TOC in maize and offer prospective routes to clone candidate genes regulating TOC for breeding program to cultivate maize varieties with the better grain quality.

Genetic dissection of quantitative trait loci for flag leaf size in bread wheat (Triticum aestivum L.).

Flag leaf size is a crucial trait influencing plant architecture and yield potential in wheat. A recombinant inbred line (RIL) population derived from the cross of W7268 and Chuanyu 12 was employed to identify quantitative trait loci (QTL) controlling flag leaf length (FLL), flag leaf width (FLW), and flag leaf area (FLA) in six environments and the best linear unbiased estimator (BLUE) datasets. Using a 55K SNP-based genetic map, six major and stable QTL were detected with 6.33-53.12% of explained phenotypic variation. Except for QFlw.cib-4B.3, the other five major QTL were co-located within two intervals on chromosomes 2B and 2D, namely QFll/Fla.cib-2B and QFll/Flw/Fla.cib-2D, respectively. Their interactions and effects on the corresponding traits and yield-related traits were also assessed based on flanking markers. QFll/Fla.cib-2B showed pleiotropic effects on spikelet number per spike (SNS). QFlw.cib-4B.3 and QFll/Flw/Fla.cib-2D had effects on grain number per spike (GNS) and thousand-grain weight (TGW). Comparison analysis suggested that QFll/Fla.cib-2B was likely a new locus. Two candidate genes, TraesCS2B03G0222800 and TraesCS2B03G0230000, associated with leaf development within the interval of QFll/Fla.cib-2B were identified based on expression-pattern analysis, gene annotation, ortholog analysis, and sequence variation. The major QTL and markers reported here provide valuable information for understanding the genetic mechanism underlying flag leaf size as well as breeding utilization in wheat.

Genetic dissection of quantitative trait loci for spikelets compactness in two Yanzhan1‐derived recombinant inbred line wheat populations

AbstractSpikelets compactness (SC) plays an important role in the yield improvement and Fusarium head blight (FHB) resistance of wheat. In this study, 151 recombinant inbred lines (RIL) derived from a cross between Yangmai 4 (YM4) and Yanzhan 1 (YZ1) and 143 RIL derived from a cross between Yangmai 5 (YM5) and YZ1 were used to construct two wheat hexaploidy genetic linkage maps, evaluate SC across three years and identify quantitative trait loci (QTL) for SC. A total of seven QTL were identified in two populations and explained the phenotypic variation ranging from 6.27% to 22.49%. Four QTL were co‐located in two genomic regions on chromosomes 2D and 6B, namely, QSc‐2D and QSc‐6B, respectively. Comparison analysis showed that QSc‐6B is a novel QTL, whereas QSc‐2D was located in the interval of Rht8. Significant additive effects of QSc‐2D and QSc‐6B on SC were observed in two RIL populations. Moreover, the positive alleles of these two QTL extremely significantly increased grain number per spike (GNS) but did not have a significantly negative effect on thousand‐grain weight (TGW), kernel length (KL) and kernel weight (KW). Whether in spray or point inoculation, QSc‐6B did not significantly influence FHB resistance in two populations. However, QSc‐2D significantly affected FHB resistance in spray inoculation in YM4/YZ1 population. A breeder‐friendly Kompetitive Allele‐Specific PCR (KASP) marker KASP_6B for QSc‐6B has been developed and verified in a set of 181 wheat cultivars/lines. Furthermore, we preliminary analysed the candidate genes within the physical interval of QSc‐6B by sequence similarity, spatial expression patterns and collinearity analysis. TraesCS6B03G1206900, TraesCS6B03G1214800 and TraesCS6B03G1218300 show specific expression in spikelet. These results provide valuable information for map‐based cloning of QSc‐6B and a useful tool for wheat breeding.

Genetic dissection of QTLs for starch content in four maize DH populations.

Starch is the principal carbohydrate source in maize kernels. Understanding the genetic basis of starch content (SC) benefits greatly in improving maize yield and optimizing end-use quality. Here, four double haploid (DH) populations were generated and were used to identify quantitative trait loci (QTLs) associated with SC. The phenotype of SC exhibited continuous and approximate normal distribution in each population. A total of 13 QTLs for SC in maize kernels was detected in a range of 3.65–16.18% of phenotypic variation explained (PVE). Among those, only some partly overlapped with QTLs previously known to be related to SC. Meanwhile, 12 genes involved in starch synthesis and metabolism located within QTLs were identified in this study. These QTLs will lay the foundation to explore candidate genes regulating SC in maize kernel and facilitate the application of molecular marker-assisted selection for a breeding program to cultivate maize varieties with a deal of grain quality.

Genetic dissection of quantitative trait loci for grain size and weight by high-resolution genetic mapping in bread wheat (Triticum aestivum L.).

Six major QTLs for wheat grain size and weight were identified on chromosomes 4A, 4B, 5A and 6A across multiple environments, and were validated in different genetic backgrounds. Grain size and weight are crucial components of wheat yield. Dissection of their genetic control is thus essential for the improvement of yield potential in wheat breeding. We used a doubled haploid (DH) population to detect quantitative trait loci (QTLs) for grain width (GW), grain length (GL), and thousand grain weight (TGW) in five environments. Six major QTLs, QGw.cib-4B.2, QGl.cib-4A, QGl.cib-5A.1, QGl.cib-6A, QTgw.cib-4B, and QTgw.cib-5A, were consistently identified in at least three individual environments and in best linear unbiased prediction (BLUP) datasets, and explained 5.65-34.06% of phenotypic variation. QGw.cib-4B.2, QTgw.cib-4B, QGl.cib-5A.1 and QGl.cib-6A had no effect on grain number per spike (GNS). In addition to QGl.cib-4A, the other major QTLs were further validated by using Kompetitive Allele Specific PCR (KASP) markers in different genetic backgrounds. Moreover, significant interactions between the three major GL QTLs and two major TGW QTLs were observed. Comparison analysis showed that QGl.cib-5A.1 and QGl.cib-6A are likely new loci. Notably, QGw.cib-4B.2 and QTgw.cib-4B were co-located on chromosome 4B and improved TGW by increasing only GW, unlike nearby or overlapped loci reported previously. Three genes associated with grain development within the QGw.cib-4B.2/QTgw.cib-4B interval were identified by searches on sequence similarity, spatial expression patterns, and orthologs. The major QTLs and KASP markers reported here will be useful for elucidating the genetic architecture of grain size and weight and for developing new wheat cultivars with high and stable yield.

QTL mapping for grain yield and three yield components in a population derived from two high-yielding spring wheat cultivars

Key messageFour genomic regions on chromosomes 4A, 6A, 7B, and 7D were discovered, each with multiple tightly linked QTL (QTL clusters) associated with two to three yield components. The 7D QTL cluster was associated with grain yield, fertile spikelet number per spike, thousand kernel weight, and heading date. It was located in the flanking region of FT-D1, a homolog gene of Arabidopsis FLOWERING LOCUS T, a major gene that regulates wheat flowering.Genetic manipulation of yield components is an important approach to increase grain yield in wheat (Triticum aestivum). The present study used a mapping population comprised of 181 doubled haploid lines derived from two high-yielding spring wheat cultivars, UI Platinum and LCS Star. The two cultivars and the derived population were assessed for six traits in eight field trials primarily in Idaho in the USA. The six traits were grain yield, fertile spikelet number per spike, productive tiller number per unit area, thousand kernel weight, heading date, and plant height. Quantitative Trait Locus (QTL) analysis of the six traits was conducted using 14,236 single-nucleotide polymorphism (SNP) markers generated from the wheat 90 K SNP and the exome and promoter capture arrays. Of the 19 QTL detected, 14 were clustered in four chromosomal regions on 4A, 6A, 7B and 7D. Each of the four QTL clusters was associated with multiple yield component traits, and these traits were often negatively correlated with one another. As a result, additional QTL dissection studies are needed to optimize trade-offs among yield component traits for specific production environments. Kompetitive allele-specific PCR markers for the four QTL clusters were developed and assessed in an elite spring wheat panel of 170 lines, and eight of the 14 QTL were validated. The two parents contain complementary alleles for the four QTL clusters, suggesting the possibility of improving grain yield via genetic recombination of yield component loci.

QTL mapping of agronomic traits in wheat using the UK Avalon × Cadenza reference mapping population grown in Kazakhstan

BackgroundThe success of wheat production is largely dependent on local breeding projects that focus on the development of high-yielding cultivars with the use of novel molecular tools. One strategy for improving wheat productivity involves the deployment of diverse germplasms with a high potential yield. An important factor for achieving success involves the dissection of quantitative trait loci (QTLs) for complex agronomic traits, such as grain yield components, in targeted environments for wheat growth.MethodsIn this study, we tested the United Kingdom (UK) spring set of the doubled haploid (DH) reference population derived from the cross between two British cultivars, Avalon (winter wheat) and Cadenza (spring wheat), in the Northern, Central, and Southern regions (Karabalyk, Karaganda, Kyzylorda) of Kazakhstan over three years (2013–2015). The DH population has previously been genotyped by UK scientists using 3647 polymorphic DNA markers. The list of tested traits includes the heading time, seed maturation time, plant height, spike length, productive tillering, number of kernels per spike, number of kernels per meter, thousand kernel weight, and yield per square meter. Windows QTL Cartographer was applied for QTL mapping using the composite interval mapping method.ResultsIn total, 83 out of 232 QTLs were identified as stable QTLs from at least two environments. A literature survey suggests that 40 QTLs had previously been reported elsewhere, indicating that this study identified 43 QTLs that are presumably novel marker-trait associations (MTA) for these environments. Hence, the phenotyping of the DH population in new environments led to the discovery of novel MTAs. The identified SNP markers associated with agronomic traits in the DH population could be successfully used in local Kazakh breeding projects for the improvement of wheat productivity.

Dissection of Quantitative Trait Loci (QTL), annotation of Single Nucleotide Polymorphism (SNP), and Identification of Candidate Genes for Grain Yield in Triticum turgidum L. var durum

Dissection of Quantitative Trait Loci (QTL), annotation of Single Nucleotide Polymorphism (SNP), and Identification of Candidate Genes for Grain Yield in Triticum turgidum L. var durum

Genetic dissection and validation of QTLs for grain shape and weight in rice and fine mapping of qGL1.3, a major QTL for grain length and weight

Grain shape is a key factor that influences both grain yield and quality of rice (Oryza sativa L.). In order to elucidate the genetic basis of grain shape, an F2 and derived F2:3 populations were developed from a cross between Guangzhan63-4S (GZ63-4S) and TGMS29, and used in a quantitative trait locus (QTL) analysis. A total of thirty-six QTLs were identified for grain shape and weight, and the phenotypic variance explained by each QTL ranged from 0.88 to 35.70%. Given that most of the QTLs were detected in only one population, a BC1F2 and derived BC1F2:3 populations were constructed with GZ63-4S as the recurrent parent to further verify the QTLs. Seventeen QTLs were validated in at least one population. qGL1.3, the major QTL for grain length in both the F2 and F2:3 populations, contributed 41.16% of the variation in grain length and 35.96% of the variation in 1000-grain weight in a random BC1F3 population, and thus was selected for further fine mapping. Based on progeny tests of 31 recombinants, qGL1.3 was delimited to a region of about 350 kb on chromosome 1. These results could lay the foundation for map-based cloning of qGL1.3 and provide genes or QTLs for breeding rice lines with high grain yield and quality.

Genetic dissection of epistatic and QTL by environment interaction effects in three bread wheat genetic backgrounds for yield-related traits under saline conditions

Salt stress represents a major impediment to global wheat production. Development of wheat varieties that offer tolerance to salt stress would increase productivity. Here we report on the results of a genetic study of salt tolerance in bread wheat across multiple genetic backgrounds and environments, with the goal of identifying quantitative trait loci (QTLs) for 9 yield-related traits that are both genetic background independent and environmentally stable. Three RIL populations derived from crosses between a super salt tolerant landrace (Roshan) and 3 bread-wheat cultivars (Falat, Sabalan, Superhead#2) that vary in salt tolerance were phenotyped in three environments. Genetic maps were constructed for each RIL population and independent analyses of each population/environment combination revealed significant associations of 92 genomic regions with the traits evaluated. Joint analyses of yield-related traits across all populations revealed a strong genetic background effect, with no QTLs shared across all genetic backgrounds. Fifty-seven QTLs identified in the independent analysis co-localized with those in the joint analysis. Overall, only 3 QTLs displayed significant epistatic interactions. Additionally, a total of 67 QTLs were identified in QTL analysis across environments, two of these (QSPL.3A, QBYI.7B-1) were both stable and not reported previously. Such novel and stable QTLs may accelerate marker-assisted breeding of new highly productive and salt tolerant bread-wheat varieties.

Dissection of two quantitative trait loci with pleiotropic effects on plant height and spike length linked in coupling phase on the short arm of chromosome 2D of common wheat (Triticum aestivum L.)

Key messageTwo QTL with pleiotropic effects on plant height and spike length linked in coupling phase on chromosome 2DS were dissected, and diagnostic marker for each QTL was developed.Plant height (PHT) is a crucial trait related to plant architecture and yield potential, and dissection of its underlying genetic basis would help to improve the efficiency of designed breeding in wheat. Here, two quantitative trait loci (QTL) linked in coupling phase on the short arm of chromosome 2D with pleiotropic effects on PHT and spike length, QPht/Sl.cau-2D.1 and QPht/Sl.cau-2D.2, were separated and characterized. QPht/Sl.cau-2D.1 is a novel QTL located between SNP makers BS00022234_51 and BobWhite_rep_c63957_1472. QPht/Sl.cau-2D.2 is mapped between two SSR markers, SSR-2062 and Xgwm484, which are located on the same genomic interval as Rht8. Moreover, the diagnostic marker tightly linked with each QTL was developed for the haplotype analysis using diverse panels of wheat accessions. The frequency of the height-reduced allele of QPht/Sl.cau-2D.1 is much lower than that of QPht/Sl.cau-2D.2, suggesting that this novel QTL may be an attractive target for genetic improvement. Consistent with a previous study of Rht8, a significant difference in cell length was observed between the NILs of QPht/Sl.cau-2D.2. By contrast, there was no difference in cell length between NILs of QPht/Sl.cau-2D.1, indicating that the underlying molecular mechanism for these two QTL may be different. Collectively, these data provide a new example of QTL dissection, and the developed diagnostic markers will be useful in marker-assisted pyramiding of QPht/Sl.cau-2D.1 and/or QPht/Sl.cau-2D.2 with the other genes in wheat breeding.

Dissection of two quantitative trait loci with pleiotropic effects on plant height and spike length linked in coupling phase on the short arm of chromosome 2D of common wheat (Triticum aestivum L.).

Two QTL with pleiotropic effects on plant height and spike length linked in coupling phase on chromosome 2DS were dissected, and diagnostic marker for each QTL was developed. Plant height (PHT) is a crucial trait related to plant architecture and yield potential, and dissection of its underlying genetic basis would help to improve the efficiency of designed breeding in wheat. Here, two quantitative trait loci (QTL) linked in coupling phase on the short arm of chromosome 2D with pleiotropic effects on PHT and spike length, QPht/Sl.cau-2D.1 and QPht/Sl.cau-2D.2, were separated and characterized. QPht/Sl.cau-2D.1 is a novel QTL located between SNP makers BS00022234_51 and BobWhite_rep_c63957_1472. QPht/Sl.cau-2D.2 is mapped between two SSR markers, SSR-2062 and Xgwm484, which are located on the same genomic interval as Rht8. Moreover, the diagnostic marker tightly linked with each QTL was developed for the haplotype analysis using diverse panels of wheat accessions. The frequency of the height-reduced allele of QPht/Sl.cau-2D.1 is much lower than that of QPht/Sl.cau-2D.2, suggesting that this novel QTL may be an attractive target for genetic improvement. Consistent with a previous study of Rht8, a significant difference in cell length was observed between the NILs of QPht/Sl.cau-2D.2. By contrast, there was no difference in cell length between NILs of QPht/Sl.cau-2D.1, indicating that the underlying molecular mechanism for these two QTL may be different. Collectively, these data provide a new example of QTL dissection, and the developed diagnostic markers will be useful in marker-assisted pyramiding of QPht/Sl.cau-2D.1 and/or QPht/Sl.cau-2D.2 with the other genes in wheat breeding.

Further dissection of QTLs for salt-induced stroke and identification of candidate genes in the stroke-prone spontaneously hypertensive rat

We previously revealed that two major quantitative trait loci (QTLs) for stroke latency of the stroke-prone spontaneously hypertensive rat (SHRSP) under salt-loading were located on chromosome (Chr) 1 and 18. Here, we attempted further dissection of the stroke-QTLs using multiple congenic strains between SHRSP and a stroke-resistant hypertensive rat (SHR). Cox hazard model among subcongenic strains harboring a chromosomal fragment of Chr-1 QTL region showed that the most promising region was a 2.1 Mbp fragment between D1Rat177 and D1Rat97. The QTL region on Chr 18 could not be narrowed down by the analysis, which may be due to multiple QTLs in this region. Nonsynonymous sequence variations were found in four genes (Cblc, Cxcl17, Cic, and Ceacam 19) on the 2.1 Mbp fragment of Chr-1 QTL by whole-genome sequence analysis of SHRSP/Izm and SHR/Izm. Significant changes in protein structure were predicted in CBL-C and CXCL17 using I-TASSER. Comprehensive gene expression analysis in the kidney with a cDNA microarray identified three candidate genes (LOC102548695 (Zinc finger protein 45-like, Zfp45L), Ethe1, and Cxcl17). In conclusion, we successfully narrowed down the QTL region on Chr 1, and identified six candidate genes in this region.

QNCLB7.02, a novel QTL for resistance to northern corn leaf blight in maize

Northern corn leaf blight (NCLB), which is caused by the hemibiotrophic fungal pathogen Setosphaeria turcica, is a devastating foliar disease that results in considerable maize yield losses. In the present study, quantitative trait locus (QTL) analysis was conducted across two environments using an ultra-high-density bin map constructed using recombinant inbred lines (RILs) derived from a cross between Ye478 and Qi319. A total of 11 QTLs, located on chromosomes 1, 4, 5, 6, 7, 8, 9, and 10, were detected that confer resistance to physiological race 0 of NCLB. Each QTL could explain 3.53–15.29% of the total phenotypic variation in disease resistance after artificial inoculation in two environments. Among these QTL, qNCLB7.02, which is located on chromosome 7, had the largest effect, accounting for 10.11 and 15.29% of the phenotypic variation in resistance in two field trials and BLUP. The common confidence interval (CI) for qNCLB7.02 was 1.4 Mb, according to the B73 RefGen_v3 sequence. The resistance effect of qNCLB7.02 was validated in 2016 by using chromosome segment substitution lines (CSSLs) derived from Qi319 as the donor in the genetic background of Ye478. The type 6 CSSL, which harbors introgressed qNCLB7.02, was found to be significantly associated with resistance to NCLB by linked marker bnlg1808 and exhibited greater resistance than the other CSSLs that did not carry this QTL (P = 0.0008). The combination of linkage mapping in RILs and validation in CSSLs is a powerful approach for the dissection of QTL for disease resistance in maize.

Genetic and Molecular Mechanisms of Quantitative Trait Loci Controlling Maize Inflorescence Architecture.

The establishment of inflorescence architecture is critical for the reproduction of flowering plant species. The maize plant generates two types of inflorescences, the tassel and the ear, and their architectures have a large effect on grain yield and yield-related traits that are genetically controlled by quantitative trait loci (QTLs). Since ear and tassel architecture are deeply affected by the activity of inflorescence meristems, key QTLs and genes regulating meristematic activity have important impacts on inflorescence development and show great potential for optimizing grain yield. Isolation of yield trait-related QTLs is challenging, but these QTLs have direct application in maize breeding. Additionally, characterization and functional dissection of QTLs can provide genetic and molecular knowledge of quantitative variation in inflorescence architecture. In this review, we summarize currently identified QTLs responsible for the establishment of ear and tassel architecture and discuss the potential genetic control of four ear-related and four tassel-related traits. In recent years, several inflorescence architecture-related QTLs have been characterized at the gene level. We review the mechanisms of these characterized QTLs.

Genetic dissection of QTLs and differentiation analysis of alleles for heading date genes in rice.

Heading date is an important agronomic trait in rice (Oryza sativa L.); it determines the geographical and seasonal adaptability of the crop. Single segment substitution lines (SSSLs) have become the preferred experimental materials in mapping functional genetic variations as the particular chromosome segments from donor genotypes can be evaluated for their impact on the phenotype in a recurrent recipient background. The phenotypic differences can be attributed to the control of quantitative trait loci (QTLs). Here, we evaluated a library consisting of 1,123 SSSLs in the same genetic background of an elite rice variety, Huajingxian74 (HJX74), and revealed four SSSLs, W05-1-11-2-7-6 (W05), W08-16-3-2 (W08), W12-28-58-03-19-1 (W12), and W22-9-5-2-4-9-3 (W22), which had a significantly different heading date compared to HJX74. To further genetically dissect the QTLs controlling heading date on chromosomes 3, 6, and 10, four SSSLs were used to develop 15 secondary SSSLs with the smaller substituted segments. The qHD-3 heading date QTL detected in W05 and W08 was delimited to an interval of 4.15 cM, whereas qHD-6-1 and qHD-6-2 heading date QTLs dissected from the substituted segments in W12 were mapped to the intervals of 2.25-cM and 2.55-cM, respectively. The qHD-10 QTL detected on the substituted segment in W22 was mapped to an interval of 6.85-cM. The nucleotide and amino acid sequence changes for those genes in the secondary SSSLs were also revealed. The allele variations of those genes might contribute to the heading date QTLs on chromosome 3 (DTH3, OsDof12, and EHD4), chromosome 6 (Hd3a, Hd17, and RFT1), and chromosome 10 (Ehd1 and Ehd2). These sequence variations in heading date genes would be useful resources for further studying the function of genes, and would be important for rice breeding. Overall, our results indicate that secondary SSSLs were powerful tools for genetic dissection of QTLs and identification of differentiation in the genes.

Genetic dissection of QTL against phosphate deficiency in the hybrid rice ‘Xieyou9308’

Phosphate (Pi) plays important roles in plant development and architecture. With the goal of identifying genomic regions that influence tolerance to Pi deficiency (TPDE) in hybrid rice (Oryza sativa L.), quantitative trait loci (QTL) were mapped using recombinant inbred lines (RILs) that were derived from a cross between tolerant ‘XieqingzaoB’ (XB) and susceptible ‘Zhonghui9308’. Six TPDE-related traits, including the root length, root dry weight, tillers number, shoot dry weight, total plant dry weight and root-to-shoot ratio, were evaluated for QTL analysis during both the tillering and heading stages. A correlation analysis showed that most of the traits were correlated with each other. Twenty-one additive QTL were detected and jointly explained between 10–49% of the trait variance, tending to cluster on chromosomes 4, 6, 10 and 11. Three QTL, qTPDE4 XB , qTPDE10 XB and qTPDE11.3 XB , were validated by the phenotypic evaluation using near isogenic lines (NILs, BC4F3) during the seedling stage. qTPDE4 XB showed the most stable tolerance against Pi deficiency. These QTL will enrich the genetic resources and accelerate hybrid rice breeding against Pi deficiency.