Locus Mapping Research Articles

Cy-1, a major QTL for tomato leaf curl New Delhi virus resistance, harbors a gene encoding a DFDGD-Class RNA-dependent RNA polymerase in cucumber (Cucumis sativus)

BackgroundTomato leaf curl New Delhi virus (ToLCNDV) (family Geminiviridae, genus Begomovirus) is a significant threat to cucumber (Cucumis sativus) production in many regions. Previous studies have reported the genetic mapping of loci related to ToLCNDV resistance, but no resistance genes have been identified.ResultsWe conducted map-based cloning of the ToLCNDV resistance gene in cucumber accession No.44. Agroinfiltration and graft-inoculation analyses confirmed the resistance of No.44 to ToLCNDV isolates from the Mediterranean and Asian countries. Initial mapping involving two rounds of phenotyping with two independent F2 populations generated by crossing the begomovirus-susceptible cultivar SHF and No.44 consistently detected major quantitative trait loci (QTLs) on chromosomes 1 and 2 that confer resistance to ToLCNDV. Fine-mapping of Cy-1, the dominant QTL on chromosome 1, using F3 populations narrowed the candidate region to a 209-kb genomic segment harboring 24 predicted genes. Among these genes, DFDGD-class RNA-dependent RNA polymerase (CsRDR3), an ortholog of Ty-1/Ty-3 of tomato and Pepy-2 of capsicum, was found to be a strong candidate conferring ToLCNDV resistance. The CsRDR3 sequence of No.44 contained multiple amino acid substitutions; the promoter region of CsRDR3 in No.44 had a large deletion; and the CsRDR3 transcript levels were greater in No.44 than in SHF. Virus-induced gene silencing (VIGS) of CsRDR3 using two chromosome segment substitution lines harboring chromosome 1 segments derived from No.44 compromised resistance to ToLCNDV.ConclusionsForward and reverse genetic approaches identified CsRDR3, which encodes a DFDGD-class RNA-dependent RNA polymerase, as the gene responsible for ToLCNDV resistance at the major QTL Cy-1 on chromosome 1 in cucumber. Marker-assisted breeding of ToLCNDV resistance in cucumber will be expedited by using No.44 and the DNA markers developed in this study.

Identifying SSR/InDel loci related to tobacco bacterial wilt resistance using association mapping

Identifying molecular markers linked to tobacco bacterial wilt resistance is crucial for developing resistant tobacco varieties, thereby enhancing tobacco production and quality. In this two-year study, we evaluated the tobacco bacterial wilt disease index (TBWDI) in a mapping population of 78 tobacco accessions using SSR/InDel markers across 1377 marker loci. Two association models, GLM_Q and MLM_Q + Kinship, were used for association analysis. By considering multiple environments, selection thresholds, and phenotype values, we identified 19 reliable marker loci (P = 7.07-E05–4.98-E02) that explained 5.37–19.10 % of the phenotypic variation. Among these, we selected 11 accessions with high resistance to tobacco bacterial wilt, each containing at least one excellent locus. The models predicted five hybrid combinations whose offspring aggregated additional excellent loci. Additionally, one locus was identified during quantitative trait loci mapping, with accessions carrying this locus exhibiting significantly different TBWDI values from those without it, confirming its reliability and stability. A candidate gene (LOC107795335) was also identified downstream at 17.58 Kbp of the locus. Our study pinpointed reliable loci associated with bacterial wilt resistance, identified important materials for breeding resistant tobacco varieities, and predicted the best hybrid combinations for low disease indexes. The results of our study offer invaluable theoretical insights for breeding varieties with high and stable resistance.

Mapping of resistance loci in wheat line Milan/S87230//Babax to South African races of Puccinia striiformis f. sp. tritici

Mapping of resistance loci in wheat line Milan/S87230//Babax to South African races of Puccinia striiformis f. sp. tritici

Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study

Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge hereafter) using retinal images and PhenoAge, a composite biomarker of phenotypic age. We used retinal photographs from the UK Biobank dataset to train a deep-learning algorithm to predict the composite score of PhenoAge. We used a deep convolutional neural network architecture with multiple layers to develop our deep-learning-based biological ageing marker, as RetiPhenoAge, with the aim of identifying patterns and features in the retina associated with variations of blood biomarkers related to renal, immune, liver functions, inflammation, and energy metabolism, and chronological age. We determined the performance of this biological ageing marker for the prediction of morbidity (cardiovascular disease and cancer events) and mortality (all-cause, cardiovascular disease, and cancer) in three independent cohorts (UK Biobank, the Singapore Epidemiology of Eye Diseases [SEED], and the Age-Related Eye Disease Study [AREDS] from the USA). We also compared the performance of RetiPhenoAge with two other known ageing biomarkers (hand grip strength and adjusted leukocyte telomere length) and one lifestyle factor (physical activity) for risk stratification of mortality and morbidity. We explored the underlying biology of RetiPhenoAge by assessing its associations with different systemic characteristics (eg, diabetes or hypertension) and blood metabolite levels. We also did a genome-wide association study to identify genetic variants associated with RetiPhenoAge, followed by expression quantitative trait loci mapping, a gene-based analysis, and a gene-set analysis. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and corresponding 95% CIs for the associations between RetiPhenoAge and the different morbidity and mortality outcomes. Retinal photographs for 34 061UK Biobank participants were used to train the model, and data for 9429participants from the SEED cohort and for 3986participants from the AREDS cohort were included in the study. RetiPhenoAge was associated with all-cause mortality (HR 1·92 [95% CI 1·42-2·61]), cardiovascular disease mortality (1·97 [1·02-3·82]), cancer mortality (2·07 [1·29-3·33]), and cardiovascular disease events (1·70[1·17-2·47]), independent of PhenoAge and other possible confounders. Similar findings were found in the two independent cohorts (HR 1·67 [1·21-2·31] for cardiovascular disease mortality in SEED and 2·07 [1·10-3·92] in AREDS). RetiPhenoAge had stronger associations with mortality and morbidity than did hand grip strength, telomere length, and physical activity. We identified two genetic variants that were significantly associated with RetiPhenoAge (single nucleotide polymorphisms rs3791224 and rs8001273), and were linked to expression quantitative trait locis in various tissues, including the heart, kidneys, and the brain. Our new deep-learning-derived biological ageing marker is a robust predictor of mortality and morbidity outcomes and could be used as a novel non-invasive method to measure ageing. Singapore National Medical Research Council and Agency for Science, Technology and Research, Singapore.

Identification and mapping of QTLs and their corresponding candidate genes controlling high night-time temperature stress tolerance in wheat (Triticum aestivum L.).

With every 1°C rise in temperature, yields are predicted to decrease by 5%-6% for both cool and warm season crops, threatening food production, which should double by 2050 to meet the global demand. While high night-time temperature (HNT) stress is expected to increase due to climate change, limited information is available on the genetic control of the trait, especially in wheat (Triticum aestivum L.). To identify genes controlling the HNT trait, we evaluated a doubled haploid (DH) population developed from a cross between an HNT tolerant line KSG1203 and KSG0057, a selection out of a mega variety PBW343 from South East Asia that turned out to be HNT susceptible. The population, along with the parents, were evaluated under 30°C night-time (HNT stress) keeping the daytime temperature to normal 22°C. The same daytime and 16°C night-time temperature were used as a control. The HNT treatment negatively impacted all agronomic traits under evaluation, with a percentage reduction of 0.5%-35% for the tolerant parent, 8%-75% for the susceptible parent, and 8%-50% for the DH population. Performed using sequencing-based genotyping, quantitative trait locus (QTL) mapping identified 19 QTLs on 13 wheat chromosomes explaining 9.72%-28.81% of cumulative phenotypic variance for HNT stress tolerance, along with 13 that were for traits under normal growing conditions. The size of QTL intervals ranged between 0.021 and 97.48 Mb, with the number of genes ranging between 2 and 867. A candidate gene analysis for the smallest six QTL intervals identified eight putative candidates for night-time heat stress tolerance.

Sesame, an Underutilized Oil Seed Crop: Breeding Achievements and Future Challenges.

Sesame seeds and their edible oil are highly nutritious and rich in mono- and polyunsaturated fatty acids. Bioactive compounds such as sterols, tocopherols, and sesamol provide significant medicinal benefits. The high oil content (50%) and favorable mono- and polyunsaturated fatty acid balance, as well as resilience to water stress, make sesame a promising candidate crop for global agricultural expansion. However, sesame production faces challenges such as low yields, poor response to agricultural inputs, and losses due to capsule dehiscence. To enhance yield, traits like determinate growth, dwarfism, a high harvest index, non-shattering capsules, disease resistance, and photoperiod sensitivity are needed. These traits can be achieved through variation or induced mutation breeding. Crossbreeding methods often result in unwanted genetic changes. The gene editing CRISPR/Cas9 technology has the potential to suppress detrimental alleles and improve the fatty acid profile by inhibiting polyunsaturated fatty acid biosynthesis. Even though sesame is an orphan crop, it has entered the genomic era, with available sequences assisting molecular breeding efforts. This progress aids in associating single-nucleotide polymorphisms (SNPs) and simple sequence repeats (SSR) with key economic traits, as well as identifying genes related to adaptability, oil production, fatty acid synthesis, and photosynthesis. Additionally, transcriptomic research can reveal genes involved in abiotic stress responses and adaptation to diverse climates. The mapping of quantitative trait loci (QTL) can identify loci linked to key traits such as capsule size, seed count per capsule, and capsule number per plant. This article reviews recent advances in sesame breeding, discusses ongoing challenges, and explores potential strategies for future improvement. Hence, integrating advanced genomic tools and breeding strategies provides promising ways to enhance sesame production to meet global demands.

Construction of the First High-Density Genetic Linkage Map and QTL Mapping of Shikimic Acid Content in Liquidambar

High-quality genetic maps are effective tools for elucidating the genetic mechanisms of complex quantitative traits and facilitating marker-assisted breeding. Species within the genus Liquidambar (commonly called sweetgum), particularly Liquidambar styraciflua and Liquidambar formosana, are significant forest resources worldwide. These sweetgum trees have been extensively utilized in medical and cosmetic applications for centuries as they contain large amounts of valuable secondary metabolites. Among these, shikimic acid is a notable metabolite with significant pharmaceutical applications. Despite advances in conventional breeding and propagation techniques for sweetgum, the genetic basis and regulatory mechanisms of valuable traits remain largely unexplored. In this study, we constructed the first high-density genetic map for sweetgum using whole-genome resequencing (WGR) of 220 progeny individuals derived from a cross of L. styraciflua × L. formosana. The genetic map spanned a total distance of 1428.51 centimorgans (cM) with an average inter-marker distance of 0.33 cM, incorporating 4268 bin markers across 16 linkage groups. To identify the genetic loci controlling the shikimic acid content, quantitative trait locus (QTL) mapping was carried out based on the genetic map. Two QTLs located on linkage group (LG) 12 were detected, encompassing a total of 213 genes within the QTL interval. Some of these genes are closely related to secondary metabolism in plants, including YUCCA and DXS genes. This study presents the first high-quality genetic map of sweetgum and provides a preliminary QTL analysis for shikimic acid content. Our findings establish a foundational framework for the genetic improvement of sweetgum through marker-assisted breeding and offer valuable insights for further research in sweetgum genetics.

Identification of Hotspot Regions for Candidate Genes Associated with Peanut (Arachis hypogaea L.) Pod and Seed Size on Chromosome A05

The size of peanut pods and seeds, which directly affects yield and quality, also has significant implications for mechanized production and market efficiency. Identifying relevant loci and mining candidate genes is crucial for cultivating high-yield peanut varieties. In this study, we employed advanced generation recombinant inbred lines developed by crossbreeding Huayu 44 and DF12 as the experimental material. Quantitative trait locus (QTL) mapping for traits related to pod and seed size was conducted across six environments. A total of 44 QTLs were detected, distributed on chromosomes A02, A05, B04, B08, and B10. An enrichment region for multiple QTLs was also identified on chromosome A05 (19.28~52.32 cm). In this region, 10 KASP markers were developed, narrowing the enrichment area to two candidate gene hotspot regions of 600.9 kb and 721.2 kb. By combining gene prediction and functional annotation within the intervals, 10 candidate genes, including those encoding cytochrome P450 protein, polyamine synthase, mannose-1-phosphate guanylyltransferase, pentatricopeptide repeat protein, and E2F transcription factor, were identified as regulators of pod and seed size. This study provides technical support for the genetic improvement and key gene identification of peanut pod and seed size.

Expression quantitative trait locus mapping of extracellular microRNAs in human plasma

MicroRNAs, crucial in regulating protein-coding gene expression, are implicated in various diseases. We performed a genome-wide association study of plasma miRNAs (ex-miRNAs) in 3,743 Framingham Heart Study (FHS) participants and identified 1,027 cis-ex-miRNA-eQTLs (cis-exQTLs) for 37 ex-miRNAs, with 55% replication in an independent study. Colocalization analyses suggested potential genetic coregulation of ex-miRNAs with whole blood mRNAs. Mendelian randomization indicated 29 ex-miRNAs potentially influencing 35 traits. Notably, the chromosome 14q23 and 14q32 miRNA clusters emerged as the top signal, contributing over 50% of the significant cis-exQTL results, and were associated with a diverse range of traits including platelet count. Correlations of 10 ex-miRNAs (such as miR-376c-3p) in 14q32 with platelet count and volume were confirmed in FHS participants. These findings shed light on the genetic basis of ex-miRNA expression and their involvement in complex traits.

Image Analysis Artificial Intelligence Technologies for Plant Phenotyping: Current State of the Art

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.

GRABSEEDS: extraction of plant organ traits through image analysis

BackgroundPhenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits.ResultsWe introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting.ConclusionGRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS.

Genetic improvement of drought stress tolerance in maize, recent advancements and future research direction

Maize is an imperative crop around the globe, and it provides several essential nutrients to humans and animals. Environmental changes seriously affect growth and productivity. Drought stress is one of the most important abiotic stresses, reducing maize growth and yield and threatening global food security. For decades, breeders have been trying to improve maize's ability to counter the toxic effects of drought stress. Drought tolerance is controlled by many genes and it complicates molecular breeding. The use of conventional breeding methods limited the development of drought tolerance in maize because of the complex nature of this trait. Hence, maize breeders have shifted their focus towards improvement of drought tolerance in maize at molecular level. Different molecular tools like quantitative trait loci (QTL) mapping, genome-wide-association-studies (GWAS), transcriptome analysis, transcription factor (TFs) analysis, and CRISPR/Cas9 have played a vital role in gene’s identification and their use in molecular breeding. These genomic regions have been proven very effective, and more studies are being conducted to increase their efficiency; however, the improvement level is limited because of the complex genetic mechanism of drought tolerance. Different review articles have been published on this aspect; however, a comprehensive and updated overview of drought tolerance needs to be included. The current review highlights the role of diverse molecular techniques to improve drought tolerance in maize. This review article will enhance the interest of researchers working on the genetic improvement of maize.

Fish Genomics and Its Application in Disease‐Resistance Breeding

ABSTRACTGlobal aquaculture production has been rising for several decades, with up to 76% of the total production from fish. However, the problem of fish diseases is becoming more and more prominent in today's context of pursuing sustainable aquaculture. Since the first fish genome assembly reported in 2002, genomic approaches have been successfully implemented in fish breeding to enhance disease resistance and reduce economic losses caused by diverse fish diseases. Here, we present a review of the current progress in fish genomics and its application in disease‐resistance breeding. First, assembly data for all publicly available fish genomes were curated and statistical analysis of these data were performed. Subsequently, genomics‐assisted breeding approaches (including quantitative trait loci mapping, genome‐wide association study, marker‐assisted selection, genomic selection, gene transfer, and genome editing) that have been applied in practical disease–resistance breeding programs are outlined. In addition, candidate genetic markers that could possibly be utilized in breeding were summarized. Finally, remaining challenges and further directions were discussed. In summary, this review provides insight into fish genomics and genomics‐assisted breeding of disease‐resistant fish varieties.

Atp1a2 and Kcnj9 are candidate genes underlying sensitivity to oxycodone-induced locomotor activation and withdrawal-induced anxiety-like behaviors in C57BL/6 substrains.

Opioid use disorder is heritable, yet its genetic etiology is largely unknown. C57BL/6J and C57BL/6NJ mouse substrains exhibit phenotypic diversity in the context of limited genetic diversity which together can facilitate genetic discovery. Here, we found C57BL/6NJ mice were less sensitive to oxycodone (OXY)-induced locomotor activation versus C57BL/6J mice in a conditioned place preference paradigm. Narrow-sense heritability was estimated at 0.22-0.31, implicating suitability for genetic analysis. Quantitative trait locus (QTL) mapping in an F2 cross identified a chromosome 1 QTL explaining 7-12% of the variance in OXY locomotion and anxiety-like withdrawal in the elevated plus maze. A second QTL for EPM withdrawal behavior on chromosome 5 near Gabra2 (alpha-2 subunit of GABA-A receptor) explained 9% of the variance. To narrow the chromosome 1 locus, we generated recombinant lines spanning 163-181 Mb, captured the QTL for OXY locomotor traits and withdrawal, and fine-mapped a 2.45-Mb region (170.16-172.61 Mb). Transcriptome analysis identified five, localized striatal cis-eQTL transcripts and two were confirmed at the protein level (KCNJ9, ATP1A2). Kcnj9 codes for a potassium channel (GIRK3) that is a major effector of mu opioid receptor signaling. Atp1a2 codes for a subunit of a Na+/K+ ATPase enzyme that regulates neuronal excitability and shows functional adaptations following chronic opioid administration. To summarize, we identified two candidate genes underlying the physiological and behavioral properties of opioids, with direct preclinical relevance to investigators employing these widely used substrains and clinical relevance to human genetic studies of opioid use disorder.

An ultra-dense linkage map identified quantitative trait loci corresponding to fruit quality- and size-related traits in red goji berry.

Goji berries are a small-fruited shrub with industrial importance whose fruit considered beneficial in both fresh and dried forms. Current germplasms of goji berries include small fruits with a short shelf life, less sweet and bitter taste, and a lack of appropriate genetic information. This study aimed to employ whole genome resequencing to generate an ultra-dense bin linkage map and to elucidate the genetic basis of goji fruit quality and size using quantitative trait loci (QTL) mapping analysis in a cross-pollinated hybrid population. To achieve this goal, human sensory tests were carried out to determine the bitter taste (BT) and sweet taste (ST), and to quantify the soluble solid content (SSC), fruit firmness (FF), and fruit size-related traits of fresh goji fruits over three or four years. The results revealed that the goji bin linkage map based on resequencing spanned a total length of 966.42 cM and an average bin interval of 0.03 cM. Subsequent variant calling and ordering resulted in 3,058 bins containing 35,331 polymorphic markers across 12 chromosomes. A total of 99 QTLs, with individual loci in different environments explaining a phenotypic variance of 1.21-16.95% were identified for the studied traits. Ten major effects, including colocalized QTLs corresponding to different traits, were identified on chromosomes 1, 3, 5, 6, 7, and 8, with a maximum Logarithm of Odds (LOD) of 29.25 and 16.95% of explained phenotypic variance (PVE). In addition, four stable loci, one for FF, one for fruit weight (FW), and two for fruit shape index (FSI), were mainly mapped on chromosomes 5, 6, and 7, elucidating 2.10-16.95% PVE. These findings offer valuable insights into the genetic architecture of goji fruit traits along with identified specific loci and markers to further improve and develop sweeter, less bitter and larger fruited goji berry cultivars with extended shelf life.

Meristic co-evolution and genomic co-localization of lateral line scales and vertebrae in Central American cichlid fishes.

Meristic traits are often treated as distinct phenotypes that can be used to differentiate and delineate recently diverged species. For instance, the number of lateral line scales and vertebrae, two traits that vary substantially among Neotropical Heroine cichlid species, have been previously suggested to co-evolve. These meristic traits could co-evolve due to shared adaptive, developmental, or genetic factors. If they were found to be genetically or developmentally non-independent, this might require a more general re-evaluation of their role in evolutionary or taxonomic studies. We expanded a previous analysis of correlated evolution of meristic traits (lateral line scales and vertebrae counts) in these fishes to include a range of phylogenetic reconstructions as well as the analyses of 13 Nicaraguan Midas cichlid species (Amphilophus spp.). Additionally, we performed qualitative traits locus (QTL) mapping in a F2 laboratory-reared hybrid population from two ecologically divergent Midas cichlid fish species to discover and evaluate whether genomic co-segregation might explain the observed patterns of meristic co-evolution. Meristic values for these traits were found to morphologically differentiate some species of the Midas cichlid adaptive radiation. Our QTL analysis pinpointed several genomic regions associated with divergence in these traits and highlighted the potential for genomic co-segregation of the lateral line and vertebrae numbers on two chromosomes. Further, our phylogenetic comparative analyses consistently recovered a significant positive evolutionary correlation between the counts of lateral line scale and vertebrae numbers in Neotropical cichlids. Hence, the findings of genomic co-segregation could partially explain the co-evolution of these two meristic traits in these species. Continuing to unravel the genetic architecture governing meristic divergence helps to better understand both trait correlations and the utility of meristic traits in taxonomic diagnoses and how traits in phenotypes might be expected to co-evolve.

Identification of a major QTL region and candidate genes responsible for harvest date combining QTL mapping and QTL-seq approaches in walnut (Juglans regia L.)

Walnut (Juglans regia L.) takes place within the Juglandaceae family and is one of the most popular nut species. In walnut breeding, several limiting factors need to be revealed regarding the genetic mechanism underlying important agronomical traits. In the current report, the monitoring of the ‘Chandler x Kaplan-86’ F1 walnut population was carried out four successive years (2015, 2016, 2017, 2018) and the distribution of the plants for harvest date from four years was ranged from August 27th to October 22nd. Then, we dissected the genomic regions associated with harvest date using classical Quantitative Trait Loci (QTLs) mapping and a rapid and cost-effective QTL-seq approach in ‘Chandler x Kaplan-86’ F1 walnut population for the first time. In classical QTL mapping, a major QTL was recognized in LG4 corresponding to Chr1 in Chandler female map with 27.6 (%) phenotyping variance. In QTL-seq, we bulked the DNAs of the genotypes with extreme harvest date separately, the whole genome resequencing was performed for a parent Chandler and both bulks (early and late) to calculate Delta SNP indexes for each Single Nucleotide Polymorphisms (SNPs) and Insertion/Deletion (InDels). Approximately 48.04 million paired reads for Chandler, 149.72, and 150.14 million paired reads were obtained from early and late harvest bulks, respectively. A total of 229,307 SNPs and 45,223 InDels were mined from the merged sequences data covered whole chromosomes. The responsible genomic fragments were identified in Chr2 and Chr11 in QTL-seq. The highest linked InDel locus detected in QTL-seq in Chr11 around 17 million base pairs distinguished the plants according to harvest date. InDel Sequence Characterized Amplified Region (SCAR) marker is a candidate for marker-assisted selection for future walnut breeding studies.

Genotype selection identified elite lines through quantitative trait loci mapping of agronomically important traits in wheat.

The online version contains supplementary material available at 10.1007/s11032-024-01496-3.

Artificial selection of mutations in two nearby genes gave rise to shattering resistance in soybean

Resistance to pod shattering is a key domestication-related trait selected for seed production in many crops. Here, we show that the transition from shattering in wild soybeans to shattering resistance in cultivated soybeans resulted from selection of mutations within the coding sequences of two nearby genes - Sh1 and Pdh1. Sh1 encodes a C2H2-like zinc finger transcription factor that promotes shattering by repressing SHAT1-5 expression, thereby reducing the secondary wall thickness of fiber cap cells in the abscission layers of pod sutures, while Pdh1 encodes a dirigent protein that orchestrates asymmetric lignin distribution in inner sclerenchyma, creating torsion in pod walls that facilitates shattering. Integration analyses of quantitative trait locus mapping, genome-wide association studies, and allele distribution in representative soybean germplasm suggest that these two genes are primary modulators underlying this domestication trait. Our study thus provides comprehensive understanding regarding the genetic, molecular, and cellular bases of shattering resistance in soybeans.

Quantitative Trait Loci Mapping of Heading Date in Wheat under Phosphorus Stress Conditions.

Wheat (Triticum aestivum L.) is a crucial cereal crop, contributing around 20% of global caloric intake. However, challenges such as diminishing arable land, water shortages, and climate change threaten wheat production, making yield enhancement crucial for global food security. The heading date (HD) is a critical factor influencing wheat's growth cycle, harvest timing, climate adaptability, and yield. Understanding the genetic determinants of HD is essential for developing high-yield and stable wheat varieties. This study used a doubled haploid (DH) population from a cross between Jinmai 47 and Jinmai 84. QTL analysis of HD was performed under three phosphorus (P) treatments (low, medium, and normal) across six environments, using Wheat15K high-density SNP technology. The study identified 39 QTLs for HD, distributed across ten chromosomes, accounting for 2.39% to 29.52% of the phenotypic variance. Notably, five stable and major QTLs (Qhd.saw-3A.7, Qhd.saw-3A.8, Qhd.saw-3A.9, Qhd.saw-4A.4, and Qhd.saw-4D.3) were consistently detected across varying P conditions. The additive effects of these major QTLs showed that favorable alleles significantly delayed HD. There was a clear trend of increasing HD delay as the number of favorable alleles increased. Among them, Qhd.saw-3A.8, Qhd.saw-3A.9, and Qhd.saw-4D.3 were identified as novel QTLs with no prior reports of HD QTLs/genes in their respective intervals. Candidate gene analysis highlighted seven highly expressed genes related to Ca2+ transport, hormone signaling, glycosylation, and zinc finger proteins, likely involved in HD regulation. This research elucidates the genetic basis of wheat HD under P stress, providing critical insights for breeding high-yield, stable wheat varieties suited to low-P environments.