Mendelian randomization with fine-mapped genetic data: Choosing from large numbers of correlated instrumental variables
File(s)
Published version
Author(s)
Burgess, Stephen
Zuber, Verena
Valdes-Marquez, Elsa
Sun, Benjamin B
Hopewell, Jemma C
Type
Journal Article
Abstract
Mendelian randomization uses genetic variants to make causal inferences about the effect of a risk factor on an outcome. With fine‐mapped genetic data, there may be hundreds of genetic variants in a single gene region any of which could be used to assess this causal relationship. However, using too many genetic variants in the analysis can lead to spurious estimates and inflated Type 1 error rates. But if only a few genetic variants are used, then the majority of the data is ignored and estimates are highly sensitive to the particular choice of variants. We propose an approach based on summarized data only (genetic association and correlation estimates) that uses principal components analysis to form instruments. This approach has desirable theoretical properties: it takes the totality of data into account and does not suffer from numerical instabilities. It also has good properties in simulation studies: it is not particularly sensitive to varying the genetic variants included in the analysis or the genetic correlation matrix, and it does not have greatly inflated Type 1 error rates. Overall, the method gives estimates that are less precise than those from variable selection approaches (such as using a conditional analysis or pruning approach to select variants), but are more robust to seemingly arbitrary choices in the variable selection step. Methods are illustrated by an example using genetic associations with testosterone for 320 genetic variants to assess the effect of sex hormone related pathways on coronary artery disease risk, in which variable selection approaches give inconsistent inferences.
Date Issued
2017-12-01
Date Acceptance
2017-08-16
Citation
Genetic Epidemiology, 2017, 41 (8), pp.714-725
ISSN
0741-0395
Publisher
Wiley
Start Page
714
End Page
725
Journal / Book Title
Genetic Epidemiology
Volume
41
Issue
8
Copyright Statement
© 2017 The Authors. Genetic Epidemiology Published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000415903800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Genetics & Heredity
Mathematical & Computational Biology
Mendelian randomization
allele score
correlated variants
summarized data
conditional analysis
GENOME-WIDE ASSOCIATION
SUMMARIZED DATA
VARIANTS
EPIDEMIOLOGY
STATISTICS
TRAITS
GWAS
Publication Status
Published
Date Publish Online
2017-09-25