Bayesian Mendelian Randomization for incomplete pedigree data, and the characterisation of Multiple Sclerosis proteins

Before the GWAS (genome-wide association study) era, many genetic determinants of disease were found via analysis of multiplex pedigrees, that is, by looking for genetic markers that run in families in a similar way as disease. GWAS advent has robbed pedigree analysis of its luster. Future scientific methodology seesaw might bring pedigree analysis back into the spotlight. After the recent discovery of hundreds of disease-associated variants, interest is focusing on the way these variants affect downstream molecular markers, such as transcripts and protein levels, and on the way the resulting changes in these markers in turn affect disease risk. Statistical methods such as Mendelian Randomization (Katan, 1986), hereafter denoted as MR, represent important tools in this effort. Most MR studies are based on data from unrelated individuals, a notable exception being Brumpton et al. (2019). In the present paper we argue that by enriching these data with data from family-related individuals, a number of difficulties that are encountered in MR can be signicantly attenuated. Motivated by the above considerations, this paper discusses extensions of MR to deal with pedigree data. We adopt the Bayesian MR framework proposed by Berzuini and colleagues (Berzuini et al., 2018), and extend it in various ways to deal with pedigree data.