Biology subjects

Howson, J. M.

Publications and source records attributed to Howson, J. M..

4 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↗

bioRxiv2019

A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits

Genome-wide association studies (GWAS) have identified thousands of genomic regions affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms involved. One approach is to use statistical colocalization to assess shared genetic aetiology across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose HyPrColoc (Hypothesis Prioritisation in multi-trait Colocalization), an efficient deterministic Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 second). We performed a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits. HyPrColoc identified 43 regions in which CHD colocalized with [≥]1 trait, including 5 potentially new CHD loci. Across the 43 loci, we further integrated gene and protein expression quantitative trait loci to identify candidate causal genes.

genetics↗

bioRxiv2019

A robust and efficient method for Mendelian randomization with hundreds of genetic variants: unravelling mechanisms linking HDL-cholesterol and coronary heart disease

Mendelian randomization (MR) investigations with large numbers of genetic variants are becoming increasingly common. However, the reliability of findings from a MR investigation is dependent on the validity of the genetic variants as instrumental variables. We developed a method to identify groups of genetic variants with similar causal effect estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Our contamination mixture method is a robust and efficient method for valid MR in the presence of invalid IVs. Compared to other robust methods, our method had the lowest mean squared error across a range of realistic scenarios. The method is fast and efficient, and can perform analysis with hundreds of variants in a fraction of a second. In a MR analysis for high-density lipoprotein (HDL) cholesterol and coronary heart disease (CHD) risk, the method identified 11 variants associated with increased HDL-cholesterol, decreased triglyceride levels, and decreased CHD risk that had the same directions of associations with platelet distribution width and other blood cell traits, suggesting a shared mechanism linking lipids and CHD risk relating to platelet aggregation.

genetics↗

bioRxiv2017

Causal Analyses, Statistical Efficiency And Phenotypic Precision Through Recall-By-Genotype Study Design

Genome-wide association studies have been useful in identifying common genetic variants related to a variety of complex traits and diseases; however, they are often limited in their ability to inform about underlying biology. Whilst bioinformatics analyses, studies of cells, animal models and applied genetic epidemiology have provided some understanding of genetic associations or causal pathways, there is a need for new genetic studies that elucidate causal relationships and mechanisms in a cost-effective, precise and statistically efficient fashion. We discuss the motivation for and the characteristics of the Recall-by-Genotype (RbG) study design, an approach that enables genotype-directed deep-phenotyping and improvement in drawing causal inferences. Specifically, we present RbG designs using single and multiple variants and discuss the inferential properties, analytical approaches and applications of both. We consider the efficiency of the RbG approach, the likely value of RbG studies for the causal investigation of disease aetiology and the practicalities of incorporating genotypic data into population studies in the context of the RbG study design. Finally, we provide a catalogue of the UK-based resources for such studies, an online tool to aid the design of new RbG studies and discuss future developments of this approach.

genetics↗