Pleiotropy robust Mendelian randomization
Lead Research Organisation:
University of Bristol
Department Name: UNLISTED
Abstract
Across the world, scientists are trying to uncover the causes of disease and ill-health, so that resources can be intelligently targeted to improve the lives of the general public. However, ethical considerations often remove the possibility of a randomized controlled trial (RCT), which is the most straightforward and reliable way of assessing causality, and decisions must be made using non-experimental data.
Since our genes are randomly assigned from parents to their children at the point of conception, they should not be related to factors such as diet and other lifestyle choices that impact an individual’s health in later life. We can therefore think of them as creating a natural randomized trial. By comparing people’s health across different genetic sub-groups, we can assess whether a risk factor (e.g. cholesterol) causally influences the risk of a disease (e.g. stroke). This is technique is called Mendelian randomization. My research focuses on developing the statistical methods required to perform this analysis, so that it is reliable and efficient.
Since our genes are randomly assigned from parents to their children at the point of conception, they should not be related to factors such as diet and other lifestyle choices that impact an individual’s health in later life. We can therefore think of them as creating a natural randomized trial. By comparing people’s health across different genetic sub-groups, we can assess whether a risk factor (e.g. cholesterol) causally influences the risk of a disease (e.g. stroke). This is technique is called Mendelian randomization. My research focuses on developing the statistical methods required to perform this analysis, so that it is reliable and efficient.
Technical Summary
The explosion in publicly available data from genome-wide association studies is accelerating the use of Mendelian randomization (MR) in bio-medicine. Summary data estimates of genetic association from large numbers of variants are now being synthesised for causal inference within the two-sample framework. Over the coming years, cohort studies such as UK Biobank will also provide a rich individual-level data resource for MR investigations. In a short time, the field has seen, and will continue to see, a dramatic rise in the power for testing causal hypotheses. There is a justified concern that when large numbers of genetic variants are included in MR analyses, with many lacking a firm biological basis for their association with the exposure, a sizeable proportion of these variants are likely to be invalid instrumental variables.
A chief concern is that genetic variants may exert an effect on the outcome not through the exposure of interest - which is referred to as horizontal pleiotropy. This programme will focus on the development of statistical methods for MR in the presence of pleiotropy and other associated biases. Building upon recent methodological advancements in this field, we will develop analysis tools that:
• Provide natural robustness to pleiotropy;
• Adjust for non-random selection into (or out of) cohort studies used for MR;
• Novel methods for data visualisation and analysis will be incorporated into statistical software platforms such as MR-Base
This programme links key players in methodological research around the world to leading biomedical scientists at the Integrative Epidemiology Unit. It will develop the tools to enable epidemiological researchers to fully capitalise on emerging large-scale data sources whilst preserving the principles and rigour of causal inference.
A chief concern is that genetic variants may exert an effect on the outcome not through the exposure of interest - which is referred to as horizontal pleiotropy. This programme will focus on the development of statistical methods for MR in the presence of pleiotropy and other associated biases. Building upon recent methodological advancements in this field, we will develop analysis tools that:
• Provide natural robustness to pleiotropy;
• Adjust for non-random selection into (or out of) cohort studies used for MR;
• Novel methods for data visualisation and analysis will be incorporated into statistical software platforms such as MR-Base
This programme links key players in methodological research around the world to leading biomedical scientists at the Integrative Epidemiology Unit. It will develop the tools to enable epidemiological researchers to fully capitalise on emerging large-scale data sources whilst preserving the principles and rigour of causal inference.
Organisations
People |
ORCID iD |
Jack Bowden (Principal Investigator) |
Publications
Anderson EL
(2020)
Education, intelligence and Alzheimer's disease: evidence from a multivariable two-sample Mendelian randomization study.
in International journal of epidemiology
Bowden J
(2018)
Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression.
in International journal of epidemiology
Bowden J
(2018)
Invited Commentary: Detecting Individual and Global Horizontal Pleiotropy in Mendelian Randomization-A Job for the Humble Heterogeneity Statistic?
in American journal of epidemiology
Bowden J
(2019)
Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption.
in International journal of epidemiology
Bowden J
(2021)
Realising the full potential of MR-PHeWAS in cancer.
in British journal of cancer
Bowden J
(2019)
Meta-analysis and Mendelian randomization: A review.
in Research synthesis methods
Brumpton B
(2020)
Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses.
in Nature communications
Bull CJ
(2020)
Adiposity, metabolites, and colorectal cancer risk: Mendelian randomization study.
in BMC medicine
Carter AR
(2021)
Mendelian randomisation for mediation analysis: current methods and challenges for implementation.
in European journal of epidemiology
Dai JY
(2018)
Diagnostics for Pleiotropy in Mendelian Randomization Studies: Global and Individual Tests for Direct Effects.
in American journal of epidemiology
Dashti HS
(2019)
Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates.
in Nature communications
Gage SH
(2018)
Investigating causality in associations between education and smoking: a two-sample Mendelian randomization study.
in International journal of epidemiology
Gill D
(2018)
Age at menarche and adult body mass index: a Mendelian randomization study.
in International journal of obesity (2005)
Gormley M
(2020)
A multivariable Mendelian randomization analysis investigating smoking and alcohol consumption in oral and oropharyngeal cancer.
in Nature communications
Hartley A
(2021)
Mendelian randomization provides evidence for a causal effect of higher serum IGF-1 concentration on risk of hip and knee osteoarthritis.
in Rheumatology (Oxford, England)
Hartwig FP
(2020)
The median and the mode as robust meta-analysis estimators in the presence of small-study effects and outliers.
in Research synthesis methods
Hemani G
(2018)
Evaluating the potential role of pleiotropy in Mendelian randomization studies.
in Human molecular genetics
Hemani G
(2018)
The MR-Base platform supports systematic causal inference across the human phenome.
in eLife
Higbee DH
(2021)
Lung function and cardiovascular disease: a two-sample Mendelian randomisation study.
in The European respiratory journal
Jenkins DA
(2021)
Estimating the causal effect of BMI on mortality risk in people with heart disease, diabetes and cancer using Mendelian randomization.
in International journal of cardiology
Jones SE
(2019)
Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms.
in Nature communications
Langan D
(2019)
A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses.
in Research synthesis methods
Lotta LA
(2021)
A cross-platform approach identifies genetic regulators of human metabolism and health.
in Nature genetics
Minelli C
(2018)
Age at puberty and risk of asthma: A Mendelian randomisation study.
in PLoS medicine
Pilling LC
(2021)
Low Vitamin D Levels and Risk of Incident Delirium in 351,000 Older UK Biobank Participants.
in Journal of the American Geriatrics Society
Richardson TG
(2020)
Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study.
in BMJ (Clinical research ed.)
Richmond RC
(2019)
Investigating causal relations between sleep traits and risk of breast cancer in women: mendelian randomisation study.
in BMJ (Clinical research ed.)
Sanderson E
(2021)
The use of negative control outcomes in Mendelian randomization to detect potential population stratification
in International Journal of Epidemiology
Sanderson E
(2021)
Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization.
in Statistics in medicine
Sanderson E
(2018)
Negative control exposure studies in the presence of measurement error: implications for attempted effect estimate calibration.
in International journal of epidemiology
Sanderson E
(2021)
Multivariable Mendelian Randomization and Mediation.
in Cold Spring Harbor perspectives in medicine
Sanderson E
(2019)
An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings.
in International journal of epidemiology
Sanderson E
(2019)
Mendelian randomisation analysis of the effect of educational attainment and cognitive ability on smoking behaviour.
in Nature communications
Schmitz S
(2018)
The use of single armed observational data to closing the gap in otherwise disconnected evidence networks: a network meta-analysis in multiple myeloma.
in BMC medical research methodology
Skrivankova VW
(2021)
Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration.
in BMJ (Clinical research ed.)
Spiller W
(2019)
Detecting and correcting for bias in Mendelian randomization analyses using Gene-by-Environment interactions.
in International journal of epidemiology
Tudball MJ
(2021)
Mendelian randomisation with coarsened exposures.
in Genetic epidemiology
Villar SS
(2018)
Response-adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?
in Pharmaceutical statistics
Wang H
(2019)
Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes.
in Nature communications
Ward-Caviness CK
(2019)
Mendelian randomization evaluation of causal effects of fibrinogen on incident coronary heart disease.
in PloS one
Windmeijer F
(2021)
The Confidence Interval Method for Selecting Valid Instrumental Variables
in Journal of the Royal Statistical Society Series B: Statistical Methodology
Yang Q
(2022)
Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization.
in European journal of epidemiology
Related Projects
Project Reference | Relationship | Related To | Start | End | Award Value |
---|---|---|---|---|---|
MC_UU_00011/1 | 01/04/2018 | 31/03/2023 | £2,864,000 | ||
MC_UU_00011/2 | Transfer | MC_UU_00011/1 | 01/04/2018 | 31/03/2023 | £965,000 |
MC_UU_00011/3 | Transfer | MC_UU_00011/2 | 01/04/2018 | 31/03/2023 | £1,011,000 |
MC_UU_00011/4 | Transfer | MC_UU_00011/3 | 01/04/2018 | 31/03/2023 | £1,329,000 |
MC_UU_00011/5 | Transfer | MC_UU_00011/4 | 01/04/2018 | 31/03/2023 | £1,254,000 |
MC_UU_00011/6 | Transfer | MC_UU_00011/5 | 01/04/2018 | 31/03/2023 | £1,640,000 |
MC_UU_00011/7 | Transfer | MC_UU_00011/6 | 01/04/2018 | 31/03/2023 | £1,083,000 |
Title | Radial MR |
Description | An R package for fitting and visualising radial inverse variance weighted and radial MR-Egger models for two sample summary data Mendelian randomization |
Type Of Technology | Software |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | The software was developed in conjunction with a paper on improving the visualisation, analysis and interpretation of summary data in Mendelian randomization. See https://www.biorxiv.org/content/early/2017/10/11/200378 |
URL | https://github.com/WSpiller |
Title | mrrobust-a tool for performing two-sample summary Mendelian randomization analyses using Stata |
Description | In recent years, Mendelian randomization analysis using summary data from genome-wide association studies has become a popular approach for investigating causal relationships in epidemiology. The mrrobust Stata package implements several of the recently developed methods. General features The package includes inverse variance weighted estimation, as well as a range of median, modal and MR-Egger estimation methods. Using mrrobust, plots can be constructed visualizing each estimate either individually or simultaneously. The package also provides statistics such as I2GX?, which are useful in assessing attenuation bias in causal estimates. Availability The software is freely available from GitHub |
Type Of Technology | Software |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | Researchers familiar with Stata (but not R) can now apply state of the art summary data MR methods |
URL | https://academic.oup.com/ije/advance-article/doi/10.1093/ije/dyy195/5096673 |