QTL.gCIMapping.GUI v2.0: An R software for detecting small-effect and linked QTLs for quantitative traits in bi-parental segregation populations

Dissection of the E8 locus in two early maturing Canadian soybean populations

Soybean [Glycine max (L.) Merr.] is a short-day crop for which breeders want to expand the cultivation range to more northern agro-environments by introgressing alleles involved in early reproductive traits. To do so, we investigated quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) regions comprised within the E8 locus, a large undeciphered region (~7.0 Mbp to 44.5 Mbp) associated with early maturity located on chromosome GM04. We used a combination of two mapping algorithms, (i) inclusive composite interval mapping (ICIM) and (ii) genome-wide composite interval mapping (GCIM), to identify major and minor regions in two soybean populations (QS15524F2:F3 and QS15544RIL) having fixed E1, E2, E3, and E4 alleles. Using this approach, we identified three main QTL regions with high logarithm of the odds (LODs), phenotypic variation explained (PVE), and additive effects for maturity and pod-filling within the E8 region: GM04:16,974,874-17,152,230 (E8-r1); GM04:35,168,111-37,664,017 (E8-r2); and GM04:41,808,599-42,376,237 (E8-r3). Using a five-step variant analysis pipeline, we identified Protein far-red elongated hypocotyl 3 (Glyma.04G124300; E8-r1), E1-like-a (Glyma.04G156400; E8-r2), Light-harvesting chlorophyll-protein complex I subunit A4 (Glyma.04G167900; E8-r3), and Cycling dof factor 3 (Glyma.04G168300; E8-r3) as the most promising candidate genes for these regions. A combinatorial eQTL mapping approach identified significant regulatory interactions for 13 expression traits (e-traits), including Glyma.04G050200 (Early flowering 3/E6 locus), with the E8-r3 region. Four other important QTL regions close to or encompassing major flowering genes were also detected on chromosomes GM07, GM08, and GM16. In GM07:5,256,305-5,404,971, a missense polymorphism was detected in the candidate gene Glyma.07G058200 (Protein suppressor of PHYA-105). These findings demonstrate that the locus known as E8 is regulated by at least three distinct genomic regions, all of which comprise major flowering genes.

Finding stable and closely linked QTLs against spot blotch in different planting dates during the adult stage in barley

The common resistance to Spot Blotch (SB) and drought stress in barley was studied using a RILs population caused Kavir × Badia cross. These lines were inoculated with Cochliobolus sativus Gonbad isolate during the adult stage and were evaluated for three crop seasons in different planting dates. The different osmotic potentials during the flowering were regulated by changing the planting dates. In total, 43 lines had resistant to SB and drought. The high-density linkage map covered 1045 cM of barley genome. A total of five stable and closely linked QTLs to SB resistance were mapped on chromosomes 2H, 3H, 4H and 7H using genome-wide composite interval mapping. Moreover, four stable and closely linked QTLs to SB susceptibility were located on chromosomes 3H, 4H, 5H and 7H. Additionally, the ISJ19-A, SCoT7-C, ISJ17-B, Bmac0144k, iPBS2415-1, Bmac0282b and EBmatc0016 markers can be used for positive screening of resistant cultivars. However, ISJ3-C, UMB310, ISJ9-B, UMB706, D03-D and iPBS2257-A markers can be used for negative screening of susceptible cultivars in marker-assisted selection. The bioinformatics studies showed that QRCsa-2H (ISJ19-A region), QRCsa-2H (SCoT7-C-ISJ17-B region), QRCsa-3H (Bmac0144k region), QRCsa-4H (iPBS2415-1 region) and QRCsa-7H (Bmac0282b-EBmatc0016 region) are involved in the carboxypeptidase, Glycosyltransferase, transcription factors, kinase and AP2/ERF, respectively.

Genetic mapping of quantitative trait loci associated with drought tolerance in chickpea (Cicer arietinum L.)

Elucidation of the genetic basis of drought tolerance is vital for genomics-assisted breeding of drought tolerant crop varieties. Here, we used genotyping-by-sequencing (GBS) to identify single nucleotide polymorphisms (SNPs) in recombinant inbred lines (RILs) derived from a cross between a drought tolerant chickpea variety, Pusa 362 and a drought sensitive variety, SBD 377. The GBS identified a total of 35,502 SNPs and subsequent filtering of these resulted in 3237 high-quality SNPs included in the eight linkage groups. Fifty-one percent of these SNPs were located in the genic regions distributed throughout the genome. The high density linkage map has total map length of 1069 cm with an average marker interval of 0.33 cm. The linkage map was used to identify 9 robust and consistent QTLs for four drought related traits viz. membrane stability index, relative water content, seed weight and yield under drought, with percent variance explained within the range of 6.29%-90.68% and LOD scores of 2.64 to 6.38, which were located on five of the eight linkage groups. A genomic region on LG 7 harbors quantitative trait loci (QTLs) explaining > 90% phenotypic variance for membrane stability index, and > 10% PVE for yield. This study also provides the first report of major QTLs for physiological traits such as membrane stability index and relative water content for drought stress in chickpea. A total of 369 putative candidate genes were identified in the 6.6 Mb genomic region spanning these QTLs. In-silico expression profiling based on the available transcriptome data revealed that 326 of these genes were differentially expressed under drought stress. KEGG analysis resulted in reduction of candidate genes from 369 to 99, revealing enrichment in various signaling pathways. Haplotype analysis confirmed 5 QTLs among the initially identified 9 QTLs. Two QTLs, qRWC1.1 and qYLD7.1, were chosen based on high SNP density. Candidate gene-based analysis revealed distinct haplotypes in qYLD7.1 associated with significant phenotypic differences, potentially linked to pathways for secondary metabolite biosynthesis. These identified candidate genes bolster defenses through flavonoids and phenylalanine-derived compounds, aiding UV protection, pathogen resistance, and plant structure.The study provides novel genomic regions and candidate genes which can be utilized in genomics-assisted breeding of superior drought tolerant chickpea cultivars.

4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs

Background

The yield and quality of soybean oil are determined by seed oil-related traits, and metabolites/lipids act as bridges between genes and traits. Although there are many studies on the mode of inheritance of metabolites or traits, studies on multi-dimensional genetic network (MDGN) are limited.

Results

In this study, six seed oil-related traits, 59 metabolites, and 107 lipids in 398 recombinant inbred lines, along with their candidate genes and miRNAs, were used to construct an MDGN in soybean. Around 175 quantitative trait loci (QTLs), 36 QTL-by-environment interactions, and 302 metabolic QTL clusters, 70 and 181 candidate genes, including 46 and 70 known homologs, were previously reported to be associated with the traits and metabolites, respectively. Gene regulatory networks were constructed using co-expression, protein–protein interaction, and transcription factor binding site and miRNA target predictions between candidate genes and 26 key miRNAs. Using modern statistical methods, 463 metabolite–lipid, 62 trait–metabolite, and 89 trait–lipid associations were found to be significant. Integrating these associations into the above networks, an MDGN was constructed, and 128 sub-networks were extracted. Among these sub-networks, the gene–trait or gene–metabolite relationships in 38 sub-networks were in agreement with previous studies, e.g., oleic acid (trait)–GmSEI–GmDGAT1a–triacylglycerol (16:0/18:2/18:3), gene and metabolite in each of 64 sub-networks were predicted to be in the same pathway, e.g., oleic acid (trait)–GmPHS–d-glucose, and others were new, e.g., triacylglycerol (16:0/18:1/18:2)–GmbZIP123–GmHD-ZIPIII-10–miR166s–oil content.

Conclusions

This study showed the advantages of MGDN in dissecting the genetic relationships between complex traits and metabolites. Using sub-networks in MGDN, 3D genetic sub-networks including pyruvate/threonine/citric acid revealed genetic relationships between carbohydrates, oil, and protein content, and 4D genetic sub-networks including PLDs revealed the relationships between oil-related traits and phospholipid metabolism likely influenced by the environment. This study will be helpful in soybean quality improvement and molecular biological research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02191-1.

SSR Linkage Maps and Identification of QTL Controlling Morpho-Phenological Traits in Two Iranian Wheat RIL Populations

Wheat is one of the essential grains grown in large areas. Identifying the genetic structure of agronomic and morphological traits of wheat can help to discover the genetic mechanisms of grain yield. In order to map the morpho-phenological traits, an experiment was conducted in the two cropping years of 2020 and 2021 on the university farm of the Faculty of Agriculture, GonbadKavous University. This study used two F8 populations, including 120 lines resulting from Gonbad × Zagros and Gonbad × Kuhdasht. The number of days to physiological maturity, number of days to flowering, number of germinated grains, number of tillers, number of tillers per plant, grain filling periods, plant height, peduncle length, spike length, awn length, spike weight, peduncle diameter, flag leaf length and weight, number of spikelets per spike, number of grains per spike, grain length, grain width, 1000-grain weight, biomass, grain yield, harvest index, straw-weight, and number of fertile spikelets per spike were measured. A total of 21 and 13 QTLs were identified for 11 and 13 traits in 2020 and 2021, respectively. In 2020, qGL-3D and qHI-1A were identified for grain length and harvest index on chromosomes 3D and 1A, explaining over 20% phenotypic variation, respectively. qNT-5B, qNTS-2D, and qSL-1D were identified on chromosomes 5B, 2D, and 1D with the LOD scores of 4.5, 4.13, and 3.89 in 2021, respectively.

Evaluation and Identification of Resistance Lines and QTLs of Maize to Seedborne Fusarium verticillioides

Internal fungal contamination in cereal grains may affect plant growth and result in health concerns for humans and animals. Fusarium verticillioides is a seedborne fungus that can systemically infect maize. However, few efforts had been devoted to studying the genetics of maize resistance to seedborne F. verticillioides. In this study, we developed a disease evaluation method to identify resistance to seedborne F. verticillioides in maize, by which a set of 121 diverse maize inbred lines were evaluated. A 160 F₁₀-generation recombinant inbred line (RIL) population derived from a cross of the resistant (BT-1) and susceptible (N6) inbred line was further used to identify major quantitative trait loci (QTLs) for seedborne F. verticillioides resistance. Eighteen inbred lines with a high resistance to seedborne F. verticillioides were characterized and could be used as potential germplasm resources for genetic improvement of maize resistance. Six QTLs with high heritability across multiple environments were detected on chromosomes 3, 4, 6, and 10, among which was a major QTL, qISFR4-1. Located on chromosome 4 at the interval of 12922609-13418025, qISFR4-1 could explain 16.63% of the total phenotypic variance. Distinct expression profiles of eight candidate genes in qISFR4-1 between BT-1 and N6 inbred lines suggested their pivotal regulatory roles in seedborne F. verticillioides resistance. Taken together, these results will improve our understanding of the resistant mechanisms of seedborne F. verticillioides and would provide valuable germplasm resources for disease resistance breeding in maize.

postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification

OBJECTIVE

The determination of the location of quantitative trait loci (QTL) (i.e., QTL mapping) is essential for identifying new genes. Various statistical methods are being incorporated into different QTL mapping functions. However, statistical errors and limitations may often occur in a QTL mapping, implying the risk of false positive errors and/or failing to detect a true positive QTL effect. We simulated the power to detect four simulated QTL in tomato using cim() and stepwiseqtl(), widely adopted QTL mapping functions, and QTL.gCIMapping(), a derivative of the composite interval mapping method. While there is general agreement that those three functions identified simulated QTL, missing or false positive QTL were observed, which were prevalent when more realistic data (such as smaller population size) were provided.

RESULTS

To address this issue, we developed postQTL, a QTL mapping R workflow that incorporates (i) both cim() and stepwiseqtl(), (ii) widely used R packages developed for model selection, and (iii) automation to increase the accuracy, efficiency, and accessibility of QTL mapping. QTL mapping experiments on tomato F₂ populations in which QTL effects were simulated or calculated showed advantages of postQTL in QTL detection.

Genetic Aspects and Molecular Causes of Seed Longevity in Plants—A Review

Seed longevity is the most important trait related to the management of gene banks because it governs the regeneration cycle of seeds. Thus, seed longevity is a quantitative trait. Prior to the discovery of molecular markers, classical genetic studies have been performed to identify the genetic determinants of this trait. Post-2000 saw the use of DNA-based molecular markers and modern biotechnological tools, including RNA sequence (RNA-seq) analysis, to understand the genetic factors determining seed longevity. This review summarizes the most important and relevant genetic studies performed in Arabidopsis (24 reports), rice (25 reports), barley (4 reports), wheat (9 reports), maize (8 reports), soybean (10 reports), tobacco (2 reports), lettuce (1 report) and tomato (3 reports), in chronological order, after discussing some classical studies. The major genes identified and their probable roles, where available, are debated in each case. We conclude by providing information about many different collections of various crops available worldwide for advanced research on seed longevity. Finally, the use of new emerging technologies, including RNA-seq, in seed longevity research is emphasized by providing relevant examples.

Identification and analysis of oil candidate genes reveals the molecular basis of cottonseed oil accumulation in Gossypium hirsutum L

Based on the integration of QTL-mapping and regulatory network analyses, five high-confidence stable QTL regions, six candidate genes and two microRNAs that potentially affect the cottonseed oil content were discovered. Cottonseed oil is increasingly becoming a promising target for edible oil with its high content of unsaturated fatty acids. In this study, a recombinant inbred line (RIL) cotton population was constructed to detect quantitative trait loci (QTLs) for the cottonseed oil content. A total of 39 QTLs were detected across eight different environments, of which five QTLs were stable. Forty-three candidate genes potentially involved in carbon metabolism, fatty acid synthesis and triacylglycerol biosynthesis processes were further obtained in the stable QTL regions. Transcriptome analysis showed that nineteen of these candidate genes expressed during  the developing cottonseed ovules and may affect the cottonseed oil content. Besides, transcription factor (TF) and microRNA (miRNA) co-regulatory network analyses based on the nineteen candidate genes suggested that six genes, two core miRNAs (ghr-miR2949b and ghr-miR2949c), and one TF GhHSL1 were considered to be closely associated with the cottonseed oil content. Moreover, four vital genes were validated by quantitative real-time PCR (qRT-PCR). These results provide insights into the oil accumulation mechanism in developing cottonseed ovules through the construction of a detailed oil accumulation model.

Domestication and improvement genes reveal the differences of seed size- and oil-related traits in soybean domestication and improvement

•

Due to reduced diversity, it is essential to map domesticated and improved genes.

•

13 known and 442 candidate genes were mined for seed size- and oil-related traits.

•

All the genes were used to explain trait changes in domestication and improvement.

•

56 domesticated and 15 improved genes may be valuable for future soybean breeding.

•

This study provides useful gene resources for future breeding and biology research.

To address domestication and improvement studies of soybean seed size- and oil-related traits, a series of domesticated and improved regions, loci, and candidate genes were identified in 286 soybean accessions using domestication and improvement analyses, genome-wide association studies, quantitative trait locus (QTL) mapping and bulked segregant analyses in this study. As a result, 534 candidate domestication regions (CDRs) and 458 candidate improvement regions (CIRs) were identified in this study and integrated with those in five and three previous studies, respectively, to obtain 952 CDRs and 538 CIRs; 1469 loci for soybean seed size- and oil-related traits were identified in this study and integrated with those in Soybase to obtain 433 QTL clusters. The two results were intersected to obtain 245 domestication and 221 improvement loci for the above traits. Around these trait-related domestication and improvement loci, 7 domestication and 7 improvement genes were found to be truly associated with these traits, and 372 candidate domestication and 87 candidate improvement genes were identified using gene expression, SNP variants in genome, miRNA binding, KEGG pathway, DNA methylation, and haplotype analysis. These genes were used to explain the trait changes in domestication and improvement. As a result, the trait changes can be explained by their frequencies of elite haplotypes, base mutations in coding region, and three factors affecting their expression levels. In addition, 56 domestication and 15 improvement genes may be valuable for future soybean breeding. This study can provide useful gene resources for future soybean breeding and molecular biology research.

Identification of Candidate Genes Regulating the Seed Coat Color Trait in Sesame (Sesamum indicum L.) Using an Integrated Approach of QTL Mapping and Transcriptome Analysis

Seed coat color is an important seed quality trait in sesame. However, the genetic mechanism of seed coat color variation remains elusive in sesame. We conducted a QTL mapping of the seed coat color trait in sesame using an F₂ mapping population. With the aid of the newly constructed superdense genetic linkage map comprised of 22,375 bins distributed in 13 linkage groups (LGs), 17 QTLs of the three indices (i.e., L, a, and b values) of seed coat color were detected in seven intervals on four LGs, with a phenotype variance explanation rate of 4.46-41.53%. A new QTL qSCa6.1 on LG 6 and a QTL hotspot containing at least four QTLs on LG 9 were further identified. Variants screening of the target intervals showed that there were 84 genes which possessed the variants that were high-impact and co-segregating with the seed coat color trait. Meanwhile, we performed the transcriptome comparison of the developing seeds of a white- and a black-seeded variety, and found that the differentially expressed genes were significantly enriched in 37 pathways, including three pigment biosynthesis related pathways. Integration of variants screening and transcriptome comparison results suggested that 28 candidate genes probably participated in the regulation of the seed coat color in sesame; of which, 10 genes had been proved or suggested to be involved in pigments biosynthesis or accumulation during seed formation. The findings gave the basis for the mechanism of seed coat color regulation in sesame, and exhibited the effects of the integrated approach of genome resequencing and transcriptome analysis on the genetics analysis of the complex traits.

QTL Analysis of Five Morpho-Physiological Traits in Bread Wheat Using Two Mapping Populations Derived from Common Parents

Traits such as plant height (PH), juvenile growth habit (GH), heading date (HD), and tiller number are important for both increasing yield potential and improving crop adaptation to climate change. In the present study, these traits were investigated by using the same bi-parental population at early (F₂ and F₂-derived F₃ families) and late (F₆ and F₇, recombinant inbred lines, RILs) generations to detect quantitative trait loci (QTLs) and search for candidate genes. A total of 176 and 178 lines were genotyped by the wheat Illumina 25K Infinium SNP array. The two genetic maps spanned 2486.97 cM and 3732.84 cM in length, for the F₂ and RILs, respectively. QTLs explaining the highest phenotypic variation were found on chromosomes 2B, 2D, 5A, and 7D for HD and GH, whereas those for PH were found on chromosomes 4B and 4D. Several QTL detected in the early generations (i.e., PH and tiller number) were not detected in the late generations as they were due to dominance effects. Some of the identified QTLs co-mapped to well-known adaptive genes (i.e., Ppd-1, Vrn-1, and Rht-1). Other putative candidate genes were identified for each trait, of which PINE1 and PIF4 may be considered new for GH and TTN in wheat. The use of a large F₂ mapping population combined with NGS-based genotyping techniques could improve map resolution and allow closer QTL tagging.

An evolutionary population structure model reveals pleiotropic effects of GmPDAT for traits related to seed size and oil content in soybean

Seed oil traits in soybean that are of benefit to human nutrition and health have been selected for during crop domestication. However, these domesticated traits have significant differences across various evolutionary types. In this study, we found that the integration of evolutionary population structure (evolutionary types) with genome-wide association studies increased the power of gene detection, and it identified one locus for traits related to seed size and oil content on chromosome 13. This domestication locus, together with another one in a 200-kb region, was confirmed by the GEMMA and EMMAX software. The candidate gene, GmPDAT, had higher expressional levels in high-oil and large-seed accessions than in low-oil and small-seed accessions. Overexpression lines had increased seed size and oil content, whereas RNAi lines had decreased seed size and oil content. The molecular mechanism of GmPDAT was deduced based on results from linkage analysis for triacylglycerols and on histocytological comparisons of transgenic soybean seeds. Our results illustrate a new approach for identifying domestication genes with pleiotropic effects.

Genetic and comparative mapping of Lupinus luteus L. highlight syntenic regions with major orthologous genes controlling anthracnose resistance and flowering time

Anthracnose susceptibility and ill-adapted flowering time severely affect Lupinus luteus yield, which has high seed protein content, is excellent for sustainable agriculture, but requires genetic improvement to fulfil its potential. This study aimed to (1) develop a genetic map; (2) define collinearity and regions of synteny with Lupinus angustifolius; and (3) map QTLs/candidate genes for anthracnose resistant and flowering time. A few linkage groups/genomic regions tended to be associated with segregation distortion, but did not affect the map. The developed map showed collinearity, and syntenic regions with L. angustifolius. Major QTLs were mapped in syntenic regions. Alleles from the wild parent and cultivar, explained 75% of the phenotypic variance for anthracnose resistance and 83% for early flowering, respectively. Marker sequences flanking the QTLs showed high homology with the Lanr1 gene and Flowering-locus-T of L. angustifolius. This suggests orthologous genes for both traits in the L. luteus genome. The findings are remarkable, revealing the potential to combine early flowering/anthracnose resistant in fulfilling yield capacity in L. luteus, and can be a major strategy in the genetic improvement and usage of this species for sustainable protein production. Allele sequences and PCR-marker tagging of these genes are being applied in marker assisted selection.

QTL mapping and GWAS for field kernel water content and kernel dehydration rate before physiological maturity in maize

Kernel water content (KWC) and kernel dehydration rate (KDR) are two main factors affecting maize seed quality and have a decisive influence on the mechanical harvest. It is of great importance to map and mine candidate genes related to KWCs and KDRs before physiological maturity in maize. 120 double-haploid (DH) lines constructed from Si287 with low KWC and JiA512 with high KWC were used as the mapping population. KWCs were measured every 5 days from 10 to 40 days after pollination, and KDRs were calculated. A total of 1702 SNP markers were used to construct a linkage map, with a total length of 1,309.02 cM and an average map distance of 0.77 cM. 10 quantitative trait loci (QTLs) and 27 quantitative trait nucleotides (QTNs) were detected by genome-wide composite interval mapping (GCIM) and multi-locus random-SNP-effect mixed linear model (mrMLM), respectively. One and two QTL hotspot regions were found on Chromosome 3 and 7, respectively. Analysis of the Gene Ontology showed that 2 GO terms of biological processes (BP) were significantly enriched (P ≤ 0.05) and 6 candidate genes were obtained. This study provides theoretical support for marker-assisted breeding of mechanical harvest variety in maize.