Biology subjects

Traylor, M.

Publications and source records attributed to Traylor, M..

8 recordsLinked to original sources

bioRxiv2025

Multi-Tissue Profiling Reveals tissue-specific protein regulation and relationships Between Protein Quantitative Trait Loci (pQTLs) and Cardiometabolic Disease

Integrating genetic data with protein levels, known as protein quantitative trait loci (pQTLs), can enhance our understanding of disease mechanisms and provide actionable insights for drug discovery, by guiding the direction of therapeutic interventions, clarifying mechanisms of action, and predicting potential side effects. However, most pQTL studies have focused on the plasma proteome, overlooking tissue-specific effects. Here, we investigate the plasma and tissue proteome and derive tissue-specific pQTLs in a unique dataset derived from a cohort of 284 STARNET patients, predominantly male, with a mean age of 65 years and a high prevalence of coronary artery disease (CAD). Importantly, our dataset includes paired tissue samples from aortic wall, mammary artery, liver, and skeletal muscle alongside plasma, allowing for a comprehensive comparative analysis across tissues--all from the same individuals. We employed the Olink Explore 3.2k platform to assess relative protein levels in each tissue. We identify 608 cis-pQTLs, the majority of which are found in plasma, reflecting greater protein variability. Notably, we find 13 proteins with exclusive tissue-specific pQTLs, underscoring distinct as well as shared genetic influences across tissues. Colocalization analyses reveal shared genetic regulation between tissue proteins and cardiometabolic traits, including LDL, HDL, and triglycerides levels, implicating proteins such as PNLIPRP2, SORT1, and PRSS53 as potential mediators of lipid regulation. Furthermore, Mendelian randomization analyses suggest a liver-specific role for SORT1 and PSRC1 in modulating CAD risk and lipid profiles. Our findings highlight the importance of profiling tissue-shared, and tissue-specific, protein expression and pQTLs to elucidate disease mechanisms and accelerate precision drug and biomarker discovery.

genetics↗

bioRxiv2024

Mapping atherogenesis mechanisms in smooth muscle cells by targeting genes linked to coronary artery disease

Recent genome-wide association studies (GWAS) have identified multiple vascular cell-expressed genes linked to coronary artery disease (CAD), suggesting that smooth muscle cells (SMCs) and SMC-derived metaplastic cells are promising targets for novel antiatherosclerosis therapies. However, the disease-promoting pathways of most GWAS-identified genes are unknown, hindering their translation into therapeutic targets. This study integrated public GWAS data for CAD and single-cell RNA sequencing (scRNA-seq) analyses of human atherosclerotic plaques to identify 20 GWAS risk genes with a putative mechanism of action in SMCs or SMC-derived cells. Gene perturbation experiments in SMCs coaxed to plaque-relevant phenotypes revealed that the selected risk genes, despite encoding very different types of proteins, regulated shared sets of genes associated with contractile functions, cell cycle pathways, NF{kappa}B, and type I interferon signaling. By integrating information about GWAS gene effect direction and a deep analysis of cholesterol- and stretch-induced gene modules in SMCs, we find evidence that cholesterol-induced signaling is a pro-atherogenic disease mechanism in SMCs that is upregulated by detrimental and downregulated by protective GWAS genes. Overall, our study identifies a set of candidate disease mechanisms in SMCs that are regulated by multiple GWAS genes across several SMC assays. Furthermore, it provides proof-of-concept for using GWAS gene effect directionality to predict the pathogenic effect of candidate disease mechanisms that can be extended to other GWAS genes and cell types in the future.

cell biology↗

bioRxiv2022

Genetic regulation of the human plasma proteome in 54,306 UK Biobank participants

The UK Biobank Pharma Proteomics Project (UKB-PPP) is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,306 UKB participants. Here, we describe results from the first phase of UKB-PPP, including protein quantitative trait loci (pQTL) mapping of 1,463 proteins that identifies 10,248 primary genetic associations, of which 85% are newly discovered. We also identify independent secondary associations in 92% of cis and 29% of trans loci, expanding the catalogue of genetic instruments for downstream analyses. The study provides an updated characterisation of the genetic architecture of the plasma proteome, leveraging population-scale proteomics to provide novel, extensive insights into trans pQTLs across multiple biological domains. We highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement proteins, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug target discovery by extending the genetic proxied effect of PCSK9 levels on lipid concentrations, cardio- and cerebro-vascular diseases, and additionally disentangle specific genes and proteins perturbed at COVID-19 susceptibility loci. This public-private partnership provides the scientific community with an open-access proteomics resource of unprecedented breadth and depth to help elucidate biological mechanisms underlying genetic discoveries and accelerate the development of novel biomarkers and therapeutics.

genetics↗

bioRxiv2019

Genetic comorbidity between major depression and cardio-metabolic disease, stratified by age at onset of major depression.

IntroductionIts imperative to understand the specific and shared aetiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression.\n\nMethodsPolygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic aetiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in eleven data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank.\n\nResultsAll cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression.\n\nConclusionsThe phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic aetiology irrespective of the age depression first presents.

genetics↗

bioRxiv2018

The Subtype Specificity of Genetic Loci Associated with Stroke in 16,664 cases and 32,792 controls

BackgroundGenome-wide association studies have identified multiple loci associated with stroke. However, the specific stroke subtypes affected, and whether loci influence both ischaemic and haemorrhagic stroke, remains unknown. For loci associated with stroke, we aimed to infer the combination of stroke subtypes likely to be affected, and in doing so assess the extent to which such loci have homogeneous effects across stroke subtypes.\n\nMethodsWe performed Bayesian multinomial regression in 16,664 stroke cases and 32,792 controls of European ancestry to determine the most likely combination of stroke subtypes affected for loci with published genome-wide stroke associations, using model selection. Cases were subtyped under two commonly used stroke classification systems, Trial of Org 10172 Acute Stroke Treatment (TOAST) and Causative Classification of Stroke (CCS). All individuals had genotypes imputed to the Haplotype Reference Consortium 1.1 Panel.\n\nResultsSixteen loci were considered for analysis. Seven loci influenced both haemorrhagic and ischaemic stroke, three of which influenced ischaemic and haemorrhagic subtypes under both TOAST and CCS. Under CCS, 4 loci influenced both small vessel stroke and intracerebral haemorrhage. An EDNRA locus demonstrated opposing effects on ischaemic and haemorrhagic stroke. No loci were predicted to influence all stroke subtypes in the same direction and only one locus (12q24) was predicted to influence all ischaemic stroke subtypes.\n\nConclusionsHeterogeneity in the influence of stroke-associated loci on stroke subtypes is pervasive, reflecting differing causal pathways. However, overlap exists between haemorrhagic and ischaemic stroke, which may reflect shared pathobiology predisposing to small vessel arteriopathy. Stroke is a complex, heterogeneous disorder requiring tailored analytic strategies to decipher genetic mechanisms.

genetics↗

bioRxiv2017

Predictors of intestinal inflammation in asymptomatic first-degree relatives of patients with Crohn’s disease

ObjectiveRelatives of individuals with Crohns disease (CD) carry an increased number of CD-associated genetic variants and are at increased risk of developing the disease. Multiple environmental and genetic factors contribute to this increased risk. We aimed to estimate the utility of genotype, smoking, family history, and a panel of biomarkers to predict risk in asymptomatic first-degree relatives (FDRs) of CD patients.\n\nDesignWe calculated a combined genotype (72 CD-associated genetic markers) and smoking relative risk score in 454 FDRs, and performed capsule endoscopy and collected 22 biomarkers in individuals from the highest and lowest risk quartiles. We then predicted small intestinal inflammation using genetic risk score, smoking status, number of relatives with CD, capsule transit time, and the panel of biomarkers in 124 individuals with complete data. Our principal analysis was to calculate the predictive utility from two machine learning classifiers: an elastic net and a random forest.\n\nResultsBoth classifiers successfully predicted FDRs with intestinal inflammation: elastic net (AUC=0.80, 95% CI: 0.62-0.98), random forest (AUC=0.87, 95% CI: 0.75-1.00). The elastic net selected a 3-predictor solution: CD family history (OR=1.31), genetic risk score (OR=1.14), and faecal calprotectin (OR=1.04). The same 3 variables were among the top 5 most important predictors as ranked by the random forest.\n\nConclusionA readily collectable panel of genetic risk variants, added to family history and faecal calprotectin, predicts those at greatest risk for developing CD with a good degree of accuracy.

epidemiology↗

bioRxiv2017

ukbtools: An R package to manage and query UK Biobank data

SummaryThe UK Biobank is a resource that includes detailed health-related data on about 500,000 individuals and is available to the research community. ukbtools removes all the upfront data wrangling required to get a single dataset for statistical analysis, and provides tools to assist in quality control, query of disease diagnoses, and retrieval of genetic metadata.\n\nAvailabilityThe package is available for installation from the Comprehensive R Archive Network (CRAN), and includes a vignette describing the use of all functionality.\n\nContact ken.b.hanscombe@kcl.ac.uk, https://github.com/kenhanscombe/ukbtools

epidemiology↗

bioRxiv2017

Pharmacogenetic testing through the direct-to-consumer genetic testing company 23andMe

BackgroundRapid advances in scientific research have led to an increase in public awareness of genetic testing and pharmacogenetics. Direct-to-consumer (DTC) genetic testing companies, such as 23andMe, allow consumers to access their genetic information directly through an online service without the involvement of healthcare professionals. Here, we evaluate the clinical relevance of such pharmacogenetic tests reported by 23andMe.\n\nMethodsThe research papers listed under each 23andMe report were evaluated, extracting information on effect size, sample size and ethnicity. A wider literature search was performed to provide a fuller assessment of the pharmacogenetic test and variants were matched to FDA recommendations. Additional evidence from CPIC guidelines, PharmGKB, and Dutch Pharmacogenetics Working Group was reviewed to determine current clinical practice. The value of the tests across ethnic groups was determined, including information on linkage disequilibrium between the tested SNP and causal pharmacogenetic variant, where relevant.\n\nResults23andMe offers 12 pharmacogenetic tests, some of which are in standard clinical practice, and others which are less widely applied. The clinical validity and clinical utility varies extensively between tests. The variants tested are likely to have different degrees of sensitivity due to different risk allele frequencies and linkage disequilibrium patterns across populations. The clinical relevance depends on the ethnicity of the individual and variability of pharmacogenetic markers. Further research is required to determine causal variants and provide more complete assessment of drug response and side effects.\n\nConclusion23andMe reports provide some useful pharmacogenetics information, mirroring clinical tests that are in standard use. Other tests are unspecific, providing limited guidance and may not be useful for patients without professional interpretation. Nevertheless, DTC companies like 23andMe act as a powerful intermediate step to integrate pharmacogenetic testing into clinical practice.

genetics↗