Quantitative Trait Loci Research Articles

Multi-omics analysis of the molecular response to glucocorticoids - insights into shared genetic risk from psychiatric to medical disorders

BackgroundAlterations in the effects of glucocorticoids have been implicated in mediating some of the negative health effects associated with chronic stress, including increased risk for psychiatric disorders as well as cardiovascular and metabolic diseases. This study investigates how genetic variants influence gene expression and DNA methylation (DNAm) in response to glucocorticoid receptor (GR)-activation, and their association with disease risk. MethodsWe measured DNAm (n=199) and gene expression (n=297) in peripheral blood before and after GR-activation with dexamethasone, with matched genotype data available for all samples. A comprehensive molecular quantitative trait locus (QTL) analysis was conducted, mapping GR-response methylation (me)QTLs, GR-response expression (e)QTLs, and GR-response expression quantitative trait methylation (eQTM). A multi-level network analysis was employed to map the complex relationships between the transcriptome, epigenome, and genetic variation. ResultsWe identified 3,772 GR-response meCpGs corresponding to 104,828 local GR-response meQTLs that did not strongly overlap with baseline meQTLs. eQTM and eQTL analyses revealed distinct genetic influences on gene expression and DNAm. Multi-level network analysis uncovered GR-response network trio QTLs, characterized by SNP-CpG-transcript combinations where meQTLs act as both eQTLs and eQTMs. GR-response trio variants were enriched in GWAS for psychiatric, respiratory, autoimmune and cardiovascular diseases and conferred a higher relative heritability per SNP than GR-response meQTL and baseline QTL SNP. ConclusionsGenetic variants modulating the molecular effects of glucocorticoids are associated with psychiatric as well as medical diseases and not uncovered in baseline QTL analyses.

DMRdb: a disease-centric Mendelian randomization database for systematically assessing causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases.

Exploring the causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases is fundamental to the life sciences. However, large-scale research using Mendelian randomization (MR) analysis is currently lacking. To address this, we introduce DMRdb (http://www.inbirg.com/DMRdb/), a disease-centric Mendelian randomization database, designed to systematically assess causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases. The database consists of three main components: (i) 6640 high-quality disease genome-wide association studies (GWASs) from public sources that were subjected to rigorous quality filtering and standardization; (ii) over 497 billion results from MR analyses involving 6640 disease GWAS datasets, 16 238 expression quantitative trait loci (eQTLs) data, 2564 protein quantitative trait loci (pQTLs) data, 12 000 methylation quantitative trait locus (meQTLs) data and 825 metabolites data and (iii) over 380 000 causal relationship pairs from 1223 literature sources relevant to MR analyses. A user-friendly online database was developed to allow users to query, search, and download all the results. In summary, we anticipate that DMRdb will be a valuable resource for advancing our understanding of disease mechanisms and identifying new biomarkers and therapeutic targets.

Fine mapping of candidate effector genes for heart rate.

An elevated resting heart rate (RHR) is associated with increased cardiovascular mortality. Genome-wide association studies (GWAS) have identified > 350 loci. Uniquely, in this study we applied genetic fine-mapping leveraging tissue specific chromatin segmentation and colocalization analyses to identify causal variants and candidate effector genes for RHR. We used RHR GWAS summary statistics from 388,237 individuals of European ancestry from UK Biobank and performed fine mapping using publicly available genomic annotation datasets. High-confidence causal variants (accounting for > 75% posterior probability) were identified, and we collated candidate effector genes using a multi-omics approach that combined evidence from colocalisation with molecular quantitative trait loci (QTLs), and long-range chromatin interaction analyses. Finally, we performed druggability analyses to investigate drug repurposing opportunities. The fine mapping pipeline indicated 442 distinct RHR signals. For 90 signals, a single variant was identified as a high-confidence causal variant, of which 22 were annotated as missense. In trait-relevant tissues, 39 signals colocalised with cis-expression QTLs (eQTLs), 3 with cis-protein QTLs (pQTLs), and 75 had promoter interactions via Hi-C. In total, 262 candidate genes were highlighted (79% had promoter interactions, 15% had a colocalised eQTL, 8% had a missense variant and 1% had a colocalised pQTL), and, for the first time, enrichment in nervous system pathways. Druggability analyses highlighted ACHE, CALCRL, MYT1 and TDP1 as potential targets. Our genetic fine-mapping pipeline prioritised 262 candidate genes for RHR that warrant further investigation in functional studies, and we provide potential therapeutic targets to reduce RHR and cardiovascular mortality.

Transcriptome-based prediction for polygenic traits in rice using different gene subsets

BackgroundTranscriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation. The selection of large-effect genes based on a priori biological knowledge is beneficial for predicting oligogenic traits; however, such a simple gene selection method is not applicable to polygenic traits because causal genes or large-effect loci are often unknown. Here, we used several gene-level features and tested whether it was possible to select a gene subset that resulted in better predictive ability than using all genes for predicting a polygenic trait.ResultsUsing the phenotypic values of shoot and root traits and transcript abundances in leaves and roots of 57 rice accessions, we evaluated the predictive abilities of the transcriptome-based prediction models. Leaf transcripts predicted shoot phenotypes, such as plant height, more accurately than root transcripts, whereas root transcripts predicted root phenotypes, such as crown root length, more accurately than leaf transcripts. Furthermore, we used the following three features to train the prediction model: (1) tissue specificity of the transcripts, (2) ontology annotations, and (3) co-expression modules for selecting gene subsets. Although models trained by a gene subset often resulted in lower predictive abilities than the model trained by all genes, some gene subsets showed improved predictive ability. For example, using genes expressed in roots but not in leaves, the predictive ability for crown root diameter was improved by more than 10% (R2 = 0.59 when using all genes; R2 = 0.66, using 1,554 root-specifically expressed genes). Similarly, genes annotated as “gibberellic acid sensitivity” showed higher predictive ability than using all genes for root dry weight.ConclusionsOur results highlight both the possibility and difficulty of selecting an appropriate gene subset to predict polygenic traits from transcript abundance, given the current biological knowledge and information. Further integration of multiple sources of information, as well as improvements in gene characterization, may enable the selection of an optimal gene set for the prediction of polygenic phenotypes.

SSR markers based QTL mapping and genetic analysis for yield and yield-attributing traits in bitter gourd (Momordica charantia L.)

The present study was conducted for QTL mapping for yield and other yield attributing traits in bitter gourd using Simple Sequence Repeat (SSR) markers. A total of 630 SSR markers were screened for polymorphism in two contrasting parents (DBGS-2 and Pusa Purvi), out of which only 35 were polymorphic. F1 plants (10 individuals) in which hybridity was ascertained (using these polymorphic markers); were further employed for development of mapping population (F2) consisting of 120 plants. Higher variation was present in the mapping population as evident from the wider range value of the characters. Continuous frequency distribution classes combined with bell-shaped, symmetrical normal distribution curve, revealed the quantitative inheritance nature of the traits studied. Higher PCV than the GCV for all the traits, along with high difference between the PCV and GCV for majority of traits indicated higher influence of environment in expression of these traits in the mapping population. Presence of transgressive segregation was also noted for majority of the traits. Amongst the various linkage groups (LG), LG 4 had the maximum number of markers, covering 171.07 cM map distance. LG4 also possessed the maximum number (nine) of QTLs while LG1 had six. QTL mapping using polymorphic SSRs resulted in detection of a total of 28 QTLs for fourteen traits viz. yield per plant (kg), earliness or flower related traits (node to first female flower, node to first male flower, days to first male flower, male to female flower ratio), fruit traits (fruit number per plant, fruit diameter (cm), fruit length/ diameter ratio, pericarp thickness (mm) and number of seed per fruit) and vegetative traits (internodal length (cm), number of primary branches, leaf width (cm), length and width ratio). The LOD score of these QTLs ranged from 3.01 to 64.47, the total phenotypic variances (PVE) ranged from 1.52 to 34.57 % and additive effects ranged from –3.69 to 17.07. Of the total, nineteen were major QTLs, having PVE >10 %. Three QTLs were detected for yield per plant while a total of seven for the traits imparting earliness viz. days to first male flower, node to first female flower and node to first male flower. Amongst all the QTLs detected, qFrtLDR-4-1 (K) had the maximum LOD (64.47) and PVE (34.57 %) value. Two hotspots were detected with multiple QTLs clustered in the LG 1. The first hotspot possessed four QTLs [qLfW-1-1, qMFR-1-1 (K), qSPF-1-1, qNFmlF-1-1 (K)] while the second had two related to yield per plant [qYldpl-1-1 (K) and qYldpl-1-1]. Many of these QTLs are also being reported for the first time in bitter gourd. The findings of the present study can be used to fasten the bitter gourd improvement by utilizing these in MAS, DNA fingerprinting, genetic mapping, genomics analysis etc.

Method of moments framework for differential expression analysis of single-cell RNA sequencing data

Differential expression analysis of single-cell RNA sequencing (scRNA-seq) data is central for characterizing how experimental factors affect the distribution of gene expression. However, distinguishing between biological and technical sources of cell-cell variability and assessing the statistical significance of quantitative comparisons between cell groups remain challenging. We introduce Memento, a tool for robust and efficient differential analysis of mean expression, variability, and gene correlation from scRNA-seq data, scalable to millions of cells and thousands of samples. We applied Memento to 70,000 tracheal epithelial cells to identify interferon-responsive genes, 160,000 CRISPR-Cas9 perturbed Tcells to reconstruct gene-regulatory networks, 1.2 million peripheral blood mononuclear cells (PBMCs) to map cell-type-specific quantitative trait loci (QTLs), and the 50-million-cell CELLxGENE Discover corpus to compare arbitrary cell groups. In all cases, Memento identified more significant and reproducible differences in mean expression compared with existing methods. It also identified differences in variability and gene correlation that suggest distinct transcriptional regulation mechanisms imparted by perturbations.

The critical involvement of monocytes/macrophages in the pathogenesis of primary Sjögren's syndrome: New evidence from Mendelian randomization and single-cell sequencing

BackgroundPrimary Sjögren's syndrome (pSS) stands as a chronic autoimmune disease characterized by an elusive pathogenesis. The synergy of single-cell RNA sequencing and Mendelian randomization (MR) analysis provides an opportunity to comprehensively unravel the contributory role of monocytes/macrophages in the intricate pathogenesis of pSS. MethodsDifferentially expressed genes (DEGs) of various types of immune cells were analyzed after annotating single-cell RNA sequencing (scRNA-seq) data. MR analysis of expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) was conducted to search for key pathogenic genes and proteins. Cellular localization of pathogenic genes was performed based on scRNA-seq data. Variations in signaling pathways between immune cells were further analyzed. ResultsA total of 1434 significant DEGs were identified. Among these, 60 genes exhibited strong relevance to the occurrence of pSS, of which 32 genes differentially expressed in monocytes/macrophages. CTSS was found to be a significant risk protein with a p-value of 0.001 and an odds ratio of 1.384 (1.147–1.669), showing pronounced expression in monocytes/macrophages. Furthermore, monocytes/macrophages displayed heightened expression levels of MXD1, AMPD2, TNFSF10, FTL, UBXN11, CSF3R, and LILRA5. The analysis of intercellular signaling revealed increased signal intensity in both incoming and outgoing signals in monocytes/macrophages. The signaling interactions between monocytes/macrophages, B cells, and T cells exhibited varying degrees of deviation. ConclusionsThis study highlights the significant involvement of monocytes/macrophages in the pathogenesis of pSS, as evidenced by MR analysis and scRNA-seq analysis. This suggests monocytes/macrophages as a focal point for pathogenesis research and potential therapeutic targeting in pSS.

Exploration of protein and genetic targets causing atrioventricular block: mendelian-randomization analyses based on eQTL data and pQTL data

BackgroundAtrioventricular block (AVB) is a heterogeneous group of arrhythmias. AVB can lead to sudden arrest of the heart and subsequent syncope or sudden cardiac death. Few scholars have investigated the underlying molecular mechanisms of AVB. Finding molecular markers can facilitate understanding of AVB and exploration of therapeutic targets.MethodsTwo-sample Mendelian randomization (MR) analysis was undertaken with inverse variance weighted (IVW) model and Wald ratio as the primary approach. Reverse MR analysis was undertaken to identify the associated protein targets and gene targets. Expression quantitative trait loci (eQTL) data from the eQTLGen database and protein quantitative trait loci (pQTL) data from three previous large-scale proteomic studies on plasma were retrieved as exposure data. Genome-wide association study (GWAS) summary data (586 cases and 379,215 controls) for AVB were retrieved from the UK Biobank database. Colocalization analyses were undertaken to identify the effect of filtered markers on outcome data. Databases (DrugBank, Therapeutic Target, PubChem) were used to identify drugs that interacted with targets.ResultsWe discovered that 692 genes and 42 proteins showed a significant correlation with the AVB phenotype. Proteins (cadherin-5, sTie-1, Notch 1) and genes (DNAJC30, ABO) were putative molecules to AVB. Drug–interaction analyses revealed anticancer drugs such as tyrosine-kinase inhibitors and TIMD3 inhibitors could cause AVB. Other substances (e.g. toxins, neurological drugs) could also cause AVB.ConclusionsWe identified the proteins (cadherin-5, sTie-1, Notch 1) and gene (DNAJC30, ABO) targets associated with AVB pathogenesis. Anticancer drugs (tyrosine-kinase inhibitors, TIMD3 inhibitors), toxins, or neurological drugs could also cause AVB.

Compiling molecular evidence from a tetraploid rose genome into a near-saturated map for the identification of pigment-related genes

With their high economic value and cultural significance, modern roses are one of the most important ornamental plants. Because of their complicated genetic background and tetraploid nature, the creation of high-density genetic maps of roses has been a challenge that has slowed the pace of molecular breeding for modern roses. The current construction of tetraploid genetic maps based on existing diploid rose genomes could lead to inaccurate marker information and genotyping results. Therefore, we generated the first high-quality tetraploid genome of Rosa chinensis ‘Yunzheng Xiawei.’ Utilizing Illumina, PacBio, and Hi-C sequencing technologies, we assembled a genome of 858.59 Mb with 14 pseudo-chromosomes. Mode of inheritance analysis using PolyOrigin indicated that modern roses show both quadrivalent and bivalent pairing. Based on this reference genome, high-density genetic maps were constructed using MSTmap with nearly saturated markers. Quantitative trait locus (QTL) analysis was conducted using WinQTLCart and R/qtl for flavonoids and carotenoids, and 11 QTL clusters were identified. By combining the genome annotation, phylogenetic analyses, and gene expression analyses, we were able to identify several key genes related to flavonoid and carotenoid biosynthesis. This study provides the basis for further genetic analyses of highly heterozygous tetraploid roses and could facilitate the progress of marker-assisted selection in modern roses.

Formalizing the law of diminishing returns in metabolic networks using an electrical analogy.

The way biological systems respond to changes in parameter values caused by mutations is a key issue in evolution and quantitative genetics, as it affects fundamental aspects such as adaptation, selective neutrality, robustness, optimality, evolutionary equilibria, etc. We address this question using the enzyme-flux relationship in a metabolic network as a model of the genotype-phenotype relationship. The lack of a suitable mathematical tool from biochemical theory to investigate this relationship led us to use an analogy between electrical circuits and metabolic networks with uni-uni reactions. We show that a behaviour of diminishing returns, which is commonly observed at various phenotypic levels, is inevitable, irrespective of the complexity of the system. We also present a possible generalization to metabolic networks with both uni-uni and bi-bi reactions.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study.

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms(SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

A Blueberry (Vaccinium L.) Crop Ontology to Enable Standardized Phenotyping for Blueberry Breeding and Research

Breeding programs around the world continually collect data on large numbers of individuals. To be able to combine data collected across regions, years, and experiments, research communities develop standard operating procedures for data collection and measurement. One such method is a crop ontology, or a standardized vocabulary for collecting data on commonly measured traits. The ontology is also computer readable to facilitate the use of data management systems such as databases. Blueberry breeders and researchers across the United States have come together to develop the first standardized crop ontology in blueberry (Vaccinium spp.). We provide an overview and report on the construction of the first blueberry crop ontology and the 178 traits and methods included within. Researchers of Vaccinium species—such as other blueberry species, cranberry, lingonberry, and bilberry—can use the described crop ontology to collect phenotypic data of greater quality and consistency, interoperability, and computer readability. Crop ontologies, as a shared data language, benefit the entire worldwide research community by enabling collaborative meta-analyses that can be used with genomic data for quantitative trait loci, genome-wide association studies, and genomic selection analysis.

Plasma Proteomic Insights for Identification of Novel Predictors and Potential Drug Targets in Atrial Fibrillation: A Prospective Cohort Study and Mendelian Randomization Analysis.

Currently, there are no reliable methods for predicting and preventing atrial fibrillation (AF) in its early stages. This study aimed to identify plasma proteins associated with AF to discover biomarkers and potential drug targets. The UK Biobank Pharma Proteomics Project examined 2923 circulating proteins using the Olink platform, forming the basis of this prospective cohort study. The UK Biobank Pharma Proteomics Project included a randomly selected discovery cohort and the consortium-selected replication cohort. The study's end point was incident AF, identified using International Classification of Diseases, Tenth Revision codes. The association between plasma proteins and incident AF was evaluated using Cox proportional hazard models in both cohorts. Proteins present in both cohorts underwent Mendelian randomization analysis to delineate causal connections, utilizing cis-protein quantitative trait loci as genetic tools. The predictive efficacy of the identified proteins for AF was assessed using the area under the receiver operating characteristic curve, and their druggability was explored. Data from 53 032 participants were included in this study. Incident AF cases were identified in the discovery cohort (1894; 5.5%) within a median follow-up of 14.5 years and in the replication cohort (451; 10.6%) within a median follow-up of 14.4 years. Twenty-one proteins linked to AF were identified in both cohorts. Specifically, COL4A1 (collagen IV α-1; odds ratio, 1.11 [95% CI, 1.04-1.19]; false discovery rate, 0.016) and RET (proto-oncogene tyrosine-protein kinase receptor Ret; odds ratio, 0.96 [95% CI, 0.94-0.98]; false discovery rate, 0.013) demonstrated a causal link with AF, and RET is druggable. COL4A1 improved the short- and long-term predictive performance of established AF models, as evidenced by significant enhancements in the area under the receiver operating characteristic, integrated discrimination improvement, and net reclassification index, all with P values below 0.05. COL4A1 and RET are associated with the development of AF. RET is identified as a potential drug target for AF prevention, while COL4A1 serves as a biomarker for AF prediction. Future studies are needed to evaluate the effectiveness of targeting these proteins to reduce AF risk.

Research progress on single-cell expression quantitative trait loci.

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.

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.

Biological aging accelerates hepatic fibrosis: Insights from the NHANES 2017–2020 and genome-wide association study analysis

Introduction and objectivesThis study aimed to investigate the association between biological aging and liver fibrosis in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Materials and methodsWe analyzed NHANES 2017–2020 data to calculate phenotypic age. Hepatic steatosis and fibrosis were identified using controlled attenuation parameters (CAP), fatty liver index (FLI) and transient elastography (TE). The odds ratios (ORs) and 95 % confidence intervals (CI) for significant MASLD fibrosis were calculated using multivariate logistic regression, and subgroup analyses were performed. We explored the potential causal relationship between telomere length and liver fibrosis using Mendelian randomization (MR). Additionally, we used the expression quantitative trait loci (eQTL) method and GSE197112 data to identify genes related to liver fibrosis and senescence. Finally, the APOLD1 expression was validated using GSE89632. ResultsPhenotypic age was associated with liver fibrosis occurrence in MASLD (OR = 1.08, 95 % CI 1.05–1.12). Subgroup analyses by BMI and age revealed differences. For obese or young to middle-aged MASLD patients, phenotypic age is significantly associated with liver fibrosis. (OR = 1.14, 95 % CI 1.10–1.18; OR = 1.07, 95 % CI 1.01–1.14 and OR = 1.14, 95 % CI 1.07–1.22). MR revealed a negative association between telomere length and liver fibrosis (IVW method: OR = 0.63288, 95 % CI 0.42498–0.94249). The gene APOLD1 was identified as a potential target through the intersection of the GEO dataset and eQTL genes. ConclusionsThis study emphasized the link between biological aging and fibrosis in young to middle-aged obese MASLD patients. We introduced phenotypic age as a clinical indicator and identified APOLD1 as a potential therapeutic target.

A single-nucleotide polymorphism in PvPW1 encoding β-1,3-glucanase 9 is associated with pod width in Phaseolus vulgaris L.

Pod width influences pod size, shape, yield, and consumer preference in snap beans (Phaseolus vulgaris L.). In this study, we map PvPW1, a quantitative trait locus associated with pod width in snap beans, through genotyping and phenotyping of recombinant plants. We identify Phvul.006G072800, encoding the β-1,3-glucanase 9 protein, as the causal gene for PvPW1. The PvPW1G3555 allele is found to positively regulate pod width, as revealed by an association analysis between pod width phenotype and the PvPW1G3555C genotype across 17 bi-parental F2 populations. 97.7% of the 133 wide pod accessions carry PvPW1G3555, while 82.1% of the 78 narrow pod accessions carry PvPW1C3555, indicating strong selection pressure on PvPW1 during common bean breeding. Re-sequencing data from 59 common bean cultivars identify an 8-bp deletion in the intron linked to PvPW1C3555, leading to the development of the InDel marker of PvM436. Genotyping 317 common bean accessions with PvM436 demonstrated that accessions with PvM436247 and PvM436227 alleles have wider pods compared to those with PvM436219 allele, establishing PvM436 as a reliable marker for molecular breeding in snap beans. These findings highlight PvPW1 as a critical gene regulating pod width and underscore the utility of PvM436 in marker-assisted selection for snap bean breeding.

Sex-Stratified Genome-Wide Association Study in the Spanish Population Identifies a Novel Locus for Lacunar Stroke.

Ischemic stroke (IS) represents a significant health burden globally, necessitating a better understanding of its genetic underpinnings to improve prevention and treatment strategies. Despite advances in IS genetics, studies focusing on the Spanish population and sex-stratified analyses are lacking. A case-control genome-wide association study was conducted with 9081 individuals (3493 IS cases and 5588 healthy controls). IS subtypes using Trial of ORG 10172 in Acute Stroke Treatment criteria were explored in a sex-stratified approach. Replication efforts involved the MEGASTROKE, GIGASTROKE, and the UK Biobank international cohorts. Post-genome-wide association study analysis included: in silico proteomic analysis, gene-based analysis, quantitative trait loci annotation, transcriptome-wide association analysis, and bioinformatic analysis using chromatin accessibility data. Identified as associated with IS and its subtypes were 4 significant and independent loci. Replication confirmed 5p15.2 as a new locus associated with small-vessel occlusion stroke, with rs59970332-T as the lead variant (beta [SE], 0.13 [0.02]; P=4.34×10-8). Functional analyses revealed CTNND2 given proximity and its implication in pathways involved in vascular integrity and angiogenesis. Integration of Hi-C data identified additional potentially modulated genes, and in silico proteomic analysis suggested a distinctive blood proteome profile associated with the lead variant. Gene-set enrichment analyses highlighted pathways consistent with small-vessel disease pathogenesis. Gene-based associations with known stroke-related genes such as F2 and FGG were also observed, reinforcing the relevance of our findings. We found CTNND2 as a potential key molecule in small-vessel occlusion stroke risk, and predominantly in males. This study sheds light on the genetic architecture of IS in the Spanish population, providing novel insights into sex-specific associations and potential molecular mechanisms. Further research, including replication in larger cohorts, is essential for a comprehensive understanding of these findings and for their translation to clinical practice.

Context transcription factors establish cooperative environments and mediate enhancer communication

Many enhancers control gene expression by assembling regulatory factor clusters, also referred to as condensates. This process is vital for facilitating enhancer communication and establishing cellular identity. However, how DNA sequence and transcription factor (TF) binding instruct the formation of high regulatory factor environments remains poorly understood. Here we developed a new approach leveraging enhancer-centric chromatin accessibility quantitative trait loci (caQTLs) to nominate regulatory factor clusters genome-wide. By analyzing TF-binding signatures within the context of caQTLs and comparing episomal versus endogenous enhancer activities, we discovered a class of regulators, ‘context-only’ TFs, that amplify the activity of cell type-specific caQTL-binding TFs, that is, ‘context-initiator’ TFs. Similar to super-enhancers, enhancers enriched for context-only TF-binding sites display high coactivator binding and sensitivity to bromodomain-inhibiting molecules. We further show that binding sites for context-only and context-initiator TFs underlie enhancer coordination, providing a mechanistic rationale for how a loose TF syntax confers regulatory specificity.

A GWAS for grip strength in cohorts of children-Advantages of analysing young participants for this trait.

Grip strength (GS) is a proxy measure for muscular strength and a predictor for bone fracture risk among other diseases. Previous genome-wide association studies (GWASs) have been conducted in large cohorts of adults focusing on scores collected for the dominant hand, therefore increasing the likelihood of confounding effects by environmental factors. Here, we perform the first GWAS meta-analyses on maximal GS with the dominant (GSD) and non-dominant (GSND) hand in two cohorts of children (ALSPAC, N = 5450; age range = 10.65-13.61; Raine Study, N = 1162, age range: 9.42-12.38 years). We identified a novel significant association for GSND (rs9546244, LINC02465, p = 3.43e-08) and replicated associations previously reported in adults including with a HOXB3 gene marker that shows an expression quantitative trait locus (eQTL) effect. Despite a much smaller sample (~3%) compared with the UK Biobank we replicated correlation analyses previously reported in this much larger adult cohort, such as a negative correlation with coronary artery disease. Although the results from the polygenic risk score (PRS) analyses did not survive multiple testing correction, we observed nominally significant associations between GS and risk of overall fracture, as previously reported, as well ADHD which will require further investigations. Finally, we observed a higher SNP-heritability (24%-41%) compared with previous studies (4%-24%) in adults. Overall, our results suggest that cohorts of children might be better suited for genetic studies of grip strength, possibly due to the shorter exposure to confounding environmental factors compared with adults.