Quantitative Trait Loci Research Articles

Copy Number Variation and Selection Signal: Exploring the Domestication History and Phenotype Differences Between Duroc and the Chinese Native Ningxiang Pigs.

The Ningxiang pig, one of the well-known Chinese native pig breeds, has the advantages of tender meat, high intramuscular fat (IMF) content, and roughage tolerance, compared to the commercial lean pig breeds. The genetic basis for complex traits in Ningxiang pigs has been previously studied through other genetic markers, such as Single Nucleotide Polymorphism (SNP), while the characteristics of copy number variation (CNV) and the selection signal have not been investigated yet. In this study, GGP 50 k genotyping data of 2242 Ningxiang pigs (NX) and 1137 Duroc pigs (Duroc) were involved in CNV atlas construction and selection signals identification. Annotations of genes and quantitative trait locus (QTLs) were performed on the target candidate regions, as follows: (1) 162 CNVs were detected in Ningxiang pigs, while 326 CNVs were detected in Duroc pigs, and there are 21 copy number variation regions (CNVRs) shared between them; (2) The CNVRs of Duroc are more abundant, with 192 CNVRs, accounting for 1.61% of the entire genome, while those of Ningxiang pigs only have 98 CNVRs, accounting for 0.49%; (3) The QTLs annotated on CNVs and selected regions of Ningxiang pigs were mainly associated with meat quality and fertility. In contrast, the Duroc QTLs' notes relate primarily to the carcass and immunity, and explain why they have a higher slaughter rate and immunity; (4) There is a presence of high-frequency acquired CNVs, specifically in Ningxiang pigs, with 24 genes significantly enriched in the sensory receptor-related pathway in this region; (5) Based on the CNVs atlas, candidate genes such as 3 inositol 1,4,5-triphosphate receptor, type 3 (ITPR3), forkhead box protein K2 (FOXK2), G-protein coupled estrogen receptor 1 (GPER1), Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), triosephosphate isomerase 1 (TPI1), and other candidate genes related to fat deposition and differentiation were screened. In general, this study improved our knowledge about copy number variation and selection signal information of Ningxiang pigs, which can not only further explain the genetic differences between Chinese native and Western commercial pig breeds, but also provide new materials for the analysis of the genetic basis of complex traits.

Mining Candidate Genes for Maize Tassel Spindle Length Based on a Genome-Wide Association Analysis

Maize tassel spindle length is closely related to the number of pollen grains and the duration of the flowering stage, ultimately affecting maize yield and adaptations to stress conditions. In this study, 182 maize inbred lines were included in an association population. A genome-wide association study was conducted on maize tassel spindle length using the Q + K model. With p ≤ 1.0 × 10−4 applied as the significance threshold, 240 SNPs significantly associated with tassel spindle length were detected, which were associated with 99 quantitative trait loci (QTLs), with 21 QTLs detected in two or more environments. Moreover, 51 candidate genes were detected in 21 co-localized QTLs. A KEGG enrichment analysis and candidate gene expression analysis indicated that Zm00001d042312 affects plant hormone signal transduction and is highly expressed in maize tassels. A haplotype analysis of Zm00001d042312 revealed three main haplotypes, with significant differences between Hap1 and Hap2. In conclusion, we propose that Zm00001d042312 is a gene that regulates maize tassel spindle length. This study has further elucidated the genetic basis of maize tassel spindle length, while also providing excellent genetic targets and germplasm resources for the genetic improvement of maize tassel spindle length and yield.

Two-Sample Mendelian Randomization Study Identifies Tissue-Dependent Risk Genes in Autoimmune Diseases

Autoimmune diseases are among the most prevalent diseases across the world with genetic and environmental factors that contribute to their etiology. Because the exact causes of autoimmune diseases are largely unknown, a Mendelian randomization (MR) approach is used here to examine the potential causal association between gene expression levels and disease risk across various tissues. Specifically, this study focuses on six autoimmune diseases including Crohn’s disease, ulcerative colitis, rheumatoid arthritis, multiple sclerosis, type 1 diabetes mellitus, and systemic lupus erythematosus. Several of these diseases are currently treatable with immunosuppressants that target specific genes, such as TNF-alpha, IL-23, CD20, and more. In this study, a two-sample MR analysis is performed with multitissue expression quantitative trait loci (eQTLs) and large-scale genome-wide association studies to investigate how gene expression can influence the risk of developing these diseases. Our results show that genes HLA-DQA1/2, HLA-DRB1/6, HLA-DQB2, C4A, CYP21A2, and HLA-DQB1-AS1 have a high causal effect across several diseases and tissues, and almost all of these findings originate from the major histocompatibility complex (MHC) region on Chromosome 6. Our findings support the current knowledge of genes associated with these diseases while also revealing novel genes that can be used for drug therapies in the future. Although several drug therapies currently exist to treat this selection of autoimmune diseases, we provide further insights into the main, common pathways responsible for autoimmune disease pathogenesis and discuss novel genes that lack research focus.

Shifts in plant architecture drive species-specific responses to drought in a Sorghum recombinant inbred line population.

Drought stress severely impedes plant growth, development, and yield. Therefore, it is critical to uncover the genetic mechanisms underlying drought resistance to ensure future food security. To identify the genetic controls of these responses in Sorghum, an agriculturally and economically important grain crop, an interspecific recombinant inbred line (RIL) population was established by crossing a domesticated inbred line of Sorghum bicolor (TX7000) with its wild relative, Sorghum propinquum. This RIL population was evaluated under drought conditions, allowing for theidentification of quantitative trait loci (QTL) that contribute to drought resistance. We detected eight QTL in the drought population that explain a significant portion of the observed variation for four traits (height, aboveground biomass, relative water content, and leaf temperature/transpiration). The allelic effects of, and the candidate genes within, these QTL emphasize: (1) the influence of domestication on drought-responsive phenotypes, such as height and aboveground biomass, and (2) how control of water uptake and/or loss can be driven by species-specific plant architecture. Our findings shed light on the interconnected roles of shoot and root responses in drought resistance as it relates to regulation of water uptake and/or loss, while the detected allelic effects demonstrate how maintenance of grain production and yield under drought is a likely result of domestication-derived drought tolerance.

The Rolled Towel Method for Hormone Response Assays in Maize.

The rolled towel assay (RTA) is a soil-free method to evaluate juvenile phenotypes in crops such as maize and soybean. Here, we provide an updated RTA-based protocol to phenotype maize seedling responses to chemicals of interest. We exemplify the protocol with two synthetic auxin herbicides (2,4-dichlorophenoxyacetic acid and picloram), an auxin precursor (indole-3-butyric acid), and an auxin inhibitor (N-1-naphthylphthalamic acid), but the method can be used with other hormones or plant growth regulators that are soluble in growth media. We also include instructions on how to annotate root traits and analyze primary root length trait data. The protocol can be scaled up for use in genetic screens, preparing tissue for gene expression analyses, carrying out genome-wide association studies (GWASs), and quantitative trait locus (QTL) identification.

Genetic Regulation of Fruit Shape in Horticultural Crops: A Review

The shape of fruits is a critical trait affecting the commercial value and consumer acceptance of horticultural crops. Genetic regulation of fruit shape involves complex interactions among multiple genes and environmental factors. This review summarizes recent advances in understanding the genetic mechanisms controlling fruit shape in several key horticultural crops, including tomato, pepper, cucumber, peach, and grape. We present the identification and characterization of genes and quantitative trait loci (QTLs) that influence fruit shape, focusing on the roles of genes such as OVATE, SUN, FAS, LC, ENO, GLOBE, CsSUN, CsFUL1, CsCRC, PpCAD1, PpOFP1, and VvSUN. This review highlights the importance of hormonal pathways, particularly those involving synthesis and concentration of cytokinins and brassinosteroids in shaping fruit morphology, and explores how these genes interact and form regulatory networks that collectively determine the final fruit shape. This knowledge provides a foundation for developing strategies to improve fruit quality and yield through genetic modification and breeding programs.

COLD6-OSM1 module senses chilling for cold tolerance via 2′,3′-cAMP signaling in rice

While it is known that temperature sensors trigger calcium (Ca2+) signaling to confer cold tolerance in cells, less is known about sensors that couple with other secondary messengers. Here, we identify a cold sensor complex of CHILLING-TOLERANCE DIVERGENCE 6 (COLD6) and osmotin-like 1 (OSM1), which triggers 2',3'-cyclic adenosine monophosphate (2',3'-cAMP) production to enhance cold tolerance in rice. COLD6, which is encoded by a major quantitative trait locus (QTL) gene, interacts with the rice G protein α subunit (RGA1) at the plasma membrane under normal conditions. Upon exposure to chilling, cold-induced OSM1 binds to COLD6, kicking out RGA1 from interaction. This triggers an elevation of 2',3'-cAMP levels for enhancing chilling tolerance. Genetic data show that COLD6 negatively regulates cold tolerance and functionally depends on OSM1 in chilling stress. COLD6 alleles were selected during rice domestication. Knockout and natural variation of COLD6 in hybrid rice enhanced chilling tolerance, hinting design potential for breeding. This highlighted a module triggering 2',3'-cAMP to improve chilling tolerance in crops.

Arginine metabolism is a biomarker of red blood cell and human aging.

Increasing global life expectancy motivates investigations of molecular mechanisms of aging and age-related diseases. This study examines age-associated changes in red blood cells (RBCs), the most numerous host cell in humans. Four cohorts, including healthy individuals and patients with sickle cell disease, were analyzed to define age-dependent changes in RBC metabolism. Over 15,700 specimens from 13,757 humans were examined, a major expansion over previous studies of RBCs in aging. Multi-omics approaches identified chronological age-related alterations in the arginine pathway with increased arginine utilization in RBCs from older individuals. These changes were consistent across healthy and sickle cell disease cohorts and were influenced by genetic variation, sex, and body mass index. Integrating multi-omics data and metabolite quantitative trait loci (mQTL) in humans and 525 diversity outbred mice functionally linked metabolism of arginine during RBC storage to increased vesiculation-a hallmark of RBC aging-and lower post-transfusion hemoglobin increments. Thus, arginine metabolism is a biomarker of RBC and organismal aging, suggesting potential new targets for addressing sequelae of aging.

Higher Incidence of Common Polymorphisms in the Genes of Folate and Methionine Cycles in Children With Orofacial Clefs and Congenital Heart Defects Compared to their Unaffected Siblings.

Uninterrupted folate metabolism plays a vital role in embryonic development, ensuring a supply of one-carbon-activated folate cofactors for essential processes. Folate deficiency has been implicated in the development of orofacial clefts (OFC) and congenital heart disease (CHD). Although both malformations have been extensively studied in lieu of folate deficiency, the results of corresponding studies are ambiguous due to the interplay of maternal and fetal genomes controlling folate metabolism in the developing fetus. We used the innovative study design to compare affected and unaffected siblings from the same mother, thus minimizing the effect of the maternal genome. Thus, it might be possible to identify genetic markers of congenital malformations that pertain exclusively to the child. This study compared demographic and environmental factors between OFC or CHD-affected and unaffected pregnancies as well as the presence of polymorphisms in genes of folate metabolism between OFC or CHD-affected and unaffected siblings. Only the maternal fever in the first trimester was a risk factor for OFC, whereas the maternal advanced age, medication administration, and common polymorphism in the FPGS gene increased the risk of CHD formation. Both OFC and CHD formation were associated with a higher number of variant loci in genes of folate-methionine cycles. Both OFC and CHD formation were associated with a higher number of mutated loci in genes of folate-methionine cycles, indicating polygenic and possibly multifactorial inheritance.

QTaHa-5DL: another major QTL regulating wheat grain hardness

Grain hardness has important effects on grain quality and the end-use of wheat. In this study, a collection of 103 common wheat germplasms and a DH population of 194 lines were used to identify new quantitative trait loci (QTL) for grain hardness. Two stable genetic loci on chromosome 5D were detected under different locations and years with one of them being the Ha locus on 5DS where the major gene Puroindolines for wheat grain hardness is located. Another locus of qTaHa-5DL also showed a significant impact on grain hardness index (HI) with HI increasing from ~ 20 to ~ 45 and hardness type changing from soft to mixed when Puroindolines are wild type. A kompetitive allele-specific PCR (KASP) marker for qTaHa-5DL was developed and the effectiveness of the QTL was confirmed in 184 breeding lines with the marker K-Ha5DL dividing wheat into two distinct categories. This new QTL can be effectively used to select soft or medium hard wheat.

Genome-wide dissection of genes shaping inflorescence morphology in 242 Chinese south–north sorghum accessions

The inflorescences morphology (IM) of sorghum (Sorghum bicolor L. Moench) affects its resistance to pests, diseases, and grain yields. However, the specific genetic factors underlying in IM are not yet fully elucidated. Here we conducted a comprehensive genome-wide association analysis (GWAS) to identify the stable and adaptive Quantitative Trait Loci (QTL) for five IM traits (panicle length, the number of cob nodes, the number of primary branches, the largest length of the primary branch, and panicle type) in a sorghum panel, which adapted to different environments from the south to north in China. Totally, 2,015,850 high quality single nucleotide polymorphisms (SNPs) were obtained. Population structure analysis showed that two distinct genetic sub-populations were divided according to their geographic origin. Seventy-one QTLs distributed in 41 genetic regions on 9 chromosomes were identified. These regions harbored 21 high-confident candidate genes that were homologous to rice domestication genes, including 7 related to IM. Two domestication-related genes (Sobic.003G052700 and Sobic.006G247700) were located into two major QTL regions (QTL3.4721839 and QTL6.58709500) which were identified in multi-environments. Allelic variations in the two genes displayed a geographical pattern, indicating that different IM traits were selected by south and north sorghum breeders, such as south sorghums had long and loose panicles in order to adapt the hot and humid climate, while north sorghums had short and compact panicle to increase planting density and grain yield per unit area due to dry climate. This work provides new breeding strategies and resources for developing locally adapted sorghum varieties.

Lactuca super-pangenome reduces bias towards reference genes in lettuce research

BackgroundBreeding of lettuce (Lactuca sativa L.), the most important leafy vegetable worldwide, for enhanced disease resistance and resilience relies on multiple wild relatives to provide the necessary genetic diversity. In this study, we constructed a super-pangenome based on four Lactuca species (representing the primary, secondary and tertiary gene pools) and comprising 474 accessions. We include 68 newly sequenced accessions to improve cultivar coverage and add important foundational breeding lines.ResultsWith the super-pangenome we find substantial presence/absence variation (PAV) and copy-number variation (CNV). Functional enrichment analyses of core and variable genes show that transcriptional regulators are conserved whereas disease resistance genes are variable. PAV-genome-wide association studies (GWAS) and CNV-GWAS are largely congruent with single-nucleotide polymorphism (SNP)-GWAS. Importantly, they also identify several major novel quantitative trait loci (QTL) for resistance against Bremia lactucae in variable regions not present in the reference lettuce genome. The usability of the super-pangenome is demonstrated by identifying the likely origin of non-reference resistance loci from the wild relatives Lactuca serriola, Lactuca saligna and Lactuca virosa.ConclusionsThe super-pangenome offers a broader view on the gene repertoire of lettuce, revealing relevant loci that are not in the reference genome(s). The provided methodology and data provide a strong basis for research into PAVs, CNVs and other variation underlying important biological traits of lettuce and other crops.

Proteogenomic analysis in population biobanks identifies new therapeutic targets across heart failure subtypes

Abstract Background Heart failure (HF) represents a multifactorial clinical syndrome with differential response to existing therapies across its subtypes. Proteogenomic analysis of genetic variants associated with changes in circulating protein level and risk of heart failure subtypes provides an opportunity for a systematic identification of proteins with subtype-specific causal effects as novel therapeutic targets. Purpose To identify proteins as potential therapeutic targets for specific heart failure subtypes by leveraging proteomics and genomics data in population biobanks. Methods We evaluated the causal effects of 2,773 proteins on 5 HF-related outcomes, including overall HF, non-ischaemic HF (ni-HF), ni-HF with reduced / preserved ejection fraction (ni-HFrEF / ni-HFpEF), and dilated cardiomyopathy (DCM). For each protein-phenotype pair, we performed a two-sample Mendelian randomisation (MR) analysis using protein-specific genetic association estimates derived from protein quantitative trait locus (pQTL) studies in 114,145 individuals from deCODE, UK Biobank, Fenland study, and SCALLOP consortium and genome-wide association studies (GWAS) of HF outcomes in 145,795 individuals by HERMES Consortium. We further prioritised candidate protein targets based on replication of results across datasets, model parameters, assays, and colocalization of causal genetic variants. Finally, we profiled the causal effects of identified proteins on 32 related cardiac traits and risk factors to explore potential mechanisms of actions and on-target side effects. Results We identified 493 proteins associated with at least one HF outcome at a false discovery rate (FDR) < 1% in the primary MR analysis. We further prioritised 44 proteins with concordant effect direction across datasets, model parameters, and assays or colocalization in at least one dataset. All 44 identified proteins showed cross association with at least one of 35 tested traits other than HF or cardiomyopathies at FDR <1%, including coronary artery disease (18 proteins), body mass index (11 proteins), type 2 diabetes (6 proteins), systolic / diastolic blood pressure (13 proteins), and chronic kidney disease (9 proteins). Conclusion We characterised the causal associations of 2,773 circulating proteins across HF-related outcomes and prioritised 44 proteins by integrating proteomics and genomics data in population biobanks. These findings extend our understanding of the molecular mechanism underlying different forms of HF and may inform the development of new therapeutic strategies.Study design

The collaborative cross mouse for studying the effect of host genetic background on memory impairments due to obesity and diabetes.

Over the past few decades, a threefold increase in obesity and type 2 diabetes (T2D) has placed a heavy burden on the health-care system and society. Previous studies have shown correlations between obesity, T2D, and neurodegenerative diseases, including dementia. It is imperative to further understand the relationship between obesity, T2D, and cognitive deficits. This investigation tested and evaluated the cognitive impact of obesity and T2D induced by high-fat diet (HFD) and the effect of the host genetic background on the severity of cognitive decline caused by obesity and T2D in collaborative cross (CC) mice. The CC mice are a genetically diverse panel derived from eight inbred strains. Our findings demonstrated significant variations in the recorded phenotypes across different CC lines compared to the reference mouse line, C57BL/6J. CC037 line exhibited a substantial increase in body weight on HFD, whereas line CC005 exhibited differing responses based on sex. Glucose tolerance tests revealed significant variations, with some lines like CC005 showing a marked increase in area under the curve (AUC) values on HFD. Organ weights, including brain, spleen, liver, and kidney, varied significantly among the lines and sexes in response to HFD. Behavioral tests using the Morris water maze indicated that cognitive performance was differentially affected by diet and genetic background. Our study establishes a foundation for future quantitative trait loci mapping using CC lines and identifying genes underlying the comorbidity of Alzheimer's disease (AD), caused by obesity and T2D. The genetic components may offer new tools for early prediction and prevention.

Association of genetically predicted human metabolomic gene expression with incident heart failure

Abstract Background Heart failure (HF) represents a significant challenge both for patients and healthcare systems worldwide. Despite ongoing efforts, therapeutic options are limited. The underlying pathophysiology involves intricate changes in cardiac metabolism that strongly influence the clinical phenotype, thus potentially offering novel options for therapeutic targeting. Systematic prioritisation of metabolic genes regarding their phenotypic impact on HF is outstanding. Purpose We investigate the association of genetically predicted metabolic gene expression with the onset of HF within UK Biobank (UKB). We followed up on identified candidate genes using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) models of cardiomyocyte hypertrophy. Methods Utilising genomic data of 484,372 UKB participants and left ventricle-specific expression quantitative trait loci (eQTL) information from Genotype-Tissue Expression (GTEx) project, we predicted genetically regulated gene expression using established methods (PrediXcan) for all metabolic genes. Subsequently, we employed per-gene Cox proportional hazards (COX-PH) models to assess the association of genetically predicted metabolic gene expression with incident HF. We adjusted models for clinical risk using established risk models (Pooled Cohort Equations to Prevent HF (PCP-HF)). To validate our findings in vitro, we subjected hiPSC-CMs to a sympathomimetic stimulus via treatment with endothelin-1 (ET-1; 10 nM). We evaluate alterations in expression via real-time quantitative polymerase chain reaction (RT-qPCR) and establish methods for assessing hypertrophic remodelling. Results COX-PH models revealed the predicted expression of 104/1515 metabolic genes to be independently associated with the onset of HF. Subsequent refinement with a meta-analysis of commonly dysregulated genes in end-stage HF narrowed our findings to 46 candidate genes, of which several were known for their role in HF pathophysiology. We further traced nine genes for validation in hiPSC-CM models and showed significant dysregulation of several genes following the treatment with ET-1. Flow cytometry measured hiPSC-CM hypertrophy following sympathomimetic stimulation (p=0.036). Extracellular flux analysis revealed canonical changes following treatment with ET-1. Conclusions The study revealed several metabolic genes associated with incident HF, thus confirming previous findings and highlighting novel, putative therapeutic target genes. We confirmed transcriptional changes in an in vitro model. Ongoing work includes further validation via silencing candidate genes in hiPSC-CMs and subjection to the established phenotypic in vitro models.

Protein quantitative trait loci identifies genetic biomarkers of proteins associated with measures of myocardial fibrosis in new-onset, treatment naive rheumatoid arthritis

Abstract Introduction The excess risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) compared to the general population is attributed to chronic inflammation. Identifying the underlying mechanisms and shared inflammatory pathways more precisely would enable tailored treatment. Purpose To identify genetic variants associated with the cardio-metabolic-inflammatory proteomic signature in patients with early, treatment-naïve RA and low CVD risk. Methods The Coronary Artery Disease Evaluation in RA (CADERA) study was a bolt-on to a RCT of patients with early, treatment-naïve RA and low CVD risk profile (maximum of 1 traditional CVD risk factor and no diabetes mellitus). Normalised expression of 334 proteins from 75 CADERA patients who underwent cardiovascular magnetic resonance imaging (CMR) was measured using the Olink Proximity Extension Immunoassay across Inflammation, Cardiovascular II, III and Cardiometabolic panels. Genome-wide single nucleotide polymorphism (SNP) data were generated using the Illumina Global Screening Array and was imputed and checked for data quality using standard approaches. Proteins associated with CMR parameters were identified using Bayesian mixed effects linear regression. Protein quantitative trait locus (pQTL) analysis was then performed using linear regression models adjusted for age, gender, and systolic blood pressure. Cis-pQTLs were defined as SNPs within 1Mb of the protein-encoding gene, and significance threshold was set at 1e-5. Due to sample size, trans-pQTLs were not considered in this analysis. Linkage disequilibrium (LD) clumping was performed using PLINK software to identify index SNPs defined as the most significant variant in LD (r2 <0.5) within a region of +/- 250Kb. Results Following quality control 318 proteins and 5,971,781 SNPs were available for analysis in 75 CADERA patients. The median age was 51 years [inter-quartile range (IQR) 40 - 61] with 52/75 (69%) females, 40/75 (53%) current/ex-smokers, and median body mass index (BMI) 26.4kg/m2 (IQR 23.3 - 28). Participants had a median RA symptom duration of 21.9 weeks (IQR 14.4 - 32.7). Only 8/75 (10%) reported a history of hypertension, hypercholesteroaemia or family history of CVD. 54 out of 318 proteins were associated with CMR measures of myocardial fibrosis and/or vascular stiffness. 460 cis-pQTLs were identified for 20 out of the 318 proteins. LD clumping revealed 40 cis-pQTLs independently associated with these 20 proteins. Of these, NOTCH3 (rs34763630) was positively associated with extracellular volume, and SELPLG (rs7980427) was negatively associated with late gadolinium enhancement as detected on CMR (table 1). No other proteins with genetic support were associated with CMR measures. Conclusion This study for the first time, implicates genetic markers associated with two proteins as putative candidate biomarkers of CMR-identified myocardial fibrosis in new-onset RA that now warrant validation in an independent cohort.

Hypnotic use and the risk of cardiovascular diseases in insomnia patients

Abstract Background Insomnia, commonly treated with hypnotic agents, is an independent risk factor of cardiovascular diseases. Previous research demonstrated the detrimental effect of hypnotics on the prognosis of cardiovascular patients. Purpose We aimed to examine the association between hypnotic agents and cardiovascular outcomes in general individuals with insomnia. Methods In a propensity score matched cohort of UK Biobank (UKB) participants with insomnia, Cox proportional hazard model was used to estimate the association between regular use of hypnotic agents and predetermined cardiovascular outcomes including incident coronary heart diseases (CHD), heart failure (HF), stroke, and cardiovascular death. Inverse probability of treatment weighting and competing risk models were further performed during sensitivity analysis. Drug-target Mendelian randomization (MR) analyses were employed for further evaluation of hypnotics-related genes and cardiovascular disease association. The genome-wide association data of CHD, HF, and stroke were there large-scale genome-wide association analyses. Tissue-specified expression quantitative trait loci (eQTL) data was derived from BrainSeq phase II. Results During a median follow-up of 14.3 years, the matched cohort documented a total of 1,019 CHD cases, 406 HF cases, 285 stroke cases, and 197 cardiovascular deaths. No significant association was detected between Z-meds and CHD, stroke, and cardiovascular mortality. Benzodiazepine use was significantly associated with the increased risk of CHD, HF, and cardiovascular mortality (Figure 1). The inverse probability of treatment weighting and competing risk models didn’t alter the above associations. Moreover, drug-target MR analyses corroborated the safety of Z-meds in the general population regarding cardiovascular health. However, GABRG1—a drug target for benzodiazepines—was associated with heightened risks for heart failure, and ischemic stroke (Figure 2). Conclusions Our findings suggested the heterogeneous associations between different categories of hypnotics and incident cardiovascular events in individuals with insomnia. Z-meds are safe regarding the risk of cardiovascular outcomes in the UKB population with insomnia.Figure 1Figure 2

Causal association of serum calcium, phosphate, vitamin D, parathyroid hormone, and FGF23 with the risk of aortic stenosis

Abstract Aim Disorders of mineral metabolism, including elevated levels of serum calcium, phosphate, 25-hydroxyvitamin D (25OH-VitD), parathyroid hormone (PTH), and fibroblast growth factor 23 (FGF23), have been reported in patients with calcific aortic valve stenosis (CAVS). However, evidence of the causal role of mineral metabolism in CAVS is still lacking. We aimed to investigate the causality between mineral metabolism and CAVS. Methods A systematic pipeline combining Mendelian randomization (MR), Steiger directionality test, colocalization analysis, protein-protein network, and enrichment analysis was applied to investigate the causal effect. Genome-wide association study (GWAS) and protein quantitative trait loci data for mineral metabolism markers were extracted from large-scale meta-analyses. Summary statistics for CAVS were obtained from two independent GWAS datasets as discovery and replication cohorts (n=374,277 and 653,867). Results In MR analysis, genetic mimicry of serum FGF23 elevation was associated with increased CAVS risk [ORdiscovery=3.081 (1.649-5.760), Pdiscovery=4.21×10-4; ORreplication=2.280 (1.461-3.558), Preplication=2.82×10-4] without evidence of reverse causation (Psteiger=7.21×10-98). Strong colocalisation association with CAVS was observed for FGF23 expression in the blood (PP.H4 = 0.96). Additionally, we identified some protein-protein interactions between FGF23 and known CAVS causative genes. Serum calcium, phosphate, 25OH-VitD, and PTH failed to show causal effects on CAVS at Bonferroni-corrected significance (all P>0.05/5=0.01). Conclusions Elevated serum FGF23 level is a causal risk factor for CAVS, and its mechanism of action in CAVS development may be independent of its function in regulating mineral metabolism. Hence, FGF23 may serve as a circulating marker and a promising preventive target for CAVS, warranting further investigation.

Therapeutic targets for lung cancer: genome-wide Mendelian randomization and colocalization analyses.

Lung cancer, categorized into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), remains a significant global health challenge. The development of drug resistance and the heterogeneity of the disease necessitate the identification of novel therapeutic targets to improve patient outcomes. We conducted a genome-wide Mendelian randomization (MR) and colocalization analysis using a comprehensive dataset of 4,302 druggable genes and cis-expressed quantitative trait loci (cis-eQTLs) from 31,884 blood samples. The study integrated genomic analysis with eQTL data to identify key genes associated with lung cancer risk. The analysis revealed five actionable therapeutic targets for NSCLC, including LTB4R, LTBP4, MPI, PSMA4, and TCN2. Notably, PSMA4 demonstrated a strong association with both NSCLC and SCLC risks, with odds ratios of 3.168 and 3.183, respectively. Colocalization analysis indicated a shared genetic etiology between these gene expressions and lung cancer risk. Our findings contribute to precision medicine by identifying druggable targets that may be exploited for subtype-specific lung cancer therapies.

Genomic Prediction and Genome-Wide Association Studies for Productivity, Conformation and Heat Tolerance Traits in Tropical Smallholder Dairy Cows.

Genomic selection (GS) and genome-wide association studies (GWAS) have not been investigated in Vietnamese dairy cattle, even for basic milk production traits, largely due to the scarcity of individual phenotype recording in smallholder dairy farms (SDFs). This study aimed to estimate heritability (h2) and test the applicability of GS and GWAS for milk production, body conformation and novel heat tolerance traits using single test day phenotypic data. Thirty-two SDFs located in either the north (a lowland vs. a highland) or the south (a lowland vs. a highland) of Vietnam were each visited for an afternoon and the next morning to collect phenotype data of all lactating cows (n = 345). Tail hair from each cow was sampled for subsequent genotyping with a 50K SNP chip at that same visit. Milk production traits (single-test day) were milk yield (MILK, kg/cow/day), energy corrected milk yield adjusted for body weight (ECMbw, kg/100 kg BW/day), fat (mFA, %), protein (mPR, %) and dry matter (mDM, %). Conformation traits were body weight (BW, kg) and body condition score (BCS, 1 = thin to 5 = obese). Heat tolerance traits were panting score (PS, 0 = normal to 4.5 = extremely heat-stressed) and infrared temperatures (IRTs, °C) at 11 areas on the external body surface of the cow (inner vulval lip, outer vulval surface, inner tail base surface, ocular area, muzzle, armpit area, paralumbar fossa area, fore udder, rear udder, forehoof and hind hoof), assessed by an Infrared Camera. Univariate linear mixed models and a 10-fold cross-validation approach were applied for GS. Univariate single SNP mixed linear models were applied for the GWAS. Estimated h2 (using the genotype information to build relationships among animals) were moderate (0.20-0.37) for ECMbw, mFA, mPR, mRE, BW, BCS and IRT at rear udder; low (0.08-0.19) for PS and other IRTs; and very low (≤ 0.07) for MILK, ECM and mDM. Accuracy of genomic estimated breeding values (GEBVs) was low (≤ 0.12) for MILK, ECM, mDM and IRT at hind hoof; and moderate to high (0.32-0.46) for all other traits. The most significant regions on chromosomes (BTA) associated with milk production traits were 0.47-1.18 Mb on BTA14. Moderate to high h2 and moderate accuracies of GEBVs for mFA, mPR, ECMbw, BCS, BW, PS and IRTs at rear udder and outer vulval surface suggested that GS using single test day phenotypic data could be applied for these traits. However, a greater sample size is required to decrease the bias of GEBVs by GS and increase the power of detecting significant quantitative trait loci (QTLs) by GWAS.