Biology subjects

Foley, C. N.

Publications and source records attributed to Foley, C. N..

9 recordsLinked to original sources

bioRxiv2024

Imputed DNA methylation outperforms measured loci associations with smoking and chronological age

Multi-locus signatures of blood-based DNA methylation are well-established biomarkers for lifestyle and health outcomes. Here, we focus on two CpGs that are strongly associated with age and smoking behaviour. Imputing these loci via epigenome-wide CpGs results in stronger associations with outcomes in external datasets compared to directly measured CpGs. If extended epigenome-wide, CpG imputation could augment historic arrays and recently-released, inexpensive but lower-content arrays, thereby yielding better-powered association studies.

genetics↗

bioRxiv2023

isGWAS: ultra-high-throughput, scalable and equitable inference of genetic associations with disease

Genome-wide association studies (GWAS) have proven a powerful tool for human geneticists to generate biological insights or hypotheses for drug discovery. Nevertheless, a dependency on sensitive individual-level data together with ever-increasing cohort sample sizes, numbers of variants and phenotypes studied put a strain on existing algorithms, limiting the GWAS approach from maximising potential. Here we present in-silico GWAS (isGWAS), a uniquely scalable algorithm to infer regression parameters in case-control GWAS from cohort-level summary data. For any sample size, isGWAS computes a variant-disease association parameter in [~]1 millisecond, or [~]11m variants in UK-Biobank within [~]4 minutes ([~]1500-fold faster than state-of-the-art). Extensive simulations and empirical tests demonstrate that isGWAS results are highly comparable to traditional regression-based approaches. We further introduce a heuristic re-sampling algorithm, leapfrog re-sampler (LRS), to extrapolate association results to semi-virtually enlarged cohorts. Owing to significant computational gains we anticipate a broad use of isGWAS and LRS which are customizable on a web interface.

genetics↗

bioRxiv2021

Genetic map of regional sulcal morphology in the human brain

The human brain is a complex organ underlying many cognitive and physiological processes, affected by a wide range of diseases. Genetic associations with macroscopic brain structure are emerging, providing insights into genetic sources of brain variability and risk for functional impairments and disease. However, specific associations with measures of local brain folding, associated with both brain development and decline, remain under-explored. Here we carried out detailed large-scale genome-wide associations of regional brain cortical sulcal measures derived from magnetic resonance imaging data of 40,169 individuals in the UK Biobank. Combining both genotyping and whole-exome sequencing data ([~]12 million variants), we discovered 388 regional brain folding associations across 77 genetic loci at p<5x10-8, which replicated at p<0.05. We found genes in associated loci to be independently enriched for expression in the cerebral cortex, neuronal development processes and differential regulation in early brain development. We integrated coding associations and brain eQTLs to refine genes for various loci and demonstrated shared signal in the pleiotropic KCNK2 locus with a cortex-specific KCNK2 eQTL. Genetic correlations with neuropsychiatric conditions highlighted emerging patterns across distinct sulcal parameters and related phenotypes. We provide an interactive 3D visualisation of our summary associations, making complex association patterns easier to interpret, and emphasising the added resolution of regional brain analyses compared to global brain measures. Our results offer new insights into the genetic architecture underpinning brain folding and provide a resource to the wider scientific community for studies of pathways driving brain folding and their role in health and disease.

genomics↗

bioRxiv2019

MR-Clust: Clustering of genetic variants in Mendelian randomization with similar causal estimates

MotivationMendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for uncertainty in the causal estimates, and it includes null and junk clusters, to provide protection against the detection of spurious clusters. ResultsOur algorithm correctly detected the number of clusters in a simulation analysis, outperforming the popular Mclust method. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A hypothesis-free search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster. Availability and ImplementationMR-Clust can be downloaded from https://github.com/cnfoley/mrclust. Contactsb452@medschl.cam.ac.uk or christopher.foley@mrc-bsu.cam.ac.uk Supplementary InformationSupplementary Material is included in the submission.

bioinformatics↗

bioRxiv2019

Genetic determinants of lipids and cardiovascular disease outcomes: a wide-angled Mendelian randomization investigation

AimsTo systematically investigate causal relationships between circulating lipids and cardiovascular outcomes, using a Mendelian randomization approach.\n\nMethods and ResultsIn the primary analysis, we performed two-sample multivariable Mendelian randomization using data from participants of European ancestry. We also conducted univariable analyses using inverse-variance weighted and robust methods, and gene-specific analyses using variants that can be considered as proxies for specific lipid-lowering medications. We obtained associations with lipid fractions from the Global Lipids Genetics Consortium, a meta-analysis of 188,577 participants, and genetic associations with cardiovascular outcomes from 367,703 participants in UK Biobank.\n\nFor LDL-cholesterol, in addition to the expected positive associations with coronary artery disease (CAD) risk (odds ratio per 1 standard deviation increase [OR], 1.45; 95% confidence interval [95%CI] 1.35-1.57) and other atheromatous outcomes (ischemic cerebrovascular disease and peripheral vascular disease), we found independent associations of genetically-predicted LDL-cholesterol with abdominal aortic aneurysm (OR 1.75; 95%CI 1.40-2.17), and aortic valve stenosis (OR 1.46; 95%CI 1.25-1.70). Genetically-predicted triglyceride levels were positively associated with CAD (OR 1.25; 95%CI 1.12-1.40), aortic valve stenosis (OR 1.29; 95%CI 1.04-1.61), and hypertension (OR 1.17; 95%CI 1.07-1.27), but inversely associated with venous thromboembolism (OR 0.79; 95%CI 0.67-0.93). The positive associations of genetically-predicted LDL-cholesterol and triglycerides with heart failure and aortic stenosis appeared to be mediated by CAD.\n\nConclusionLowering LDL-cholesterol is likely to prevent abdominal aortic aneurysm and aortic stenosis, in addition to CAD and other atheromatous cardiovascular outcomes. Lowering triglycerides is likely to prevent CAD and aortic valve stenosis, but may increase risk of thromboembolism.

epidemiology↗

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↗

bioRxiv2019

Shared mechanisms between coronary heart disease and depression: findings from a large UK general population-based cohort

While comorbidity between coronary heart disease (CHD) and depression is evident, it is unclear whether the two diseases have shared underlying mechanisms. We performed a range of analyses in 367,703 unrelated middle-aged participants of European ancestry from UK Biobank, a population based cohort study, to assess whether comorbidity is primarily due to genetic or environmental factors, and to test whether cardiovascular risk factors and CHD are likely to be causally related to depression using Mendelian randomization. We showed family history of heart disease was associated with a 20% increase in depression risk (95% confidence interval [CI] 16% to 24%, p<0.0001), but a genetic risk score that is strongly associated with CHD risk was not associated with depression. An increase of one standard deviation in the CH D genetic risk score was associated with 71% higher CHD risk, but 1% higher depression risk (95% CI 0% to 3%; p=0.11). Mendelian randomization analyses suggested that triglycerides, interleukin-6 (IL-6), and C-reactive protein (CRP) are likely causal risk factors for depression. The odds ratio for depression per standard deviation increase in genetically-predicted triglycerides was 1.18 (95% CI 1.09 to 1.27; p=2x10-5); per unit increase in genetically-predicted log-transformed I L-6 was 0.74 (95% CI 0.62 to 0.89; p=0.0012); and per unit increase in genetically-predicted log-transformed CRP was 1.18 (95% CI 1.07 to 1.29; p=0.0009). Our analyses suggest that comorbidity between depression and CHD arises largely from shared environmental factors. I L-6, CRP and triglycerides, are likely to be causally linked with depression, so could be targets for treatment and prevention of depression.

genetics↗

bioRxiv2019

Factorial Mendelian randomization: using genetic variants to assess interactions

BackgroundFactorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 x 2 approach, using dichotomized genetic scores to divide the population into 4 subgroups as in a factorial randomized trial. MethodsWe describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure. ResultsSimulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between four- and ten-fold improvement in efficiency is demonstrated over the 2 x 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal sized groups. ConclusionsPrevious factorial Mendelian randomization analyses may have been under-powered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 x 2 approach. Key messagesO_LIFactorial Mendelian randomization is an extension of the Mendelian randomization paradigm to answer questions about interactions. C_LIO_LIThere are two contexts in which factorial Mendelian randomization can be used: for investigating interactions between risk factors, and interactions between pharmacological interventions on risk factors. C_LIO_LIWhile most applications of factorial Mendelian randomization have dichotomized the population as in a 2 x 2 factorial randomized trial, this approach is generally inefficient for detecting statistical interactions. C_LIO_LIIn the first context, efficiency is maximized by including all genetic variants and their cross-terms as instrumental variables for the two risk factors and their product term. C_LIO_LIIn the second context, efficiency is maximized by using continuous genetic scores rather than dichotomized scores. C_LI

epidemiology↗