High resolution mapping of a novel non-transgressive hybrid susceptibility locus in barley exploited by P. teres f. maculata.

Hybrid genotypes can provide significant yield gains over conventional inbred varieties due to heterosis or hybrid vigor. However, hybrids can also display unintended negative attributes or phenotypes such as extreme pathogen susceptibility. The necrotrophic pathogen Pyrenophora teres f. maculata (Ptm) causes spot form net blotch, which has caused significant yield losses to barley worldwide. Here, we report on a non-transgressive hybrid susceptibility locus in barley identified between the three parental lines CI5791, Tifang and Golden Promise that are resistant to Ptm isolate 13IM.3. However, F2 progeny from CI5791 × Tifang and CI5791 × Golden Promise crosses exhibited extreme susceptibility. The susceptible phenotype segregated in a ratio of 1 resistant:1 susceptible representing a genetic segregation ratio of 1 parental (res):2 heterozygous (sus):1 parental (res) suggesting a single hybrid susceptibility locus. Genetic mapping using a total of 715 CI5791 × Tifang F2 individuals (1430 recombinant gametes) and 149 targeted SNPs delimited the hybrid susceptibility locus designated Susceptibility to Pyrenophora teres 2 (Spt2) to an ~ 198 kb region on chromosome 5H of the Morex V3 reference assembly. This single locus was independently mapped with 83 CI5791 × Golden Promise F2 individuals (166 recombinant gametes) and 180 genome wide SNPs that colocalized to the same Spt2 locus. The CI5791 genome was sequenced using PacBio Continuous Long Read technology and comparative analysis between CI5791 and the publicly available Golden Promise genome assembly determined that the delimited region contained a single high confidence Spt2 candidate gene predicted to encode a pentatricopeptide repeat-containing protein.

GWAS elucidated grain yield genetics in Indian spring wheat under diverse water conditions.

KEY MESSAGE: Underpinned natural variations and key genes associated with yield under different water regimes, and identified genomic signatures of genetic gain in the Indian wheat breeding program. A novel KASP marker for TKW under water stress was developed and validated. A comprehensive genome-wide association study was conducted on 300 spring wheat genotypes to elucidate the natural variations associated with grain yield and its eleven contributing traits under fully irrigated, restricted water, and simulated no water conditions. Utilizing the 35K Wheat Breeders' Array, we identified 1155 quantitative trait nucleotides (QTNs), with 207 QTNs exhibiting stability across diverse conditions. These QTNs were further delimited into 539 genomic regions using a genome-wide LD value of 3.0 Mbp, revealing pleiotropic control across traits and conditions. Sub-genome A was significantly associated with traits under irrigated conditions, while sub-genome B showed more QTNs under water stressed conditions. Favourable alleles with significantly associated QTNs were delineated, with a notable pyramiding effect for enhancing trait performance. Additionally, allele of only 921 QTNs significantly affected the population mean. Allele profiling highlighted C-306 as a most potential source of drought tolerance. Moreover, 762 genes overlapping significant QTNs were identified, narrowing down to 27 putative candidate genes overlapping 29 novel and functional SNPs expressing (≥ 0.5 tpm) relevance across various growth conditions. A new KASP assay was developed, targeting a gene TraesCS2A03G1123700 regulating thousand kernel weight under severe drought condition. Genomic selection models (GBLUP, BayesB, MxE, and R-Norm) demonstrated an average prediction accuracy of 0.06-0.58 across environments, indicating potential for trait selection. Retrospective analysis of the Indian wheat breeding program supported a genetic gain in GY at the rate of ca. 0.56% per breeding cycle, since 1960, supporting the identification of genomic signatures driving trait selection and genetic gain. These findings offer insight into improving the rate of genetic gain in wheat breeding programs globally.

Optical Genome Mapping for Applications in Repeat Expansion Disorders.

Short tandem repeat (STR) expansions are associated with more than 60 genetic disorders. The size and stability of these expansions correlate with the severity and age of onset of the disease. Therefore, being able to accurately detect the absolute length of STRs is important. Current diagnostic assays include laborious lab experiments, including repeat-primed PCR and Southern blotting, that still cannot precisely determine the exact length of very long repeat expansions. Optical genome mapping (OGM) is a cost-effective and easy-to-use alternative to traditional cytogenetic techniques and allows the comprehensive detection of chromosomal aberrations and structural variants >500 bp in length, including insertions, deletions, duplications, inversions, translocations, and copy number variants. Here, we provide methodological guidance for preparing samples and performing OGM as well as running the analysis pipelines and using the specific repeat expansion workflows to determine the exact repeat length of repeat expansions expanded beyond 500 bp. Together these protocols provide all details needed to analyze the length and stability of any repeat expansion with an expected repeat size difference from the expected wild-type allele of >500 bp. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Genomic ultra-high-molecular-weight DNA isolation, labeling, and staining Basic Protocol 2: Data generation and genome mapping using the Bionano Saphyr® System Basic Protocol 3: Manual De Novo Assembly workflow Basic Protocol 4: Local guided assembly workflow Basic Protocol 5: EnFocus Fragile X workflow Basic Protocol 6: Molecule distance script workflow.

Pentatricopeptide repeat 153 (PPR153) restores maize C-type cytoplasmic male sterility in conjunction with RF4.

Maize (Zea mays L.) C-type cytoplasmic male sterility (CMS-C) is a highly used CMS system for maize commercial hybrid seed production. Rf4 is the major dominant restorer gene for CMS-C. Inbreds were recently discovered which contain the restoring Rf4 allele yet are unable to restore fertility due to the lack of an additional gene required for Rf4's restoration. To find this additional gene, QTL mapping and positional cloning were performed using an inbred that contained Rf4 but was incapable of restoring CMS-C. The QTL was mapped to a 738-kb interval on chromosome 2, which contains a Pentatricopeptide Repeat (PPR) gene cluster. Allele content comparisons of the inbreds identified three potential candidate genes responsible for fertility restoration in CMS-C. Complementation via transformation of these three candidate genes showed that PPR153 (Zm00001eb114660) is required for Rf4 to restore fertility to tassels. The PPR153 sequence is present in B73 genome, but it is not capable of restoring CMS-C without Rf4. Analysis using NAM lines revealed that Rf4 requires the presence of PPR153 to restore CMS-C in diverse germplasms. This research uncovers a major CMS-C genetic restoration pathway and can be used for identifying inbreds suitable for maize hybrid production with CMS-C cytoplasm.

Map-Based Cloning of the Causal Gene for a Seed Dormancy Quantitative Trait Locus in Barley.

Seed dormancy is an important agronomic trait in cereal crops. Throughout the domestication of cereals, seed dormancy has been reduced to obtain uniform germination. However, grain crops must retain moderate levels of seed dormancy to prevent problems such as preharvest sprouting in wheat (Triticum aestivum) and barley (Hordeum vulgare). To produce modern cultivars with the appropriate seed dormancy levels, it is important to identify the genes responsible for seed dormancy. With recent advances in sequencing technology, several causal genes for seed dormancy quantitative trait loci (QTLs) have been identified in barley and wheat. Here, we present a method to identify causal genes for seed dormancy QTLs in barley, a method that is also applicable to other cereals.

Quantitative trait locus analysis of gray leaf spot resistance in the maize IBM Syn10 DH population.

KEY MESSAGE: The exploration and dissection of a set of QTLs and candidate genes for gray leaf spot disease resistance using two fully assembled parental genomes may help expedite maize resistance breeding. The fungal disease of maize known as gray leaf spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is a significant concern in China, Southern Africa, and the USA. Resistance to GLS is governed by multiple genes with an additive effect and is influenced by both genotype and environment. The most effective way to reduce the cost of production is to develop resistant hybrids. In this study, we utilized the IBM Syn 10 Doubled Haploid (IBM Syn10 DH) population to identify quantitative trait loci (QTLs) associated with resistance to gray leaf spot (GLS) in multiple locations. Analysis of seven distinct environments revealed a total of 58 QTLs, 49 of which formed 12 discrete clusters distributed across chromosomes 1, 2, 3, 4, 8 and 10. By comparing these findings with published research, we identified colocalized QTLs or GWAS loci within eleven clustering intervals. By integrating transcriptome data with genomic structural variations between parental individuals, we identified a total of 110 genes that exhibit both robust disparities in gene expression and structural alterations. Further analysis revealed 19 potential candidate genes encoding conserved resistance gene domains, including putative leucine-rich repeat receptors, NLP transcription factors, fucosyltransferases, and putative xyloglucan galactosyltransferases. Our results provide a valuable resource and linked loci for GLS marker resistance selection breeding in maize.

Unraveling the genetic basis of quantitative resistance to diseases in tomato: a meta-QTL analysis and mining of transcript profiles.

KEY MESSAGE: Whole-genome QTL mining and meta-analysis in tomato for resistance to bacterial and fungal diseases identified 73 meta-QTL regions with significantly refined/reduced confidence intervals. Tomato production is affected by a range of biotic stressors, causing yield losses and quality reductions. While sources of genetic resistance to many tomato diseases have been identified and characterized, stability of the resistance genes or quantitative trait loci (QTLs) across the resources has not been determined. Here, we examined 491 QTLs previously reported for resistance to tomato diseases in 40 independent studies and 54 unique mapping populations. We identified 29 meta-QTLs (MQTLs) for resistance to bacterial pathogens and 44 MQTLs for resistance to fungal pathogens, and were able to reduce the average confidence interval (CI) of the QTLs by 4.1-fold and 6.7-fold, respectively, compared to the average CI of the original QTLs. The corresponding physical length of the CIs of MQTLs ranged from 56 kb to 6.37 Mb, with a median of 921 kb, of which 27% had a CI lower than 500 kb and 53% had a CI lower than 1 Mb. Comparison of defense responses between tomato and Arabidopsis highlighted 73 orthologous genes in the MQTL regions, which were putatively determined to be involved in defense against bacterial and fungal diseases. Intriguingly, multiple genes were identified in some MQTL regions that are implicated in plant defense responses, including PR-P2, NDR1, PDF1.2, Pip1, SNI1, PTI5, NSL1, DND1, CAD1, SlACO, DAD1, SlPAL, Ph-3, EDS5/SID1, CHI-B/PR-3, Ph-5, ETR1, WRKY29, and WRKY25. Further, we identified a number of candidate resistance genes in the MQTL regions that can be useful for both marker/gene-assisted breeding as well as cloning and genetic transformation.

Identification of candidate gene associated with maize northern leaf blight resistance in a multi-parent population.

KEY MESSAGE: QTL mapping combined with genome-wide association studies, revealed a potential candidate gene for  resistance to northern leaf blight in the tropical CATETO-related maize line YML226, providing a basis for marker-assisted selection of maize varieties Northern leaf blight (NLB) is a foliar disease that can cause severe yield losses in maize. Identifying and utilizing NLB-resistant genes is the most effective way to prevent and control this disease. In this study, five important inbred lines of maize were used as parental lines to construct a multi-parent population for the identification of NLB-resistant loci. QTL mapping and GWAS analysis revealed that QTL qtl_YML226_1, which had the largest phenotypic variance explanation (PVE) of 9.28%, and SNP 5-49,193,921 were co-located in the CATETO-related line YML226. This locus was associated with the candidate gene Zm00001d014471, which encodes a pentatricopeptide repeat (PPR) protein. In the coding region of Zm00001d014471, YML226 had more specific SNPs than the other parental lines. qRT-PCR showed that the relative expressions of Zm00001d014471 in inoculated and uninoculated leaves of YML226 were significantly higher, indicating that the expression of the candidate gene was correlated with NLB resistance. The analysis showed that the higher expression level in YML226 might be caused by SNP mutations. This study identified NLB resistance candidate loci and genes in the tropical maize inbred line YML226 derived from the CATETO germplasm, thereby providing a theoretical basis for using modern marker-assisted breeding techniques to select genetic resources resistant to NLB.

Refinement of rice blast disease resistance QTLs and gene networks through meta-QTL analysis.

Rice blast disease is the most devastating disease constraining crop productivity. Vertical resistance to blast disease is widely studied despite its instability. Clusters of genes or QTLs conferring blast resistance that offer durable horizontal resistance are important in resistance breeding. In this study, we aimed to refine the reported QTLs and identify stable meta-QTLs (MQTLs) associated with rice blast resistance. A total of 435 QTLs were used to project 71 MQTLs across all the rice chromosomes. As many as 199 putative rice blast resistance genes were identified within 53 MQTL regions. The genes included 48 characterized resistance gene analogs and related proteins, such as NBS-LRR type, LRR receptor-like kinase, NB-ARC domain, pathogenesis-related TF/ERF domain, elicitor-induced defense and proteins involved in defense signaling. MQTL regions with clusters of RGA were also identified. Fifteen highly significant MQTLs included 29 candidate genes and genes characterized for blast resistance, such as Piz, Nbs-Pi9, pi55-1, pi55-2, Pi3/Pi5-1, Pi3/Pi5-2, Pikh, Pi54, Pik/Pikm/Pikp, Pb1 and Pb2. Furthermore, the candidate genes (42) were associated with differential expression (in silico) in compatible and incompatible reactions upon disease infection. Moreover, nearly half of the genes within the MQTL regions were orthologous to those in O. sativa indica, Z. mays and A. thaliana, which confirmed their significance. The peak markers within three significant MQTLs differentiated blast-resistant and susceptible lines and serve as potential surrogates for the selection of blast-resistant lines. These MQTLs are potential candidates for durable and broad-spectrum rice blast resistance and could be utilized in blast resistance breeding.

Haplotype-based association mapping of genomic regions associated with Zymoseptoria tritici resistance using 217 diverse wheat genotypes.

BACKGROUND: Septoria tritici blotch (STB) is considered to be one of the most destructive foliar wheat diseases and is caused by Zymoseptoria tritici. The yield losses are severe and in Northwestern Europe can reach up to 50%. The efficacy of fungicides is diminishing due to changes in the genetic structure of the pathogen. Therefore, resistance breeding is the most effective strategy of disease management. Recently, genome-wide association studies (GWAS) have become more popular due to their robustness in dissecting complex traits, including STB resistance in wheat. This was made possible by the use of large mapping populations and new sequencing technologies. High-resolution mapping benefits from historical recombination and greater allele numbers in GWAS. RESULTS: In our study, 217 wheat genotypes of diverse origin were phenotyped against five Z. tritici isolates (IPO323, IPO88004, IPO92004, IPO86036 and St1-03) and genotyped on the DArTseq platform. In polytunnel tests two disease parameters were evaluated: the percentage of leaf area covered by necrotic lesions (NEC) and the percentage of leaf area covered by lesions bearing pycnidia (PYC). The disease escape parameters heading date (Hd) and plant height (Ht) were also measured. Pearson's correlation showed a positive effect between disease parameters, providing additional information. The Structure analysis indicated four subpopulations which included from 28 (subpopulation 2) to 79 genotypes (subpopulation 3). All of the subpopulations showed a relatively high degree of admixture, which ranged from 60% of genotypes with less than 80% of proportions of the genome attributed to assigned subpopulation for group 2 to 85% for group 4. Haplotype-based GWAS analysis allowed us to identify 27 haploblocks (HBs) significantly associated with analysed traits with a p-value above the genome-wide significance threshold (5%, which was -log10(p) > 3.64) and spread across the wheat genome. The explained phenotypic variation of identified significant HBs ranged from 0.2% to 21.5%. The results of the analysis showed that four haplotypes (HTs) associated with disease parameters cause a reduction in the level of leaf coverage by necrosis and pycnidia, namely: Chr3A_HB98_HT2, Chr5B_HB47_HT1, Chr7B_HB36_HT1 and Chr5D_HB10_HT3. CONCLUSIONS: GWAS analysis enabled us to identify four significant chromosomal regions associated with a reduction in STB disease parameters. The list of valuable HBs and wheat varieties possessing them provides promising material for further molecular analysis of resistance loci and development of breeding programmes.

Mining for QTL controlling maize low-phosphorus response genes combined with deep resequencing of RIL parental genomes and in silico GWAS analysis.

KEY MESSAGE: Extensive and comprehensive phenotypic data from a maize RIL population under both low- and normal-Pi treatments were used to conduct QTL mapping. Additionally, we integrated parental resequencing data from the RIL population, GWAS results, and transcriptome data to identify candidate genes associated with low-Pi stress in maize. Phosphorus (Pi) is one of the essential nutrients that greatly affect the maize yield. However, the genes underlying the QTL controlling maize low-Pi response remain largely unknown. In this study, a total of 38 traits at both seedling and maturity stages were evaluated under low- and normal-Pi conditions using a RIL population constructed from X178 (tolerant) and 9782 (sensitive), and most traits varied significantly between low- and normal-Pi treatments. Twenty-nine QTLs specific to low-Pi conditions were identified after excluding those with common intervals under both low- and normal-Pi conditions. Furthermore, 45 additional QTLs were identified based on the index value ((Trait_under_LowPi-Trait_under_NormalPi)/Trait_under_NormalPi) of each trait. These 74 QTLs collectively were classified as Pi-dependent QTLs. Additionally, 39 Pi-dependent QTLs were clustered in nine HotspotQTLs. The Pi-dependent QTL interval contained 19,613 unique genes, 6,999 of which exhibited sequence differences with non-synonymous mutation sites between X178 and 9782. Combined with in silico GWAS results, 277 consistent candidate genes were identified, with 124 genes located within the HotspotQTL intervals. The transcriptome analysis revealed that 21 genes, including the Pi transporter ZmPT7 and the strigolactones pathway-related gene ZmPDR1, exhibited consistent low-Pi stress response patterns across various maize inbred lines or tissues. It is noteworthy that ZmPDR1 in maize roots can be sharply up-regulated by low-Pi stress, suggesting its potential importance as a candidate gene for responding to low-Pi stress through the strigolactones pathway.

Defining genomic landscape for identification of potential candidate resistance genes associated with major rice diseases through MetaQTL analysis.

Rice production is severely affected by various diseases such as bacterial leaf blight (BLB), brown spot (BS), false smut (FS), foot rot (FR), rice blast (RB), and sheath blight (SB). In recent years, several quantitative trait loci (QTLs) studies involving different populations have been carried out, resulting in the identification of hundreds of resistance QTLs for each disease. These QTLs can be integrated and analyzed using meta-QTL (MQTL) analysis for better understanding of the genetic architecture underlying multiple disease resistance (MDR). This study involved an MQTL analysis on 661 QTLs (378, 161, 21, 41, 44, and 16 QTLs for SB, RB, BLB, BS, FS, and FR, respectively) retrieved from 50 individual studies published from 1995 to 2021. Of these, 503 QTLs were projected finally onto the consensus map saturated with 6,275 markers, resulting in 73 MQTLs, including 27 MDR-MQTLs conferring resistance to three or more diseases. Forty-seven MQTLs were validated using marker-trait associations identified in published genome-wide association studies. A total of 3,310 genes, including both R and defense genes, were also identified within some selected high-confidence MQTL regions that were investigated further for the syntenic relationship with barley, wheat, and maize genomes. Thirty-nine high-confidence candidate genes were selected based on their expression patterns and recommended for future studies involving functional validation, genetic engineering, and gene editing. Nineteen MQTLs were co-localized with 39 known R genes for BLB and RB diseases. These results could pave the way to utilize candidate genes in a marker-assisted breeding program for MDR in rice.

Cytogenetic identification and molecular mapping for the wheat-Thinopyrum ponticum introgression line with resistance to Fusarium head blight.

KEY MESSAGE: Xinong 511, a new wheat-Thinopyrum ponticum variety with excellent fusarium head blight resistance, the QTLs were mapped to the wheat chromosomes 5B and 7A with named QFhb.nwafu-5B and QFhb.nwafu-7A, respectively. Novel Fusarium head blight (FHB) resistance germplasms and genes are valuable for wheat improvement and breeding efforts. Thinopyrum ponticum, a wild relative of common wheat, is a valuable germplasm of disease resistance for wheat improvement and breeding. Xinong 511 (XN511) is a high-quality wheat variety widely cultivated in the Yellow and Huai Rivers Valley of China with stable FHB-resistance. Through analysis of pedigree materials of the wheat cultivar XN511, we found that the genetic material and FHB resistance from Th. ponticum were transmitted to the introgression line, indicating that the FHB resistance in XN511 likely originates from Th. ponticum. To further explore the genetic basis of FHB resistance in XN511, QTL mapping was conducted using the RILs population of XN511 and the susceptible line Aikang 58 (AK58). Survey with makers closely-linked to Fhb1, Fhb2, Fhb4, Fhb5, and Fhb7, indicated that both XN511 and the susceptible lines do not contain these QTL. Using bulked segregant analysis RNA-seq (BSR-Seq) and newly developed allele-specific PCR (AS-PCR) markers, QTLs in XN511 were successfully located on wheat chromosomes 5B and 7A. These findings are significant for further understanding and utilizing FHB resistance genes in wheat improvement.

Consensus genetic linkage map and QTL mapping allow to capture the genomic regions associated with agronomic traits in pearl millet.

MAIN CONCLUSION: A genetic linkage map representing the pearl millet genome was constructed with SNP markers. Major and stable QTL associated with flowering, number of productive tillers, ear head length, and test weight were mapped on chromosomes 1 and 3. Pearl millet (Pennisetum glaucum) is a major cereal and fodder crop in arid and semi-arid regions of Asia and Africa. Agronomic traits are important traits in pearl millet breeding and genetic and environmental factors highly influence them. In the present study, an F9 recombinant inbred line (RIL) population derived from a cross between PT6029 and PT6129 was evaluated for agronomic traits in three environments. Utilizing a genotyping by sequencing approach, a dense genetic map with 993 single nucleotide polymorphism markers covering a total genetic distance of 1035.4 cM was constructed. The average interval between the markers was 1.04 cM, and the seven chromosomes varied from 115.39 to 206.72 cM. Quantitative trait loci (QTL) mapping revealed 35 QTL for seven agronomic traits, and they were distributed on all pearl millet chromosomes. These QTL individually explained 11.35 to 26.71% of the phenotypic variation, with LOD values ranging from 2.74 to 5.80. Notably, four QTL (qDFF1.1, qNPT3.1, qEHL3.1, and qTW1.1) associated with days to fifty percent flowering, the number of productive tillers, ear head length, and test weight were found to be major and stable QTL located on chromosomes 1 and 3. Collectively, our results provide an important base for understanding the genetic architecture of agronomic traits in pearl millet, which is useful for accelerating the genetic gain toward crop improvement.

Cold tolerance SNPs and candidate gene mining in the soybean germination stage based on genome-wide association analysis.

KEY MESSAGE: Three QTLs associated with low-temperature tolerance were identified by genome-wide association analysis, and 15 candidate genes were identified by haplotype analysis and gene expression analyses. Low temperature is a critical factor affecting the geographical distribution, growth, development, and yield of soybeans, with cold stress during seed germination leading to substantial productivity loss. In this study, an association panel comprising 260 soybean accessions was evaluated for four germination traits and four cold tolerance index traits, revealing extensive variation in cold tolerance. Genome-wide association study (GWAS) identified 10 quantitative trait nucleotides (QTNs) associated with cold tolerance, utilizing 30,799 single nucleotide polymorphisms (SNPs) and four GWAS models. Linkage disequilibrium (LD) analysis positioned these QTNs within three cold-tolerance quantitative trait loci (QTL) and, with QTL19-1, was positioned by three multi-locus models, underscoring its importance as a key QTL. Integrative haplotype analysis, supplemented by transcriptome analysis, uncovered 15 candidate genes. The haplotypes within the genes Glyma.18G044200, Glyma.18G044300, Glyma.18G044900, Glyma.18G045100, Glyma.19G222500, and Glyma.19G222600 exhibited significant phenotypic variations, with differential expression in materials with varying cold tolerance. The QTNs and candidate genes identified in this study offer substantial potential for marker-assisted selection and gene editing in breeding cold-tolerant soybeans, providing valuable insights into the genetic mechanisms underlying cold tolerance during soybean germination.

Detection of closely linked QTLs and candidate genes controlling germination indices in response to drought and salinity stresses in barley.

The aim of current study was to identify closely linked QTLs and candidate genes related to germination indices under control, salinity and drought conditions in barley. A total of nine (a major), 28 (eight major) and 34 (five major) closely linked QTLs were mapped on the seven chromosomes in response to control, drought and salinity conditions using genome-wide composite interval mapping, respectively. The major QTLs can be used in marker-assisted selection (MAS) projects to increase tolerance to drought and salinity stresses during the germination. Overall, 422 unique candidate genes were associated with most major QTLs. Moreover, gene ontology analysis showed that candidate genes mostly involved in biological process related to signal transduction and response to stimulus in the pathway of resistance to drought and salinity stresses. Also, the protein-protein interaction network was identified 10 genes. Furthermore, 10 genes were associated with receptor-like kinase family. In addition, 16 transcription factors were detected. Three transcription factors including B3, bHLH, and FAR1 had the most encoding genes. Totally, 60 microRNAs were traced to regulate the target genes. Finally, the key genes are a suitable and reliable source for future studies to improve resistance to abiotic stress during the germination of barley.

Enhanced recombination empowers the detection and mapping of Quantitative Trait Loci.

Modern plant breeding, such as genomic selection and gene editing, is based on the knowledge of the genetic architecture of desired traits. Quantitative trait loci (QTL) analysis, which combines high throughput phenotyping and genotyping of segregating populations, is a powerful tool to identify these genetic determinants and to decipher the underlying mechanisms. However, meiotic recombination, which shuffles genetic information between generations, is limited: Typically only one to two exchange points, called crossovers, occur between a pair of homologous chromosomes. Here we test the effect on QTL analysis of boosting recombination, by mutating the anti-crossover factors RECQ4 and FIGL1 in Arabidopsis thaliana full hybrids and lines in which a single chromosome is hybrid. We show that increasing recombination ~6-fold empowers the detection and resolution of QTLs, reaching the gene scale with only a few hundred plants. Further, enhanced recombination unmasks some secondary QTLs undetected under normal recombination. These results show the benefits of enhanced recombination to decipher the genetic bases of traits.

Identification, validation and candidate gene analysis of major QTL for Supernumerary spikelets in wheat.

BACKGROUND: The number of spikelets per spike is a key trait that affects the yield of bread wheat (Triticum aestivum L.). Identification of the QTL for spikelets per spike and its genetic effects that could be used in molecular assistant breeding in the future. RESULTS: In this study, four recombinant inbred line (RIL) populations were generated and used, having YuPi branching wheat (YP), with Supernumerary Spikelets (SS) phenotype, as a common parent. QTL (QSS.sicau-2 A and QSS.sicau-2D) related to SS trait were mapped on chromosomes 2 A and 2D through bulked segregant exome sequencing (BSE-Seq). Fourteen molecular markers were further developed within the localization interval, and QSS.sicau-2 A was narrowed to 3.0 cM covering 7.6 Mb physical region of the reference genome, explaining 13.7 - 15.9% the phenotypic variance. Similarly, the QSS.sicau-2D was narrowed to 1.8 cM covering 2.4 Mb physical region of the reference genome, and it explained 27.4 - 32.9% the phenotypic variance. These two QTL were validated in three different genetic backgrounds using the linked markers. QSS.sicau-2 A was identified as WFZP-A, and QSS.sicau-2D was identified a novel locus, different to the previously identified WFZP-D. Based on the gene expression patterns, gene annotation and sequence analysis, TraesCS2D03G0260700 was predicted to be a potential candidate gene for QSS.sicau-2D. CONCLUSION: Two significant QTL for SS, namely QSS.sicau-2 A and QSS.sicau-2D were identified in multiple environments were identified and their effect in diverse genetic populations was assessed. QSS.sicau-2D is a novel QTL associated with the SS trait, with TraesCS2D03G0260700 predicted as its candidate gene.

Identification of candidate genes controlling cold tolerance at the early seedling stage from Dongxiang wild rice by QTL mapping, BSA-Seq and RNA-Seq.

BACKGROUND: The cold tolerance of rice is closely related to its production and geographic distribution. The identification of cold tolerance-related genes is of important significance for developing cold-tolerant rice. Dongxiang wild rice (Oryza rufipogon Griff.) (DXWR) is well-adapted to the cold climate of northernmost-latitude habitats ever found in the world, and is one of the most valuable rice germplasms for cold tolerance improvement. RESULTS: Transcriptome analysis revealed genes differentially expressed between Xieqingzao B (XB; a cold sensitive variety) and 19H19 (derived from an interspecific cross between DXWR and XB) in the room temperature (RT), low temperature (LT), and recovery treatments. The results demonstrated that chloroplast genes might be involved in the regulation of cold tolerance in rice. A high-resolution SNP genetic map was constructed using 120 BC5F2 lines derived from a cross between 19H19 and XB based on the genotyping-by-sequencing (GBS) technique. Two quantitative trait loci (QTLs) for cold tolerance at the early seedling stage (CTS), qCTS12 and qCTS8, were detected. Moreover, a total of 112 candidate genes associated with cold tolerance were identified based on bulked segregant analysis sequencing (BSA-seq). These candidate genes were divided into eight functional categories, and the expression trend of candidate genes related to 'oxidation-reduction process' and 'response to stress' differed between XB and 19H19 in the RT, LT and recovery treatments. Among these candidate genes, the expression level of LOC_Os12g18729 in 19H19 (related to 'response to stress') decreased in the LT treatment but restored and enhanced during the recovery treatment whereas the expression level of LOC_Os12g18729 in XB declined during recovery treatment. Additionally, XB contained a 42-bp deletion in the third exon of LOC_Os12g18729, and the genotype of BC5F2 individuals with a survival percentage (SP) lower than 15% was consistent with that of XB. Weighted gene coexpression network analysis (WGCNA) and modular regulatory network learning with per gene information (MERLIN) algorithm revealed a gene interaction/coexpression network regulating cold tolerance in rice. In the network, differentially expressed genes (DEGs) related to 'oxidation-reduction process', 'response to stress' and 'protein phosphorylation' interacted with LOC_Os12g18729. Moreover, the knockout mutant of LOC_Os12g18729 decreased cold tolerance in early rice seedling stage signifcantly compared with that of wild type. CONCLUSIONS: In general, study of the genetic basis of cold tolerance of rice is important for the development of cold-tolerant rice varieties. In the present study, QTL mapping, BSA-seq and RNA-seq were integrated to identify two CTS QTLs qCTS8 and qCTS12. Furthermore, qRT-PCR, genotype sequencing and knockout analysis indicated that LOC_Os12g18729 could be the candidate gene of qCTS12. These results are expected to further exploration of the genetic mechanism of CTS in rice and improve cold tolerance of cultivated rice by introducing the cold tolerant genes from DXWR through marker-assisted selection.

A catalogue of recombination coldspots in interspecific tomato hybrids.

Increasing natural resistance and resilience in plants is key for ensuring food security within a changing climate. Breeders improve these traits by crossing cultivars with their wild relatives and introgressing specific alleles through meiotic recombination. However, some genomic regions are devoid of recombination especially in crosses between divergent genomes, limiting the combinations of desirable alleles. Here, we used pooled-pollen sequencing to build a map of recombinant and non-recombinant regions between tomato and five wild relatives commonly used for introgressive tomato breeding. We detected hybrid-specific recombination coldspots that underscore the role of structural variations in modifying recombination patterns and maintaining genetic linkage in interspecific crosses. Crossover regions and coldspots show strong association with specific TE superfamilies exhibiting differentially accessible chromatin between somatic and meiotic cells. About two-thirds of the genome are conserved coldspots, located mostly in the pericentromeres and enriched with retrotransposons. The coldspots also harbor genes associated with agronomic traits and stress resistance, revealing undesired consequences of linkage drag and possible barriers to breeding. We presented examples of linkage drag that can potentially be resolved by pairing tomato with other wild species. Overall, this catalogue will help breeders better understand crossover localization and make informed decisions on generating new tomato varieties.