Multi-environment Quantitative Trait Loci Research Articles

Quantitative Trait Loci for Phenology, Yield, and Phosphorus Use Efficiency in Cowpea.

Cowpea is an important legume crop in sub-Saharan Africa (SSA) and beyond. However, access to phosphorus (P), a critical element for plant growth and development, is a significant constraint in SSA. Thus, it is essential to have high P-use efficiency varieties to achieve increased yields in environments where little-to- no phosphate fertilizers are applied. In this study, crop phenology, yield, and grain P efficiency traits were assessed in two recombinant inbred line (RIL) populations across ten environments under high- and low-P soil conditions to identify traits' response to different soil P levels and associated quantitative trait loci (QTLs). Single-environment (SEA) and multi-environment (MEA) QTL analyses were conducted for days to flowering (DTF), days to maturity (DTM), biomass yield (BYLD), grain yield (GYLD), grain P-use efficiency (gPUE) and grain P-uptake efficiency (gPUpE). Phenotypic data indicated significant variation among the RILs, and inadequate soil P had a negative impact on flowering, maturity, and yield traits. A total of 40 QTLs were identified by SEA, with most explaining greater than 10% of the phenotypic variance, indicating that many major-effect QTLs contributed to the genetic component of these traits. Similarly, MEA identified 23 QTLs associated with DTF, DTM, GYLD, and gPUpE under high- and low-P environments. Thirty percent (12/40) of the QTLs identified by SEA were also found by MEA, and some of those were identified in more than one P environment, highlighting their potential in breeding programs targeting PUE. QTLs on chromosomes Vu03 and Vu08 exhibited consistent effects under both high- and low-P conditions. In addition, candidate genes underlying the QTL regions were identified. This study lays the foundation for molecular breeding for PUE and contributes to understanding the genetic basis of cowpea response in different soil P conditions. Some of the identified genomic loci, many being novel QTLs, could be deployed in marker-aided selection and fine mapping of candidate genes.

Multi-environment QTL mapping identifies major genetic loci influencing soybean main stem node architecture.

Soybean plant architecture has a significant impact on yield potential, but the genetic underpinnings of key architectural traits remain elusive. The primary objective of this study was to explore the genetic foundations underlying main stem node number (MSN) in soybeans. Recombinant inbred lines (RILs) contained a 234 individual derived from crosses between two cultivars Zhonghuang35 (ZH35) and Jindou21 (JD21) was evaluated for seed hardness across 3 years (2013, 2014, and 2015 in Gansu). Markedly, the parent varieties, shown significant differences in MSN. Also, the RIL population exhibited a wide range of genetic variation in MSN. A high-density genetic map composed of 8,078 specific-locus amplified fragment (SLAF) markers, spanning 3,480.98 centimorgans (cM) with an average inter-marker distance of 0.59 cM were used to construct linkage map. Using ICIM analysis identified a total of 23 Quantitative Trait Loci (QTLs) across the 20 chromosome, of which five QTLs were detected in multiple years in Chr.6. Notably, we identified a stable major QTL, qMSN-6-4, explaining up to 24.81% of phenotypic variation. This QTL govern seven candidate genes with potential roles in regulating MSN development in soybean, including Glyma.06G027500 with a domain of unknown function, Glyma.06G027600 involved in proton transport, Glyma.06G027700 linked to proteolysis, Glyma.06G027900 related to transcriptional regulation, and Glyma.06G028000 and Glyma.06G028050 associated with membrane functions. The RT-PCR analysis confirmed that these genes were expressed differently between the parental lines this supports the idea that they may play a role in determining MSN. Glyma.06G027500 and Glyma.06G027600 showing higher expression in JD21 leaves and nodes, while Glyma.06G027700 and Glyma.06G028000 exhibited increased expression in ZH35 stems, highlighting their distinct roles in transcription regulation, membrane activities, and protein degradation that contribute to MSN formation in soybean. This study offers valuable insights into the genetic mechanisms governing soybean MSN, providing a foundation for future research and crop improvement efforts.

Genome-wide association study of seedling nitrogen-use efficiency-associated traits in common wheat (Triticum aestivum L.)

Nitrogen (N) fertilizer application is essential for crop-plant growth and development. Identifying genetic loci associated with N-use efficiency (NUE) could increase wheat yields and reduce environmental pollution caused by overfertilization. We subjected a panel of 389 wheat accessions to N and chlorate (a nitrate analog) treatments to identify quantitative trait loci (QTL) controlling NUE-associated traits at the wheat seedling stage. Genotyping the panel with a 660K single-nucleotide polymorphism (SNP) array, we identified 397 SNPs associated with N-sensitivity index and chlorate inhibition rate. These SNPs were merged into 49 QTL, of which eight were multi-environment stable QTL and 27 were located near previously reported QTL. A set of 135 candidate genes near the 49 QTL included TaBOX (F-box family protein) and TaERF (ethylene-responsive transcription factor). A Tabox mutant was more sensitive to low-N stress than the wild-type plant. We developed two functional markers for Hap 1, the favorable allele of TaBOX.

Identification of robust yield quantitative trait loci derived from cultivated emmer for durum wheat improvement.

Durum wheat (Triticum turgidum ssp. durum L.) is an important world food crop used to make pasta products. Compared to bread wheat (Triticum aestivum L.), fewer studies have been conducted to identify genetic loci governing yield-component traits in durum wheat. A potential source of diversity for durum is its immediate progenitor, cultivated emmer (T. turgidum ssp. dicoccum). We evaluated two biparental populations of recombinant inbred lines (RILs) derived from crosses between the durum lines Ben and Rusty and the cultivated emmer wheat accessions PI 41025 and PI 193883, referred to as the Ben×PI 41025 (BP025) and Rusty×PI 193883 (RP883) RIL populations, respectively. Both populations were evaluated under field conditions in three seasons with an aim to identify quantitative trait loci (QTLs) associated with yield components and seed morphology that were expressed in multiple environments. A total of 44 and 34 multi-environment QTLs were identified in the BP025 and RP883 populations, respectively. As expected, genetic loci known to govern domestication and development were associated with some of the QTLs, but novel QTLs derived from the cultivated emmer parents and associated with yield components including spikelet number, grain weight, and grain size were identified. These QTLs offer new target loci for durum wheat improvement, and toward that goal, we identified five RILs with increased grain weight and size compared to the durum parents. These materials along with the knowledge of stable QTLs and associated markers can help to expedite the development of superior durum varieties.

Multi-environment association study highlights candidate genes for robust agronomic quantitative trait loci in a novel worldwide Capsicum core collection.

Investigating crop diversity through genome-wide association studies (GWAS) on core collections helps in deciphering the genetic determinants of complex quantitative traits. Using the G2P-SOL project world collection of 10038 wild and cultivated Capsicum accessions from 10 major genebanks, we assembled a core collection of 423 accessions representing the known genetic diversity. Since complex traits are often highly dependent upon environmental variables and genotype-by-environment (G × E) interactions, multi-environment GWAS with a 10195-marker genotypic matrix were conducted on a highly diverse subset of 350 Capsicum annuum accessions, extensively phenotyped in up to six independent trials from five climatically differing countries. Environment-specific and multi-environment quantitative trait loci (QTLs) were detected for 23 diverse agronomic traits. We identified 97 candidate genes potentially implicated in 53 of the most robust and high-confidence QTLs for fruit flavor, color, size, and shape traits, and for plant productivity, vigor, and earliness traits. Investigating the genetic architecture of agronomic traits in this way will assist the development of genetic markers and pave the way for marker-assisted selection. The G2P-SOL pepper core collection will be available upon request as a unique and universal resource for further exploitation in future gene discovery and marker-assisted breeding efforts by the pepper community.

QTL and Candidate Genes: Techniques and Advancement in Abiotic Stress Resistance Breeding of Major Cereals.

At least 75% of the world's grain production comes from the three most important cereal crops: rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). However, abiotic stressors such as heavy metal toxicity, salinity, low temperatures, and drought are all significant hazards to the growth and development of these grains. Quantitative trait locus (QTL) discovery and mapping have enhanced agricultural production and output by enabling plant breeders to better comprehend abiotic stress tolerance processes in cereals. Molecular markers and stable QTL are important for molecular breeding and candidate gene discovery, which may be utilized in transgenic or molecular introgression. Researchers can now study synteny between rice, maize, and wheat to gain a better understanding of the relationships between the QTL or genes that are important for a particular stress adaptation and phenotypic improvement in these cereals from analyzing reports on QTL and candidate genes. An overview of constitutive QTL, adaptive QTL, and significant stable multi-environment and multi-trait QTL is provided in this article as a solid framework for use and knowledge in genetic enhancement. Several QTL, such as DRO1 and Saltol, and other significant success cases are discussed in this review. We have highlighted techniques and advancements for abiotic stress tolerance breeding programs in cereals, the challenges encountered in introgressing beneficial QTL using traditional breeding techniques such as mutation breeding and marker-assisted selection (MAS), and the in roads made by new breeding methods such as genome-wide association studies (GWASs), the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system, and meta-QTL (MQTL) analysis. A combination of these conventional and modern breeding approaches can be used to apply the QTL and candidate gene information in genetic improvement of cereals against abiotic stresses.

Multienvironment QTL analysis delineates a major locus associated with homoeologous exchanges for water-use efficiency and seed yield in canola.

Canola varieties exhibit variation in drought avoidance and drought escape traits, reflecting adaptation to water‐deficit environments. Our understanding of underlying genes and their interaction across environments in improving crop productivity is limited. A doubled haploid population was analysed to identify quantitative trait loci (QTL) associated with water‐use efficiency (WUE) related traits. High WUE in the vegetative phase was associated with low seed yield. Based on the resequenced parental genome data, we developed sequence‐capture‐based markers and validated their linkage with carbon isotope discrimination (Δ13C) in an F2 population. RNA sequencing was performed to determine the expression of candidate genes underlying Δ13C QTL. QTL contributing to main and QTL × environment interaction effects for Δ13C and yield were identified. One multiple‐trait QTL for Δ13C, days to flower, plant height, and seed yield was identified on chromosome A09. Interestingly, this QTL region overlapped with a homoeologous exchange (HE) event, suggesting its association with the multiple traits. Transcriptome analysis revealed 121 significantly differentially expressed genes underlying Δ13C QTL on A09 and C09, including in HE regions. Sorting out the negative relationship between vegetative WUE and seed yield is a priority. Genetic and genomic resources and knowledge so developed could improve canola WUE and yield.

Genetic control and phenotypic characterization of panicle architecture and grain yield-related traits in foxtail millet (Setaria italica).

Multi-environment QTL mapping identified 23 stable loci and 34 co-located QTL clusters for panicle architecture and grain yield-related traits, which provide a genetic basis for foxtail millet yield improvement. Panicle architecture and grain weight, both of which are influenced by genetic and environmental factors, have significant effects on grain yield potential. Here, we used a recombinant inbred line (RIL) population of 333 lines of foxtail millet, which were grown in 13 trials with varying environmental conditions, to identify quantitative trait loci (QTL) controlling nine agronomic traits related to panicle architecture and grain yield. We found that panicle weight, grain weight per panicle, panicle length, panicle diameter, and panicle exsertion length varied across different geographical locations. QTL mapping revealed 159 QTL for nine traits. Of the 159 QTL, 34 were identified in 2 to 12 environments, suggesting that the genetic control of panicle architecture in foxtail millet is sensitive to photoperiod and/or other environmental factors. Eighty-eight QTL controlling different traits formed 34 co-located QTL clusters, including the triple QTL cluster qPD9.2/qPL9.5/qPEL9.3, which was detected 23 times in 13 environments. Several candidate genes, including Seita.2G388700, Seita.3G136000, Seita.4G185300, Seita.5G241500, Seita.5G243100, Seita.9G281300, and Seita.9G342700, were identified in the genomic intervals of multi-environmental QTL or co-located QTL clusters. Using available phenotypic and genotype data, we conducted haplotype analysis for Seita.2G002300 and Seita.9G064000,which showed high correlations with panicle weight and panicle exsertion length, respectively. These results not only provided a basis for further fine mapping, functional studies and marker-assisted selection of traits related to panicle architecture in foxtail millet, but also provide information for comparative genomics analyses of cereal crops.

Multi-Environment Quantitative Trait Loci Mapping for Grain Iron and Zinc Content Using Bi-parental Recombinant Inbred Line Mapping Population in Pearl Millet.

Pearl millet is a climate-resilient, nutritious crop with low input requirements that could provide economic returns in marginal agro-ecologies. In this study, we report quantitative trait loci (QTLs) for iron (Fe) and zinc (Zn) content from three distinct production environments. We generated a genetic linkage map using 210 F6 recombinant inbred line (RIL) population derived from the (PPMI 683 × PPMI 627) cross using genome-wide simple sequence repeats (SSRs). The molecular linkage map (seven linkage groups) of 151 loci was 3,273.1 cM length (Kosambi). The content of grain Fe in the RIL population ranged between 36 and 114 mg/Kg, and that of Zn from 20 to 106 mg/Kg across the 3 years (2014–2016) at over the three locations (Delhi, Dharwad, and Jodhpur). QTL analysis revealed a total of 22 QTLs for grain Fe and Zn, of which 14 were for Fe and eight were for Zn on three consecutive years at all locations. The observed phenotypic variance (R2) explained by different QTLs for grain Fe and Zn content ranged from 2.85 (QGFe.E3.2014–2016_Q3) to 19.66% (QGFe.E1.2014–2016_Q3) and from 2.93 (QGZn.E3.2014–2016_Q3) to 25. 95% (QGZn.E1.2014–2016_Q1), respectively. Two constitutive expressing QTLs for both Fe and Zn co-mapped in this population, one on LG 2 and second one on LG 3. Inside the QTLs candidate genes such as Ferritin gene, Al3+ Transporter, K+ Transporters, Zn2+ transporters and Mg2+ transporters were identified using bioinformatics approaches. The identified QTLs and candidate genes could be useful in pearl millet population improvement programs, seed, restorer parents, and marker-assisted selection programs.

QTL × environment interactions underlie ionome divergence in switchgrass.

Ionomics measures elemental concentrations in biological organisms and provides a snapshot of physiology under different conditions. In this study, we evaluate genetic variation of the ionome in outbred, perennial switchgrass in three environments across the species’ native range, and explore patterns of genotype-by-environment interactions. We grew 725 clonally replicated genotypes of a large full sib family from a four-way linkage mapping population, created from deeply diverged upland and lowland switchgrass ecotypes, at three common gardens. Concentrations of 18 mineral elements were determined in whole post-anthesis tillers using ion coupled plasma mass spectrometry (ICP-MS). These measurements were used to identify quantitative trait loci (QTL) with and without QTL-by-environment interactions (QTLxE) using a multi-environment QTL mapping approach. We found that element concentrations varied significantly both within and between switchgrass ecotypes, and GxE was present at both the trait and QTL level. Concentrations of 14 of the 18 elements were under some genetic control, and 77 QTL were detected for these elements. Seventy-four percent of QTL colocalized multiple elements, half of QTL exhibited significant QTLxE, and roughly equal numbers of QTL had significant differences in magnitude and sign of their effects across environments. The switchgrass ionome is under moderate genetic control and by loci with highly variable effects across environments.

Genetic analysis and QTLs identification of stigma traits in japonica rice (Oryza sativa L.)

Short stigma is one of the main reasons of low yield of F1 hybrid seed production, especially in japonica rice. Genetics analysis and identifying favorable alleles for stigma traits are necessary for improving the parents suitable for F1 hybrid seed production. In this study, we investigated the stigma length (STL) and the sum of stigma and style length (TSSL) of 254 recombinant inbred lines from population derived from Xiushui 79 × C Bao cross in three environments, and a multi-trait multi-environment quantitative trait loci (QTL) analysis was conducted. There existed rich phenotypic variances, which provides a basis for mining favorable alleles. The correlation analysis showed that STL was significant positive correlation with TSSL. For STL, we identified 5 stable QTLs, in which 3 QTLs were found novelty, with the phenotypic variance explained (PVE) from 2.87 to 7.38%. The QTL qSTL10 has the largest PVE (7.38%). The synergistic allele of qSTL10 was from C Bao (an inbred parent that contributes the favorable alleles that increase the traits) and could increase STL 0.03 mm. For TSSL, 4 of 7 stable QTLs were novel, with the PVE from 2.17 to 8.32%. The synergistic allele of qTSSL12 was from C Bao and could increase TSSL 0.05 mm. These results provide a basis for the further fine-mapping of STL and TSSL in japonica rice and the selection male-sterile lines with long stigma in japonica rice by molecular marker-assisted selection.

QTL mapping for foxtail millet plant height in multi-environment using an ultra-high density bin map.

Using a fixed RIL population derived from a widely used foxtail millet backbone breeding line and an elite cultivar, we constructed a high-density bin map and identified six novel multi-environment effect QTLs and seven candidate genes for dwarf phenotype. Plant height is an important trait that determines tradeoffs between competition and resource allocation, which is crucial for yield potential. To improve the C4 model plant foxtail millet (Setaria italica) productivity, it is necessary to isolate plant height-related genes that contribute to ideal plant architecture in breeding. In the present study, we generated a foxtail millet population of 333 recombinant inbred lines (RILs) derived from a cross between a backbone line Ai 88 and an elite cultivar Liaogu 1. We evaluated plant height in 13 environmental conditions across 4years, the mean plant height of the RIL population ranged from 89.5 to 149.9cm. Using deep re-sequencing data, we constructed a high-density bin map with 3744 marker bins. Quantitative trait locus (QTL) mapping identified 26 QTLs significantly associated with plant height. Of these, 13 QTLs were repeatedly detected under multiple environments, including six novel QTLs that have not been reported before. Seita.1G242300, a gene encodes gibberellin 2-oxidase-8, which was detected in nine environments in a 1.54-Mb interval of qPH1.3, was considered as an important candidate gene. Moreover, other six genes involved in GA biosynthesis or signaling pathways, and fifteen genes encode F-box domain proteins which might function as E3 ligases, were also considered as candidate genes in different QTLs. These QTLs and candidate genes identified in this study will help to elucidate the genetic basis of foxtail millet plant height, and the linked markers will be useful for marker-assistant selection of varieties with ideal plant architecture and high yield potential.

Genome-wide association reveals a complex architecture for rust resistance in 2300 worldwide bread wheat accessions screened under various Australian conditions.

We utilized 2300 wheat accessions including worldwide landraces, cultivars and primary synthetic-derived germplasm with three Australian cultivars: Annuello, Yitpi and Correll, to investigate field-based resistance to leaf (Lr) rust, stem (Sr) rust and stripe (Yr) rust diseases across a range of Australian wheat agri-production zones. Generally, the resistance in the modern Australian cultivars, synthetic derivatives, South and North American materials outperformed other geographical subpopulations. Different environments for each trait showed significant correlations, with average r values of 0.53, 0.23 and 0.66 for Lr, Sr and Yr, respectively. Single-trait genome-wide association studies (GWAS) revealed several environment-specific and multi-environment quantitative trait loci (QTL). Multi-trait GWAS confirmed a cluster of Yr QTL on chromosome 3B within a 4.4-cM region. Linkage disequilibrium and comparative mapping showed that at least three Yr QTL exist within the 3B cluster including the durable rust resistance gene Yr30. An Sr/Lr QTL on chromosome 3D was found mainly in the synthetic-derived germplasm from Annuello background which is known to carry the Agropyron elongatum 3D translocation involving the Sr24/Lr24 resistance locus. Interestingly, estimating the SNP effects using a BayesR method showed that the correlation among the highest 1% of QTL effects across environments (excluding GWAS QTL) had significant correlations, with average r values of 0.26, 0.16 and 0.55 for Lr, Sr and Yr, respectively. These results indicate the importance of small effect QTL in achieving durable rust resistance which can be captured using genomic selection.

Multi-environment QTL mapping of crown root traits in a maize RIL population

Crown root traits, including crown root angle (CRA), diameter (CRD), and number (CRN), are major determining factors of root system architecture, which influences crop production. In maize, the genetic mechanisms determining crown root traits in the field are largely unknown. CRA, CRD, and CRN were evaluated in a recombinant inbred line population in three field trials. High phenotypic variation was observed for crown root traits, and all measured traits showed significant genotype–environment interactions. Single-environment (SEA) and multi-environment (MEA) quantitative trait locus (QTL) analyses were conducted for CRA, CRD, and CRN. Of 46 QTL detected by SEA, most explained less than 10% of the phenotypic variation, indicating that a large number of minor-effect QTL contributed to the genetic component of these traits. MEA detected 25 QTL associated with CRA, CRD, and CRN, and 2 and 1 QTL were identified with significant QTL-by-environment interaction effects for CRA and CRD, respectively. A total of 26.1% (12/46) of the QTL identified by SEA were also detected by MEA, with many being detected in more than one environment. These findings contribute to our understanding of the phenotypic and genotypic patterns of crown root traits in different environments. The identified environment-specific QTL and stable QTL may be used to improve root traits in maize breeding.

Multi-environment QTL analysis of plant and flower morphological traits in tetraploid rose

Key messageRose morphological traits such as prickles or petal number are influenced by a few key QTL which were detected across different growing environments—necessary for genomics-assisted selection in non-target environments.Rose, one of the world’s most-loved and commercially important ornamental plants, is predominantly tetraploid, possessing four rather than two copies of each chromosome. This condition complicates genetic analysis, and so the majority of previous genetic studies in rose have been performed at the diploid level. However, there may be advantages to performing genetic analyses at the tetraploid level, not least because this is the ploidy level of most breeding germplasm. Here, we apply recently developed methods for quantitative trait loci (QTL) detection in a segregating tetraploid rose population (F1 = 151) to unravel the genetic control of a number of key morphological traits. These traits were measured both in the Netherlands and Kenya. Since ornamental plant breeding and selection are increasingly being performed at locations other than the production sites, environment-neutral QTL are required to maximise the effectiveness of breeding programmes. We detected a number of robust, multi-environment QTL for such traits as stem and petiole prickles, petal number and stem length that were localised on the recently developed high-density SNP linkage map for rose. Our work explores the complex genetic architecture of these important morphological traits at the tetraploid level, while helping to advance the methods for marker–trait exploration in polyploid species.

Major Contribution of Flowering Time and Vegetative Growth to Plant Production in Common Bean As Deduced from a Comparative Genetic Mapping.

Determinacy growth habit and accelerated flowering traits were selected during or after domestication in common bean. Both processes affect several presumed adaptive traits such as the rate of plant production. There is a close association between flowering initiation and vegetative growth; however, interactions among these two crucial developmental processes and their genetic bases remain unexplored. In this study, with the aim to establish the genetic relationships between these complex processes, a multi-environment quantitative trait locus (QTL) mapping approach was performed in two recombinant inbred line populations derived from inter-gene pool crosses between determinate and indeterminate genotypes. Additive and epistatic QTLs were found to regulate flowering time, vegetative growth, and rate of plant production. Moreover, the pleiotropic patterns of the identified QTLs evidenced that regions controlling time to flowering traits, directly or indirectly, are also involved in the regulation of plant production traits. Further QTL analysis highlighted one QTL, on the lower arm of the linkage group Pv01, harboring the Phvul.001G189200 gene, homologous to the Arabidopsis thaliana TERMINAL FLOWER1 (TFL1) gene, which explained up to 32% of phenotypic variation for time to flowering, 66% for vegetative growth, and 19% for rate of plant production. This finding was consistent with previous results, which have also suggested Phvul.001G189200 (PvTFL1y) as a candidate gene for determinacy locus. The information here reported can also be applied in breeding programs seeking to optimize key agronomic traits, such as time to flowering, plant height and an improved reproductive biomass, pods, and seed size, as well as yield.

Exploring the quantitative resistance to Pseudomonas syringae pv. phaseolicola in common bean (Phaseolus vulgaris L.)

Pseudomonas syringae pv. phaseolicola is an important disease that causes halo blight in common bean. The genetic mechanisms underlying quantitative halo blight resistance are poorly understood in this species, as most disease studies have focused on qualitative resistance. The present work examines the genetic basis of quantitative resistance to the nine halo blight races in different organs (primary and trifoliate leaf, stem and pod) of an Andean recombinant inbred line (RIL) progeny. Using a multi-environment quantitative trait locus (QTL) mapping approach, 76 and 101 main-effect and epistatic QTLs were identified, respectively. Most of the epistatic interactions detected were due to loci without detectable QTL additive main effects. Main and epistatic QTLs detected were mainly consistent across the environment conditions. The homologous genomic regions corresponding to 26 of the 76 main-effect detected QTLs were positive for the presence of resistance-associated gene cluster encoding nucleotide-binding and leucine-rich repeat (NL) proteins and known defence genes. Main-effect QTLs for resistance to races 3, 4 and 5 in leaf, stem and pod were located on chromosome 2 within a 3.01-Mb region, where a cluster of nine NL genes was detected. The NL gene Phvul.002G323300 is located in this region, which can be considered an important putative candidate gene for the non-organ-specific QTL identified here. The present research provides essential information not only for the better understanding of the plant-pathogen interaction but also for the application of genomic assisted breeding for halo blight resistance in common bean.

Multi-environment QTL mapping reveals genetic architecture of fruit cracking in a tomato RIL Solanum lycopersicum×S. pimpinellifolium population.

QTL and codominant genetic markers for fruit cracking have been identified in a tomato genetic map derived from a RIL population, providing molecular tools for marker-assisted breeding of this trait. In tomato, as well as in other fleshy fruits, one of the main disorders that widely limit quality and production is fruit cracking or splitting of the epidermis that is observed on the fruit skin and flesh at any stage of fruit growth and maturation. To elucidate the genetic basis of fruit cracking, a quantitative trait loci (QTL) analysis was conducted in a recombinant inbred line (RIL) population derived from a cross between tomato (Solanum lycopersicum) and the wild-relative species S. pimpinellifolium. The RIL population was evaluated for fruit cracking during three consecutive growing seasons. Construction of a high-density linkage map based on codominant markers, covering more than 1000cM of the whole genome, led to the identification of both main and epistatic QTL controlling fruit cracking on the basis of a single-environment as well as multiple-environment analysis. This information will enhance molecular breeding for novel cracking resistant varieties and simultaneously assist the identification of genes underlying these QTL, helping to reveal the genetic basis of fruit cracking in tomato.

Multi-trait and multi-environment QTL analysis reveals the impact of seed colour on seed composition traits in Brassica napus

Brassica napus seed composition traits (fibre, protein, oil and fatty acid profiles), seed colour and yield-associated traits are regulated by a complex network of genetic factors. Although previous studies have attempted to dissect the underlying genetic basis for these traits, a more complete picture of the available quantitative trait loci (QTL) variation and any interaction between the different traits is required. In this study, QTL mapping for eleven seed composition traits, seed colour and a yield-related trait (TSW) was conducted in a spring-type canola-quality B. napus doubled haploid (DH) population from a cross between black-seeded (DH12075) and yellow-seeded (YN01-429) lines across five environments. A major QTL associated with fibre traits (acid detergent fibre, acid detergent lignin and neutral detergent fibre) and seed colour (whiteness index) was mapped on chromosome N9 across the five environments. Multi-trait analysis identified QTL which had pleiotropic effect for seed colour and other composition traits. Multi-environment analysis revealed genetic (QTL) × environment effects on most QTL. These findings provide a more detailed insight into the complex QTL networks controlling seed composition and yield-associated traits in canola-quality B. napus.

Multi-environment QTL analysis of grain morphology traits and fine mapping of a kernel-width QTL in Zheng58 × SK maize population

Sixteen major QTLs regulating maize kernel traits were mapped in multiple environments and one of them, qKW - 9.2 , was restricted to 630Kb, harboring 28 putative gene models. To elucidate the genetic basis of kernel traits, a quantitative trait locus (QTL) analysis was conducted in a maize recombinant inbred line population derived from a cross between two diverse parents Zheng58 and SK, evaluated across eight environments. Construction of a high-density linkage map was based on 13,703 single-nucleotide polymorphism markers, covering 1860.9cM of the whole genome. In total, 18, 26, 23, and 19 QTLs for kernel length, width, thickness, and 100-kernel weight, respectively, were detected on the basis of a single-environment analysis, and each QTL explained 3.2-23.7% of the phenotypic variance. Sixteen major QTLs, which could explain greater than 10% of the phenotypic variation, were mapped in multiple environments, implying that kernel traits might be controlled by many minor and multiple major QTLs. The major QTL qKW-9.2 with physical confidence interval of 1.68Mbp, affecting kernel width, was then selected for fine mapping using heterogeneous inbred families. At final, the location of the underlying gene was narrowed down to 630Kb, harboring 28 putative candidate-gene models. This information will enhance molecular breeding for kernel traits and simultaneously assist the gene cloning underlying this QTL, helping to reveal the genetic basis of kernel development in maize.