Please use this identifier to cite or link to this item: http://hdl.handle.net/10077/29170
Title: Bayesian Mendelian Randomization for incomplete pedigree data, and the characterisation of Multiple Sclerosis proteins
Authors: Fazia, Teresa
Egidi, Leonardo
Ayoglu, Burku
Beechan, Ashley
Botti, Pier Paolo
Ticca, Anna
McCauley, Jacob L.
Keywords: Genetic determinantsPedigreeMendelian RandomizationBayesian methods
Issue Date: 2019
Publisher: EUT Edizioni Università di Trieste
Source: Teresa Fazia, Leonardo Egidi, Burcu Ayoglu, Ashley Beecham, Pier Paolo Bitti, Anna Ticca, "Bayesian Mendelian Randomization for incomplete pedigree data, and the characterisation of Multiple Sclerosis proteins", Trieste, EUT Edizioni Università di Trieste, 2019
Series/Report no.: DEAMS Research Paper Series 
Contents: DEAMS Research Paper Series, 2019, 1
Pages: 30
Abstract: 
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 significantly 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.
Type: Book
URI: http://hdl.handle.net/10077/29170
Appears in Collections:DEAMS Research Paper Series 2019, 1

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