Combining QTL-seq and linkage mapping to fine map a wild soybean allele characteristic of greater plant height

Identification of major QTLs for drought tolerance in soybean, together with a novel candidate gene, GmUAA6

Drought tolerance is a complex trait in soybean that is controlled by polygenetic quantitative trait loci (QTLs). In this study, wilting score, days-to-wilting, leaf relative water content, and leaf relative conductivity were used to identify QTLs associated with drought tolerance in recombinant inbred lines derived from a cross between a drought-sensitive variety, Lin, and a drought-tolerant variety, Meng. A total of 33 drought-tolerance QTLs were detected. Of these 17 were major QTLs. In addition, 15 were novel drought-tolerance QTLs. The most predominant QTL was on chromosome 11. This was detected in at least three environments. The overlapped mapping interval of the four measured traits was 0.2 cM in genetic distance (about 220 kb in physical length). Glyma.11g143500 (designated as GmUAA6), which encodes a UDP-N-acetylglucosamine transporter, was identified as the most likely candidate gene. The allele of GmUAA6 from Lin (GmUAA6Lin) was associated with improved soybean drought tolerance. Overexpression of GmUAA6Lin in Arabidopsis and soybean hairy roots enhanced drought tolerance. Furthermore, a 3-bp insertion/deletion (InDel) in the coding sequence of GmUAA6 explained up to 49.9% of the phenotypic variation in drought tolerance-related traits, suggesting that this InDel might be used in future marker-assisted selection of drought-tolerant lines in soybean breeding programs.

Deploying QTL-seq rapid identification and separation of the major QTLs of tassel branch number for fine-mapping in advanced maize populations

The tassel competes with the ear for nutrients and shields the upper leaves, thereby reducing the yield of grain. The tassel branch number (TBN) is a pivotal determinant of tassel size, wherein the reduced TBN has the potential to enhance the transmission of light and reduce the consumption of nutrients, which should ultimately result in increased yield. Consequently, the TBN has emerged as a vital target trait in contemporary breeding programs that focus on compact maize varieties. In this study, QTL-seq technology and advanced population mapping were used to rapidly identify and dissect the major effects of the TBN on QTL. Advanced mapping populations (BC4F2 and BC4F3) were derived from the inbred lines 18-599 (8-11 TBN) and 3237 (0-1 TBN) through phenotypic recurrent selection. First, 13 genomic regions associated with the TBN were detected using quantitative trait locus (QTL)-seq and were located on chromosomes 2 and 5. Subsequently, validated loci within these regions were identified by QTL-seq. Three QTLs for TBN were identified in the BC4F2 populations by traditional QTL mapping, with each QTL explaining the phenotypic variation of 6.13-18.17%. In addition, for the major QTL (qTBN2-2 and qTBN5-1), residual heterozygous lines (RHLs) were developed from the BC4F2 population. These two major QTLs were verified in the RHLs by QTL mapping, with the phenotypic variation explained (PVE) of 21.57% and 30.75%, respectively. Near-isogenic lines (NILs) of qTBN2-2 and qTBN5-1 were constructed. There were significant differences between the NILs in TBN. These results will enhance our understanding of the genetic basis of TBN and provide a solid foundation for the fine-mapping of TBN.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01431-y.

OsBBX11 on qSTS4 links to salt tolerance at the seeding stage in Oryza sativa L. ssp. Japonica

Rice has been reported to be highly sensitive to salt stress at the seedling stage. However, the lack of target genes that can be used for improving salt tolerance has resulted in several saline soils unsuitable for cultivation and planting. To characterize new salt-tolerant genes, we used 1,002 F_2:3 populations derived from Teng-Xi144 and Long-Dao19 crosses as the phenotypic source to systematically characterize seedlings' survival days and ion concentration under salt stress. Utilizing QTL-seq resequencing technology and a high-density linkage map based on 4,326 SNP markers, we identified qSTS4 as a major QTL influencing seedling salt tolerance, which accounted for 33.14% of the phenotypic variation. Through functional annotation, variation detection and qRT-PCR analysis of genes within 46.9 Kb of qSTS4, it was revealed that there was one SNP in the promoter region of OsBBX11, which resulted in a significant response difference between the two parents to salt stress. Transgenic plants using knockout-based technology and demonstrated that Na⁺ and K⁺ in the roots of the functional-loss-type OsBBX11 were translocated largely to the leaves under 120 mmol/L NaCl compared with the wild-type, causing osbbx11 leaves to die after 12 days of salt stress due to an imbalance in osmotic pressure. In conclusion, this study identified OsBBX11 as a salt-tolerance gene, and one SNPs in the OsBBX11 promoter region can be used to identify its interacting transcription factors. This provides a theoretical basis for finding the molecular mechanism of OsBBX11 upstream and downstream regulation of salt tolerance and molecular design breeding in the future.

Functional Bph14 from Rathu Heenati promotes resistance to BPH at the early seedling stage of rice (Oryza sativa L.) as revealed by QTL-seq

A QTL associated with BPH resistance at the early seedling stage was identified on chromosome 3. Functional Bph14 in Rathu Heenati was associated with BPH resistance at the early seedling stage. Brown planthopper (BPH; Nilaparvata lugens Stål) is considered the most important rice pest in many Asian countries. Several BPH resistance genes have previously been identified. However, there are few reports of genes specific for BPH resistance at the early seedling stage, a crucial stage for direct-seeding cultivation. In this study, we performed a QTL-seq analysis using two bulks (20 F₂ lines in each bulk) of the F₂ population (n = 300) derived from a cross of Rathu Heenati (RH) × HCS-1 to identify QTL/genes associated with BPH resistance at the early seedling stage. An important QTL was identified on chromosome 3 and Bph14 was identified as a potential candidate gene based on the differences in gene expression and sequence variation when compared with the two parents. All plants in the resistant bulks possessed the functional Bph14 from RH and all plants in the susceptible bulk and HCS-1 contained a large deletion (2703 bp) in Bph14. The functional Bph14 gene of RH appears to be important for BPH resistance at the early seedling stage of rice and could be used in conjunction with other BPH resistance genes in rice breeding programs that confer resistance to BPH at the early and later growth stages.

Fine mapping and candidate gene analysis of proportion of four-seed pods by soybean CSSLs

Soybean yield, as one of the most important and consistent breeding goals, can be greatly affected by the proportion of four-seed pods (PoFSP). In this study, QTL mapping was performed by PoFSP data and BLUE (Best Linear Unbiased Estimator) value of the chromosome segment substitution line population (CSSLs) constructed previously by the laboratory from 2016 to 2018, and phenotype-based bulked segregant analysis (BSA) was performed using the plant lines with PoFSP extreme phenotype. Totally, 5 ICIM QTLs were repeatedly detected, and 6 BSA QTLs were identified in CSSLs. For QTL (qPoFSP13-1) repeated in ICIM and BSA results, the secondary segregation populations were constructed for fine mapping and the interval was reduced to 100Kb. The mapping results showed that the QTL had an additive effect of gain from wild parents. A total of 14 genes were annotated in the delimited interval by fine mapping. Sequence analysis showed that all 14 genes had genetic variation in promoter region or CDS region. The qRT-PCR results showed that a total of 5 candidate genes were differentially expressed between the plant lines having antagonistic extreme phenotype (High PoFSP > 35.92%, low PoFSP< 17.56%). The results of haplotype analysis showed that all five genes had two or more major haplotypes in the resource population. Significant analysis of phenotypic differences between major haplotypes showed all five candidate genes had haplotype differences. And the genotypes of the major haplotypes with relatively high PoFSP of each gene were similar to those of wild soybean. The results of this study were of great significance to the study of candidate genes affecting soybean PoFSP, and provided a basis for the study of molecular marker-assisted selection (MAS) breeding and four-seed pods domestication.

Progress and prospectus in genetics and genomics of Phytophthora root and stem rot resistance in soybean (Glycine max L.)

Soybean is one of the largest sources of protein and oil in the world and is also considered a "super crop" due to several industrial advantages. However, enhanced acreage and adoption of monoculture practices rendered the crop vulnerable to several diseases. Phytophthora root and stem rot (PRSR) caused by Phytophthora sojae is one of the most prevalent diseases adversely affecting soybean production globally. Deployment of genetic resistance is the most sustainable approach for avoiding yield losses due to this disease. PRSR resistance is complex in nature and difficult to address by conventional breeding alone. Genetic mapping through a cost-effective sequencing platform facilitates identification of candidate genes and associated molecular markers for genetic improvement against PRSR. Furthermore, with the help of novel genomic approaches, identification and functional characterization of Rps (resistance to Phytophthora sojae) have also progressed in the recent past, and more than 30 Rps genes imparting complete resistance to different PRSR pathotypes have been reported. In addition, many genomic regions imparting partial resistance have also been identified. Furthermore, the adoption of emerging approaches like genome editing, genomic-assisted breeding, and genomic selection can assist in the functional characterization of novel genes and their rapid introgression for PRSR resistance. Hence, in the near future, soybean growers will likely witness an increase in production by adopting PRSR-resistant cultivars. This review highlights the progress made in deciphering the genetic architecture of PRSR resistance, genomic advances, and future perspectives for the deployment of PRSR resistance in soybean for the sustainable management of PRSR disease.

Harnessing the potential of bulk segregant analysis sequencing and its related approaches in crop breeding

Most plant traits are governed by polygenes including both major and minor genes. Linkage mapping and positional cloning have contributed greatly to mapping genomic loci controlling important traits in crop species. However, they are low-throughput, time-consuming, and have low resolution due to which their efficiency in crop breeding is reduced. In this regard, the bulk segregant analysis sequencing (BSA-seq) and its related approaches, viz., quantitative trait locus (QTL)-seq, bulk segregant RNA-Seq (BSR)-seq, and MutMap, have emerged as efficient methods to identify the genomic loci/QTLs controlling specific traits at high resolution, accuracy, reduced time span, and in a high-throughput manner. These approaches combine BSA with next-generation sequencing (NGS) and enable the rapid identification of genetic loci for qualitative and quantitative assessments. Many previous studies have shown the successful identification of the genetic loci for different plant traits using BSA-seq and its related approaches, as discussed in the text with details. However, the efficiency and accuracy of the BSA-seq depend upon factors like sequencing depth and coverage, which enhance the sequencing cost. Recently, the rapid reduction in the cost of NGS together with the expected cost reduction of third-generation sequencing in the future has further increased the accuracy and commercial applicability of these approaches in crop improvement programs. This review article provides an overview of BSA-seq and its related approaches in crop breeding together with their merits and challenges in trait mapping.

Identification of superior haplotypes in a diverse natural population for breeding desirable plant height in soybean

KEY MESSAGE

Plant height of soybean is associated with a haplotype block on chromosome 19, which classified 211 soybean accessions into five distinct groups showing significant differences for the target trait. Genetic variation is pivotal for crop improvement. Natural populations are precious genetic resources. However, efficient strategies for the targeted utilization of these resources for quantitative traits, such as plant height (PH), are scarce. Being an important agronomic trait associated with soybean yield and quality, it is imperative to unravel the genetic mechanisms underlying PH in soybean. Here, a genome-wide association study (GWAS) was performed to identify single nucleotide polymorphisms (SNPs) significantly associated with PH in a natural population of 211 cultivated soybeans, which was genotyped with NJAU 355 K Soy SNP Array and evaluated across six environments. A total of 128 SNPs distributed across 17 chromosomes were found to be significantly associated with PH across six environments and a combined environment. Three significant SNPs were consistently identified in at least three environments on Chr.02 (AX-93958260), Chr.17 (AX-94154834), and Chr.19 (AX-93897200). Genomic regions of ~ 130 kb flanking these three consistent SNPs were considered as stable QTLs, which included 169 genes. Of these, 22 genes (including Dt1) were prioritized and defined as putative candidates controlling PH. The genomic region flanking 12 most significant SNPs was in strong linkage disequilibrium (LD). These SNPs formed a single haplotype block containing five haplotypes for PH, namely Hap-A, Hap-B, Hap-C, Hap-D, and Hap-E. Deployment of such superior haplotypes in breeding programs will enable development of improved soybean varieties with desirable plant height.

Construction of Chromosome Segment Substitution Lines and Inheritance of Seed-Pod Characteristics in Wild Soybean

Genetic populations provide the basis for genetic and genomic research, and chromosome segment substitution lines (CSSLs) are a powerful tool for the fine mapping of quantitative traits, new gene mining, and marker-assisted breeding. In this study, 213 CSSLs were obtained by self-crossing, backcrossing, and marker-assisted selection between cultivated soybean (Glycine max [L.] Merr.) variety Suinong14 (SN14) and wild soybean (Glycine soja Sieb. et Zucc.) ZYD00006. The genomes of these 213 CSSLs were resequenced and 580,524 single-nucleotide polymorphism markers were obtained, which were divided into 3,780 bin markers. The seed-pod-related traits were analyzed by quantitative trait locus (QTL) mapping using CSSLs. A total of 170 QTLs were detected, and 32 QTLs were detected stably for more than 2 years. Through epistasis analysis, 955 pairs of epistasis QTLs related to seed-pod traits were obtained. Furthermore, the hundred-seed weight QTL was finely mapped to the region of 64.4 Kb on chromosome 12, and Glyma.12G088900 was identified as a candidate gene. Taken together, a set of wild soybean CSSLs was constructed and upgraded by a resequencing technique. The seed-pod-related traits were studied by bin markers, and a candidate gene for the hundred-seed weight was finely mapped. Our results have revealed the CSSLs can be an effective tool for QTL mapping, epistatic effect analysis, and gene cloning.

Protein Biofortification in Lentils (Lens culinaris Medik.) Toward Human Health

Lentil (Lens culinaris Medik.) is a nutritionally dense crop with significant quantities of protein, low-digestible carbohydrates, minerals, and vitamins. The amino acid composition of lentil protein can impact human health by maintaining amino acid balance for physiological functions and preventing protein-energy malnutrition and non-communicable diseases (NCDs). Thus, enhancing lentil protein quality through genetic biofortification, i.e., conventional plant breeding and molecular technologies, is vital for the nutritional improvement of lentil crops across the globe. This review highlights variation in protein concentration and quality across Lens species, genetic mechanisms controlling amino acid synthesis in plants, functions of amino acids, and the effect of antinutrients on the absorption of amino acids into the human body. Successful breeding strategies in lentils and other pulses are reviewed to demonstrate robust breeding approaches for protein biofortification. Future lentil breeding approaches will include rapid germplasm selection, phenotypic evaluation, genome-wide association studies, genetic engineering, and genome editing to select sequences that improve protein concentration and quality.

Combining QTL-seq and linkage mapping to uncover the genetic basis of single vs. paired spikelets in the advanced populations of two-ranked maize×teosinte

BACKGROUND

Teosinte ear bears single spikelet, whereas maize ear bears paired spikelets, doubling the number of grains in each cupulate during maize domestication. In the past 20 years, genetic analysis of single vs. paired spikelets (PEDS) has been stagnant. A better understanding of genetic basis of PEDS could help fine mapping of quantitative trait loci (QTL) and cloning of genes.

RESULTS

In this study, the advanced mapping populations (BC₃F₂ and BC₄F₂) of maize × teosinte were developed by phenotypic recurrent selection. Four genomic regions associated with PEDS were detected using QTL-seq, located on 194.64-299.52 Mb, 0-162.80 Mb, 12.82-97.17 Mb, and 125.06-157.01 Mb of chromosomes 1, 3, 6, and 8, respectively. Five QTL for PEDS were identified in the regions of QTL-seq using traditional QTL mapping. Each QTL explained 1.12-38.05% of the phenotypic variance (PVE); notably, QTL qPEDS3.1 with the average PVE of 35.29% was identified in all tests. Moreover, 14 epistatic QTL were detected, with the total PVE of 47.57-66.81% in each test. The QTL qPEDS3.1 overlapped with, or was close to, one locus of 7 epistatic QTL. Near-isogenic lines (NILs) of QTL qPEDS1.1, qPEDS3.1, qPEDS6.1, and qPEDS8.1 were constructed. All individuals of NIL-qPEDS6.1(MT1) and NIL-qPEDS8.1(MT1) showed paired spikelets (PEDS = 0), but the flowering time was 7 days shorter in the NIL-qPEDS8.1(MT1). The ratio of plants with PEDS > 0 was low (1/18 to 3/18) in the NIL-qPEDS1.1(MT1) and NIL-qPEDS3.1(MT1), maybe due to the epistatic effect.

CONCLUSION

Our results suggested that major QTL, minor QTL, epistasis and photoperiod were associated with the variation of PEDS, which help us better understand the genetic basis of PEDS and provide a genetic resource for fine mapping of QTL.

QTL mapping for low temperature germination in rapeseed

Rapeseed, a major oil crop in the world, is easily affected by low-temperature stress. A low temperature delays seed germination and increases seedling mortality, adversely affecting rapeseed growth and production. In the present study, a tolerant cultivar (Huyou21) was crossed with a susceptible genotype (3429) to develop a mapping population consisting of 574 F₂ progenies and elucidate the genetic mechanisms of seed germination under low temperatures. Two quantitative trait loci (QTL) for low-temperature germination (LTG) were detected, one on chromosome A09 (named qLTGA9-1) and the other on chromosome C01 (named qLTGC1-1), using the QTL-seq approach and confirmed via linkage analysis in the mapping population. Further, qLTGA9-1 was mapped to a 341.86 kb interval between the SSR markers Nys9A212 and Nys9A215. In this region, 69 genes including six specific genes with moderate or high effect function variants were identified based on the Ningyou7 genome sequence. Meanwhile, qLTGC1-1 was mapped onto a 1.31 Mb interval between SSR markers Nys1C96 and Nys1C117. In this region, 133 genes including five specific genes with moderate effect function variants were identified. These specific genes within the two QTL could be used for further studies on cold tolerance and as targets in rapeseed breeding programs.

Identification and Validation of a QTL for Bacterial Leaf Streak Resistance in Rice (Oryza sativa L.) against Thai Xoc Strains

Rice is one of the most important food crops in the world and is of vital importance to many countries. Various diseases caused by fungi, bacteria and viruses constantly threaten rice plants and cause yield losses. Bacterial leaf streak disease (BLS) caused by Xanthomonas oryzae pv. oryzicola (Xoc) is one of the most devastating rice diseases. However, most modern rice varieties are susceptible to BLS. In this study, we applied the QTL-seq approach using an F2 population derived from the cross between IR62266 and Homcholasit (HSC) to rapidly identify the quantitative trait loci (QTL) that confers resistance to BLS caused by a Thai Xoc isolate, SP7-5. The results showed that a single genomic region at the beginning of chromosome 5 was highly associated with resistance to BLS. The gene xa5 was considered a potential candidate gene in this region since most associated single nucleotide polymorphisms (SNPs) were within this gene. A Kompetitive Allele-Specific PCR (KASP) marker was developed based on two consecutive functional SNPs in xa5 and validated in six F2 populations inoculated with another Thai Xoc isolate, 2NY2-2. The phenotypic variance explained by this marker (PVE) ranged from 59.04% to 70.84% in the six populations. These findings indicate that xa5 is a viable candidate gene for BLS resistance and may help in breeding programs for BLS resistance.

Genetic mapping high protein content QTL from soybean 'Nanxiadou 25' and candidate gene analysis

BACKGROUND

Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein content (SPC) is a valuable quality trait controlled by multiple genes in soybean.

RESULTS

In this study, we performed quantitative trait loci (QTL) mapping, QTL-seq, and RNA sequencing (RNA-seq) to reveal the genes controlling protein content in the soybean by using the high protein content variety Nanxiadou 25. A total of 50 QTL for SPC distributed on 14 chromosomes except chromosomes 4, 12, 14, 17, 18, and 19 were identified by QTL mapping using 178 recombinant inbred lines (RILs). Among these QTL, the major QTL qSPC_20-1 and qSPC_20-2 on chromosome 20 were repeatedly detected across six tested environments, corresponding to the location of the major QTL detected using whole-genome sequencing-based QTL-seq. 329 candidate DEGs were obtained within the QTL region of qSPC_20-1 and qSPC_20-2 via gene expression profile analysis. Nine of which were associated with SPC, potentially representing candidate genes. Clone sequencing results showed that different single nucleotide polymorphisms (SNPs) and indels between high and low protein genotypes in Glyma.20G088000 and Glyma.16G066600 may be the cause of changes in this trait.

CONCLUSIONS

These results provide the basis for research on candidate genes and marker-assisted selection (MAS) in soybean breeding for seed protein content.

Genome-wide association analysis for yield-related traits at the R6 stage in a Chinese soybean mini core collection

BACKGROUND

Soybean (Glycine max (L.) Merr.) is an economically important crop for vegetable oil and protein production, and yield is a critical trait for grain/vegetable uses of soybean. However, our knowledge of the genes controlling the vegetable soybean yield remains limited.

OBJECTIVE

To better understand the genetic basis of the vegetable soybean yield.

METHODS

The 100-pod fresh weight (PFW), 100-seed fresh weight (SFW), kernel percent (KP) and moisture content of fresh seeds (MCFS) at the R6 stage are four yield-related traits for vegetable soybean. We investigated a soybean mini core collection composed of 224 germplasm accessions for four yield-related traits in two consecutive years. Based on 1514 single nucleotide polymorphisms (SNPs), genome-wide association studies (GWAS) were conducted using a mixed linear model (MLM).

RESULTS

Extensive phenotypic variation existed in the soybean mini core collection and significant positive correlations were shown among most of traits. A total of 16 SNP markers for PFW, SFW, KP and MCFS were detected in all environments via GWAS. Nine SNP markers were repeatedly identified in two environments. Among these markers, eight were located in or near regions where yield-related QTLs have been reported in previous studies, and one was a novel genetic locus identified in this study. In addition, we conducted candidate gene analysis to the large-effect SNP markers, a total of twelve genes were proposed as potential candidate genes of soybean yield at the R6 stage.

CONCLUSION

These results will be beneficial for understanding the genetic basis of soybean yield at the R6 stage and facilitating the pyramiding of favourable alleles for future high-yield breeding by marker-assisted selection in vegetable soybean.

Transgressive resistance to Heterodera glycines in chromosome segment substitution lines derived from susceptible soybean parents

Chromosome segment substitution lines (CSSLs) are valuable genetic resources for quantitative trait loci (QTL) mapping of complex agronomic traits especially suitable for minor effect QTL. Here, 162 BC3 F7 -BC7 F3 CSSLs derived from crossing two susceptible parent lines, soybean [Glycine max (L.) Merr.] 'Suinong14' (recurrent parent) × wild soybean (G. soja Siebold & Zucc.) ZYD00006, were used for QTL mapping of soybean cyst nematode (SCN, Heterodera glycine Ichinohe) resistance based on female index (FI) and cysts per gram root (CGR) through phenotypic screening and whole-genome resequencing of CSSLs. Phenotypic results displayed a wide range of distribution and transgressive lines in both HG Type 2.5.7 FI and CGR and demonstrated a higher correlation between CGR and root weight (R2 = .5424) compared with than between FI and CGR (R2 = .0018). Using the single-marker analysis nonparametric mapping test, 33 significant QTL were detected on 18 chromosomes contributing resistance to FI and CGR. Fourteen QTL contributing 5.6-15.5% phenotypic variance (PVE) to FI were revealed on 11 chromosomes, and 16 QTL accounting for 6.1-36.2% PVE in CGR were detected on 14 chromosomes with strong additive effect by multiple-QTL model (MQM) mapping. Twenty-five and 13 out of all 38 QTL identified for FI and CGR on 20 chromosomes were from ZYD00006 and Suinong14, respectively. The CSSLs with the combination of positive alleles for FI, CGR, and root weight exhibited low nematode reproduction. For the first time, QTL associated with CGR have been detected, and both FI and CGR should be considered for breeding purposes in the absence of strong resistance genes such as rhg1 and Rhg4.

Combining QTL-seq and linkage mapping to fine map a candidate gene in qCTS6 for cold tolerance at the seedling stage in rice

BACKGROUND

Cold stress caused by low temperatures is an important factor restricting rice production. Identification of cold-tolerance genes that can stably express in cold environments is crucial for molecular rice breeding.

RESULTS

In this study, we employed high-throughput quantitative trait locus sequencing (QTL-seq) analyses in a 460-individual F2:3 mapping population to identify major QTL genomic regions governing cold tolerance at the seedling stage in rice. A novel major QTL (qCTS6) controlling the survival rate (SR) under low-temperature conditions of 9°C/10 days was mapped on the 2.60-Mb interval on chromosome 6. Twenty-seven single-nucleotide polymorphism (SNP) markers were designed for the qCST6 region based on re-sequencing data, and local QTL mapping was conducted using traditional linkage analysis. Eventually, we mapped qCTS6 to a 96.6-kb region containing 13 annotated genes, of which seven predicted genes contained 13 non-synonymous SNP loci. Quantitative reverse transcription PCR analysis revealed that only Os06g0719500, an OsbZIP54 transcription factor, was strongly induced by cold stress. Haplotype analysis confirmed that +376 bp (T>A) in the OsbZIP54 coding region played a key role in regulating cold tolerance in rice.

CONCLUSION

We identified OsbZIP54 as a novel regulatory gene associated with rice cold-responsive traits, with its Dongfu-104 allele showing specific cold-induction expression serving as an important molecular variation for rice improvement. This result is expected to further exploration of the genetic mechanism of rice cold tolerance at the seedling stage and improve cold tolerance in rice varieties by marker-assisted selection.

Genomic mapping and identification of candidate genes encoding nulliplex-branch trait in sea-island cotton (Gossypium barbadense L.) by multi-omics analysis

Nulliplex branch is a key architectural trait in sea-island cotton (Gossypium barbadense L.), but its genetic basis is not well understood. Here we investigated the genetic basis of the nulliplex-branch trait in cotton by combining newly created bulked segregant analysis (BSA)-seq data, published RNA-seq data, and published whole-genome resequencing (WGR) data. We delimited the nulliplex-branch locus (qD07-NB) to D07, region 14.8-17.1 Mb, using various BSA methods and markers. We integrated our BSA data with WGR data of sea-island cotton varieties and detected a missense single-nucleotide polymorphism in the candidate gene (Gbar_D07G011870) of qD07-NB. This gene was under positive selection during sea-island cotton breeding in the Xinjiang Uygur Autonomous Region, China. Notably, the nulliplex-branch varieties possessed a better fiber quality than the long-branch varieties, and a set of high-quality molecular markers was identified for molecular breeding of the nulliplex-branch trait in cotton. We combined BSA-seq and RNA-seq data to compare gene expression profiles between two elite sea-island cotton varieties during three developmental stages. We identified eleven relevant candidate genes, five downregulated and six upregulated, in the qD07-NB locus. This research will expand our understanding of the genetic basis of the nulliplex-branch trait and provide guidance for architecture-focused breeding in cotton.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01229-w.

Understanding Omics Driven Plant Improvement and de novo Crop Domestication: Some Examples

In the current era, one of biggest challenges is to shorten the breeding cycle for rapid generation of a new crop variety having high yield capacity, disease resistance, high nutrient content, etc. Advances in the "-omics" technology have revolutionized the discovery of genes and bio-molecules with remarkable precision, resulting in significant development of plant-focused metabolic databases and resources. Metabolomics has been widely used in several model plants and crop species to examine metabolic drift and changes in metabolic composition during various developmental stages and in response to stimuli. Over the last few decades, these efforts have resulted in a significantly improved understanding of the metabolic pathways of plants through identification of several unknown intermediates. This has assisted in developing several new metabolically engineered important crops with desirable agronomic traits, and has facilitated the de novo domestication of new crops for sustainable agriculture and food security. In this review, we discuss how "omics" technologies, particularly metabolomics, has enhanced our understanding of important traits and allowed speedy domestication of novel crop plants.

Identification of Candidate Genes Conferring Cold Tolerance to Rice (Oryza sativa L.) at the Bud-Bursting Stage Using Bulk Segregant Analysis Sequencing and Linkage Mapping

Low-temperature tolerance during the bud-bursting stage is an important characteristic of direct-seeded rice. The identification of cold-tolerance quantitative trait loci (QTL) in species that can stably tolerate cold environments is crucial for the molecular breeding of rice with such traits. In our study, high-throughput QTL-sequencing analyses were performed in a 460-individual F_{2 : 3} mapping population to identify the major QTL genomic regions governing cold tolerance at the bud-bursting (CTBB) stage in rice. A novel major QTL, qCTBB9, which controls seed survival rate (SR) under low-temperature conditions of 5°C/9 days, was mapped on the 5.40-Mb interval on chromosome 9. Twenty-six non-synonymous single-nucleotide polymorphism (nSNP) markers were designed for the qCTBB9 region based on re-sequencing data and local QTL mapping conducted using traditional linkage analysis. We mapped qCTBB9 to a 483.87-kb region containing 58 annotated genes, among which six predicted genes contained nine nSNP loci. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis revealed that only Os09g0444200 was strongly induced by cold stress. Haplotype analysis further confirmed that the SNP 1,654,225 bp in the Os09g0444200 coding region plays a key role in regulating the cold tolerance of rice. These results suggest that Os09g0444200 is a potential candidate for qCTBB9. Our results are of great significance to explore the genetic mechanism of rice CTBB and to improve the cold tolerance of rice varieties by marker-assisted selection.

Genome-wide association studies of plant architecture-related traits and 100-seed weight in soybean landraces

BACKGROUND

Plant architecture-related traits (e.g., plant height (PH), number of nodes on main stem (NN), branch number (BN) and stem diameter (DI)) and 100-seed weight (100-SW) are important agronomic traits and are closely related to soybean yield. However, the genetic basis and breeding potential of these important agronomic traits remain largely ambiguous in soybean (Glycine max (L.) Merr.).

RESULTS

In this study, we collected 133 soybean landraces from China, phenotyped them in two years at two locations for the above five traits and conducted a genome-wide association study (GWAS) using 82,187 single nucleotide polymorphisms (SNPs). As a result, we found that a total of 59 SNPs were repeatedly detected in at least two environments. There were 12, 12, 4, 4 and 27 SNPs associated with PH, NN, BN, DI and 100-SW, respectively. Among these markers, seven SNPs (AX-90380587, AX-90406013, AX-90387160, AX-90317160, AX-90449770, AX-90460927 and AX-90520043) were large-effect markers for PH, NN, BN, DI and 100-SW, and 15 potential candidate genes were predicted to be in linkage disequilibrium (LD) decay distance or LD block. In addition, real-time quantitative PCR (qRT-PCR) analysis was performed on four 100-SW potential candidate genes, three of them showed significantly different expression levels between the extreme materials at the seed development stage. Therefore, Glyma.05 g127900, Glyma.05 g128000 and Glyma.05 g129000 were considered as candidate genes with 100-SW in soybean.

CONCLUSIONS

These findings shed light on the genetic basis of plant architecture-related traits and 100-SW in soybean, and candidate genes could be used for further positional cloning.

Genomic resources in plant breeding for sustainable agriculture

Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.

Targeted amplicon sequencing + next-generation sequencing-based bulked segregant analysis identified genetic loci associated with preharvest sprouting tolerance in common buckwheat (Fagopyrum esculentum)

BACKGROUND

Common buckwheat (2n = 2x = 16) is an outcrossing pseudocereal whose seeds contain abundant nutrients and potential antioxidants. As these beneficial compounds are damaged by preharvest sprouting (PHS) and PHS is likely to increase with global warming, it is important to find efficient ways to develop new PHS-tolerant lines. However, genetic loci and selection markers associated with PHS in buckwheat have not been reported.

RESULTS

By next-generation sequencing (NGS) of whole-genome of parental lines, we developed a genome-wide set of 300 markers. By NGS- based bulked segregant analysis (NGS-BSA), we developed 100 markers linked to PHS tolerance. To confirm the effectiveness of marker development from NGS-BSA data, we developed 100 markers linked to the self-compatibility (SC) trait from previous NGS-BSA data. Using these markers, we developed genetic maps with AmpliSeq technology, which can quickly detect polymorphisms by amplicon-based multiplex targeted NGS, and performed quantitative trait locus (QTL) analysis for PHS tolerance in combination with NGS-BSA. QTL analysis detected two major and two minor QTLs for PHS tolerance in a segregating population developed from a cross between the PHS-tolerant 'Kyukei 29' and the self-compatible susceptible 'Kyukei SC7'. We found different major and minor QTLs in other segregating populations developed from the PHS-tolerant lines 'Kyukei 28' and 'NARO-FE-1'. Candidate markers linked to PHS developed by NGS-BSA were located near these QTL regions. We also investigated the effectiveness of markers linked to these QTLs for selection of PHS-tolerant lines among other segregating populations.

CONCLUSIONS

We efficiently developed genetic maps using a method combined with AmpliSeq technology and NGS-BSA, and detected QTLs associated with preharvest sprouting tolerance in common buckwheat. This is the first report to identify QTLs for PHS tolerance in buckwheat. Our marker development system will accelerate genetic research and breeding in common buckwheat.

Next Generation Sequencing Based Forward Genetic Approaches for Identification and Mapping of Causal Mutations in Crop Plants: A Comprehensive Review

The recent advancements in forward genetics have expanded the applications of mutation techniques in advanced genetics and genomics, ahead of direct use in breeding programs. The advent of next-generation sequencing (NGS) has enabled easy identification and mapping of causal mutations within a short period and at relatively low cost. Identifying the genetic mutations and genes that underlie phenotypic changes is essential for understanding a wide variety of biological functions. To accelerate the mutation mapping for crop improvement, several high-throughput and novel NGS based forward genetic approaches have been developed and applied in various crops. These techniques are highly efficient in crop plants, as it is relatively easy to grow and screen thousands of individuals. These approaches have improved the resolution in quantitative trait loci (QTL) position/point mutations and assisted in determining the functional causative variations in genes. To be successful in the interpretation of NGS data, bioinformatics computational methods are critical elements in delivering accurate assembly, alignment, and variant detection. Numerous bioinformatics tools/pipelines have been developed for such analysis. This article intends to review the recent advances in NGS based forward genetic approaches to identify and map the causal mutations in the crop genomes. The article also highlights the available bioinformatics tools/pipelines for reducing the complexity of NGS data and delivering the concluding outcomes.

Whole-genome resequencing-based QTL-seq identified candidate genes and molecular markers for fresh seed dormancy in groundnut

The subspecies fastigiata of cultivated groundnut lost fresh seed dormancy (FSD) during domestication and human-made selection. Groundnut varieties lacking FSD experience precocious seed germination during harvest imposing severe losses. Development of easy-to-use genetic markers enables early-generation selection in different molecular breeding approaches. In this context, one recombinant inbred lines (RIL) population (ICGV 00350 × ICGV 97045) segregating for FSD was used for deploying QTL-seq approach for identification of key genomic regions and candidate genes. Whole-genome sequencing (WGS) data (87.93 Gbp) were generated and analysed for the dormant parent (ICGV 97045) and two DNA pools (dormant and nondormant). After analysis of resequenced data from the pooled samples with dormant parent (reference genome), we calculated delta-SNP index and identified a total of 10,759 genomewide high-confidence SNPs. Two candidate genomic regions spanning 2.4 Mb and 0.74 Mb on the B05 and A09 pseudomolecules, respectively, were identified controlling FSD. Two candidate genes-RING-H2 finger protein and zeaxanthin epoxidase-were identified in these two regions, which significantly express during seed development and control abscisic acid (ABA) accumulation. QTL-seq study presented here laid out development of a marker, GMFSD1, which was validated on a diverse panel and could be used in molecular breeding to improve dormancy in groundnut.

PyBSASeq: a simple and effective algorithm for bulked segregant analysis with whole-genome sequencing data

BACKGROUND

Bulked segregant analysis (BSA), coupled with next-generation sequencing, allows the rapid identification of both qualitative and quantitative trait loci (QTL), and this technique is referred to as BSA-Seq here. The current SNP index method and G-statistic method for BSA-Seq data analysis require relatively high sequencing coverage to detect significant single nucleotide polymorphism (SNP)-trait associations, which leads to high sequencing cost.

RESULTS

We developed a simple and effective algorithm for BSA-Seq data analysis and implemented it in Python; the program was named PyBSASeq. Using PyBSASeq, the significant SNPs (sSNPs), SNPs likely associated with the trait, were identified via Fisher's exact test, and then the ratio of the sSNPs to total SNPs in a chromosomal interval was used to detect the genomic regions that condition the trait of interest. The results obtained this way are similar to those generated via the current methods, but with more than five times higher sensitivity. This approach was termed the significant SNP method here.

CONCLUSIONS

The significant SNP method allows the detection of SNP-trait associations at much lower sequencing coverage than the current methods, leading to ~ 80% lower sequencing cost and making BSA-Seq more accessible to the research community and more applicable to the species with a large genome.

Nested association mapping of important agronomic traits in three interspecific soybean populations

KEY MESSAGE

Glycine soja germplasm can be used to successfully introduce new alleles with the potential to add valuable new genetic diversity to the current elite soybean gene pool. Given the demonstrated narrow genetic base of the US soybean production, it is essential to identify beneficial alleles from exotic germplasm, such as wild soybean, to enhance genetic gain for favorable traits. Nested association mapping (NAM) is an approach to population development that permits the comparison of allelic effects of the same QTL in multiple parents. Seed yield, plant maturity, plant height and plant lodging were evaluated in a NAM panel consisting of 392 recombinant inbred lines derived from three biparental interspecific soybean populations in eight environments during 2016 and 2017. Nested association mapping, combined with linkage mapping, identified three major QTL for plant maturity in chromosomes 6, 11 and 12 associated with alleles from wild soybean resulting in significant increases in days to maturity. A significant QTL for plant height was identified on chromosome 13 with the allele increasing plant height derived from wild soybean. A significant grain yield QTL was detected on chromosome 17, and the allele from Glycine soja had a positive effect of 166 kg ha-1; RIL's with the wild soybean allele yielded on average 6% more than the lines carrying the Glycine max allele. These findings demonstrate the usefulness and potential of alleles from wild soybean germplasm to enhance important agronomic traits in a soybean breeding program.

Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height

Plant height is an important target for soybean breeding. It is a typical quantitative trait controlled by multiple genes and is susceptible to environmental influences. Here, we carried out phenotypic analysis of 156 recombinant inbred lines derived from "Dongnong L13" and "Henong 60" in nine environments at four locations over 6 years using interval mapping and inclusive composite interval mapping methods. We performed quantitative trait locus (QTL) analysis by applying pre-built simple-sequence repeat maps. We detected 48 QTLs, including nine significant QTLs detected by multiple methods and in multiple environments. Meanwhile, genotyping of all lines using the SoySNP660k BeadChip produced 54,836 non-redundant single-nucleotide polymorphism (SNP) genotypes. We used five multi-locus genome-wide association analysis methods to locate 10 quantitative trait nucleotides (QTNs), four of which overlap with previously located QTLs. Five candidate genes related to plant height are predicted to lie within 200 kb of these four QTNs. We identified 19 homologous genes in Arabidopsis, two of which may be associated with plant height. These findings further our understanding of the multi-gene regulatory network and genetic determinants of soybean plant height, which will be important for breeding high-yielding soybean.

Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean

Genomic selection uses single-nucleotide polymorphisms (SNPs) to predict quantitative phenotypes for enhancing traits in breeding populations and has been widely used to increase breeding efficiency for plants and animals. Existing statistical methods rely on a prior distribution assumption of imputed genotype effects, which may not fit experimental datasets. Emerging deep learning technology could serve as a powerful machine learning tool to predict quantitative phenotypes without imputation and also to discover potential associated genotype markers efficiently. We propose a deep-learning framework using convolutional neural networks (CNNs) to predict the quantitative traits from SNPs and also to investigate genotype contributions to the trait using saliency maps. The missing values of SNPs are treated as a new genotype for the input of the deep learning model. We tested our framework on both simulation data and experimental datasets of soybean. The results show that the deep learning model can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than currently available other well-known statistical methods. It can also effectively and efficiently identify significant markers of SNPs and SNP combinations associated in genome-wide association study.

Deciphering the Genetic Architecture of Plant Height in Soybean Using Two RIL Populations Sharing a Common M8206 Parent

Plant height (PH) is an important agronomic trait that is closely related to soybean yield and quality. However, it is a complex quantitative trait governed by multiple genes and is influenced by environment. Unraveling the genetic mechanism involved in PH, and developing soybean cultivars with desirable PH is an imperative goal for soybean breeding. In this regard, the present study used high-density linkage maps of two related recombinant inbred line (RIL) populations viz., MT and ZM evaluated in three different environments to detect additive and epistatic effect quantitative trait loci (QTLs) as well as their interaction with environments for PH in Chinese summer planting soybean. A total of eight and 12 QTLs were detected by combining the composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) methods in MT and ZM populations, respectively. Among these QTLs, nine QTLs viz., QPH-2, qPH-6-2_MT, QPH-6, qPH-9-1_ZM, qPH-10-1_ZM, qPH-13-1_ZM, qPH-16-1_MT, QPH-17 and QPH-19 were consistently identified in multiple environments or populations, hence were regarded as stable QTLs. Furthermore, Out of these QTLs, three QTLs viz., qPH-4-2_ZM, qPH-15-1_MT and QPH-17 were novel. In particular, QPH-17 could detect in both populations, which was also considered as a stable and major QTL in Chinese summer planting soybean. Moreover, eleven QTLs revealed significant additive effects in both populations, and out of them only six showed additive by environment interaction effects, and the environment-independent QTLs showed higher additive effects. Finally, six digenic epistatic QTLs pairs were identified and only four additive effect QTLs viz., qPH-6-2_MT, qPH-19-1_MT/QPH-19, qPH-5-1_ZM and qPH-17-1_ZM showed epistatic effects. These results indicate that environment and epistatic interaction effects have significant influence in determining genetic basis of PH in soybean. These results would not only increase our understanding of the genetic control of plant height in summer planting soybean but also provide support for implementing marker assisted selection (MAS) in developing cultivars with ideal plant height as well as gene cloning to elucidate the mechanisms of plant height.

Identification of heat-tolerance QTLs and high-temperature stress-responsive genes through conventional QTL mapping, QTL-seq and RNA-seq in tomato

BACKGROUND

High temperature is one of the major abiotic stresses in tomato and greatly reduces fruit yield and quality. Identifying high-temperature stress-responsive (HSR) genes and breeding heat-tolerant varieties is an effective way to address this issue. However, there are few reports on the fine mapping of heat-tolerance quantitative trait locus (QTL) and the identification of HSR genes in tomato. Here, we applied three heat tolerance-related physiological indexes, namely, relative electrical conductivity (REC), chlorophyll content (CC) and maximum photochemical quantum efficiency (Fv/Fm) of PSII (photosystem II), as well as the phenotypic index, the heat injury index (HII), and conventional QTL analysis combined with QTL-seq technology to comprehensively detect heat-tolerance QTLs in tomato seedlings. In addition, we integrated the QTL mapping results with RNA-seq to identify key HSR genes within the major QTLs.

RESULTS

A total of five major QTLs were detected: qHII-1-1, qHII-1-2, qHII-1-3, qHII-2-1 and qCC-1-5 (qREC-1-3). qHII-1-1, qHII-1-2 and qHII-1-3 were located, respectively, in the intervals of 1.43, 1.17 and 1.19 Mb on chromosome 1, while the interval of qHII-2-1 was located in the intervals of 1.87 Mb on chromosome 2. The locations observed with conventional QTL mapping and QTL-seq were consistent. qCC-1-5 and qREC-1-3 for CC and REC, respectively, were located at the same position by conventional QTL mapping. Although qCC-1-5 was not detected in QTL-seq analysis, its phenotypic variation (16.48%) and positive additive effect (0.22) were the highest among all heat tolerance QTLs. To investigate the genes involved in heat tolerance within the major QTLs in tomato, RNA-seq analysis was performed, and four candidate genes (SlCathB2, SlGST, SlUBC5, and SlARG1) associated with heat tolerance were finally detected within the major QTLs by DEG analysis, qRT-PCR screening and biological function analysis.

CONCLUSIONS

In conclusion, this study demonstrated that the combination of conventional QTL mapping, QTL-seq analysis and RNA-seq can rapidly identify candidate genes within major QTLs for a complex trait of interest to replace the fine-mapping process, thus greatly shortening the breeding process and improving breeding efficiency. The results have important applications for the fine mapping and identification of HSR genes and breeding for improved thermotolerance.

Characterization and Rapid Gene-Mapping of Leaf Lesion Mimic Phenotype of spl-1 Mutant in Soybean (Glycine max (L.) Merr.)

In plants, lesion mimic mutants (LMMs) reveal spontaneous disease-like lesions in the absence of pathogen that constitutes powerful genetic material to unravel genes underlying programmed cell death (PCD), particularly the hypersensitive response (HR). However, only a few LMMs are reported in soybean, and no related gene has been cloned until now. In the present study, we isolated a new LMM named spotted leaf-1 (spl-1) from NN1138-2 cultivar through ethyl methanesulfonate (EMS) treatment. The present study revealed that lesion formation might result from PCD and excessive reactive oxygen species (ROS) accumulation. The chlorophyll content was significantly reduced but antioxidant activities, viz., superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), as well as the malondialdehyde (MDA) contents, were detected higher in spl-1 than in the wild-type. According to segregation analysis of mutant phenotype in two genetic populations, viz., W82×spl-1 and PI378692×spl-1, the spotted leaf phenotype of spl-1 is controlled by a single recessive gene named lm1. The lm1 locus governing mutant phenotype of spl-1 was first identified in 3.15 Mb genomic region on chromosome 04 through MutMap analysis, which was further verified and fine mapped by simple sequence repeat (SSR) marker-based genetic mapping. Genetic linkage analysis narrowed the genomic region (lm1 locus) for mutant phenotype to a physical distance of ~76.23 kb. By searching against the Phytozome database, eight annotated candidate genes were found within the lm1 region. qRT-PCR expression analysis revealed that, among these eight genes, only Glyma.04g242300 showed highly significant expression levels in wild-type relative to the spl-1 mutant. However, sequencing data of the CDS region showed no nucleotide difference between spl-1 and its wild type within the coding regions of these genes but might be in the non-coding regions such as 5' or 3' UTR. Hence, the data of the present study are in favor of Glyma.04g242300 being the possible candidate genes regulating the mutant phenotype of spl-1. However, further validation is needed to prove this function of the gene as well as its role in PCD, which in turn would be helpful to understand the mechanism and pathways involved in HR disease resistance of soybean.