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Abstract
Background: Mendelian randomisation (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions.
Methods: Here, a new method –the mode-based estimate (MBE) – is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk.
Results: The MBE presented less bias and type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared to the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia.
Conclusions: The MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in a sensitivity analysis.
Methods: Here, a new method –the mode-based estimate (MBE) – is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk.
Results: The MBE presented less bias and type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared to the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia.
Conclusions: The MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in a sensitivity analysis.
Original language | English |
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Article number | dyx102 |
Pages (from-to) | 1985-1998 |
Number of pages | 14 |
Journal | International Journal of Epidemiology |
Volume | 46 |
Issue number | 6 |
Early online date | 12 Jul 2017 |
DOIs | |
Publication status | Published - 1 Dec 2017 |
Keywords
- Causality
- Instrumental variables
- Genetic variation
- Mendelian randomization
- Genetic pleiotropy
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Dive into the research topics of 'Robust inference in summary data Mendelian randomisation via the zero modal pleiotropy assumption'. Together they form a unique fingerprint.Projects
- 3 Finished
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Jack Bowden fellowship transfer
Gaunt, L. F. (Principal Investigator)
1/08/15 → 31/03/18
Project: Research
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IEU Theme 3
Windmeijer, F. (Principal Investigator), Tilling, K. M. (Researcher) & Tilling, K. M. (Principal Investigator)
1/06/13 → 31/03/18
Project: Research
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MRC UoB UNITE Unit - Programme 1
Davey Smith, G. (Principal Investigator)
1/06/13 → 31/03/18
Project: Research