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Mendelian randomization is a widely-used method to estimate the unconfoundedeffect of an exposure on an outcome by using genetic variants as instrumentalvariables. Mendelian randomization analyses which use variants from a singlegenetic region (cis-MR) have gained popularity for being an economical way toprovide supporting evidence for drug target validation. This paper proposesmethods for cis-MR inference which use the explanatory power of many correlatedvariants to make valid inferences even in situations where those variants onlyhave weak effects on the exposure. In particular, we exploit the highlystructured nature of genetic correlations in single gene regions to reduce thedimension of genetic variants using factor analysis. These genetic factors arethen used as instrumental variables to construct tests for the causal effect ofinterest. Since these factors may often be weakly associated with the exposure,size distortions of standard t-tests can be severe. Therefore, we consider twoapproaches based on conditional testing. First, we extend results ofcommonly-used identification-robust tests to account for the use of estimatedfactors as instruments. Secondly, we propose a test which appropriately adjustsfor first-stage screening of genetic factors based on their relevance. Ourempirical results provide genetic evidence to validate cholesterol-loweringdrug targets aimed at preventing coronary heart disease.

Conditional inference in cis-Mendelian randomization using weak genetic factors