Inclusive Composite Interval Mapping of QTL by Environment Interactions in Biparental Populations

Understanding genome structure facilitates the use of wild lentil germplasm for breeding: A case study with shattering loci

Plant breeders are generally reluctant to cross elite crop cultivars with their wild relatives to introgress novel desirable traits due to associated negative traits such as pod shattering. This results in a genetic bottleneck that could be reduced through better understanding of the genomic locations of the gene(s) controlling this trait. We integrated information on parental genomes, pod shattering data from multiple environments, and high-density genetic linkage maps to identify pod shattering quantitative trait loci (QTLs) in three lentil interspecific recombinant inbred line populations. The broad-sense heritability on a multi-environment basis varied from 0.46 (in LR-70, Lens culinaris × Lens odemensis) to 0.77 (in LR-68, Lens orientalis × L. culinaris). Genetic linkage maps of the interspecific populations revealed reciprocal translocations of chromosomal segments that differed among the populations, and which were associated with reduced recombination. LR-68 had a 2-5 translocation, LR-70 had 1-5, 2-6, and 2-7 translocations, and LR-86 had a 2-7 translocation in one parent relative to the other. Segregation distortion was also observed for clusters of single nucleotide polymorphisms on multiple chromosomes per population, further affecting introgression. Two major QTL, on chromosomes 4 and 7, were repeatedly detected in the three populations and contain several candidate genes. These findings will be of significant value for lentil breeders to strategically access novel superior alleles while minimizing the genetic impact of pod shattering from wild parents.

Pathogenicity, Host Resistance, and Genetic Diversity of Fusarium Species under Controlled Conditions from Soybean in Canada

Fusarium spp. are commonly associated with the root rot complex of soybean (Glycine max). Previous surveys identified six common Fusarium species from Manitoba, including F. oxysporum, F. redolens, F. graminearum, F. solani, F. avenaceum, and F. acuminatum. This study aimed to determine their pathogenicity, assess host resistance, and evaluate the genetic diversity of Fusarium spp. isolated from Canada. The pathogenicity of these species was tested on two soybean cultivars, 'Akras' (moderately resistant) and 'B150Y1' (susceptible), under greenhouse conditions. The aggressiveness of the fungal isolates varied, with root rot severities ranging from 1.5 to 3.3 on a 0-4 scale. Subsequently, the six species were used to screen a panel of 20 Canadian soybean cultivars for resistance in a greenhouse. Cluster and principal component analyses were conducted based on the same traits used in the pathogenicity study. Two cultivars, 'P15T46R2' and 'B150Y1', were consistently found to be tolerant to F. oxysporum, F. redolens, F. graminearum, and F. solani. To investigate the incidence and prevalence of Fusarium spp. in Canada, fungi were isolated from 106 soybean fields surveyed across Manitoba, Saskatchewan, Ontario, and Quebec. Eighty-three Fusarium isolates were evaluated based on morphology and with multiple PCR primers, and phylogenetic analyses indicated their diversity across the major soybean production regions of Canada. Overall, this study contributes valuable insights into host resistance and the pathogenicity and genetic diversity of Fusarium spp. in Canadian soybean fields.

Aspartyl proteases identified as candidate genes of a fiber length QTL, qFLD05, that regulates fiber length in cotton (Gossypium hirsutum L.)

KEY MESSAGE

GhAP genes were identified as the candidates involved in cotton fiber length under the scope of fine mapping a stable fiber length QTL, qFLD05. Moreover, the transcription factor GhWRKY40 positively regulated GhAP3 to decrease fiber length. Fiber length (FL) is an economically important fiber quality trait. Although several genes controlling cotton fiber development have been identified, our understanding of this process remains limited. In this study, an FL QTL (qFLD05) was fine-mapped to a 216.9-kb interval using a secondary F2:3 population derived from the upland hybrid cultivar Ji1518. This mapped genomic segment included 15 coding genes, four of which were annotated as aspartyl proteases (GhAP1-GhAP4). GhAPs were identified as candidates for qFLD05 as the sequence variations in GhAPs were associated with FL deviations in the mapping population, and functional validation of GhAP3 and GhAP4 indicated a longer FL following decreases in their expression levels through virus-induced gene silencing (VIGS). Subsequently, the potential involvement of GhWRKY40 in the regulatory network was revealed: GhWRKY40 positively regulated GhAP3's expression according to transcriptional profiling, VIGS, yeast one-hybrid assays and dual-luciferase experiments. Furthermore, alterations in the expression of the eight previously reported cotton FL-responsive genes from the above three VIGS lines (GhAP3, GhAP4 and GhWRKY40) implied that MYB5_A12 was involved in the GhWRKY40-GhAP network. In short, we unveiled the unprecedented FL regulation roles of GhAPs in cotton, which was possibly further regulated by GhWRKY40. These findings will reveal the genetic basis of FL development associated with qFLD05 and be beneficial for the marker-assisted selection of long-staple cotton.

Identification of reliable QTLs and designed QTL breeding for grain shape and milling quality in the reciprocal introgression lines in rice

Milling quality (MQ) and grain shape (GS) of rice (Oryza sativa L.) are correlated traits, both determine farmers' final profit. More than one population under multiple environments may provide valuable information for breeding selection on these MQ-GS correlations. However, suitable analytical methods for reciprocal introgression lines with linkage map for this kind of correlation remains unclear. In this study, our major tasks were (1) to provide a set of reciprocal introgression lines (composed of two BC2RIL populations) suitable for mapping by linkage mapping using markers/bins with physical positions; (2) to test the mapping effects of different methods by using MQ-GS correlation dissection as sample case; (3) to perform genetic and breeding simulation on pyramiding favorite alleles of QTLs for representative MQ-GS traits. Finally, with four analysis methods and data collected under five environments, we identified about 28.4 loci on average for MQ-GS traits. Notably, 52.3% of these loci were commonly detected by different methods and eight loci were novel. There were also nine regions harboring loci for different MQ-GS traits which may be underlying the MQ-GS correlations. Background independent (BI) loci were also found for each MQ and GS trait. All these information may provide useful resources for rice molecular breeding.

Genetic mapping and transcriptome profiling of a chickpea (Cicer arietinum L.) mutant identifies a novel locus (CaEl) regulating organ size and early vigor

Chickpea is among the top three legumes produced and consumed worldwide. Early plant vigor, characterized by good germination and rapid seedling growth, is an important agronomic trait in many crops including chickpea, and shows a positive correlation with seed size. In this study, we report a gamma-ray-induced chickpea mutant with a larger organ and seed size. The mutant (elm) exhibits increased early vigor and contains higher proline that contributes to a better tolerance under salt stress at germination, seedling, and early vegetative phase. The trait is governed as monogenic recessive, with wild-type allele being incompletely dominant over the mutant. Genetic mapping of this locus (CaEl) identified it as a previously uncharacterized gene (101503252) in chromosome 1 of the chickpea genome. There is a deletion of this gene in the mutant with a complete loss of expression. In silico analysis suggests that the gene is present as a single copy in chickpea and related legumes of the galegoid clade. In the mutant, cell division and expansion are affected. Transcriptome profiling identified differentially regulated transcripts related to cell division, expansion, cell wall organization, and metabolism in the mutant. The mutant can be exploited in chickpea breeding programs for increasing plant vigor and seed size.

Identification of quantitative trait loci and candidate genes for seed folate content in soybean

KEY MESSAGE

From 61 QTL mapped, a stable QTL cluster of 992 kb was discovered on chromosome 5 for folate content and a putative candidate gene, Glyma.05G237500, was identified. Folate (vitamin B9) is one of the most essential micronutrients whose deficiencies lead to various health defects in humans. Herein, we mapped the quantitative trait loci (QTL) underlying seed folate content in soybean using recombinant inbred lines developed from cultivars, ZH35 and ZH13, across four environments. We identified 61 QTL on 12 chromosomes through composite interval mapping, with phenotypic variance values ranging from 1.68 to 24.68%. A major-effect QTL cluster (qFo-05) was found on chromosome 5, spanning 992 kb and containing 134 genes. Through gene annotation and single-locus haplotyping analysis of qFo-05 in a natural soybean population, we identified seven candidate genes significantly associated with 5MTHF and total folate content in multiple environments. RNA-seq analysis showed a unique expression pattern of a hemerythrin RING zinc finger gene, Glyma.05G237500, between both parental cultivars during seed development, which suggest the gene might regulate folate content in soybean. This is the first study to investigate QTL underlying folate content in soybean and provides new insight for molecular breeding to improve folate content in soybean.

Identification of QTN-by-environment interactions for yield related traits in maize under multiple abiotic stresses

INTRODUCTION

Quantitative trait nucleotide (QTN)-by-environment interactions (QEIs) play an increasingly essential role in the genetic dissection of complex traits in crops as global climate change accelerates. The abiotic stresses, such as drought and heat, are the major constraints on maize yields. Multi-environment joint analysis can improve statistical power in QTN and QEI detection, and further help us to understand the genetic basis and provide implications for maize improvement.

METHODS

In this study, 3VmrMLM was applied to identify QTNs and QEIs for three yield-related traits (grain yield, anthesis date, and anthesis-silking interval) of 300 tropical and subtropical maize inbred lines with 332,641 SNPs under well-watered and drought and heat stresses.

RESULTS

Among the total 321 genes around 76 QTNs and 73 QEIs identified in this study, 34 known genes were reported in previous maize studies to be truly associated with these traits, such as ereb53 (GRMZM2G141638) and thx12 (GRMZM2G016649) associated with drought stress tolerance, and hsftf27 (GRMZM2G025685) and myb60 (GRMZM2G312419) associated with heat stress. In addition, among 127 homologs in Arabidopsis out of 287 unreported genes, 46 and 47 were found to be significantly and differentially expressed under drought vs well-watered treatments, and high vs. normal temperature treatments, respectively. Using functional enrichment analysis, 37 of these differentially expressed genes were involved in various biological processes. Tissue-specific expression and haplotype difference analysis further revealed 24 candidate genes with significantly phenotypic differences across gene haplotypes under different environments, of which the candidate genes GRMZM2G064159, GRMZM2G146192, and GRMZM2G114789 around QEIs may have gene-by-environment interactions for maize yield.

DISCUSSION

All these findings may provide new insights for breeding in maize for yield-related traits adapted to abiotic stresses.

QTL Mapping for Important Agronomic Traits Using a Wheat55K SNP Array-Based Genetic Map in Tetraploid Wheat

Wheat yield is highly correlated with plant height, heading date, spike characteristics, and kernel traits. In this study, we used the wheat55K single nucleotide polymorphism array to genotype a recombinant inbred line population of 165 lines constructed by crossing two tetraploid wheat materials, Icaro and Y4. A genetic linkage map with a total length of 6244.51 cM was constructed, covering 14 chromosomes of tetraploid wheat. QTLs for 12 important agronomic traits, including plant height (PH), heading date (HD), awn color (AC), spike-branching (SB), and related traits of spike and kernel, were mapped in multiple environments, while combined QTL-by-environment interactions and epistatic effects were analyzed for each trait. A total of 52 major or stable QTLs were identified, among which may be some novel loci controlling PH, SB, and kernel length-width ratio (LWR), etc., with LOD values ranging from 2.51 to 54.49, thereby explaining 2.40-66.27% of the phenotypic variation. Based on the 'China Spring' and durum wheat reference genome annotations, candidate genes were predicted for four stable QTLs, QPH.nwafu-2B.2 (165.67-166.99 cM), QAC.nwafu-3A.1 (419.89-420.52 cM), QAC.nwafu-4A.1 (424.31-447.4 cM), and QLWR.nwafu-7A.1 (166.66-175.46 cM). Thirty-one QTL clusters and 44 segregation distortion regions were also detected, and 38 and 18 major or stable QTLs were included in these clusters and segregation distortion regions, respectively. These results provide QTLs with breeding application potential in tetraploid wheat that broadens the genetic basis of important agronomic traits such as PH, HD, AC, SB, etc., and benefits wheat breeding.

QTL cluster analysis and marker development for kernel traits based on DArT markers in spring bread wheat (Triticum aestivum L.)

Genetic dissection of yield component traits including kernel characteristics is essential for the continuous improvement in wheat yield. In the present study, one recombinant inbred line (RIL) F₆ population derived from a cross between Avocet and Chilero was used to evaluate the phenotypes of kernel traits of thousand-kernel weight (TKW), kernel length (KL), and kernel width (KW) in four environments at three experimental stations during the 2018-2020 wheat growing seasons. The high-density genetic linkage map was constructed with the diversity arrays technology (DArT) markers and the inclusive composite interval mapping (ICIM) method to identify the quantitative trait loci (QTLs) for TKW, KL, and KW. A total of 48 QTLs for three traits were identified in the RIL population on the 21 chromosomes besides 2A, 4D, and 5B, accounting for 3.00%-33.85% of the phenotypic variances. Based on the physical positions of each QTL, nine stable QTL clusters were identified in the RILs, and among these QTL clusters, TaTKW-1A was tightly linked to the DArT marker interval 3950546-1213099, explaining 10.31%-33.85% of the phenotypic variances. A total of 347 high-confidence genes were identified in a 34.74-Mb physical interval. TraesCS1A02G045300 and TraesCS1A02G058400 were among the putative candidate genes associated with kernel traits, and they were expressed during grain development. Moreover, we also developed high-throughput kompetitive allele-specific PCR (KASP) markers of TaTKW-1A, validated in a natural population of 114 wheat varieties. The study provides a basis for cloning the functional genes underlying the QTL for kernel traits and a practical and accurate marker for molecular breeding.

Mapping quantitative trait loci and predicting candidate genes for Striga resistance in maize using resistance donor line derived from Zea diploperennis

The parasitic weed, Striga is a major biological constraint to cereal production in sub-Saharan Africa (SSA) and threatens food and nutrition security. Two hundred and twenty-three (223) F_2:3 mapping population involving individuals derived from TZdEI 352 x TZEI 916 were phenotyped for four Striga-adaptive traits and genotyped using the Diversity Arrays Technology (DArT) to determine the genomic regions responsible for Striga resistance in maize. After removing distorted SNP markers, a genetic linkage map was constructed using 1,918 DArTseq markers which covered 2092.1 cM. Using the inclusive composite interval mapping method in IciMapping, twenty-three QTLs influencing Striga resistance traits were identified across four Striga-infested environments with five stable QTLs (qGY4, qSC2.1, qSC2.2, qSC5, and qSC6) detected in more than one environment. The variations explained by the QTLs ranged from 4.1% (qSD2.3) to 14.4% (qSC7.1). Six QTLs each with significant additive × environment interactions were also identified for grain yield and Striga damage. Gene annotation revealed candidate genes underlying the QTLs, including the gene models GRMZM2G077002 and GRMZM2G404973 which encode the GATA transcription factors, GRMZM2G178998 and GRMZM2G134073 encoding the NAC transcription factors, GRMZM2G053868 and GRMZM2G157068 which encode the nitrate transporter protein and GRMZM2G371033 encoding the SBP-transcription factor. These candidate genes play crucial roles in plant growth and developmental processes and defense functions. This study provides further insights into the genetic mechanisms of resistance to Striga parasitism in maize. The QTL detected in more than one environment would be useful for further fine-mapping and marker-assisted selection for the development of Striga resistant and high-yielding maize cultivars.

Combining QTL mapping and RNA-Seq Unravels candidate genes for Alfalfa (Medicago sativa L.) leaf development

BACKGROUND

Leaf size affects crop canopy morphology and photosynthetic efficiency, which can influence forage yield and quality. It is of great significance to mine the key genes controlling leaf development for breeding new alfalfa varieties. In this study, we mapped leaf length (LL), leaf width (LW), and leaf area (LA) in an F1 mapping population derived from a cultivar named ZhongmuNo.1 with larger leaf area and a landrace named Cangzhou with smaller leaf area.

RESULTS

This study showed that the larger LW was more conducive to increasing LA. A total of 24 significant quantitative trait loci (QTL) associated with leaf size were identified on both the paternal and maternal linkage maps. Among them, nine QTL explained about 11.50-22.45% phenotypic variation. RNA-seq analysis identified 2,443 leaf-specific genes and 3,770 differentially expressed genes. Combining QTL mapping, RNA-seq alalysis, and qRT-PCR, we identified seven candidate genes associated with leaf development in five major QTL regions.

CONCLUSION

Our study will provide a theoretical basis for marker-assisted breeding and lay a foundation for further revealing molecular mechanism of leaf development in alfalfa.

GAHP: An integrated software package on genetic analysis with bi-parental immortalized heterozygous populations

GAHP is a freely available software package for genetic analysis with bi-parental immortalized heterozygous and pure-line populations. The package is project-based and integrated with multiple functions. All operations and running results are properly saved in a project, which can be recovered when the project is re-open by the package. Four functionalities have been implemented in the current version of GAHP, i.e., 1) MHP: visualization of genetic linkage maps; 2) VHP: analysis of variance (ANOVA) and estimation of heritability on phenotypic data; 3) QHP: quantitative trait locus (QTL) mapping on both genotypic and phenotypic data; 4) SHP: simulation of bi-parental immortalized heterozygous and pure-line populations, and power analysis of QTL mapping. VHP and QHP can be conducted in individual populations, as well as in multiple populations by the combined analysis. Input files are arranged either in the plain text format with an extension name same as the functionality or in the MS Excel formats. Output files have the same prefix name as the input file, but with different extensions to indicate their contents. Three characters before the extension names stand for the types of populations used in analysis. In the interface of the software package, input files are grouped by functionality, and output files are grouped by individual or combined mapping populations. In addition to the text-format outputs, the constructed linkage map can be visualized per chromosome or for a number of selected chromosomes; line plots and bi-plots can be drawn from QTL mapping results and phenotypic data. Functionalities and analysis methods available in GAHP help the investigation of genetic architectures of complex traits and the mechanism of heterosis in plants.

GBS-Based SNP Map Pinpoints the QTL Associated With Sorghum Downy Mildew Resistance in Maize (Zea mays L.)

Sorghum downy mildew (SDM), caused by the biotrophic fungi Peronosclerospora sorghi, threatens maize production worldwide, including India. To identify quantitative trait loci (QTL) associated with resistance to SDM, we used a recombinant inbred line (RIL) population derived from a cross between resistant inbred line UMI936 (w) and susceptible inbred line UMI79. The RIL population was phenotyped for SDM resistance in three environments [E1-field (Coimbatore), E2-greenhouse (Coimbatore), and E3-field (Mandya)] and also utilized to construct the genetic linkage map by genotyping by sequencing (GBS) approach. The map comprises 1516 SNP markers in 10 linkage groups (LGs) with a total length of 6924.7 cM and an average marker distance of 4.57 cM. The QTL analysis with the phenotype and marker data detected nine QTL on chromosome 1, 2, 3, 5, 6, and 7 across three environments. Of these, QTL namely qDMR1.2, qDMR3.1, qDMR5.1, and qDMR6.1 were notable due to their high phenotypic variance. qDMR3.1 from chromosome 3 was detected in more than one environment (E1 and E2), explaining the 10.3% and 13.1% phenotypic variance. Three QTL, qDMR1.2, qDMR5.1, and qDMR6.1 from chromosomes 1, 5, and 6 were identified in either E1 or E3, explaining 15.2%–18% phenotypic variance. Moreover, genome mining on three QTL (qDMR3.1, qDMR5.1, and qDMR6.1) reveals the putative candidate genes related to SDM resistance. The information generated in this study will be helpful for map-based cloning and marker-assisted selection in maize breeding programs.

Genome-Wide QTL Mapping for Stripe Rust Resistance in Winter Wheat Pindong 34 Using a 90K SNP Array

Winter wheat cultivar Pindong 34 has both adult-plant resistance (APR) and all-stage resistance (ASR) to stripe rust, which is caused by Puccinia striiformis f. sp. tritici (Pst). To map the quantitative trait loci (QTL) for stripe rust resistance, an F_6-10 recombinant inbred line (RIL) population from a cross of Mingxian 169 × Pingdong 34 was phenotyped for stripe rust response over multiple years in fields under natural infection conditions and with selected Pst races under controlled greenhouse conditions, and genotyping was performed with a 90K single nucleotide polymorphism (SNP) array chip. Inclusive composite interval mapping (ICIM) identified 12 APR resistance QTLs and 3 ASR resistance QTLs. Among the 12 APR resistance QTLs, QYrpd.swust-1BL (explaining 9.24-13.33% of the phenotypic variation), QYrpd.swust-3AL.1 (11.41-14.80%), QYrpd.swust-3AL.2 (11.55-16.10%), QYrpd.swust-6BL (9.39-12.78%), QYrpd.swust-6DL (9.52-16.36%), QYrpd.swust-7AL (9.09-17.0%), and QYrpd.swust-7DL (8.87-11.38%) were more abundant than in the five tested environments and QYrpd.swust-1AS (11.05-12.72%), QYrpd.swust-1DL (9.81-13.05%), QYrpd.swust-2BL.1 (9.69-10.57%), QYrpd.swust-2BL.2 (10.36-12.97%), and QYrpd.swust-2BL.3 (9.54-13.15%) were significant in some of the tests. The three ASR resistance QTLs QYrpd.swust-2AS (9.69-13.58%), QYrpd.swust-2BL.4 (9.49-12.07%), and QYrpd.swust-7AS (16.16%) were detected based on the reactions in the seedlings tested with the CYR34 Pst race. Among the 15 QTLs detected in Pindong 34, the ASR resistance gene QYrpd.swust-7AS mapped on the short arm of chromosome 7A was likely similar to the previously reported QTL Yr61 in the region. The QTLs identified in the present study and their closely linked molecular markers could be useful for developing wheat cultivars with durable resistance to stripe rust.

Wheat glutamine synthetase TaGSr-4B is a candidate gene for a QTL of thousand grain weight on chromosome 4B

Glutamine synthetase TaGSr-4B is a candidate gene for a QTL of thousand grain weight on 4B, and the gene marker is ready for wheat breeding. A QTL for thousand grain weight (TGW) in wheat was previously mapped on chromosome 4B in a DH population of Westonia × Kauz. For identifying the candidate genes of the QTL, wheat 90 K SNP array was used to saturate the existing linkage map, and four field trials plus one glasshouse experiment over five locations were conducted to refine the QTL. Three nitrogen levels were applied to two of those field trials, resulting in a TGW phenotype data set from nine environments. A robust TGW QTL cluster including 773 genes was detected in six environments with the highest LOD value of 13.4. Based on differentiate gene expression within the QTL cluster in an RNAseq data of Westonia and Kauz during grain filling, a glutamine synthesis gene (GS: TaGSr-4B) was selected as a potential candidate gene for the QTL. A SNP on the promoter region between Westonia and Kauz was used to develop a cleaved amplified polymorphic marker for TaGSr-4B gene mapping and QTL reanalysing. As results, TGW QTL appeared in seven environments, and in four out of seven environments, the TGW QTL were localized on the TaGSr-4B locus and showed significant contributions to the phenotype. Based on the marker, two allele groups of Westonia and Kauz formed showed significant differences on TGW in eight environments. In agreement with the roles of GS genes on nitrogen and carbon remobilizations, TaGSr-4B is likely the candidate gene of the TGW QTL on 4B and the TaGSr-4B gene marker is ready for wheat breeding.

A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies

Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits.

Identification of quantitative trait loci (QTLs) and candidate genes of seed Iron and zinc content in soybean [Glycine max (L.) Merr.]

BACKGROUND

Deciphering the hereditary mechanism of seed iron (Fe) and zinc (Zn) content in soybean is important and sustainable to address the "hidden hunger" that presently affects approximately 2 billion people worldwide. Therefore, in order to detect genomic regions related to soybean seed Fe and Zn content, a recombinant inbred line (RIL) population with 248 lines was assessed in four environments to detect Quantitative Trait Loci (QTLs) related to soybean seed Fe and Zn content.

RESULT

Wide variation was found in seed Fe and Zn content in four environments, and genotype, environment, and genotype × environment interactions had significant influences on both the seed Fe and Zn content. A positive correlation was observed between seed Fe content and seed Zn content, and broad-sense heritability (H2) of seed Fe and Zn content were 0.73 and 0.75, respectively. In this study, five QTLs for seed Fe content were detected with 4.57 - 32.71% of phenotypic variation explained (PVE) and logarithm of odds (LOD) scores ranging from 3.60 to 33.79. Five QTLs controlling the seed Zn content were detected, and they individually explained 3.35 to 26.48% of the phenotypic variation, with LOD scores ranging from 3.64 to 20.4. Meanwhile, 409,541 high-quality single-nucleotide variants (SNVs) and 85,102 InDels (except intergenic regions) between two bi-parental lines were identified by whole genome resequencing. A total of 12 candidate genes were reported in one major QTL for seed Fe content and two major QTLs for seed Zn content, with the help of RNA-Seq analysis, gene ontology (GO) enrichment, gene annotation, and bi-parental whole genome sequencing (WGS) data.

CONCLUSIONS

Limited studies were performed about microelement of soybean, so these results may play an important role in the biofortification of Fe and Zn and accelerate the development of marker-assisted selection (MAS) for breeding soybeans fortified with iron and zinc.

A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions

Detecting small and linked quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits is a difficult issue in immortalized F2 and F2:3 design, especially in the era of global climate change and environmental plasticity research. Here we proposed a compressed variance component mixed model. In this model, a parametric vector of QTL genotype and environment combination effects replaced QTL effects, environmental effects and their interaction effects, whereas the combination effect polygenic background replaced the QTL and QEI polygenic backgrounds. Thus, the number of variance components in the mixed model was greatly reduced. The model was incorporated into our genome-wide composite interval mapping (GCIM) to propose GCIM-QEI-random and GCIM-QEI-fixed, respectively, under random and fixed models of genetic effects. First, potentially associated QTLs and QEIs were selected from genome-wide scanning. Then, significant QTLs and QEIs were identified using empirical Bayes and likelihood ratio test. Finally, known and candidate genes around these significant loci were mined. The new methods were validated by a series of simulation studies and real data analyses. Compared with ICIM, GCIM-QEI-random had 29.77 ± 18.20% and 24.33 ± 10.15% higher average power, respectively, in 0.5–3.0% QTL and QEI detection, 43.44 ± 9.53% and 51.47 ± 15.70% higher average power, respectively, in linked QTL and QEI detection, and identified 30 more known genes for four rice yield traits, because GCIM-QEI-random identified more small genes/loci, being 2.69 ± 2.37% for additional genes. GCIM-QEI-random was slightly better than GCIM-QEI-fixed. In addition, the new methods may be extended into backcross and genome-wide association studies. This study provides effective methods for detecting small-effect and linked QTLs and QEIs.

Construction of Genetic Linkage Maps From a Hybrid Family of Large Yellow Croaker (Larimichthys crocea)

Consensus and sex-specific genetic linkage maps for large yellow croaker (Larimichthys crocea) were constructed using samples from an F₁ family produced by crossing a Daiqu female and a Mindong male. A total of 20,147 single nucleotide polymorphisms (SNPs) by restriction site associated DNA sequencing were assigned to 24 linkage groups (LGs). The total length of the consensus map was 1757.4 centimorgan (cM) with an average marker interval of 0.09 cM. The total length of female and male linkage map was 1533.1 cM and 1279.2 cM, respectively. The average female-to-male map length ratio was 1.2 ± 0.23. Collapsed markers in the genetic maps were re-ordered according to their relative positions in the ASM435267v1 genome assembly to produce integrated genetic linkage maps with 9885 SNPs distributed across the 24 LGs. The recombination pattern of most LGs showed sigmoidal patterns of recombination, with higher recombination in the middle and suppressed recombination at both ends, which corresponds with the presence of sub-telocentric and acrocentric chromosomes in the species. The average recombination rate in the integrated female and male maps was respectively 3.55 cM/Mb and 3.05 cM/Mb. In most LGs, higher recombination rates were found in the integrated female map, compared to the male map, except in LG12, LG16, LG21, LG22, and LG24. Recombination rate profiles within each LG differed between the male and the female, with distinct regions indicating potential recombination hotspots. Separate quantitative trait loci (QTL) and association analyses for growth related traits in 6 months fish were performed, however, no significant QTL was detected. The study indicates that there may be genetic differences between the two strains, which may have implications for the application of DNA-information in the further breeding schemes.

Discovery of genomic regions associated with resistance to late wilt disease caused by Harpophora maydis (Samra, Sabet and Hing) in maize (Zea mays L.)

Late wilt disease (LWD) caused by Harpophora maydis (Samra, Sabet and Hing) is emerging as major production constraint in maize across the world. As a prelude to develop maize hybrid resistance to LWD, genetic basis of resistance was investigated. Two F_2:3 mapping populations (derived from CV156670 × 414-33 (P-1) and CV156670 × CV143587 (P-2)) were challenged with LWD at two locations (Kallinayakanahalli and Muppadighatta) during 2017 post-rainy season. A wider range of LWD scores was observed at both locations in both the populations. LWD response was influenced by significant genotype × location interaction. Six and 56 F_2:3 progeny families showed resistance level better than resistant parent. A total of 150 and 199 polymorphic single nucleotide polymorphism markers were used to genotype P-1 and P-2, respectively. Inclusive composite interval mapping was performed to detect significant Quantitative Trait Loci (QTL), QTL × QTL, QTL × location interaction effects. Three major and four minor QTL controlling LWD resistance were detected on chromosome-1. The position and effect of the QTL varied with the location. Significant di-QTL interactions involving QTL (with significant and/or non-significant effects) located within and between all the ten chromosomes were detected. Five of the seven detected QTL showed significant QTL × location interaction. Though two major QTL (q-lw-1.5 and q-lw-1.6) with lower Q×L interaction effects could be considered as stable, their phenotypic variance is not large enough to deploy them in Marker Assisted Selection (MAS). However, these QTL are of paramount importance in accumulating positive alleles for LWD resistance breeding.

Yield-Related QTL Clusters and the Potential Candidate Genes in Two Wheat DH Populations

In the present study, four large-scale field trials using two doubled haploid wheat populations were conducted in different environments for two years. Grain protein content (GPC) and 21 other yield-related traits were investigated. A total of 227 QTL were mapped on 18 chromosomes, which formed 35 QTL clusters. The potential candidate genes underlying the QTL clusters were suggested. Furthermore, adding to the significant correlations between yield and its related traits, correlation variations were clearly shown within the QTL clusters. The QTL clusters with consistently positive correlations were suggested to be directly utilized in wheat breeding, including 1B.2, 2A.2, 2B (4.9–16.5 Mb), 2B.3, 3B (68.9–214.5 Mb), 4A.2, 4B.2, 4D, 5A.1, 5A.2, 5B.1, and 5D. The QTL clusters with negative alignments between traits may also have potential value for yield or GPC improvement in specific environments, including 1A.1, 2B.1, 1B.3, 5A.3, 5B.2 (612.1–613.6 Mb), 7A.1, 7A.2, 7B.1, and 7B.2. One GPC QTL (5B.2: 671.3–672.9 Mb) contributed by cultivar Spitfire was positively associated with nitrogen use efficiency or grain protein yield and is highly recommended for breeding use. Another GPC QTL without negatively pleiotropic effects on 2A (50.0–56.3 Mb), 2D, 4D, and 6B is suggested for quality wheat breeding.

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.

Mapping and Validation of a Stable Quantitative Trait Locus Conferring Maize Resistance to Gibberella Ear Rot

Gibberella ear rot (GER), a prevalent disease caused by Fusarium graminearum, can result in significant yield loss and carcinogenic mycotoxin contamination in maize worldwide. However, only a few quantitative trait loci (QTLs) for GER resistance have been reported. In this study, we evaluated a Chinese recombinant inbred line (RIL) population comprising 204 lines, developed from a cross between a resistant parent DH4866 and a susceptible line T877, in three field trials under artificial inoculation with F. graminearum. The RIL population and their parents were genotyped with an Affymetrix microarray CGMB56K SNP Array. Based on the genetic linkage map constructed using 1,868 bins as markers, 11 QTLs, including five stable QTLs, were identified by individual environment analysis. Joint multiple environments analysis and epistatic interaction analysis revealed six additive and six epistatic (additive × additive) QTLs, respectively. None of the QTLs could explain more than 10% of phenotypic variation, suggesting that multiple minor-effect QTLs contributed to the genetic component of resistance to GER, and both additive and epistatic effects contributed to the genetic architecture of resistance to GER. A novel QTL, qGER4.09, with the largest effect, identified and validated using 588 F₂ individuals, was colocalized with genomic regions for Fusarium ear rot and Aspergillus ear rot, indicating that this genetic locus likely confers resistance to multiple pathogens and can potentially be utilized in breeding maize varieties aimed at improving the resistance not only to GER but also other ear rot diseases.

Identification of Genetic Loci and Candidate Genes Related to Grain Zinc and Iron Concentration Using a Zinc-Enriched Wheat 'Zinc-Shakti'

The development of nutritionally enhanced wheat (Triticum aestivum L.) with higher levels of grain iron (Fe) and zinc (Zn) offers a sustainable solution to micronutrient deficiency among resource-poor wheat consumers. One hundred and ninety recombinant inbred lines (RILs) from 'Kachu' × 'Zinc-Shakti' cross were phenotyped for grain Fe and Zn concentrations and phenological and agronomically important traits at Ciudad Obregon, Mexico in the 2017-2018, 2018-2019, and 2019-2020 growing seasons and Diversity Arrays Technology (DArT) molecular marker data were used to determine genomic regions controlling grain micronutrients and agronomic traits. We identified seven new pleiotropic quantitative trait loci (QTL) for grain Zn and Fe on chromosomes 1B, 1D, 2B, 6A, and 7D. The stable pleiotropic QTL identified have expanded the diversity of QTL that could be used in breeding for wheat biofortification. Nine RILs with the best combination of pleiotropic QTL for Zn and Fe have been identified to be used in future crossing programs and to be screened in elite yield trials before releasing as biofortified varieties. In silico analysis revealed several candidate genes underlying QTL, including those belonging to the families of the transporters and kinases known to transport small peptides and minerals (thus assisting mineral uptake) and catalyzing phosphorylation processes, respectively.

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 (R ²) 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.

Genome-Wide Mapping of Loci for Adult-Plant Resistance to Stripe Rust in Durum Wheat Svevo Using the 90K SNP Array

Stripe rust is a foliar disease in wheat caused by Puccinia striiformis f. tritici. The best way to protect wheat from this disease is by growing resistant cultivars. Tetraploid wheat can serve as a good source of valuable genetic diversity for various traits. Here, we report the mapping of nine stripe rust resistance quantitative trait loci (QTL) effective against P. striiformis f. tritici in China and Israel. We used recombinant inbred lines (RILs) developed from a cross between the durum wheat cultivar Svevo and Triticum dicoccoides accession Zavitan. By genotyping the RIL population of 137 lines using the wheat 90K single-nucleotide polymorphism array, we mapped an adult-plant resistance locus QYrsv.swust-1BL.1, the most effective QTL, within a 0.75-centimorgan region in T. turgidum subsp. durum 'Svevo' on chromosome arm 1BL, corresponding to the region of 670.7 to 671.5 Mb on the Chinese Spring chromosome arm 1BL. Of the other eight minor-effect stripe rust QTL, seven were from Svevo and mapped on chromosomes 1A, 1B, 2B, 3A, 4A, and 5A, and one was from Zavitan and mapped on chromosome 2A. Several QTL with epistatic effects were identified as well. The markers linked to the resistance QTL can be useful in marker-assisted selection for incorporation of these resistance QTL into both durum and common wheat cultivars.

Genetic Mapping by Integration of 55K SNP Array and KASP Markers Reveals Candidate Genes for Important Agronomic Traits in Hexaploid Wheat

Agronomic traits such as heading date (HD), plant height (PH), thousand grain weight (TGW), and spike length (SL) are important factors affecting wheat yield. In this study, we constructed a high-density genetic linkage map using the Wheat55K SNP Array to map quantitative trait loci (QTLs) for these traits in 207 recombinant inbred lines (RILs). A total of 37 QTLs were identified, including 9 QTLs for HD, 7 QTLs for PH, 12 QTLs for TGW, and 9 QTLs for SL, which explained 3.0-48.8% of the phenotypic variation. Kompetitive Allele Specific PCR (KASP) markers were developed based on sequencing data and used for validation of the stably detected QTLs on chromosomes 3A, 4B and 6A using 400 RILs. A QTL cluster on chromosome 4B for PH and TGW was delimited to a 0.8 Mb physical interval explaining 12.2-22.8% of the phenotypic variation. Gene annotations and analyses of SNP effects suggested that a gene encoding protein Photosynthesis Affected Mutant 68, which is essential for photosystem II assembly, is a candidate gene affecting PH and TGW. In addition, the QTL for HD on chromosome 3A was narrowed down to a 2.5 Mb interval, and a gene encoding an R3H domain-containing protein was speculated to be the causal gene influencing HD. The linked KASP markers developed in this study will be useful for marker-assisted selection in wheat breeding, and the candidate genes provide new insight into genetic study for those traits in wheat.

The 55K SNP-Based Exploration of QTLs for Spikelet Number Per Spike in a Tetraploid Wheat (Triticum turgidum L.) Population: Chinese Landrace "Ailanmai" × Wild Emmer

Spikelet number per spike (SNS) is the primary factor that determines wheat yield. Common wheat breeding reduces the genetic diversity among elite germplasm resources, leading to a detrimental effect on future wheat production. It is, therefore, necessary to explore new genetic resources for SNS to increase wheat yield. A tetraploid landrace "Ailanmai" × wild emmer wheat recombinant inbred line (RIL) population was used to construct a genetic map using a wheat 55K single- nucleotide polymorphism (SNP) array. The linkage map containing 1,150 bin markers with a total genetic distance of 2,411.8 cm was obtained. Based on the phenotypic data from the eight environments and best linear unbiased prediction (BLUP) values, five quantitative trait loci (QTLs) for SNS were identified, explaining 6.71-29.40% of the phenotypic variation. Two of them, QSns.sau-AM-2B.2 and QSns.sau-AM-3B.2, were detected as a major and novel QTL. Their effects were further validated in two additional F₂ populations using tightly linked kompetitive allele-specific PCR (KASP) markers. Potential candidate genes within the physical intervals of the corresponding QTLs were predicted to participate in inflorescence development and spikelet formation. Genetic associations between SNS and other agronomic traits were also detected and analyzed. This study demonstrates the feasibility of the wheat 55K SNP array developed for common wheat in the genetic mapping of tetraploid population and shows the potential application of wheat-related species in wheat improvement programs.

Identification and validation of quantitative trait loci for kernel traits in common wheat (Triticum aestivum L.)

BACKGROUND

Kernel weight and morphology are important traits affecting cereal yields and quality. Dissecting the genetic basis of thousand kernel weight (TKW) and its related traits is an effective method to improve wheat yield.

RESULTS

In this study, we performed quantitative trait loci (QTL) analysis using recombinant inbred lines derived from the cross 'PuBing3228 × Gao8901' (PG-RIL) to dissect the genetic basis of kernel traits. A total of 17 stable QTLs related to kernel traits were identified, notably, two stable QTLs QTkw.cas-1A.2 and QTkw.cas-4A explained the largest portion of the phenotypic variance for TKW and kernel length (KL), and the other two stable QTLs QTkw.cas-6A.1 and QTkw.cas-7D.2 contributed more effects on kernel width (KW). Conditional QTL analysis revealed that the stable QTLs for TKW were mainly affected by KW. The QTLs QTkw.cas-7D.2 and QKw.cas-7D.1 associated with TKW and KW were delimited to the physical interval of approximately 3.82 Mb harboring 47 candidate genes. Among them, the candidate gene TaFT-D1 had a 1 bp insertions/deletion (InDel) within the third exon, which might be the reason for diversity in TKW and KW between the two parents. A Kompetitive Allele-Specific PCR (KASP) marker of TaFT-D1 allele was developed and verified by PG-RIL and a natural population consisted of 141 cultivar/lines. It was found that the favorable TaFT-D1 (G)-allele has been positively selected during Chinese wheat breeding. Thus, these results can be used for further positional cloning and marker-assisted selection in wheat breeding programs.

CONCLUSIONS

Seventeen stable QTLs related to kernel traits were identified. The stable QTLs for thousand kernel weight were mainly affected by kernel width. TaFT-D1 could be the candidate gene for QTLs QTkw.cas-7D.2 and QKw.cas-7D.1.

Genetic mapping of northern corn leaf blight-resistant quantitative trait loci in maize

Northern corn leaf blight (NCLB), a corn disease infected by Exserohilum turcicum, can cause loss of harvest and economy. Identification or evaluation of NCLB-resistant quantitative trait loci (QTL) and genes could improve maize breeds. This study aimed to identify novel QTLs for NCLB-resistance.Two maize strains (BB and BC) were utilized to generate B73 × B97 and B73 × CML322 and constructed the genetic linkage using high-throughput single nucleotide polymorphism (SNP) linkage map analysis of 170 (BB) and 163(BC) recombinant inbred line (RIL) genomic DNA samples. NCLB-resistant QTL was associated with phenotypic data from the field trial of 170 BB and 163 BC strains over two years using these 1100 SNPs to identify high-density NCLB-resistant QTLs.In BB, QTL of the NCLB resistance was on chromosome 1 and 3 (LOD scores between 2.74 and 5.44); in BC, QTL of NCLB resistance was on chromosome 1, 2, 4, 8, and 9 (LOD scores between 2.52 and 8.53). A number of genes or genetic information related to NCLB resistance in both BB and BC were identified with the maximum number of genes/NCLB resistance-related QTL on chromosome 3 for BB and on chromosome 1 for BC.This study successfully mapped and identified NCLB-resistant QTL and genes for these 2 different maize strains, which provides insightful information for future study of NCLB-resistance and selection of NCLB-resistant maize variants.

Characterization of Genetic Diversity and Genome-Wide Association Mapping of Three Agronomic Traits in Qingke Barley (Hordeum Vulgare L.) in the Qinghai-Tibet Plateau

Barley (Hordeum vulgare L.) is one of the most important cereal crops worldwide. In the Qinghai-Tibet Plateau, six-rowed hulless (or naked) barley, called “qingke” in Chinese or “nas” in Tibetan, is produced mainly in Tibet. The complexity of the environment in the Qinghai-Tibet Plateau has provided unique opportunities for research on the breeding and adaptability of qingke barley. However, the genetic architecture of many important agronomic traits for qingke barley remains elusive. Heading date (HD), plant height (PH), and spike length (SL) are three prominent agronomic traits in barley. Here, we used genome-wide association (GWAS) mapping and GWAS with eigenvector decomposition (EigenGWAS) to detect quantitative trait loci (QTL) and selective signatures for HD, PH, and SL in a collection of 308 qingke barley accessions. The accessions were genotyped using a newly-developed, proprietary genotyping-by-sequencing (tGBS) technology, that yielded 14,970 high quality single nucleotide polymorphisms (SNPs). We found that the number of SNPs was higher in the varieties than in the landraces, which suggested that Tibetan varieties and varieties in the Tibetan area may have originated from different landraces in different areas. We have identified 62 QTLs associated with three important traits, and the observed phenotypic variation is well-explained by the identified QTLs. We mapped 114 known genes that include, but are not limited to, vernalization, and photoperiod genes. We found that 83.87% of the identified QTLs are located in the non-coding regulatory regions of annotated barley genes. Forty-eight of the QTLs are first reported here, 28 QTLs have pleotropic effects, and three QTL are located in the regions of the well-characterized genes HvVRN1, HvVRN3, and PpD-H2. EigenGWAS analysis revealed that multiple heading-date-related loci bear signatures of selection. Our results confirm that the barley panel used in this study is highly diverse, and showed a great promise for identifying the genetic basis of adaptive traits. This study should increase our understanding of complex traits in qingke barley, and should facilitate genome-assisted breeding for qingke barley improvement.

Detecting CSSLs and yield QTLs with additive, epistatic and QTL×environment interaction effects from Oryza sativa × O. nivara IRGC81832 cross

Chromosome segment substitution lines (CSSLs) are useful tools for precise mapping of quantitative trait loci (QTLs) and the evaluation of gene action and interaction in inter-specific crosses. In this study, a set of 90 back cross lines at BC2F8 generation derived from Swarna x Oryza nivara IRGC81832 was evaluated for yield traits under irrigated conditions in wet seasons of 3 consecutive years. We identified a set of 70 chromosome segment substitution lines, using genotyping data from 140 SSR markers covering 94.4% of O. nivara genome. Among these, 23 CSSLs were significantly different for 7 traits. 22 QTLs were detected for 11 traits with 6.51 to 46.77% phenotypic variation in 90 BILs. Three pleiotropic genomic regions associated with yield traits were mapped on chromosomes 1, 8 and 11. The marker interval RM206-RM144 at chromosome 11 was recurrently detected for various yield traits. Ten QTLs were identified consistently in the three consecutive years of testing. Seventeen pairs of significant epistatic QTLs (E-QTLs) were detected for days to flowering, days to maturity and plant height. Chromosome segments from O. nivara contributed trait enhancing alleles. The significantly improved lines and the stable QTLs identified in this study are valuable resource for gene discovery and yield improvement.

Dynamic effects of interacting genes underlying rice flowering-time phenotypic plasticity and global adaptation

The phenotypic variation of living organisms is shaped by genetics, environment, and their interaction. Understanding phenotypic plasticity under natural conditions is hindered by the apparently complex environment and the interacting genes and pathways. Herein, we report findings from the dissection of rice flowering-time plasticity in a genetic mapping population grown in natural long-day field environments. Genetic loci harboring four genes originally discovered for their photoperiodic effects (Hd1, Hd2, Hd5, and Hd6) were found to differentially respond to temperature at the early growth stage to jointly determine flowering time. The effects of these plasticity genes were revealed with multiple reaction norms along the temperature gradient. By coupling genomic selection and the environmental index, accurate performance predictions were obtained. Next, we examined the allelic variation in the four flowering-time genes across the diverse accessions from the 3000 Rice Genomes Project and constructed haplotypes at both individual-gene and multigene levels. The geographic distribution of haplotypes revealed their preferential adaptation to different temperature zones. Regions with lower temperatures were dominated by haplotypes sensitive to temperature changes, whereas the equatorial region had a majority of haplotypes that are less responsive to temperature. By integrating knowledge from genomics, gene cloning and functional characterization, and environment quantification, we propose a conceptual model with multiple levels of reaction norms to help bridge the gaps among individual gene discovery, field-level phenotypic plasticity, and genomic diversity and adaptation.

Mapping quantitative trait loci (QTLs) and estimating the epistasis controlling stem rot resistance in cultivated peanut (Arachis hypogaea)

A total of 33 additive stem rot QTLs were identified in peanut genome with nine of them consistently detected in multiple years or locations. And 12 pairs of epistatic QTLs were firstly reported for peanut stem rot disease. Stem rot in peanut (Arachis hypogaea) is caused by the Sclerotium rolfsii and can result in great economic loss during production. In this study, a recombinant inbred line population from the cross between NC 3033 (stem rot resistant) and Tifrunner (stem rot susceptible) that consists of 156 lines was genotyped by using 58 K peanut single nucleotide polymorphism (SNP) array and phenotyped for stem rot resistance at multiple locations and in multiple years. A linkage map consisting of 1451 SNPs and 73 simple sequence repeat (SSR) markers was constructed. A total of 33 additive quantitative trait loci (QTLs) for stem rot resistance were detected, and six of them with phenotypic variance explained of over 10% (qSR.A01-2, qSR.A01-5, qSR.A05/B05-1, qSR.A05/B05-2, qSR.A07/B07-1 and qSR.B05-1) can be consistently detected in multiple years or locations. Besides, 12 pairs of QTLs with epistatic (additive × additive) interaction were identified. An additive QTL qSR.A01-2 also with an epistatic effect interacted with a novel locus qSR.B07_1-1 to affect the percentage of asymptomatic plants in a row. A total of 193 candidate genes within 38 stem rot QTLs intervals were annotated with functions of biotic stress resistance such as chitinase, ethylene-responsive transcription factors and pathogenesis-related proteins. The identified stem rot resistance QTLs, candidate genes, along with the associated SNP markers in this study, will benefit peanut molecular breeding programs for improving stem rot resistance.

Narrowing Down a Major QTL Region Conferring Pod Fiber Contents in Yardlong Bean (Vigna unguiculata), a Vegetable Cowpea

Yardlong bean (Vigna unguiculata (L.) Walp. ssp. sesquipedalis), a subgroup of cowpea, is an important vegetable legume crop of Asia where its young pods are consumed in both fresh and cooked forms. Pod fiber contents (cellulose, hemicellulose and lignin) correlates with pod tenderness (softness/hardness) and pod shattering. In a previous study using populations derived from crosses between yardlong bean and wild cowpea (V. unguiculata ssp. unguiculata var. spontanea), three major quantitative trait loci (QTLs), qCel7.1, qHem7.1 and qLig7.1, controlling these fibers were identified on linkage group 7 (cowpea chromosome 5) and are co-located with QTLs for pod tenderness and pod shattering. The objective of this study was to identify candidate gene(s) controlling the pod fiber contents. Fine mapping for qCel7.1, qHem7.1 and qLig7.1 was conducted using F₂ and F_2:3 populations of 309 and 334 individuals, respectively, from the same cross combination. New DNA markers were developed from cowpea reference genome sequence and used for fine mapping. A QTL analysis showed that in most cases, each pod fiber content was controlled by one major and one minor QTLs on the LG7. The major QTLs for cellulose, hemicellulose and lignin in pod were always mapped to the same regions or close to each other. In addition, a major QTL for pod shattering was also located in the region. Although there were several annotated genes relating to pod fiber contents in the region, two genes including Vigun05g266600 (VuBGLU12) encoding a beta glucosidase and Vigun05g273500 (VuMYB26b) encoding a transcription factor MYB26 were identified as candidate genes for the pod fiber contents and pod shattering. Function(s) of these genes in relation to pod wall fiber biosynthesis and pod shattering was discussed.

Genome-wide quantitative trait loci reveal the genetic basis of cotton fibre quality and yield-related traits in a Gossypium hirsutum recombinant inbred line population

Cotton is widely cultivated globally because it provides natural fibre for the textile industry and human use. To identify quantitative trait loci (QTLs)/genes associated with fibre quality and yield, a recombinant inbred line (RIL) population was developed in upland cotton. A consensus map covering the whole genome was constructed with three types of markers (8295 markers, 5197.17 centimorgans (cM)). Six fibre yield and quality traits were evaluated in 17 environments, and 983 QTLs were identified, 198 of which were stable and mainly distributed on chromosomes 4, 6, 7, 13, 21 and 25. Thirty-seven QTL clusters were identified, in which 92.8% of paired traits with significant medium or high positive correlations had the same QTL additive effect directions, and all of the paired traits with significant medium or high negative correlations had opposite additive effect directions. In total, 1297 genes were discovered in the QTL clusters, 414 of which were expressed in two RNA-Seq data sets. Many genes were discovered, 23 of which were promising candidates. Six important QTL clusters that included both fibre quality and yield traits were identified with opposite additive effect directions, and those on chromosome 13 (qClu-chr13-2) could increase fibre quality but reduce yield; this result was validated in a natural population using three markers. These data could provide information about the genetic basis of cotton fibre quality and yield and help cotton breeders to improve fibre quality and yield simultaneously.

Identification of QTLs for resistance to maize rough dwarf disease using two connected RIL populations in maize

Maize rough dwarf disease (MRDD) is a significant viral disease caused by rice black-streaked dwarf virus (RBSDV) in China, which results in 30% yield losses in affected summer maize-growing areas. In this study, two connected recombinant inbred line (RIL) populations were constructed to elucidate the genetic basis of resistance during two crop seasons. Ten quantitative trait loci (QTLs) for resistance to MRDD were detected in the two RILs. Individual QTLs accounted for 4.97-23.37% of the phenotypic variance explained (PVE). The resistance QTL (qZD-MRDD8-1) with the largest effect was located in chromosome bin 8.03, representing 16.27-23.37% of the PVE across two environments. Interestingly, one pair of common significant QTLs was located in the similar region on chromosome 4 in both populations, accounting for 7.11-9.01% of the PVE in Zheng58×D863F (RIL-ZD) and 9.43-13.06% in Zheng58×ZS301 (RIL-ZZ). A total of five QTLs for MRDD resistance trait showed significant QTL-by-Environment interactions (QEI). Two candidate genes associated with resistance (GDSL-lipase and RPP13-like gene) which were higher expressed in resistant inbred line D863F than in susceptible inbred line Zheng58, were located in the physical intervals of the major QTLs on chromosomes 4 and 8, respectively. The identified QTLs will be studied further for application in marker-assisted breeding in maize genetic improvement of MRDD resistance.

Effect of drought stress on the genetic architecture of photosynthate allocation and remobilization in pods of common bean (Phaseolus vulgaris L.), a key species for food security

BACKGROUND

Common bean is the most important staple grain legume for direct human consumption and nutrition. It complements major sources of carbohydrates, including cereals, root crop, or plantain, as a source of dietary proteins. It is also a significant source of vitamins and minerals like iron and zinc. To fully play its nutritional role, however, its robustness against stresses needs to be strengthened. Foremost among these is drought, which commonly affects its productivity and seed quality. Previous studies have shown that photosynthate remobilization and partitioning is one of the main mechanisms of drought tolerance and overall productivity in common bean.

RESULTS

In this study, we sought to determine the inheritance of pod harvest index (PHI), a measure of the partitioning of pod biomass to seed biomass, relative to that of grain yield. We evaluated a recombinant inbred population of the cross of ICA Bunsi and SXB405, both from the Mesoamerican gene pool, to determine the effects of intermittent and terminal drought stresses on the genetic architecture of photosynthate allocation and remobilization in pods of common bean. The population was grown for two seasons, under well-watered conditions and terminal and intermittent drought stress in one year, and well-watered conditions and terminal drought stress in the second year. There was a significant effect of the water regime and year on all the traits, at both the phenotypic and QTL levels. We found nine QTLs for pod harvest index, including a major (17% of variation explained), stable QTL on linkage group Pv07. We also found eight QTLs for yield, three of which clustered with PHI QTLs, underscoring the importance of photosynthate remobilization in productivity. We also found evidence for substantial epistasis, explaining a considerable part of the variation for yield and PHI.

CONCLUSION

Our results highlight the genetic relationship between PHI and yield and confirm the role of PHI in selection of both additive and epistatic effects controlling drought tolerance. These results are a key component to strengthen the robustness of common bean against drought stresses.

High-density genetic map construction and quantitative trait loci identification for growth traits in (Taxodium distichum var. distichum × T. mucronatum) × T. mucronatum

BACKGROUND

'Zhongshanshan' is the general designation for the superior interspecific hybrid clones of Taxodium species, which is widely grown for economic and ecological purposes in southern China. Growth is the priority objective in 'Zhongshanshan' tree improvement. A high-density linkage map is vital to efficiently identify key quantitative trait loci (QTLs) that affect growth.

RESULTS

In total, 403.16 Gb of data, containing 2016,336 paired-end reads, was obtained after preprocessing. The average sequencing depth was 28.49 in T. distichum var. distichum, 25.18 in T. mucronatum, and 11.12 in each progeny. In total, 524,662 high-quality SLAFs were detected, of which 249,619 were polymorphic, and 6166 of the polymorphic markers met the requirements for use in constructing a genetic map. The final map harbored 6156 SLAF markers on 11 linkage groups, and was 1137.86 cM in length, with an average distance of 0.18 cM between adjacent markers. Separate QTL analyses of traits in different years by CIM detected 7 QTLs. While combining multiple-year data, 13 QTLs were detected by ICIM. 5 QTLs were repeatedly detected by the two methods, and among them, 3 significant QTLs (q6-2, q4-2 and q2-1) were detected in at least two traits. Bioinformatic analysis discoveried a gene annotated as a leucine-rich repeat receptor-like kinase gene within q4-2.

CONCLUSIONS

This map is the most saturated one constructed in a Taxodiaceae species to date, and would provide useful information for future comparative mapping, genome assembly, and marker-assisted selection.

Genomic and environmental determinants and their interplay underlying phenotypic plasticity

Observed phenotypic variation in living organisms is shaped by genomes, environment, and their interactions. Flowering time under natural conditions can showcase the diverse outcome of the gene-environment interplay. However, identifying hidden patterns and specific factors underlying phenotypic plasticity under natural field conditions remains challenging. With a genetic population showing dynamic changes in flowering time, here we show that the integrated analyses of genomic responses to diverse environments is powerful to reveal the underlying genetic architecture. Specifically, the effect continuum of individual genes (Ma₁ , Ma₆ , FT, and ELF3) was found to vary in size and in direction along an environmental gradient that was quantified by photothermal time, a combination of two environmental factors (photoperiod and temperature). Gene-gene interaction was also contributing to the observed phenotypic plasticity. With the identified environmental index to quantitatively connect environments, a systematic genome-wide performance prediction framework was established through either genotype-specific reaction-norm parameters or genome-wide marker-effect continua. These parallel genome-wide approaches were demonstrated for in-season and on-target performance prediction by simultaneously exploiting genomics, environment profiling, and performance information. Improved understanding of mechanisms for phenotypic plasticity enables a concerted exploration that turns challenge into opportunity.

Genotyping-by-Sequencing Derived High-Density Linkage Map and its Application to QTL Mapping of Flag Leaf Traits in Bread Wheat

Winter wheat parents ‘Harry’ (drought tolerant) and ‘Wesley’ (drought susceptible) were used to develop a recombinant inbred population with future goals of identifying genomic regions associated with drought tolerance. To precisely map genomic regions, high-density linkage maps are a prerequisite. In this study genotyping-by- sequencing (GBS) was used to construct the high-density linkage map. The map contained 3,641 markers distributed on 21 chromosomes and spanned 1,959 cM with an average distance of 1.8 cM between markers. The constructed linkage map revealed strong collinearity in marker order across 21 chromosomes with POPSEQ-v2.0, which was based on a high-density linkage map. The reliability of the linkage map for QTL mapping was demonstrated by co-localizing the genes to previously mapped genomic regions for two highly heritable traits, chaff color, and leaf cuticular wax. Applicability of linkage map for QTL mapping of three quantitative traits, flag leaf length, width, and area, identified 21 QTLs in four environments, and QTL expression varied across the environments. Two major stable QTLs, one each for flag leaf length (Qfll.hww-7A) and flag leaf width (Qflw.hww-5A) were identified. The map constructed will facilitate QTL and fine mapping of quantitative traits, map-based cloning, comparative mapping, and in marker-assisted wheat breeding endeavors.

Investigation of the Influence of Leaf Thickness on Canopy Reflectance and Physiological Traits in Upland and Pima Cotton Populations

Many systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation from the crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology's nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland (Gossypium hirsutum L.) recombinant inbred line (RIL) population of 95 lines and a Pima (G. barbadense L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered (WW) conditions, across 3 years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum r = -0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination ([Formula: see text] = -0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants.

Enriching an intraspecific genetic map and identifying QTL for fiber quality and yield component traits across multiple environments in Upland cotton (Gossypium hirsutum L.)

Cotton is a significant commercial crop that plays an indispensable role in many domains. Constructing high-density genetic maps and identifying stable quantitative trait locus (QTL) controlling agronomic traits are necessary prerequisites for marker-assisted selection (MAS). A total of 14,899 SSR primer pairs designed from the genome sequence of G. raimondii were screened for polymorphic markers between mapping parents CCRI 35 and Yumian 1, and 712 SSR markers showing polymorphism were used to genotype 180 lines from a (CCRI 35 × Yumian 1) recombinant inbred line (RIL) population. Genetic linkage analysis was conducted on 726 loci obtained from the 712 polymorphic SSR markers, along with 1379 SSR loci obtained in our previous study, and a high-density genetic map with 2051 loci was constructed, which spanned 3508.29 cM with an average distance of 1.71 cM between adjacent markers. Marker orders on the linkage map are highly consistent with the corresponding physical orders on a G. hirsutum genome sequence. Based on fiber quality and yield component trait data collected from six environments, 113 QTLs were identified through two analytical methods. Among these 113 QTLs, 50 were considered stable (detected in multiple environments or for which phenotypic variance explained by additive effect was greater than environment effect), and 18 of these 50 were identified with stability by both methods. These 18 QTLs, including eleven for fiber quality and seven for yield component traits, could be priorities for MAS.

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population.

QTL Mapping of Grain Zn and Fe Concentrations in Two Hexaploid Wheat RIL Populations with Ample Transgressive Segregation

More than 50% of undernourished children live in Asia and more than 25% live in Africa. Coupled with an inadequate food supply, mineral deficiencies are widespread in these populations; particularly zinc (Zn) and iron (Fe) deficiencies that lead to retarded growth, adverse effects on both the immune system and an individual's cognitive abilities. Biofortification is one solution aimed at reducing the incidence of these deficiencies. To efficiently breed a biofortified wheat variety, it is important to generate knowledge of the genomic regions associated with grain Zn (GZn) and Fe (GFe) concentration. This allows for the introgression of favorable alleles into elite germplasm. In this study we evaluated two bi-parental populations of 188 recombinant inbred lines (RILs) displaying a significant range of transgressive segregation for GZn and GFe during three crop cycles in CIMMYT, Mexico. Parents of the RILs were derived from Triticum spelta L. and synthetic hexaploid wheat crosses. QTL analysis identified a number of significant QTL with a region denominated as QGZn.cimmyt-7B_1P2 on chromosome 7B explaining the largest (32.7%) proportion of phenotypic variance (PVE) for GZn and leading to an average additive effect of -1.3. The QTL with the largest average additive effect for GFe (-0.161) was found on chromosome 4A (QGFe.cimmyt-4A_P2), with 21.14% of the PVE. The region QGZn.cimmyt-7B_1P2 co-localized closest to the region QGZn.cimmyt-7B_1P1 in a consensus map built from the linkage maps of both populations. Pleiotropic or tightly linked QTL were also found on chromosome 3B, however of minor effects and PVE between 4.3 and 10.9%. Further efforts are required to utilize the QTL information in marker assisted backcrossing schemes for wheat biofortification. A strategy to follow is to intercross the transgressive individuals from both populations and then utilize them as sources in biofortification breeding pipelines.

Genetic Analysis and QTL Detection on Fiber Traits Using Two Recombinant Inbred Lines and Their Backcross Populations in Upland Cotton

Cotton fiber, a raw natural fiber material, is widely used in the textile industry. Understanding the genetic mechanism of fiber traits is helpful for fiber quality improvement. In the present study, the genetic basis of fiber quality traits was explored using two recombinant inbred lines (RILs) and corresponding backcross (BC) populations under multiple environments in Upland cotton based on marker analysis. In backcross populations, no significant correlation was observed between marker heterozygosity and fiber quality performance and it suggested that heterozygosity was not always necessarily advantageous for the high fiber quality. In two hybrids, 111 quantitative trait loci (QTL) for fiber quality were detected using composite interval mapping, in which 62 new stable QTL were simultaneously identified in more than one environment or population. QTL detected at the single-locus level mainly showed additive effect. In addition, a total of 286 digenic interactions (E-QTL) and their environmental interactions [QTL × environment interactions (QEs)] were detected for fiber quality traits by inclusive composite interval mapping. QE effects should be considered in molecular marker-assisted selection breeding. On average, the E-QTL explained a larger proportion of the phenotypic variation than the main-effect QTL did. It is concluded that the additive effect of single-locus and epistasis with few detectable main effects play an important role in controlling fiber quality traits in Upland cotton.

Genetic Analysis and QTL Detection on Fiber Traits Using Two Recombinant Inbred Lines and Their Backcross Populations in Upland Cotton

Cotton fiber, a raw natural fiber material, is widely used in the textile industry. Understanding the genetic mechanism of fiber traits is helpful for fiber quality improvement. In the present study, the genetic basis of fiber quality traits was explored using two recombinant inbred lines (RILs) and corresponding backcross (BC) populations under multiple environments in Upland cotton based on marker analysis. In backcross populations, no significant correlation was observed between marker heterozygosity and fiber quality performance and it suggested that heterozygosity was not always necessarily advantageous for the high fiber quality. In two hybrids, 111 quantitative trait loci (QTL) for fiber quality were detected using composite interval mapping, in which 62 new stable QTL were simultaneously identified in more than one environment or population. QTL detected at the single-locus level mainly showed additive effect. In addition, a total of 286 digenic interactions (E-QTL) and their environmental interactions [QTL × environment interactions (QEs)] were detected for fiber quality traits by inclusive composite interval mapping. QE effects should be considered in molecular marker-assisted selection breeding. On average, the E-QTL explained a larger proportion of the phenotypic variation than the main-effect QTL did. It is concluded that the additive effect of single-locus and epistasis with few detectable main effects play an important role in controlling fiber quality traits in Upland cotton.

Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton

The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Toward this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across 2010-2012. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate-to-high broad-sense heritabilities, as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy traits and agronomic traits, such as lint yield, displayed a time-dependent relationship. We also found that the genomic position of some QTL controlling HTPP canopy traits were shared with those of QTL identified for agronomic and physiological traits. This work demonstrates the novel use of a field-based HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars.

Genotype by watering regime interaction in cultivated tomato: lessons from linkage mapping and gene expression

KEY MESSAGE

In tomato, genotype by watering interaction resulted from genotype re-ranking more than scale changes. Interactive QTLs according to watering regime were detected. Differentially expressed genes were identified in some intervals.

ABSTRACT

As a result of climate change, drought will increasingly limit crop production in the future. Studying genotype by watering regime interactions is necessary to improve plant adaptation to low water availability. In cultivated tomato (Solanum lycopersicum L.), extensively grown in dry areas, well-mastered water deficits can stimulate metabolite production, increasing plant defenses and concentration of compounds involved in fruit quality, at the same time. However, few tomato Quantitative Trait Loci (QTLs) and genes involved in response to drought are identified or only in wild species. In this study, we phenotyped a population of 119 recombinant inbred lines derived from a cross between a cherry tomato and a large fruit tomato, grown in greenhouse under two watering regimes, in two locations. A large genetic variability was measured for 19 plant and fruit traits, under the two watering treatments. Highly significant genotype by watering regime interactions were detected and resulted from re-ranking more than scale changes. The population was genotyped for 679 SNP markers to develop a genetic map. In total, 56 QTLs were identified among which 11 were interactive between watering regimes. These later mainly exhibited antagonist effects according to watering treatment. Variation in gene expression in leaves of parental accessions revealed 2259 differentially expressed genes, among which candidate genes presenting sequence polymorphisms were identified under two main interactive QTLs. Our results provide knowledge about the genetic control of genotype by watering regime interactions in cultivated tomato and the possible use of deficit irrigation to improve tomato quality.