Quantitative Trait Loci Mapping Research Articles

Identification of quantitative trait locus and positional candidate loci influencing chicken egg quality under tropical conditions.

Egg quality is a vital factor in the poultry industry. High-quality eggs not only meet consumer expectations for appearance, taste, and nutritional value but also have high marketability, profitability, and consumer satisfaction. Accordingly, we executed our research with the purpose of determining chromosomal regions and genetic markers associated with egg quality in an F2 cross-bred chicken population under tropical conditions; we determined these through a genome-wide association study and quantitative trait locus (QTL) mapping. This population was created by cross-breeding the L2 line of Taiwan Country chickens, which is adapted to local conditions in Taiwan, with an experimental line (R-line) of Rhode Island Red layer chickens, which was developed by the French National Research Institute for Agriculture, Food and the Environment. A 60K single nucleotide polymorphism (SNP) genotyping array for chickens was employed to execute the analysis. Our analysis revealed 40 QTLs associated with egg quality under tropical conditions, namely 20 QTLs with genome-wide statistical significance and 20 QTLs with chromosome-wide statistical significance. Furthermore, we identified 93 SNPs exerting discernible effects on egg quality, with 10 of these effects exhibiting genome-wide significance and 83 exhibiting potential significance. The majority of the detected QTL regions and SNPs agreed with those identified as having an association with egg quality or production traits in previous studies, thus supporting the interrelationships determined between the studied characteristics. The findings of this study enhance the understanding regarding the genetic regulation governing chicken egg quality, thereby serving as a valuable reference for future functional investigations.

Experimental evaluation of effectiveness of genomic selection for resistance to northern corn leaf blight in maize.

Northern corn leaf blight (NLB) caused by Setosphaeria turcica (Luttrell) Leonard & Suggs is a severe foliar disease in maize. Resistance to NLB is complexly inherited and controlled by several quantitative trait loci (QTL) distributed across the genome. Phenotype and DNA marker-based selection for resistance to NLB is expected to be effective. Hence, an investigation was carried out to predict the genetic value of selection candidates for resistance to NLB and compare the accuracies of genomic prediction in two F2:3 populations of two crosses (CM212 × MAI172; CM202 × SKV50) derived from contrasting parents. Linkage analysis using 297 polymorphic SNPs in population-1 and 290 polymorphic SNPs in population-2 revealed ten linkage groups spanning 3623.88cM and 4261.92cM with an average distance of 12.40cM and 14.9cM in population-1 and population-2, respectively. Location-wise and pooled data across locations identified common QTLs on linkage groups 1 and 6 in population-1 and 3 and 8 in population-2. The prediction accuracy of the QTL mapping (9.92 and 9.10 for population-1 and population-2, respectively) was based on only a few markers, which explained higher percent phenotypic variation. The prediction accuracies of the genomic estimated breeding values in the present investigation were relatively low in population-1 (0.24 to 0.26) and population-2 (0.29-0.32) compared to the expected accuracies. This could be due to fewer polymorphic markers and a small training/population size. Though the GS prediction accuracies were relatively low, they were significantly higher than QTL mapping, which promises better genetic gain per cycle. The resistant progenies from both populations were advanced to derive inbred lines and crossed with four different testers in line × tester mating design to test for their combining ability and effectiveness of genomic selection. High overall general combining ability was exhibited by 21 inbred lines. Among F1s, 48% were assigned high overall specific combining ability status. Out of the 136 single crosses, seven recorded significant positive standard heterosis over the best check for grain yield. Twenty-five inbreds with high GEBVs were crossed with four testers to obtain 100 F1s and evaluated for their response to NLB. The majority of hybrids displayed moderate to resistant reaction to NLB either in combination with susceptible or resistant testers indicating the effectiveness of selection based on high GEBVs.

Genome-wide mapping of quantitative trait loci conferring resistance to stripe rust in spring wheat line PI 660072.

Two major QTL for resistance to stripe rust were mapped on chromosome 2BL and 4BL in spring wheat PI 660072, and their KASP markers were developed. Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases of wheat worldwide. Identifying resistance genes is crucial for developing resistant cultivars to control the disease. Spring wheat PI 660072 (Triticum aestivum) has been identified to possess both adult-plant resistance (APR) and all-stage resistance (ASR) to stripe rust. To elucidate the genetic basis of the resistance in PI 660072, a mapping population consisting of 211 F5-F7 recombinant-inbred lines (RILs) was developed from a cross of PI 660072 with susceptible spring wheat Avocet S. The mapping population was phenotyped for stripe rust responses across five field environments from 2020 to 2022 and genotyped using the 15K SNP (single nucleotide polymorphism) array to map stripe rust resistance loci. The mapping population was also tested at the seedling stage with predominant Chinese Pst races CYR31, CYR32, CYR34 and PST-YX1-3-1 in the greenhouse. Stripe rust resistance genes were identified using the quantitative trait locus (QTL) mapping approach. Two QTL were identified with QYrPI660072.swust-2BL mapped on the long arm of chromosome 2B for ASR and QYrPI660072.swust-4BL on the long arm of chromosome 4B for APR. To facilitate marker-assisted selection breeding, Kompetitive allele specific PCR (KASP) markers, KASP-1269 for QYrPI660072.swust-2BL and KASP-3209 for QYrPI660072.swust-4BL, were developed. These markers could be used to introgress the effective resistance QTL into new wheat cultivars.

Mapping of quantitative trait loci (QTL) in Brassica napus L. for tolerance to water stress.

Brassica napus L. plants are sensitive to water stress conditions throughout their life cycle from seed germination to seed setting. This study aims at identifying quantitative trait loci (QTL) linked to B. napus tolerance to water stress mimicked by applications of 10% polyethylene glycol-6000 (PEG-6000). Two doubled haploid populations, each consisting of 150 genotypes, were used for this research. Plants at the two true leaf stage of development were grown in the absence (control) or presence (stress) of PEG-6000 under controlled environmental conditions for 48h, and the drought stress index was calculated for each genotype. All genotypes, along with their parents, were genotyped using the Brassica Infinium 90K SNP BeadChip Array. Inclusive composite interval mapping was used to identify QTL. Six QTL and 12 putative QTL associated with water stress tolerance were identified across six chromosomes (A2, A3, A4, A9, C3, and C7). Collectively, 2154 candidate genes for water stress tolerance were identified for all the identified QTL. Among them, 213 genes were identified as being directly associated with water stress (imposed by PEG-6000) tolerance based on nine functional annotations. These results can be incorporated into future breeding initiatives to select plant material with the ability to cope effectively with water stress.

Discovery of key volatile compound and regulatory gene governing the ‘taro-like’ aroma in wax gourd (Benincasa hispida)

The distinctive ‘taro-like’ aroma in wax gourd has become a crucial flavor characteristic, significantly enhancing its market value. Despite its importance, the volatile compounds responsible for this aroma and their genetic regulatory mechanisms have not been sufficiently studied. Utilizing headspace-gas chromatography-quadrupole time of flight-mass spectrometry (HS-GC-QTOF-MS), this research examined the volatile compounds in wax gourd germplasms with and without the ‘taro-like’ aroma, identifying 2-acetyl-1-pyrroline (2-AP) as the primary volatile responsible for this aroma. To uncover the genetic basis of this trait, segregating populations and recombinant inbred lines (RILs) were developed from MY-1 x GX-71 germplasms, which exhibit significant differences in aroma expression. Genetic analysis revealed that a single recessive gene governs the ‘taro-like’ aroma trait in wax gourd. Through bulked segregant analysis sequencing (BSA-seq) and quantitative trait loci (QTL) mapping, the candidate region for this aroma gene was localized to a 285.29 kb interval on Chromosome 2. Further examination of candidate gene expression and sequence variations highlighted the BhBADH gene's critical role in the formation of the ‘taro-like’ aroma. Additionally, RNA-seq analysis revealed significant enrichment of differentially expressed genes (DEGs) associated with metabolic pathways involved in environmental information processing, which contribute to 2-AP synthesis in wax gourd with a ‘taro-like’ aroma. Our research provides a theoretical foundation for the molecular breeding to enhance the aroma quality of wax gourd.

Quantitative Trait Locus Mapping Combined with RNA Sequencing Identified Candidate Genes for Resistance to Powdery Mildew in Bitter Gourd (Momordica charantia L.)

Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by hybridizing the powdery mildew-resistant material MC18 and the powdery mildew-susceptible material MC402. A high-density genetic map of 1222.04 cM was constructed via incorporating 1,996,505 SNPs generated by resequencing data from 180 lines, and quantitative trait locus (QTL) positioning was performed using phenotypic data at different inoculation stages. A total of seven QTLs related to powdery mildew resistance were identified on four chromosomes, among which qPm-3-1 was detected multiple times and at multiple stages after inoculation. By selecting 18 KASP markers that were evenly distributed throughout the region, 250 lines and parents were genotyped, and the interval was narrowed to 207.22 kb, which explained 13.91% of the phenotypic variation. Through RNA-seq analysis of the parents, 11,868 differentially expressed genes (DEGs) were screened. By combining genetic analysis, gene coexpression, and sequence comparison analysis of extreme materials, two candidate genes controlling powdery mildew resistance in bitter gourd were identified (evm.TU.chr3.2934 (C3H) and evm.TU.chr3.2946 (F-box-LRR)). These results represent a step forward in understanding the genetic regulatory network of powdery mildew resistance in bitter gourd and lay a molecular foundation for the genetic improvement in powdery mildew resistance.

SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data.

Traditional expression quantitative trait locus (eQTL) mapping associates single nucleotide polymorphisms (SNPs) with gene expression, where the SNPs are derived from large-scale whole-genome sequencing (WGS) data or transcriptome data. While WGS provides a high SNP density, it also incurs substantial sequencing costs. In contrast, RNA-seq data, which are more accessible and less expensive, can simultaneously yield gene expressions and SNPs. Thus, eQTL analysis based on RNA-seq offers significant potential applications. Two primary strategies were employed for eQTL in this study. The first involved analyzing expression levels in relation to variant sites detected between populations from RNA-seq data. The second approach utilized kmers, which are sequences of length k derived from RNA-seq reads, to represent variant sites and associated these kmer genotypes with gene expression. We discovered 87 significant association signals involving eGene on the basis of the SNP-based eQTL analysis. These genes include DYNLT1, NMNAT1, and MRLC2, which are closely related to neurological functions such as motor coordination and homeostasis, play a role in cellular energy metabolism, and function in regulating calcium-dependent signaling in muscle contraction, respectively. This study compared the results obtained from eQTL mapping using RNA-seq identified SNPs and gene expression with those derived from kmers. We found that the vast majority (23/30) of the association signals overlapping the two methods could be verified by haplotype block analysis. This comparison elucidates the strengths and limitations of each method, providing insights into their relative efficacy for eQTL identification.

QTL Mapping-Based Identification of Visceral White-Nodules Disease Resistance Genes in Larimichthys polyactis.

Disease outbreaks in aquaculture have recently intensified. In particular, visceral white-nodules disease, caused by Pseudomonas plecoglossicida, has severely hindered the small yellow croaker (Larimichthys polyactis) aquaculture industry. However, research on this disease is limited. To address this gap, the present study employed a 100K SNP chip to genotype individuals from an F1 full-sib family, identify single nucleotide polymorphisms (SNPs), and construct a genetic linkage map for this species. A high-density genetic linkage map spanning a total length of 1395.72 cM with an average interval of 0.08 cM distributed across 24 linkage groups was obtained. Employing post-infection survival time as an indicator of disease resistance, 13 disease resistance-related quantitative trait loci (QTLs) were detected, and these regions included 169 genes. Functional enrichment analyses pinpointed 11 candidate disease resistance-related genes. RT-qPCR analysis revealed that the genes of chmp1a and arg1 are significantly differentially expressed in response to P. plecoglossicida infection in spleen and liver tissues, indicating their pivotal functions in disease resistance. In summary, in addition to successfully constructing a high-density genetic linkage map, this study reports the first QTL mapping for visceral white-nodules disease resistance. These results provide insight into the intricate molecular mechanisms underlying disease resistance in the small yellow croaker.

Quantitative trait locus mapping for water-soluble protein content in soybean across six environments

Quantitative trait locus mapping for water-soluble protein content in soybean across six environments

CRISPR/Cas9 and Anther Culture for Precision Double Haploid Line Production in Controlled Environments

ABSTRACTThe development of mapping populations and quantitative trait loci (QTL) analysis face constraints, in crops exhibiting male sterility and self‐incompatibility under field conditions. Addressing these challenges requires the integration of advanced techniques, including the temporal alteration or excision of centromere histone H3 (CENH3) protein and the use of gene editing tools such as MATRILINEAL (MTL) knockout. Specifically, this can be achieved through Cas9/gRNA‐mediated mutagenesis or Cas9/gRNA‐driven promoter expression systems. These technologies offer efficient means to advance mapping populations and QTL analysis in male sterile and self‐incompatible crops within controlled ecosystems. The doubled haploid (DH) mapping population, traditionally requiring 3 years of generation time via anther culture method, can now be expedited to 2–3 years of generation time using gene editing techniques within controlled environmental systems. Notably, DH mapping populations can be efficiently generated in various crops, including rice, wheat, maize, barley and oats by leveraging gene editing tools. Among these tools, the novel approach of CENH3 protein temporal alteration/excision emerges as highly efficient compared to MTL knockout using Cas9/gRNA‐mediated mutation or Cas9/gRNA promoter expression. However, further investigation is warranted to optimise the regeneration of double haploid populations and enhance QTL analysis in male sterile and self‐incompatible crops under controlled systems.

Mining of Oil Content Genes in Recombinant Maize Inbred Lines with Introgression from Temperate and Tropical Germplasm.

The oil content of maize kernels is essential to determine its nutritional and economic value. A multiparent population (MPP) consisting of five recombinant inbred line (RIL) subpopulations was developed to elucidate the genetic basis of the total oil content (TOC) in maize. The MPP used the subtropical maize inbred lines CML312 and CML384, along with the tropical maize inbred lines CML395, YML46, and YML32 as the female parents, and Ye107 as the male parent. A genome-wide association study (GWAS) was performed using 429 RILs of the multiparent population across three environments, employing 584,847 high-quality single nucleotide polymorphisms (SNPs). Furthermore, linkage analysis was performed in the five subpopulations to identify quantitative trait loci (QTL) linked to TOC in maize. Through QTL mapping and GWAS, 18 QTLs and 60 SNPs that were significantly associated with TOC were identified. Two novel candidate genes, Zm00001d029550 and Zm00001d029551, related to TOC in maize and located on chromosome 1 were reported, which have not been previously reported. These genes are involved in biosynthesis, lipid signal transduction, plant development and metabolism, and stress responses, potentially influencing maize TOC. Haplotype analysis of Zm00001d029550 and Zm00001d029551 revealed that Hap3 could be considered a superior haplotype for increasing TOC in maize. A co-located SNP (SNP-75791466) on chromosome 1, located 5648 bp and 11,951 bp downstream of the candidate genes Zm00001d029550 and Zm00001d029551, respectively, was found to be expressed in various maize tissues. The highest expression was observed in embryos after pollination, indicating that embryos are the main tissue for oil accumulation in maize. This study provides a theoretical basis for understanding the genetic mechanisms underlying maize TOC and developing high-quality, high-oil maize varieties.

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.

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.

QTL mapping and omics analysis to identify genes controlling kernel dehydration in maize.

This study mapped and screened three candidate genes related to kernel dehydration in maize. The slow development rate of maize kernels during later stages leads to high kernel moisture content at harvest, posing a challenge for mechanized maize harvesting in China. This study utilized a recombinant inbred line population derived from Zheng 58 (slow dehydration) and PH6WC (fast dehydration) as parents. After four years of trait investigation and analysis, 25 quantitative trait loci (QTLs) associated with kernel dehydration rate and moisture content were identified, with six QTLs showing a significant contribution value exceeding 10% in the phenotype. Furthermore, a comparison was made between the QTLs identified in this study and those from previous research on maize kernel moisture content and dehydration rate, followed by screening through the omics analysis of the parental lines. Three candidate genes related to kernel dehydration rate were identified, primarily involving carbohydrate metabolism, energy metabolism processes (Zm00001d014030 and Zm00001d006476), and stimulus resistance (Zm00001d040113). These findings provide valuable insights to assist and guide future breeding efforts for mechanical harvesting of maize.

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.

Genetic dissection of stem and leaf rachis prickles in diploid rose using a pedigree-based QTL analysis.

Prickles are often deemed undesirable traits in many crops, including roses (Rosa sp.), and there is demand for rose cultivars with no or very few prickles. This study aims to identify new and/or validate reported quantitative trait loci (QTLs) associated with stem and leaf rachis prickle density, characterize the effects of functional haplotypes for major QTLs, and identify the sources of QTL-alleles associated with increased/decreased prickle density in roses. QTL mapping using pedigree-based analysis (PBA), and haplotype analysis were conducted on two multi-parental diploid rose populations (TX2WOB and TX2WSE). Twelve QTLs were identified on linkage groups (LGs) 2, 3, 4, and 6. The major QTLs for the stem prickle density were located between 42.25 and 45.66 Mbp on chromosome 3 of the Rosa chinensis genome assembly, with individual QTLs explaining 18 to 49% of phenotypic variance (PVE). The remaining mapped QTLs were minor. As for the rachis prickle density, several QTLs were detected on LG3, 4, and 6 with PVE 8 to 17%. Also, this study identified that ancestors R. wichurana 'Basye's Thornless', 'Old Blush', and the pollen parent of M4-4 were common sources of favorable alleles (q) associated with decreased prickle density, whereas 'Little Chief' and 'Srche Europy' were the source of unfavorable alleles (Q) in the TX2WOB and TX2WSE populations, respectively. The outcomes of this work complement other studies to locate factors that affect prickle density. These results can also be utilized to develop high-throughput DNA tests and apply parental selection to develop prickle-free rose cultivars.

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.

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.