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
(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
(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)
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
Related Projects
Project Reference | Relationship | Related To | Start | End | Award Value |
---|---|---|---|---|---|
MC_UU_00011/1 | 31/03/2018 | 30/03/2023 | £2,864,000 | ||
MC_UU_00011/2 | Transfer | MC_UU_00011/1 | 31/03/2018 | 30/03/2023 | £965,000 |
MC_UU_00011/3 | Transfer | MC_UU_00011/2 | 31/03/2018 | 30/03/2023 | £1,011,000 |
MC_UU_00011/4 | Transfer | MC_UU_00011/3 | 31/03/2018 | 30/03/2023 | £1,329,000 |
MC_UU_00011/5 | Transfer | MC_UU_00011/4 | 31/03/2018 | 30/03/2023 | £1,254,000 |
MC_UU_00011/6 | Transfer | MC_UU_00011/5 | 31/03/2018 | 30/03/2023 | £1,640,000 |
MC_UU_00011/7 | Transfer | MC_UU_00011/6 | 31/03/2018 | 30/03/2023 | £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 |