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

Identification of QTLs and Key Genes Enhancing Lodging Resistance in Soybean Through Chemical and Physical Trait Analysis

Lodging of soybean (Glycine max (L.) Merril.) significantly reduces seed yield and quality, particularly in high-yielding environments. This phenomenon occurs when stems weaken under the weight of the plants, complicating harvesting. This study investigated the relationship between soybean stem chemical composition, physical traits, and lodging resistance to improve yield and resilience. We found that as plant density increased, stem hardness decreased, and the elasticity increased, heightening the risk of lodging. Conversely, high temperature (28 °C) boosted lignin, cellulose and pectin content in the stem cell walls, enhancing the lodging resistance. Additionally, after excluding differences in phylogenetic relationships through cluster analysis, we mapped environment-stable genes linked to lodging resistance and identified new QTLs on Chr3 and Chr16. Candidate genes associated with these QTLs were confirmed using qRT-PCR and hormone treatments across diverse soybean varieties. It was found that the expression of stem tip genes was closely related to stem node diameter. These findings provide a theoretical foundation for breeding high-yielding soybean varieties with improved lodging resistance, and advance efforts to develop resilient soybean cultivars.

QTL mapping and BSR-seq revealed loci and candidate genes associated with the sporadic multifoliolate phenotype in soybean (Glycine max)

KEY MESSAGE

The QTLs and candidate genes governing the multifoliolate phenotype were identified by combining linkage mapping with BSR-seq, revealing a possible interplay between genetics and the environment in soybean leaf development. Soybean, as a legume, is typified by trifoliolate leaves. Although multifoliolate leaves (compound leaves with more than three leaflets each) have been reported in soybean, including sporadic appearances in the first compound leaves in a recombinant inbred line (RIL) population from a cross between cultivated soybean C08 and wild soybean W05 from this study, the genetic basis of this phenomenon is still unclear. Here, we integrated quantitative trait locus (QTL) mapping with bulked segregant RNA sequencing (BSR-seq) to identify the genetic loci associated with the multifoliolate phenotype in soybean. Using linkage mapping, ten QTLs related to the multifoliolate trait were identified. Among these, a significant and major QTL, qMF-2-1 on chromosome 2 and consistently detected across biological replicates, explained more than 10% of the phenotypic variation. Together with BSR-seq analyses, which analyzed the RILs with the highest multifoliolate frequencies and those with the lowest frequencies as two distinct bulks, two candidate genes were identified: Glyma.06G204300 encoding the transcription factor TCP5, and Glyma.06G204400 encoding LONGIFOLIA 2 (LNG2). Transcriptome analyses revealed that stress-responsive genes were significantly differentially expressed between high-multifoliolate occurrence lines and low occurrence ones, indicating environmental factors probably influence the appearance of multifoliolate leaves in soybean through stress-responsive genes. Hence, this study offers new insights into the genetic mechanism behind the multifoliolate phenotype in soybean.

Combined BSA-Seq and RNA-Seq Analysis to Identify Candidate Genes Associated with Aluminum Toxicity in Rapeseed (Brassica napus L.)

Exchangeable aluminum (Al) ions released from acidic soils with pH < 5.5 inhibit root elongation of crops, ultimately leading to yield reduced. It is necessary to identify the quantitative trait locus (QTLs) and candidate genes that confer toxicity resistance to understand the mechanism and improve tolerance of rapeseed. In this study, an F2 segregating population was derived from a cross between Al-tolerance inbred line FDH188 (R178) and -sensitive inbred line FDH152 (S169), and the F2:3 were used as materials to map QTLs associated with the relative elongation of taproot (RET) under Al toxicity stress. Based on bulked segregant analysis sequencing (BSA-seq), three QTLs (qAT-A07-1, qAT-A07-2, and qAT-A09-1) were detected as significantly associated with RET, and 656 candidate genes were screened. By combined BSA and RNA-seq analysis, 55 candidate genes showed differentially expressed, including genes encoding ABC transporter G (ABCG), zinc finger protein, NAC, ethylene-responsive transcription factor (ERF), etc. These genes were probably positive factors in coping with Al toxicity stress in rapeseed. This study provides new insight into exploring the QTLs and candidate genes' response to Al toxicity stress by combined BSA-seq and RNA-seq and is helpful to further research on the mechanism of Al resistance in rapeseed.

Fine mapping and identification of ERF transcription factor ERF017 as a candidate gene for cold tolerance in pumpkin

KEY MESSAGE

Two major QTLs for cold tolerance in pumpkin were localised, and CmoERF017 was identified as a key candidate gene within these QTLs via RNA-seq. Functional analysis revealed that CmoERF017 was a positive regulator of pumpkin in response to low-temperature stress. Low temperature is a key environmental factor that affects the protected cultivation of cucumber (Cucumis sativus L.) in winter, and the cold tolerance of cucumber/pumpkin-grafted seedlings depends on the rootstock. Pumpkin (Cucurbita spp.) has a well-developed root system, high resistance and wide adaptation, commonly used as rootstock for cucumber to improve the cold tolerance of grafted seedlings. This study used two high-generation inbred lines of Cucurbita moschata with significant differences in cold tolerance. We identified key candidate genes within the major cold tolerance QTL of rootstocks using QTL-seq and RNA-seq and investigated the function and molecular mechanisms of these genes in response to low-temperature stress. Results showed that QTL-seq located two cold tolerance QTLs, qCII-1 and qCII-2, while RNA-seq located 28 differentially expressed genes within these QTLs. CmoERF017 was finally identified as a key candidate gene. Functional validation results indicated that CmoERF017 is a positive regulator of pumpkin in response to low-temperature stress and affected root ABA synthesis and signalling by directly regulating the expression of SDR7 and ABI5. This study identified a key gene for low-temperature stress tolerance in rootstock pumpkin and clarified its role in the molecular mechanism of hormone-mediated plant cold tolerance. The study findings enrich the theoretical understanding of low-temperature stress tolerance in pumpkin and are valuable for the selection and breeding of cold-tolerant varieties of pumpkin used for rootstocks.

QTL-Seq identified a genomic region on chromosome 1 for soil-salinity tolerance in F2 progeny of Thai salt-tolerant rice donor line "Jao Khao"

Introduction

Owing to advances in high-throughput genome sequencing, QTL-Seq mapping of salt tolerance traits is a major platform for identifying soil-salinity tolerance QTLs to accelerate marker-assisted selection for salt-tolerant rice varieties. We performed QTL-BSA-Seq in the seedling stage of rice from a genetic cross of the extreme salt-sensitive variety, IR29, and "Jao Khao" (JK), a Thai salt-tolerant variety.

Methods

A total of 462 F2 progeny grown in soil and treated with 160 mM NaCl were used as the QTL mapping population. Two high- and low-bulk sets, based on cell membrane stability (CMS) and tiller number at the recovery stage (TN), were equally sampled. The genomes of each pool were sequenced, and statistical significance of QTL was calculated using QTLseq and G prime (G') analysis, which is based on calculating the allele frequency differences or Δ(SNP index).

Results

Both methods detected the overlapping interval region, wherein CMS-bulk was mapped at two loci in the 38.41-38.85 Mb region with 336 SNPs on chromosome 1 (qCMS1) and the 26.13-26.80 Mb region with 1,011 SNPs on chromosome 3 (qCMS3); the Δ(SNP index) peaks were -0.2709 and 0.3127, respectively. TN-bulk was mapped at only one locus in the overlapping 38.26-38.95 Mb region on chromosome 1 with 575 SNPs (qTN1) and a Δ(SNP index) peak of -0.3544. These identified QTLs in two different genetic backgrounds of segregating populations derived from JK were validated. The results confirmed the colocalization of the qCMS1 and qTN1 traits on chromosome 1. Based on the CMS trait, qCMS1/qTN1 stably expressed 6%-18% of the phenotypic variance in the two validation populations, while qCMS1/qTN1 accounted for 16%-20% of the phenotypic variance in one validation population based on the TN trait.

Conclusion

The findings confirm that the CMS and TN traits are tightly linked to the long arm of chromosome 1 rather than to chromosome 3. The validated qCMS-TN1 QTL can be used for gene/QTL pyramiding in marker-assisted selection to expedite breeding for salt resistance in rice at the seedling stage.

Refining flowering date enhances sesame yield independently of day-length

BACKGROUND

The transition from vegetative to reproductive growth is a key factor in yield maximization. Sesame (Sesamum indicum), an indeterminate short-day oilseed crop, is rapidly being introduced into new cultivation areas. Thus, decoding its flowering mechanism is necessary to facilitate adaptation to environmental conditions. In the current study, we uncover the effect of day-length on flowering and yield components using F2 populations segregating for previously identified quantitative trait loci (Si_DTF QTL) confirming these traits.

RESULTS

Generally, day-length affected all phenotypic traits, with short-day preceding days to flowering and reducing yield components. Interestingly, the average days to flowering required for yield maximization was 50 to 55 days, regardless of day-length. In addition, we found that Si_DTF QTL is more associated with seed-yield and yield components than with days to flowering. A bulk-segregation analysis was applied to identify additional QTL differing in allele frequencies between early and late flowering under both day-length conditions. Candidate genes mining within the identified major QTL intervals revealed two flowering-related genes with different expression levels between the parental lines, indicating their contribution to sesame flowering regulation.

CONCLUSIONS

Our findings demonstrate the essential role of flowering date on yield components and will serve as a basis for future sesame breeding.

Mapping and Omics Integration: Towards Precise Rice Disease Resistance Breeding

Rice (Oryza sativa), as a staple crop feeding a significant portion of the global population, particularly in Asian countries, faces constant threats from various diseases jeopardizing global food security. A precise understanding of disease resistance mechanisms is crucial for developing resilient rice varieties. Traditional genetic mapping methods, such as QTL mapping, provide valuable insights into the genetic basis of diseases. However, the complex nature of rice diseases demands a holistic approach to gain an accurate knowledge of it. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, enable a comprehensive analysis of biological molecules, uncovering intricate molecular interactions within the rice plant. The integration of various mapping techniques using multi-omics data has revolutionized our understanding of rice disease resistance. By overlaying genetic maps with high-throughput omics datasets, researchers can pinpoint specific genes, proteins, or metabolites associated with disease resistance. This integration enhances the precision of disease-related biomarkers with a better understanding of their functional roles in disease resistance. The improvement of rice breeding for disease resistance through this integration represents a significant stride in agricultural science because a better understanding of the molecular intricacies and interactions underlying disease resistance architecture leads to a more precise and efficient development of resilient and productive rice varieties. In this review, we explore how the integration of mapping and omics data can result in a transformative impact on rice breeding for enhancing disease resistance.

OcBSA: An NGS-based bulk segregant analysis tool for outcross populations

Constructing inbred lines for self-incompatible species and species with long generation times is challenging, making the use of F1 outcross/segregating populations the main strategy for genetic studies of such species. However, there is a lack of dedicated algorithms/tools for rapid quantitative trait locus (QTL) mapping using the F1 populations. To this end, we have designed and developed an algorithm/tool called OcBSA specifically for QTL mapping of F1 populations. OcBSA transforms the four-haplotype inheritance problem from the two heterozygous diploid parents of the F1 population into the two-haplotype inheritance problem common in current genetic studies by removing the two haplotypes from the heterozygous parent that do not contribute to phenotype segregation in the F1 population. Testing of OcBSA on 1800 simulated F1 populations demonstrated its advantages over other currently available tools in terms of sensitivity and accuracy. In addition, the broad applicability of OcBSA was validated by QTL mapping using seven reported F1 populations of apple, pear, peach, citrus, grape, tea, and rice. We also used OcBSA to map the QTL for flower color in a newly constructed F1 population of potato generated in this study. The OcBSA mapping result was verified by the insertion or deletion markers to be consistent with a previously reported locus harboring the ANTHOCYANIN 2 gene, which regulates potato flower color. Taken together, these results highlight the power and broad utility of OcBSA for QTL mapping using F1 populations and thus a great potential for functional gene mining in outcrossing species. For ease of use, we have developed both Windows and Linux versions of OcBSA, which are freely available at: https://gitee.com/Bioinformaticslab/OcBSA.

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other "omics" approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These "omics" approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with "omics" tools can allow rapid gene identification and eventually accelerate crop-improvement programs.