2020/02/12 by Teresa Fazia, Leonardo Egidi, Fazia, Teresa +21
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · #Applications (stat.AP) #Bayesian probability #Biology #Causation #Computer science #Confounding #Econometrics #Evolution and Genetic Dynamics #FOS: Computer and information sciences #Gene #Genetic Associations and Epidemiology #Genetics #Mathematics #Medicine #Mendelian randomization #Missing data #Statistics #Wheat and Barley Genetics and Pathology #stat.AP
paper · pdf · doi:10.48550/arxiv.2002.04872
published in Research Explorer (The University of Manchester) (University of Manchester)
openalex publication_date 2020/02/12 · arxiv created 2020/02/14 · arxiv updated 2020/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We expand Mendelian Randomization (MR) methodology to deal with randomly missing data on either the exposure or the outcome variable, and furthermore with data from nonindependent individuals (eg components of a family). Our method rests on the Bayesian MR framework proposed by Berzuini et al (2018), which we apply in a study of multiplex Multiple Sclerosis (MS) Sardinian families to characterise the role of certain plasma proteins in MS causation. The method is robust to presence of pleiotropic effects in an unknown number of instruments, and is able to incorporate inter-individual kinship information. Introduction of missing data allows us to overcome the bias introduced by the (reverse) effect of treatment (in MS cases) on level of protein. From a substantive point of view, our study results confirm recent suspicion that an increase in circulating IL12A and STAT4 protein levels does not cause an increase in MS risk, as originally believed, suggesting that these two proteins may not be suitable drug targets for MS.