Quantitative Trait Loci Analysis Research Articles

Epigenome-Wide Association Study of Asthma Exacerbations in Europeans.

Asthma exacerbations (AEs) represent the major contributor to the global asthma burden. Although genetic and environmental factors have been associated with AEs, the role of epigenetics remains uncovered. This study aimed to identify whole blood DNA methylation (DNAm) markers associated with AEs in Europeans. DNAm was assessed in 406 blood samples from Spanish individuals using the Infinium MethylationEPIC microarray (Illumina). An epigenome-wide association study was conducted to test the association of DNAm with AEs at differentially methylated positions, regions, and epigenetic modules. CpGs suggestively associated with AEs(false discovery rate [FDR] < 0.1) were followed up for replication in 222 European individuals, and the genome-wide significance (p < 9 × 10-8) was declared after meta-analyzing the discovery and replication samples. Additional assessment was performed using nasal tissue DNAm data from 155 Spanish individuals. The effects of genetic variation on DNAm were assessed through cis-methylation quantitative trait loci (meQTL) analysis. Enrichment analyses of previous EWAS signals were conducted. Four CpGs were associated with AEs, and two were replicated and reached genomic significance in the meta-analysis (annotated to ZBTB16 and BAIAP2). Of those, CpG cg25345365 (ZBTB16) was cross-tissue validated in nasal epithelium (p= 0.003) and associated with five independent meQTLs (FDR < 0.05). Additionally, four differentially methylated regions and one module were significantly associated with AEs. Enrichment analyses revealed an overrepresentation of prior epigenetic associations with prenatal and environmental exposures, immune-mediated diseases, and mortality. DNAm in whole blood and nasal samples may contribute to AEs in Europeans, capturing genetic and environmental risk factors.

QTL mapping and whole-genome sequencing analysis for novel genetic resources associated with sucrose content in soybean [Glycine max (L.) Merr.]

Key messageA major QTL for sucrose content was mapped on chromosome 8 in PI 506593. The novel genetic variants and candidate genes were further identified within the major QTL.Sucrose in soybean [Glycine max (L.) Merr.] contribute to animal feed efficiency and natural sweetness of soy products. Thus, identifying novel genetic resources, such as quantitative trait loci (QTL), associated with sucrose content in soybean is essential for enhancing seed values. In this study, two recombinant inbred line populations derived from the same high sucrose donor parent, PI 506593, were used to identify significant QTLs. A total of 11 sucrose-related regions on chromosomes (Chrs.) 4, 5, 6, 8, 10, and 13 were identified using QTL analysis. Among them, four QTLs (qSUC_08.1, qSUC_08.2, qSUC_08.3, and qSUC_08.4) were clustered in the interval of 40,597,410–42,861,364 bp on Chr. 8, which was considered major QTL region. A desirable marker at 41,834,095 bp was tested in two populations, showing that two phenotypically extreme groups were efficiently differentiated. We further identified 44 and 54 candidate genes with non-synonymous mutations in the major QTL region based on the annotations of Wm82.a2.v1 and Wm82.a5.v1 assemblies, respectively. Among 54 candidate genes from Wm82.a5.v1, Protein Variation Effect Analyzer (PROVEAN) revealed that 18 genes contained 34 variants that had deleterious impacts on biological functions. RNA-seq analysis highlighted five candidate genes that were highly expressed in pod and seed tissues during reproductive stages and other plant parts. A gene, Gm_Wm82_23219 (Glyma.08G293800, Wm82.a2.v1) encoding proline-rich protein 4-like, was highlighted in both PROVEAN and RNA-seq analyses. Novel findings in this study will be valuable genetic resources in soybean breeding programs that aim to improve efficiency in animal feed and human food.

LD-informed deep learning for Alzheimer's gene loci detection using WGS data.

The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk. The Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7416 non-Hispanic white (NHW) participants (3150 cognitively normal older adults (CN), 4266 AD). 30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. Expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions provided functional evidence for the identified variants. The results were cross-validated against established AD-associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog. The Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework's findings are supported by tissue-specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep-Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large-scale sequencing studies. Growing genomic datasets require advanced tools to identify genetic loci in sequencing.Deep-Block, a novel AI framework, was used to process large-scale ADSP WGS data.Deep-Block identified both known and novel AD-associated genetic loci.rs429358 (APOE) was key; rs11556505 (TOMM40), rs34342646 (NECTIN2) were significant.The AI framework uses biological knowledge to enhance detection of Alzheimer's loci.

Mapping and validation of QTkw.cau-3DL, a major QTL controlling thousand-kernel weight in wheat.

A novel major QTL, QTkw.cau-3DL, for thousand-kernel weight has been identified on the wheat chromosome arm 3DL and enhances grain yield by 6.2% under field conditions. Increasing kernel weight is an effective way to improve yield potential in wheat. The identification of major quantitative trait loci (QTL) for kernel weight, without negative effects on other yield-related traits, is crucial for continuous yield improvement. We developed a population of F6 recombinant inbred lines from Jimai 120 × Jimai 325 and identified eight QTL for thousand-kernel weight, kernel length, and kernel width across five environments. The population was genotyped using Wheat15K SNP arrays and QTL analysis found that one QTL, QTkw.cau-3DL, on the chromosome arm 3DL consistently showed major effects on TKW and KL in five field experiments. This QTL accounted for up to 16.43% and 13.87% of phenotypic variation, respectively. QTkw.cau-3DL was confined to a 5.72-Mb (3.48cM) interval between 554.39Mb and 560.11Mb. This QTL was validated in a pair of NILs and in a new population. QTkw.cau-3DL increased kernel weight per spike without any negative effect on heading data, plant height, spike length, spikelet number per spike, or kernels number per spike. It increased grain yield by 6.2% under regular field production conditions. Haplotype analysis and geographical distribution in a nationwide collection of 630 wheat cultivars showed that QTkw.cau-3DL has not been widely deployed in Chinese wheat breeding programs. QTkw.cau-3DL is a novel QTL for increasing TKW through increasing KL; therefore, it is an important locus for enhancing wheat grain yield. The tightly linked, user-friendly markers developed in this study should facilitate map-based cloning and marker-assisted selection of the QTL in wheat breeding programs.

Genome-wide association study identifies QTL and candidate genes for grain size and weight in a Triticum turgidum collection.

Wheat breeders are constantly looking for genes and alleles that increase grain yield. One key strategy is finding new genetic resources in the wild and domesticated gene pools of related species with genes affecting grain size. This study explored a natural population of Triticum turgidum (L.) phenotyped for grain weight and size-related traits in three field trials and genotyped with single nucleotide polymorphism markers spread across the entire genome. The genome-wide association study analysis identified 39 quantitative trait loci (QTL) for 1000-kernel weight, grain length, grain width, grain area, and grain aspect consistent in at least two and across environments. Interestingly, 23 QTL for grain-related traits were grouped in nine QTL clusters located on chromosomes 1A, 1B, 2B, 3B, 4B, 5A, and 6B, respectively. Moreover, most of these QTL support findings from previous QTL analyses and are further strengthened by the known functions of the genes (such as BG2, GS5, and SRS3) and their similarity to genes in other cereal species. QTL clusters harbored genes that participate in various metabolic processes potentially involved in seed development, phytohormone signaling, sugar transport, mitogen-activated protein kinases signaling, and transcriptional factors (such as MADS-box and WRKY). Identifying loci controlling grain-related traits will provide information on the genetic resources available to breeders to improve grain yield, as well as the opportunity to develop close gene markers to be used in marker-assisted selection programs.

Phenotypic variation among IR64 × TOG5681 rice (Oryza sativa × O. glaberrima) chromosome segment substitution lines (CSSL) in response to iron toxicity, and its associated QTLs

Iron (Fe) toxicity presents a significant challenge to rice production in lowland ecosystems globally. The identification of genetic factors responsible for Fe toxicity tolerance is crucial for the development of tolerant rice varieties. This study aimed to unravel the genetic bases of Fe toxicity tolerance using quantitative trait locus (QTL) mapping. We conducted phenotypic evaluations for Fe toxicity tolerance on 54 chromosome segment substitution lines (CSSLs) obtained from a cross between the moderately susceptible IR64 (O. sativa) and the tolerant donor TOG5681 (O. glaberrima) under Fe toxicity stress. QTL analysis was performed using agro-morphological traits and microsatellite genotypic data. We observed high heritability estimates for key traits like leaf bronzing score (LBS) and grain yield. Several loci associated with agronomic traits, including plant height (qPH2.1), panicle number (qPN4.1), grain weight (qGW4.1), harvest index (qHI4.1), maturity (qMat6.1), and shoot weight (qSW6.1 and qSW11.1), were identified. From the field experiments, three CSSL lines (CCSLOG254, CCSLOG255, and CCSLOG256) that exhibit both high yield and tolerance to iron toxicity have been identified, positioning them as promising candidates for breeding programs. Additionally, a major QTL (qLBS11.1) linked to leaf bronzing (LBS) was identified, housing the OsbHLHq11 gene involved in iron homeostasis regulation. A comparative analysis revealed colocation with previously reported QTLs, validating their significance. OsbHLHq11 was found to be conserved across diverse rice germplasm, including lowland NERICA (NEw RICe for Africa) varieties, which were developed through crossing O. sativa and O. glaberrima. The discovery of qLBS11.1 and the candidate gene OsbHLHq11 offers insights into the genetic mechanisms governing Fe toxicity tolerance, highlighting potential targets for breeding tolerant rice varieties using marker-assisted selection or genetic engineering strategies.

Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.

Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic architecture of complex traits in other crops, its application in cotton has been limited. Here, we applied five machine learning models-AdaBoost, Gradient Boosting Regressor, LightGBM, Random Forest, and XGBoost-to identify loci associated with fiber quality and flowering days in cotton. We compared two SNP dataset down-sampling methods for model training and found that selecting SNPs with an Fscale value greater than 0 outperformed randomly selected SNPs in terms of model accuracy. We further performed machine learning quantitative trait loci (mlQTLs) analysis for 13 traits related to fiber quality and flowering days. These mlQTLs were then compared to those identified through genome-wide association studies (GWAS), revealing that the machine learning approach not only confirmed known loci but also identified novel QTLs. Additionally, we evaluated the effect of population size on model accuracy and found that larger population sizes resulted in better predictive performance. Finally, we proposed candidate genes for the identified mlQTLs, including two argonaute 5 proteins, Gh_A09G104100 and Gh_A09G104400, for the FL3/FS2 locus, as well as GhFLA17 and Syntaxin-121 (Gh_D09G143700) for the FSD09_2/FED09_2 locus. Our findings demonstrate the efficacy of machine learning in enhancing the identification of genetic loci in cotton, providing valuable insights for improving cotton breeding strategies.

Identification of potential druggable targets for endometriosis through Mendelian randomization analysis.

Endometriosis (EM) is a widely recognized disorder in gynecological endocrinology. Although hormonal therapies are frequently employed for EM, their side effects and outcome limitations underscore the need to explore the genetic basis and potential drug targets for developing innovative therapeutic approaches. This study aimed to identify both cerebrospinal fluid (CSF) and plasma protein markers as promising therapeutic targets for EM. We utilized Mendelian randomization (MR) analysis to explore potential disease-causing proteins, utilizing genetic datasets from genome-wide association studies (GWAS) and protein quantitative trait loci (pQTL) analyses. We applied a range of validation techniques, including reverse causality detection, phenotype scanning, Bayesian co-localization (BC) analysis, and external validations to substantiate our findings. Additionally, we conducted a protein-protein interaction (PPI) network as well as functional enrichment analyses to unveil potential associations among target proteins. MR analysis revealed that a decrease of one standard deviation (SD) in plasma R-Spondin 3 (RSPO3) level had a protective effect on EM (OR = 1.0029; 95% confidence interval (95% CI): 1.0015-1.0043; P = 3.2567e-05; Bonferroni P < 5.63 × 10-5). BC analysis showed that RSPO3 shared the same genetic variant with EM (coloc.abf-PPH4 = 0.874). External validation further supported this causal association. Galectin-3 (LGALS3; OR = 0.9906; 95% CI: 0.9835-0.9977; P = 0.0101), carboxypeptidase E (CPE; OR = 1.0147; 95% CI: 1.0009-1.0287; P = 0.0366), and alpha-(1,3)-fucosyltransferase 5 (FUT5; OR = 1.0053; 95% CI: 1.0013-1.0093; P = 0.002) were detected as potential targets for EM in CSF. PPI analysis showed that fibronectin (FN1) had the highest combined score. Furthermore, several EM-linked proteins were involved in the glycan degradation pathway. In conclusion, this comprehensive study offers valuable insights into potential drug targets for EM, with RSPO3 emerging as a promising candidate. Additionally, mechanistic roles of FN1, glycan degradation pathway, LGALS3, CPE, and FUT5 in EM warrant further investigation.

Improved Annotation of Asthma Gene Variants with Cell Type Deconvolution of Nasal and Lung Expression-Quantitative Trait Loci.

Asthma is a genetically complex inflammatory airway disease associated with over 200 Single nucleotide polymorphisms (SNPs). However, the functional effects of many asthma-associated SNPs in lung and airway epithelial samples are unknown. Here, we aimed to conduct expression quantitative trait loci (eQTL) analysis using a meta-analysis of nasal and lung samples. We hypothesize that incorporating cell-type proportions of airway and lung samples enhances eQTL analysis outcomes. Nasal brush (n=792) and lung tissue (n=1087) samples were investigated separately. Initially, a general eQTL analysis identified genetic variants associated with gene expression levels. Estimated cell-type proportions were adjusted based on the Human Lung Cell Atlas. Additionally, the presence of significant interaction effects between asthma-associated SNPs and each cell type proportion was explored and considered evidence for cell-type associated eQTL. In nasal brush and lung parenchyma samples, 44 and 116 asthma-associated SNPs were identified as eQTLs. Adjusting for cell-type proportions revealed eQTLs for an additional 17 genes (e.g., FCER1G, CD200R1, and GABBR2) and 16 Genes (e.g., CYP2C8, SLC9A2, and SGCD) in nose and lung, respectively. Moreover, we identified eQTLs for 9 SNPs annotated to genes such as VASP, FOXA3, PCDHB12 displayed significant interactions with cell type proportions of Club, Goblet, and alveolar macrophages. Our findings demonstrate increased power for identifying eQTLs among asthma-associated SNPs by considering cell-type proportion of the bulk-RNA-seq data from nasal and lung tissues. Integration of cell-type deconvolution and eQTL analysis enhances our understanding of asthma genetics and cellular mechanisms, uncovering potential therapeutic targets for personalized interventions.

Novel synaptic markers predict early tau pathology and cognitive deficit in an asymptomatic population at risk of Alzheimer's disease.

Cognitive dysfunction in Alzheimer's disease (AD) correlates closely with pathology in the neuronal microtubule-associated protein tau. Tau pathology may spread via neural synapses. In a population of cognitively unimpaired elderly at elevated risk of AD, we investigated four cerebrospinal (CSF) markers of synaptic dysfunction and degeneration. Three of these (SYT1, SNAP25, and ADAM23) are derived from pre-synaptic structures, while ADAM22 reflects post-synaptic changes. All four markers correlated strongly with tau protein measures. In statistical models, SYT1 accounted for more than half the total variance in both total- and P(181)-tau levels. Observed correlations with CSF levels of Alzheimer amyloid-β (Aβ42) were somewhat weaker. In longitudinal data, baseline levels of ADAM22 and ADAM23 robustly predicted increase over time in both total- and P-tau. CSF SYT1 levels also correlated with PET image uptake of tau and (at a trend level) Aβ in areas of interest for early AD pathology. CSF SYT1 and SNAP25 levels correlated inversely with a global psychometric score and several of its domain subscales. In quantitative trait loci analyses, all four synaptic markers were associated with at least one AD genetic risk locus. Upon "staging" participants by their evidence of amyloid and tau pathology (A/T/N framework), the CSF synaptic markers were unexpectedly reduced in participants with CSF evidence of amyloid but not tau pathology. They were clearly elevated, however, in the CSF of persons with indications of both tau and amyloid pathology. These observations provide evidence for clear pre-synaptic degeneration in cognitively unimpaired persons with biomarker evidence of early AD pathology.

A novel susceptibility locus to Erysiphe necator (SEN2) identified by genetic mapping of automated microscopy computer vision data in grapevines.

Powdery mildew, caused by the fungus Erysiphe necator, is one of the primary causes of grape yield loss across the globe. While numerous resistance loci have been identified in various grapevine species, the genetic determinants of susceptibility to E. necator remain largely unexplored. Understanding the genetics of susceptibility for pathogenesis is equally important for developing durable resistance grapevines against this pathogen. To identify these factors in Vitis interspecific hybrid 'Chambourcin', a controlled leaf disc assay was conducted for two years using an automated microscopy phenotyping system with 273 F1 genotypes from a cross of 'Chambourcin' and V. vinifera 'Cabernet Sauvignon'. Additionally, a high-resolution linkage map using the same number of 'Chambourcin'-derived hybrids was constructed with 355 simple sequence repeats (SSR) and 1,394 RNaseH2-dependent amplicon sequencing (rhAmpSeq)-derived haplotype markers that clustered into 19 linkage groups. A quantitative trait locus (QTL) analysis identified a susceptibility locus (here named Sen2) on linkage group (LG) 7 explaining 8.90 % - 12.57 % of the total phenotypic variance. The markers associated with this susceptibility locus were used to identify 78 accessions in the USDA-ARS cold hardy Vitis collection at Geneva, NY that carry Sen2 and can be used to selectively exclude susceptible progenies. Additionally, 6 accessions carry the alternative haplotype encoding recessive resistance and can be used for resistance breeding. The identification of powdery mildew susceptibility loci is crucial for identifying genes that could be targeted for gene knock-out, gene editing, RNA interference (RNAi), or selection among breeding genotypes to enhance sustainable protection against pathogens.

Genetic variation in patent foramen ovale: a case-control genome-wide association study.

Patent foramen ovale (PFO) is a congenital defect between the atria, resulting in abnormal hemodynamics. We conducted a genome-wide association study (GWAS) to identify common genetic variants associated with PFO. We performed a whole genome sequencing in a discovery cohort of 3,227 unrelated Chinese participants screened for PFO via contrast transthoracic echocardiography (cTTE). Single-nucleotide polymorphisms (SNPs) associated with PFO were further validated by Sanger sequencing and subsequently were evaluated in a validation cohort. Expression quantitative trait loci (eQTL) analysis was conducted using the GTEx database. Single-cell sequencing analyses with pseudotime trajectory modeling were employed to evaluate their expression in human fetal hearts. The case-control GWAS of discovery cohort ultimately included 517 cases and 517 demographically matched controls. Of the 7,040,407 variants assessed, we identified rs1227675732 (OR = 2.903; 95% CI, 1.961 to 4.297; p = 3.05 × 10-8), rs62206790 (OR = 2.780; 95% CI, 1.864 to 4.146; p = 2.02 × 10-7), rs879176184 (OR = 2.724; 95% CI, 1.822 to 4.073; p = 4.30 × 10-7) and rs13115019 (OR = 2.437; 95% CI, 1.702 to 3.488; p = 5.80 × 10-7) as high-risk variants for PFO, while rs57922961 (OR = 0.5081; 95% CI, 0.388 to 0.666; p = 6.82 × 10-7) was identified as protective variant. These variations were replicated in the validation cohort (111 cases and 152 controls). Single-cell sequencing showed that CNOT2, KCNMB4, MLLT10, IGBP1, and FRG1 were highly expressed with significant changes during heart development. The identification of susceptible loci for PFO might provide insights into the pathogenesis of PFO and contribute to understanding heart development. https://www.chictr.org.cn/showproj.html?proj=40590, identifier ChiCTR1900024623.

Association of antihypertensive drug target genes with alzheimer’s disease: a mendelian randomization study

BackgroundEpidemiological and genetic studies have elucidated associations between antihypertensive medication and Alzheimer’s disease (AD), with the directionality of these associations varying upon the specific class of antihypertensive agents.MethodsGenetic instruments for the expression of antihypertensive drug target genes were identified using expression quantitative trait loci (eQTL) in blood, which are associated with systolic blood pressure (SBP). Exposure was derived from existing eQTL data in blood from the eQTLGen consortium and in the brain from the PsychENCODE and subsequently replicated in GTEx V8 and BrainMeta V2. We performed two-sample Mendelian randomization (MR) to estimate the potential effect of different antihypertensive drug classes on AD using summary statistics from a meta-analysis (111,326 cases and 677,663 controls) and further replicated in FinnGen cohorts (9301 cases and 367,976 controls). The reverse causality detection, assessing horizontal pleiotropy, Bayesian co-localization, phenotype scanning, and protein quantitative trait loci (pQTL) analysis were implemented to consolidate the MR findings further.ResultsA 1-standard deviation (SD) lower expression of the angiotensin-converting enzyme (ACE) gene in blood was associated with a lower SBP of 3.92 (95% confidence interval (CI), 2.69–5.15) mmHg but an increased risk of AD (odds ratio (OR), 2.46; 95% CI, 1.82–3.33). A similar direction of association was also observed between ACE expression in prefrontal cortex (OR, 1.19; 95% CI, 1.10–1.28), frontal cortex (OR, 1.19; 95% CI, 1.11–1.27), cerebellum (OR, 1.13; 95% CI, 1.09–1.17), cortex (OR, 1.59; 95% CI, 1.33–1.28) and ACE protein levels in plasma (OR, 1.13; 95% CI, 1.09–1.17) and AD risk. Colocalization supports these results. Similar results were found in external validation. We found no evidence for an association between genetically estimated blood pressure (BP) and AD risk.ConclusionsThere findings suggest an adverse association of lower ACE messenger RNA and protein levels with an elevated risk of AD, irrespective of its BP-lowering effects. These findings warrant greater pharmacovigilance and further investigation into the effect of ACE inhibitors, particularly those that are centrally acting, on neurodegenerative symptoms in patients with AD.

Quantitative Trait Loci for Phenology, Yield, and Phosphorus Use Efficiency in Cowpea.

Cowpea is an important legume crop in sub-Saharan Africa (SSA) and beyond. However, access to phosphorus (P), a critical element for plant growth and development, is a significant constraint in SSA. Thus, it is essential to have high P-use efficiency varieties to achieve increased yields in environments where little-to- no phosphate fertilizers are applied. In this study, crop phenology, yield, and grain P efficiency traits were assessed in two recombinant inbred line (RIL) populations across ten environments under high- and low-P soil conditions to identify traits' response to different soil P levels and associated quantitative trait loci (QTLs). Single-environment (SEA) and multi-environment (MEA) QTL analyses were conducted for days to flowering (DTF), days to maturity (DTM), biomass yield (BYLD), grain yield (GYLD), grain P-use efficiency (gPUE) and grain P-uptake efficiency (gPUpE). Phenotypic data indicated significant variation among the RILs, and inadequate soil P had a negative impact on flowering, maturity, and yield traits. A total of 40 QTLs were identified by SEA, with most explaining greater than 10% of the phenotypic variance, indicating that many major-effect QTLs contributed to the genetic component of these traits. Similarly, MEA identified 23 QTLs associated with DTF, DTM, GYLD, and gPUpE under high- and low-P environments. Thirty percent (12/40) of the QTLs identified by SEA were also found by MEA, and some of those were identified in more than one P environment, highlighting their potential in breeding programs targeting PUE. QTLs on chromosomes Vu03 and Vu08 exhibited consistent effects under both high- and low-P conditions. In addition, candidate genes underlying the QTL regions were identified. This study lays the foundation for molecular breeding for PUE and contributes to understanding the genetic basis of cowpea response in different soil P conditions. Some of the identified genomic loci, many being novel QTLs, could be deployed in marker-aided selection and fine mapping of candidate genes.

Analysis of quantitative trait loci and candidate gene exploration associated with cold tolerance in rice (Oryza sativa L.) during the seedling stage.

Cold stress during the seedling stage significantly threatens rice (Oryza sativa L.) production, specifically in temperate climates. This study aimed to identify quantitative trait loci (QTLs) associated with cold tolerance at the seedling stage. QTL analysis was conducted on a doubled haploid (DH) population derived from a cross between the cold-sensitive indica cultivar 93-11 and the cold-tolerant japonica cultivar Milyang352. Phenotypic analysis was conducted over 2 years (2022-2023) under cold water treatment (13°C) at the Chuncheon Substation, South Korea. Cold tolerance scores were used to classify the DH populations and parental lines. In 2022, three QTLs were identified on chromosomes 3, 10, and 11; in 2023, a single QTL was identified on chromosome 10. The QTL qCTS1022/23 on chromosome 10 was consistently observed across both years, explaining up to 16.06% and 40.55% of the phenotypic variance, respectively. Fine-mapping of qCTS1022/23 narrowed the candidate region to a 300-kb interval containing 44 polymorphic single-nucleotide polymorphisms. Among the candidate genes, Os10g0409400 was significantly expressed in the cold-tolerant japonica parent Milyang352 under cold stress, indicating its role in conferring cold tolerance. These findings offer valuable insights into the genetic mechanisms of cold tolerance and highlight qCTS1022/23 as a potential target for marker-assisted selection in rice breeding programs to enhance cold tolerance.

Myeloid dendritic cells and periodontal disease association: integrated study of single-cell sequencing and Mendelian randomization analysis.

Periodontal disease is a widespread inflammatory condition that compromises the supporting structures of the teeth, potentially resulting in tooth loss if left untreated. Despite advancements in therapeutic interventions and an enhanced understanding of its pathophysiology, emerging techniques such as single-cell RNA sequencing (scRNA-seq) and Mendelian randomization (MR) present new opportunities for precision medicine in the management of periodontal disease. Data derived from the GSE152042 dataset underwent rigorous quality control, normalization, and dimensionality reduction using Seurat and the MonacoImmuneData framework. Marker genes were identified to delineate subgroups for subsequent analysis utilizing CellChat and ClusterProfilerR. MR analysis of the expression quantitative trait loci (eQTLs) for these genes was conducted to determine causal relationships with periodontal disease, leveraging data from the IEU Open GWAS project. Single-cell analysis revealed distinct immune cell subtypes and indicated an increased presence of myeloid dendritic cells (mDCs) in patients with periodontal disease. MR analysis identified twenty-six significant genes, with LIMA1 (LIM domain and actin-binding 1) demonstrating a robust causal association with the progression of periodontal disease. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses highlighted crucial pathways involved in periodontal inflammation and tissue destruction. Visualization at the single-cell level elucidated the role of LIMA1 in disease progression, alongside differences in cell communication dynamics between LIMA1-positive and -negative populations. This study underscores the utility of scRNA-seq and MR in elucidating essential factors in the pathogenesis of periodontal disease, thereby reinforcing the necessity for targeted therapeutic strategies. The identification of LIMA1 as a pivotal gene in periodontal disease progression opens new avenues for precision medicine approaches, potentially enhancing treatment efficacy and patient outcomes in periodontal management.

Association between METTL14 gene polymorphisms and risk of ovarian endometriosis.

Endometriosis, a prevalent chronic gynecological condition, is frequently associated with infertility and pelvic pain. Despite numerous studies indicating a correlation between epigenetic regulation and endometriosis, its precise genetic etiology remains elusive. Methyltransferase-like 14 (METTL14), a crucial component of the N6-methyladenosine (m6A) RNA methyltransferase complex and an RNA binding scaffold, is known to play a pivotal role in various human diseases. The possibility that single nucleotide polymorphisms (SNPs) in the METTL14 gene contribute to susceptibility of endometriosis has not been thoroughly investigated. We assessed the genotype frequencies of five potential functional METTL14 SNPs (rs298982G>A, rs62328061A>G, rs9884978G>A, rs4834698C>T, and rs1064034A>T) in a Chinese population consisting of 458 patients with ovarian endometriosis and 462 healthy controls. We employed unconditional logistic regression and stratified analyses to evaluate their genotypic associations with the risk of ovarian endometriosis. Among the five SNPs examined, we found that the rs298982 A allele was significantly associated with increased risk, whereas the rs62328061G allele was linked to a decreased risk of ovarian endometriosis. Individuals harboring two unfavorable genotypes demonstrated a significantly elevated risk of ovarian endometriosis (adjusted odds ratio (AOR) = 1.57, 95% confidence interval (CI) = 1.16-2.13, P = 0.004) compared with those with no risk genotypes. Stratified analysis revealed the risk effect of rs298982 GA/AA genotypes in the gravidity≤1, parity≤1, rASRM stage I, and rASRM stage II + III + IVsubgroups. Haplotype analysis showed that individuals with the GATAA haplotype were at higher risk of ovarian endometriosis (AOR = 5.54, 95% CI = 1.63-18.87, P = 0.006), whereas the AGTTG haplotype exhibited protective effects (AOR = 0.55, 95% CI = 0.31-0.97, P = 0.039) compared with wild-type GACAG haplotype carriers. Additionally, Bayesian false discovery probability and false positive report probability analysis confirmed the robustness of the significant findings. Expression quantitative trait loci analysis revealed a significant association between the rs9884978 GA/AA genotypes and elevated METTL14 mRNA levels in fibroblasts and adrenal gland. Conversely, the rs298982GA/GG genotypes were significantly associated with reduced METTL14 mRNA levels in the nucleus accumbens and frontal cortex. Our results demonstrate that METTL14 polymorphisms are associated with susceptibility to ovarian endometriosis among Chinese women.

QTL-Based Evidence of Population Genetic Divergence in Male Territorial Aggressiveness of the Japanese Freshwater Threespine Stickleback.

Territorial aggression is widespread across the animal kingdom and is expressed in diverse ecological and social contexts. In addition, there are marked variations in the degree of male reproductive territoriality within and between species. These differences are often attributed to genetic components. However, the evolutionary genetic mechanisms in wild animals are poorly understood. This study explored the genetic basis of divergent male territorial aggressiveness between two Japanese freshwater populations, Gifu (GF) and Tomakomai (TM), in the threespine stickleback, which is a well-known model system for both behavioral ecology and evolutionary genetics. First, our field survey indicated that the distribution of reproductive territories differed greatly across breeding habitats between the focal populations, and the density of reproductive territories was much greater in the GF population. Second, a one-on-one arena aquarium experiment on male-male combat using wild-caught and common-garden-reared males revealed that GF males were genetically more aggressive than TM males. Finally, we performed quantitative trait loci (QTL) analysis using an F2 hybrid cross between the two populations to identify the causal genomic regions contributing to the divergence in male territorial aggressiveness. Our QTL analysis identified a single significant locus in an aggression-related behavioral component, that is, the number of bites of focal F2 males toward a GF stimulus intruder. Two notable behavior-related genes, HTR2A and MAO-A, are found near this locus. These genes have often been suggested to influence of aggressive behavior in animals; therefore, they are regarded as important candidate genes for further functional analyses. Thus, we are the first to provide a QTL-based genetic basis for population divergence in male territorial aggressiveness in the threespine stickleback.

Integrated Analysis of Bulk RNA Sequencing, eQTL, GWAS, and Single-Cell RNA Sequencing Reveals Key Genes in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) poses a continual therapeutic challenge owing to its elevated incidence and unfavourable prognosis, underscoring the critical need for the discovery of new molecular targets for detection and therapy. This work included the analysis of three publically accessible HCC datasets from TCGA and GEO. Instrumental variables (IVs) were derived via expression quantitative trait loci (eQTL) analysis, then followed by two-sample Mendelian randomisation (MR) analysis utilising publically available summary statistics. Key disease-associated genes were identified by assessing odds ratios and connecting them with differentially expressed genes across the datasets. The potential molecular mechanisms of these genes were clarified by functional enrichment analysis, clinical data analysis, single-cell RNA sequencing, GSEA, immune cell infiltration, and immune checkpoint analysis. These findings were then confirmed by Western blotting, immunohistochemistry, and quantitative PCR. By synthesising the outcomes from differential analysis of the databases, we found two genes, SERPING1 and STEAP3, that may be crucial in the beginning and development of HCC. These genes have a role in vital biological pathways and functions, such as metabolic regulation and macrophage activation. The significance of immunological-mediated processes in HCC was further highlighted by CIBERSORT analysis, which revealed a specific pattern of immune cell infiltration and the location of immunological checkpoints in the illness. The findings elucidate the molecular mechanisms of HCC and underscore critical genes implicated in its pathogenesis. SERPING1 and STEAP3 affect tumour cells and modify the tumour microenvironment (TME), indicating that targeting these genes may offer a viable immunotherapeutic approach for HCC in clinical settings.

Integrated analyses of Mendelian randomization, eQTL, and single-cell transcriptome identify CCN3 as a potential biomarker in aortic dissection

Plasma secretory proteins are associated with various diseases, including aortic dissection (AD). However, current research on the correlation between AD and plasma protein levels is scarce or lacks specificity. This study aimed to explore plasma secretory proteins as potential biomarkers for AD. Through genome-wide association studies, expression quantitative trait locus (eQTL) analysis, and human plasma protein profiling, we identified DBNL, NPC2, SUMF2, and TFPI as high-risk genes and CCN3, PRKCSH, TEX264, and TGFBR3 as low-risk genes for AD. Further cell localization and differential expression analysis of these eight genes were conducted using single-cell data. We also examined their expression in three Gene Expression Omnibus datasets, measured their mRNA levels in AD versus normal tissues using qPCR, and assessed their protein levels in patients’ blood versus healthy individuals using enzyme-linked immunosorbent assay. Our findings suggest that CCN3, consistently downregulated in both mRNA and plasma levels during AD, may have a protective role. Initial enrichment analyses of differentially expressed CCN3 cells suggested their involvement in focal adhesion, actin cytoskeleton regulation, and the PI3K-Akt signaling pathway.