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Background  In a study performed on multiplex Multiple Sclerosis (MS) Sardinian familiesto identify disease causing plasma proteins, application of MendelianRandomization (MR) methods encounters difficulties due to relatedness ofindividuals, correlation between finely mapped genotype instrumental variables(IVs) and presence of missing exposures.  Method  We specialize the method of Berzuini et al (2018) to deal with thesedifficulties. The proposed method allows pedigree structure to enter thespecification of the outcome distribution via kinship matrix, and treatingmissing exposures as additional parameters to be estimated from the data. Italso acknowledges possible correlation between instruments by replacing theoriginally proposed independence prior for IV-specific pleiotropic effect witha g-prior. Based on correlated (r2< 0.2) IVs, we analysed the data of fourcandidate MS-causing proteins by using both the independence and the g-prior.  Results  95% credible intervals for causal effect for proteins IL12A and STAT4 laywithin the strictly negative real semiaxis, in both analyses, suggestingpotential causality. Those instruments whose estimated pleiotropic effectexceeded 85% of total effect on outcome were found to act in trans. Analysisvia frequentist MR gave inconsistent results. Replacing the independence with ag-prior led to smaller credible intervals for causal effect.  Conclusions  Bayesian MR may be a good way to study disease causation at a protein levelbased on family data and moderately correlated instruments.

Bayesian Mendelian Randomization identifies disease causing proteins via pedigree data, partially observed exposures and correlated instruments