Biomedical subjects

Martin D Tobin

Publications and source records attributed to Martin D Tobin.

15 recordsLinked to original sources

PubMed2026

Hypertrophic cardiomyopathy: a genome-wide association meta-analysis and polygenic risk score.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a heritable trait with marked variability in expression and outcomes. Our aims were to discover new genetic loci associated with HCM and to test the effect of a new polygenic risk score (PRS) on incidence, phenotype and outcomes stratified by genotype status. METHODS: A discovery genome-wide association study (GWAS) was performed on 2284 HCM cases and 4525 controls. Two fixed-effects meta-analyses combined our discovery GWAS with single-trait and multi-trait results from a published study. Discovered loci underwent comprehensive bioinformatic analysis including functional and druggability annotations. A PRS using loci from the two meta-analyses was evaluated for association with HCM diagnosis in 411 213 individuals from UK Biobank (UKBB); imaging phenotypes in individuals without HCM; a composite endpoint (including all-cause mortality and transplantation); and sudden cardiac death (SCD) in 1756 HCM cases. PRS analyses were stratified by genotype status. RESULTS: Three loci were found in the discovery GWAS (BAG3, FHOD3 and novel locus PPP1R3A). In the meta-analyses, 70 unique loci were identified, four novel (MYPN, YWHAE, NOS1AP and OBSCN). Bioinformatic analyses identified NOS1AP as a candidate HCM gene. A new PRS was significantly associated with HCM diagnosis (HR=3.19, 95% CI 2.46 to 4.14 for top 5% vs lower 95%; HR=1.88, 95% CI 1.72 to 2.06 per SD increase). Significant associations were found between PRS and greater left ventricular (LV) wall thickness and higher LV ejection fraction in UKBB participants without HCM. Genotype-negative HCM cases in the top 20% of the PRS distribution had an increased risk of SCD (HR=2.72, 95% CI 1.03 to 7.17). CONCLUSIONS: We report novel HCM loci. A new PRS predicted the risk of HCM development and associated imaging characteristics in the UKBB and outcomes in an HCM cohort.

Cardiomyopathies↗

PubMed2026

Proteome-wide Mendelian randomisation of lung function to identify potential therapeutic targets for respiratory disease.

BACKGROUND: Despite multiple clinical trials, disease-modifying treatments for COPD are currently limited. Since many drugs target proteins, identifying causality between proteins and lung function informs understanding of COPD pathophysiology and may suggest novel targets. We used Mendelian randomisation (MR) to prioritise proteins as potentially causal for imparied lung function. For prioritised proteins, we explored their potential suitability as drug targets by predicting their effects on a range of clinical outcomes. METHODS: We used genome-wide association study (GWAS) data on 2923 proteins (n=48 195, UK Biobank) to identify single genetic variants (protein quantitative trait loci (cis-pQTLs)) associated with protein levels (p≤5×10-9, variant ≤100 kb of a transcription start site). We performed cis-pQTL-MR analyses of four spirometric traits (n=149 166, 36 independent cohorts). Sensitivity analyses included colocalisation and reverse direction MR. We report associations between cis-pQTLs for prioritised proteins and multiple clinical respiratory outcomes, and use phenome-wide analysis to explore potential adverse effects or drug repurposing opportunities. FINDINGS: 1841 proteins had a suitable cis-pQTL. We implicated 16 proteins as potentially causal for lung function (p<1.71×10-5): seven proteins have not been implicated by previous lung function GWAS or MR (CCND2, DTD1, PILRA, PTPRK, TDRKH, GRHPR, NUDT5), and we provide corroborative evidence for 10 proteins. We add to the literature identifying surfactant protein D (SFTPD) as a candidate, yet predict that integrin subunit alpha V (ITGAV) inhibition could impair some lung function measures, mimicking adverse results from a recent trial. INTERPRETATION: Our approach identifies proteins (some novel) that are potentially therapeutic targets for respiratory disease, and which warrant follow-up for utility and safety.

Journal Article↗

PubMed2026

Genome-wide association and multi-omics functional screens reveal the genetic architecture of foveal development.

Foveal hypoplasia causes visual impairment across congenital eye disorders, yet the genetic programmes governing foveal development remain poorly characterised and no tractable model exists for foveal disease. In the first genome-wide association study of foveal hypoplasia, we identified 42 sentinel variants mapping to 54 effector genes supported by ≥ 2 criteria from a variant-to-gene framework incorporating developmental multi-omics. Disruption of six effector genes using mutant lines and CRISPR knockouts in the zebrafish high acuity zone recapitulates structural, functional, and ultrastructural hallmarks of foveal hypoplasia, establishing the first vertebrate disease model. Integration with human foetal single-cell and spatial transcriptomics reveals two temporal waves of effector gene expression and identifies Müller glia as critical mediators of foveal patterning. Phenome-wide analyses reveal foveal variants are pleiotropic with refractive, lenticular, and metabolic traits, connecting foveal development to anterior segment and systemic disease biology. These findings should inform mechanistic studies of macular disease.

Journal Article↗

PubMed2026

Multi-trait polygenic scores for COPD and COPD exacerbations implicate druggable proteins.

BACKGROUNDWe constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting.METHODSPRSmix+, a multi-trait PRS framework, is used to train a composite PRS (PRSmulti) in COPDGene non-Hispanic White participants (n = 6,647). Associations of PRSmulti with COPD status (GOLD 2-4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American (n = 2,466), ECLIPSE (n = 1,858), Mass General Brigham Biobank (n = 15,152), and All of Us (n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRSmulti and were validated with proteomic data in COPDGene (n = 5,173) and UK Biobank (n = 5,012).RESULTSPRSmix+ selected 7 traits for PRSmulti. In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations.CONCLUSIONSMulti-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management.TRIAL REGISTRATIONCOPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Humans↗

PubMed2025

Genome-Wide Association Study of Varenicline-Aided Smoking Cessation.

INTRODUCTION: Varenicline is an α4β2 nicotinic acetylcholine receptor partial agonist with the highest therapeutic efficacy of any pharmacological smoking cessation aid and a 12-month cessation rate of 26%. Genetic variation may be associated with varenicline response, but to date, no genome-wide association studies of varenicline response have been published. METHODS: In this study, we investigated the genetic contribution to varenicline effectiveness using two electronic health record-derived phenotypes. We defined short-term varenicline effectiveness (SVE) and long-term varenicline effectiveness (LVE) by assessing smoking status at 3 and 12 months, respectively, after initiating varenicline treatment. In Stage 1, comprising five European cohort studies, we tested genome-wide associations with SVE (1405 cases, 2074 controls) and LVE (1576 cases, 2555 controls), defining sentinel variants (the most strongly associated variant within 1 Mb) with p-value < 5 × 10-6 to follow up in Stage 2. In Stage 2, we tested association between sentinel variants and comparable smoking cessation endpoints in varenicline randomized controlled trials. We subsequently meta-analyzed Stages 1 and 2. RESULTS: No variants reached genome-wide significance in the meta-analysis. In Stage 1, 10 sentinel variants were associated with SVE and five with LVE at a suggestive significance threshold (p-value < 5 × 10-6); none of these sentinels were previously implicated in varenicline-aided smoking cessation or in genetic studies of smoking behavior. CONCLUSIONS: We provide initial insights into the biological underpinnings of varenicline-aided smoking cessation, through implicating genes involved in various processes, including gene expression, cilium assembly, and early-stage development. IMPLICATIONS: Leveraging electronic health records, we undertook the largest genetic study of varenicline-aided smoking cessation to date, and the only such study to test genome-wide associations. We showed distinct genetic variants associated (p-value < 5 × 10-6) with varenicline-aided smoking cessation which implicate diverse cellular functions, including transcriptional regulation, RNA modification, and cilium assembly. These provide insights which, if independently corroborated, will improve understanding of varenicline response. The growing availability of biobank resources with genetic and varenicline response data will provide future opportunities for larger studies using the approach we developed.

Humans↗

PubMed2006

Public health professionals' perceptions toward provision of health protection in England: a survey of expectations of Primary Care Trusts and Health Protection Units in the delivery of health protection.

BACKGROUND: Effective health protection requires systematised responses with clear accountabilities. In England, Primary Care Trusts and the Health Protection Agency both have statutory responsibilities for health protection. A Memorandum of Understanding identifies responsibilities of both parties, but there is a potential lack of clarity about responsibility for specific health protection functions. We aimed to investigate professionals' perceptions of responsibility for different health protection functions, to inform future guidance for, and organisation of, health protection in England. METHODS: We sent a postal questionnaire to all health protection professionals in England from the following groups: (a) Directors of Public Health in Primary Care Trusts; (b) Directors of Health Protection Units within the Health Protection Agency; (c) Directors of Public Health in Strategic Health Authorities and; (d) Regional Directors of the Health Protection Agency RESULTS: The response rate exceeded 70%. Variations in perceptions of who should be, and who is, delivering health protection functions were observed within, and between, the professional groups (a)-(d). Concordance in views of which organisation should, and which does deliver was high (> or =90%) for 6 of 18 health protection functions, but much lower (< or =80%) for 6 other functions, including managing the implications of a case of meningitis out of hours, of landfill environmental contamination, vaccination in response to mumps outbreaks, nursing home infection control, monitoring sexually transmitted infections and immunisation training for primary care staff. The proportion of respondents reporting that they felt confident most or all of the time in the safe delivery of a health protection function was strongly correlated with the concordance (r = 0.65, P = 0.0038). CONCLUSION: Whilst we studied professionals' perceptions, rather than actual responses to incidents, our study suggests that there are important areas of health protection where consistent understanding of responsibility for delivery is lacking. There are opportunities to clarify the responsibility for health protection in England, perhaps learning from the approaches used for those health protection functions where we found consistent perceptions of accountability.

Administrative Personnel↗

PubMed2005

Association of WNK1 gene polymorphisms and haplotypes with ambulatory blood pressure in the general population.

BACKGROUND: Blood pressure (BP) is a heritable trait of major public health concern. The WNK1 and WNK4 genes, which encode proteins in the WNK family of serine-threonine kinases, are involved in renal electrolyte homeostasis. Mutations in the WNK1 and WNK4 genes cause a rare monogenic hypertensive syndrome, pseudohypoaldosteronism type II. We investigated whether polymorphisms in these WNK genes influence BP in the general population. METHODS AND RESULTS: Associations between 9 single-nucleotide polymorphisms (SNPs) in WNK1 and 1 in WNK4 with ambulatory BP were studied in a population-based sample of 996 subjects from 250 white European families. The heritability estimates of mean 24-hour systolic BP (SBP) and diastolic BP (DBP) were 63.4% and 67.9%, respectively. We found statistically significant (P<0.05) associations of several common SNPs and haplotypes in WNK1 with mean 24-hour SBP and/or DBP. The minor allele (C) of rs880054, with a frequency of 44%, reduced mean 24-hour SBP and DBP by 1.37 (95% confidence interval, -2.45 to -0.23) and 1.14 (95% confidence interval, -1.93 to -0.38) mm Hg, respectively, per copy of the allele. CONCLUSIONS: Common variants in WNK1 contribute to BP variation in the general population. This study shows that a gene causing a rare monogenic form of hypertension also plays a significant role in BP regulation in the general population. The findings provide a basis to identify functional variants of WNK1, elucidate any interactions of these variants with dietary intake or with response to antihypertensive drugs, and determine their impact on cardiovascular morbidity and mortality.

Adolescent↗

PubMed2005

Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure.

A population-based study of a quantitative trait may be seriously compromised when the trait is subject to the effects of a treatment. For example, in a typical study of quantitative blood pressure (BP) 15 per cent or more of middle-aged subjects may take antihypertensive treatment. Without appropriate correction, this can lead to substantial shrinkage in the estimated effect of aetiological determinants of scientific interest and a marked reduction in statistical power. Correction relies upon imputation, in treated subjects, of the underlying BP from the observed BP having invoked one or more assumptions about the bioclinical setting. There is a range of different assumptions that may be made, and a number of different analytical models that may be used. In this paper, we motivate an approach based on a censored normal regression model and compare it with a range of other methods that are currently used or advocated. We compare these methods in simulated data sets and assess the estimation bias and the loss of power that ensue when treatment effects are not appropriately addressed. We also apply the same methods to real data and demonstrate a pattern of behaviour that is consistent with that in the simulation studies. Although all approaches to analysis are necessarily approximations, we conclude that two of the adjustment methods appear to perform well across a range of realistic settings. These are: (1) the addition of a sensible constant to the observed BP in treated subjects; and (2) the censored normal regression model. A third, non-parametric, method based on averaging ordered residuals may also be advocated in some settings. On the other hand, three approaches that are used relatively commonly are fundamentally flawed and should not be used at all. These are: (i) ignoring the problem altogether and analysing observed BP in treated subjects as if it was underlying BP; (ii) fitting a conventional regression model with treatment as a binary covariate; and (iii) excluding treated subjects from the analysis. Given that the more effective methods are straightforward to implement, there is no argument for undertaking a flawed analysis that wastes power and results in excessive bias.

Aged↗

PubMed2005

Meta-analysis of genetic studies using Mendelian randomization--a multivariate approach.

In traditional epidemiological studies the association between phenotype (risk factor) and disease is often biased by confounding and reverse causation. As a person's genotype is assigned by a seemingly random process, genes are potentially useful instrumental variables for adjusting for such bias. This type of adjustment combines information on the genotype-disease association and the genotype-phenotype association to estimate the phenotype-disease association and has become known as Mendelian randomization. The information on genotype-disease and genotype-phenotype may well come from a meta-analysis. In such a synthesis, a multivariate approach needs to be used whenever some studies provide evidence on both the genotype-phenotype and genotype-disease associations. This paper presents two multivariate meta-analytical models, which differ in their treatment of the heterogeneities (between-study variances). Heterogeneities on the genotype-phenotype and genotype-disease associations may be highly correlated, but a multivariate model that parameterizes the heterogeneity directly is difficult to fit because that correlation is poorly estimated. We advocate an alternative model that treats the heterogeneities on genotype-phenotype and phenotype-disease as being independent. This model fits readily and implicitly defines the correlation between the heterogeneities on genotype-phenotype and genotype-disease. We show how either maximum likelihood or a Bayesian approach with vague prior distributions can be used to fit the alternative model.

Coronary Disease↗

PubMed2005

Covariance components models for longitudinal family data.

A longitudinal family study is an epidemiological design that involves repeated measurements over time in a sample that includes families. Such studies, that may also include relative pairs and unrelated individuals, allow closer investigation of not only the factors that cause a disease to arise, but also the genetic and environmental determinants that modulate the subsequent progression of that disease. Knowledge of such determinants may pay high dividends in terms of prognostic assessment and in the development of new treatments that may be tailored to the prognostic profile of individual patients. Unfortunately longitudinal family studies are difficult to analyse. They conflate the complex within-family correlation structure of a cross-sectional family study with the correlation over time that is intrinsic to longitudinal repeated measures. Here we describe an approach to analysis that is relatively straightforward to implement, yet is flexible in its application. It represents a natural extension of a Gibbs-sampling-based approach to the analysis of cross-sectional family studies that we have described previously. The approach can be applied to pedigrees of arbitrary complexity. It is applicable to continuous traits, repeated binary disease states, and repeated counts or rates with a Poisson distribution. It not only supports the analysis of observed determinants, including measured genotypes, but also allows decomposition of the correlation structure, thereby permitting conclusions to be drawn about the effect of unobserved genes and environment on key features of disease progression, and hence to estimate the heritability of these features. We demonstrate the efficacy of our methods using a range of simulated data analyses, and illustrate its practical application to longitudinal blood pressure data measured in families from the Framingham Heart Study.

Blood Pressure↗

PubMed2004

An integrated approach to the meta-analysis of genetic association studies using Mendelian randomization.

A natural randomization process, sometimes called Mendelian randomization, occurs at conception to determine a person's genotype. By combining information from genotype-disease and genotype-phenotype studies, it is possible to use this Mendelian randomization to derive an estimate of the association between phenotype (risk factor) and disease that is free of the confounding and reverse causation typical of classical epidemiology. When one is synthesizing evidence, studies evaluating genotype-phenotype associations, studies evaluating genotype-disease associations, and studies evaluating both are encountered, and methods should be used that allow for this structure. Plotting the log odds ratio of genotype-disease against the mean genotype-phenotype difference may help investigators detect departures from the assumptions underlying Mendelian randomization. Testing for differences between studies reporting on only the genotype-phenotype or genotype-disease association and those reporting on both associations may help in detecting reporting bias. This integrated approach to the meta-analysis of genotype-phenotype and genotype-disease studies is illustrated here using the example of the methylenetetrahydrofolate reductase (MTHFR) gene, homocysteine level, and coronary heart disease. An integrated meta-analytical approach may increase the precision of this estimate and provide information on the assumptions underlying Mendelian randomization. Serious biases may arise if the assumptions behind the analysis based on Mendelian randomization are not met.

Confidence Intervals↗

PubMed2004

Genotypes and haplotypes predisposing to myocardial infarction: a multilocus case-control study.

AIM: To identify polymorphisms and haplotypes in candidate genes that predispose to myocardial infarction (MI) using a multilocus approach. METHODS AND RESULTS: 1052 subjects, comprising 547 acute MI cases and 505 controls were studied. The association between MI and 58 SNPs in 35 candidate genes (generating 61 016 individual genotypes), and between MI and estimated haplotypes at 14 loci encompassing 16 genes was investigated. Two individual gene variants and haplotypes at two loci showed statistical association with MI. The alpha-adducin 460trp variant (OR 0.73, 95% CI 0.59-0.91, P=0.006) and the cholesteryl ester transfer protein -629A variant (OR 0.82, 95% CI 0.68-0.97, P=0.025) were both associated with a significant protective effect on MI, as was the paraoxonase 1/paraoxonase 2 haplotype comprising met55 and gln192 in paraoxonase 1 and cys311 in paraoxonase 2 (OR 0.52, 95% CI 0.39-0.77, P=0.001). The apolipoprotein C III haplotypes CCTTCG and ATCCCG at positions -641*-482*-455*1100*3175*3206 were associated with an increased risk of MI, odds ratios 1.41 (95% CI 1.06-1.76, P=0.023) and 1.71 (95% CI 1.28-2.14, P=0.038), respectively. CONCLUSIONS: We report associations of two polymorphisms and haplotypes at two loci with risk of MI that warrants testing in future studies. Furthermore, we demonstrate the application of a multilocus assay in the setting of a large association study and the additional benefit gained from the study of haplotypes to identify variants influencing risk of coronary heart disease.

Case-Control Studies↗

PubMed2004

Commentary: development of Mendelian randomization: from hypothesis test to 'Mendelian deconfounding'.

Explore the source record for details and available documents.

Causality↗

PubMed2003

Longitudinal variance components models for systolic blood pressure, fitted using Gibbs sampling.

This paper describes an analysis of systolic blood pressure (SBP) in the Genetic Analysis Workshop 13 (GAW13) simulated data. The main aim was to assess evidence for both general and specific genetic effects on the baseline blood pressure and on the rate of change (slope) of blood pressure with time. Generalized linear mixed models were fitted using Gibbs sampling in WinBUGS, and the additive polygenic random effects estimated using these models were then used as continuous phenotypes in a variance components linkage analysis. The first-stage analysis provided evidence for general genetic effects on both the baseline and slope of blood pressure, and the linkage analysis found evidence of several genes, again for both baseline and slope.

Adult Children↗

PubMedDate not supplied

Key concepts in genetic epidemiology.

This article is the first in a series of seven that will provide an overview of central concepts and topical issues in modern genetic epidemiology. In this article, we provide an overall framework for investigating the role of familial factors, especially genetic determinants, in the causation of complex diseases such as diabetes. The discrete steps of the framework to be outlined integrate the biological science underlying modern genetics and the population science underpinning mainstream epidemiology. In keeping with the broad readership of The Lancet and the diverse background of today's genetic epidemiologists, we provide introductory sections to equip readers with basic concepts and vocabulary. We anticipate that, depending on their professional background and specialist knowledge, some readers will wish to skip some of this article.

Epidemiology↗