A5087932701- Genetic Associations and Epidemiology
- Genetic and phenotypic traits in livestock
- Liver Disease Diagnosis and Treatment
- Advanced Causal Inference Techniques
- Lipoproteins and Cardiovascular Health
- Genetic Mapping and Diversity in Plants and Animals
- Nutrition, Genetics, and Disease
- Birth, Development, and Health
- Folate and B Vitamins Research
- Bioinformatics and Genomic Networks
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Statistical Methods in Clinical Trials
- Obesity, Physical Activity, Diet
- Nutritional Studies and Diet
- Vitamin D Research Studies
- Diabetes Treatment and Management
- Alcohol Consumption and Health Effects
- Cancer, Lipids, and Metabolism
- Gene expression and cancer classification
- Epigenetics and DNA Methylation
- Cardiovascular Disease and Adiposity
- Cancer-related molecular mechanisms research
- Regulation of Appetite and Obesity
- Health Systems, Economic Evaluations, Quality of Life
- BRCA gene mutations in cancer
MRC Biostatistics Unit
2015-2025
University of Cambridge
2016-2025
British Heart Foundation
2019-2025
Medical Research Council
2016-2025
University of Bristol
2015-2024
MRC Epidemiology Unit
2016-2024
University of Copenhagen
2015-2024
Lung Institute
2023-2024
Ottawa Public Health
2024
Imperial College London
2023-2024
Background: The number of Mendelian randomization analyses including large numbers genetic variants is rapidly increasing. This due to the proliferation genome-wide association studies, and desire obtain more precise estimates causal effects. However, some may not be valid instrumental variables, in particular them having than one proximal phenotypic correlate (pleiotropy). Methods: We view with multiple instruments as a meta-analysis, show that bias caused by pleiotropy can regarded...
ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic data have made application Mendelian randomization relatively straightforward. However, obtaining reliable results from a investigation remains problematic, as conventional inverse‐variance weighted method only gives consistent estimates if all variants analysis are valid instrumental variables. We present novel median estimator for combining on multiple into single causal estimate....
Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly GWAS results often insufficiently curated, undermining efficient implementation of approach. We therefore developed MR-Base ( http://www.mrbase.org ): platform that integrates curated database complete (no restrictions...
Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source data for Mendelian randomization investigations. We demonstrate how such from multiple can be combined in analysis to estimate causal effect risk factor on an outcome. The bias and efficiency estimates based summarized compared those individual-level simulation studies. investigate impact gene-gene...
Mendelian randomization-Egger (MR-Egger) is an analysis method for randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test directional pleiotropy, (2) causal effect, and (3) estimate the effect. While conventional methods assume that all variants satisfy instrumental variable assumptions, able to assess whether have pleiotropic effects on outcome differ average from zero (directional pleiotropy), as well provide consistent under weaker assumption—the InSIDE...
Abstract Summary PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates ‘phenome scans’, where variants are cross-referenced for with many phenotypes different types. Here we present major update (‘PhenoScanner V2’), including over 150 million and more than 65 billion associations (compared to 350 V1) diseases traits, gene expression, metabolite protein levels, epigenetic markers. The query options...
MendelianRandomization is a software package for the R open-source environment that performs Mendelian randomization analyses using summarized data. The core functionality to implement inverse-variance weighted, MR-Egger and weighted median methods multiple genetic variants. Several options are available user, such as use of robust regression, fixed- or random-effects models penalization weights variants with heterogeneous causal estimates. Extensions these methods, allowing be correlated,...
Mendelian randomization investigations are becoming more powerful and simpler to perform, due the increasing size coverage of genome-wide association studies availability summarized data on genetic associations with risk factors disease outcomes. However, when using multiple variants from different gene regions in a analysis, it is highly implausible that all satisfy instrumental variable assumptions. This means simple analysis alone should not be relied give causal conclusion. In this...
<ns4:p>This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, journal editors reviewers assess manuscripts. The are divided into nine sections: motivation scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary sensitivity (one section on robust methods one other approaches), presentation, interpretation. These will be updated...
ABSTRACT Mendelian randomization analyses are often performed using summarized data. The causal estimate from a one‐sample analysis (in which data taken single source) with weak instrumental variables is biased in the direction of observational association between risk factor and outcome, whereas two‐sample on outcome non‐overlapping datasets) less any bias null. When genetic consortia that have partially overlapping sets participants, extent uncertain. In this paper, we perform simulation...
Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published is an attractive analysis strategy obtaining precise estimates the causal effects risk factors outcomes. We detail necessary steps conducting investigations using data, and present novel statistical methods combining multiple (correlated or...
Abstract Summary: PhenoScanner is a curated database of publicly available results from large-scale genetic association studies. This tool aims to facilitate ‘phenome scans’, the cross-referencing variants with many phenotypes, help aid understanding disease pathways and biology. The currently contains over 350 million 10 unique variants, mostly single nucleotide polymorphisms. It accompanied by web-based that queries for associations user-specified providing according same effect non-effect...
A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with interest as instrumental variables. However, in some cases, such case triglyceride level for cardiovascular disease, it may be difficult to find relevant variant is not also related factors, other lipid fractions. Such known pleiotropic. In this paper, we propose extension uses multiple several measured factors simultaneously...
<ns4:p>This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, journal editors reviewers assess manuscripts. The are divided into nine sections: motivation scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary sensitivity (one section on robust statistical methods one other approaches), presentation, interpretation. These will...
Mendelian randomization (MR) is a method for estimating the causal relationship between an exposure and outcome using genetic factor as instrumental variable (IV) exposure. In traditional MR setting, data on IV, exposure, are available all participants. However, obtaining complete may be difficult in some settings, due to high measurement costs or lack of appropriate biospecimens. We used simulated sets assess statistical power bias when subset (or independent set) show that participants...
Mendelian randomization is the use of genetic instrumental variables to obtain causal inferences from observational data. Two recent developments for combining information on multiple uncorrelated (IVs) into a single estimate are as follows: (i) allele scores, in which individual-level data IVs aggregated univariate score, used IV, and (ii) summary statistic method, estimates calculated each IV using summarized combined an inverse-variance weighted meta-analysis. To avoid bias weak...
<h3>Importance</h3> The prevalence of cardiometabolic multimorbidity is increasing. <h3>Objective</h3> To estimate reductions in life expectancy associated with multimorbidity. <h3>Design, Setting, and Participants</h3> Age- sex-adjusted mortality rates hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689 300 participants; 91 cohorts; years baseline surveys: 1960-2007; latest follow-up: April 2013; 128 843 deaths). HRs...
BackgroundLow-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous disease.MethodsWe did a combined analysis of three large-scale sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, the UK Biobank). We characterised...
Mendelian randomization (MR) is an increasingly important tool for appraising causality in observational epidemiology. The technique exploits the principle that genotypes are not generally susceptible to reverse causation bias and confounding, reflecting their fixed nature Mendel’s first second laws of inheritance. approach is, however, subject limitations assumptions that, if unaddressed or compounded by poor study design, can lead erroneous conclusions. Nevertheless, advent 2-sample...