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Mendelian randomization uses genetic variants to make causal inferences aboutthe effect of a risk factor on an outcome. With fine-mapped genetic data, theremay be hundreds of genetic variants in a single gene region any of which couldbe used to assess this causal relationship. However, using too many geneticvariants in the analysis can lead to spurious estimates and inflated Type 1error rates. But if only a few genetic variants are used, then the majority ofthe data is ignored and estimates are highly sensitive to the particular choiceof variants. We propose an approach based on summarized data only (geneticassociation and correlation estimates) that uses principal components analysisto form instruments. This approach has desirable theoretical properties: ittakes the totality of data into account and does not suffer from numericalinstabilities. It also has good properties in simulation studies: it is notparticularly sensitive to varying the genetic variants included in the analysisor the genetic correlation matrix, and it does not have greatly inflated Type 1error rates. Overall, the method gives estimates that are not so precise asthose from variable selection approaches (such as using a conditional analysisor pruning approach to select variants), but are more robust to seeminglyarbitrary choices in the variable selection step. Methods are illustrated by anexample using genetic associations with testosterone for 320 genetic variantsto assess the effect of sex hormone-related pathways on coronary artery diseaserisk, in which variable selection approaches give inconsistent inferences.

Mendelian randomization with fine-mapped genetic data: choosing from large numbers of correlated instrumental variables