Reworking GWAS Data to Understand the Role of Nongenetic Factors in MS Etiopathogenesis.

Rosella Mechelli,Virginia Rinaldi,Grazia Manfrè,Giovanni Ristori,Gianmarco Bellucci,Renato Umeton,Maria Chiara Buscarinu,Michela Ferraldeschi,Silvia Romano,Marco Salvetti,Rachele Bigi

doi:10.3390/genes11010097

Abstract

Genome-wide association studies have identified more than 200 multiple sclerosis (MS)-associated loci across the human genome over the last decade, suggesting complexity in the disease etiology. This complexity poses at least two challenges: the definition of an etiological model including the impact of nongenetic factors, and the clinical translation of genomic data that may be drivers for new druggable targets. We reviewed studies dealing with single genes of interest, to understand how MS-associated single nucleotide polymorphism (SNP) variants affect the expression and the function of those genes. We then surveyed studies on the bioinformatic reworking of genome-wide association studies (GWAS) data, with aggregate analyses of many GWAS loci, each contributing with a small effect to the overall disease predisposition. These investigations uncovered new information, especially when combined with nongenetic factors having possible roles in the disease etiology. In this context, the interactome approach, defined as “modules of genes whose products are known to physically interact with environmental or human factors with plausible relevance for MS pathogenesis”, will be reported in detail. For a future perspective, a polygenic risk score, defined as a cumulative risk derived from aggregating the contributions of many DNA variants associated with a complex trait, may be integrated with data on environmental factors affecting the disease risk or protection.

Highlights

Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system causing neurological disability
Current multiple sclerosis (MS) research has been significantly affected by the increasing number of disease-modifying therapies (DMTs) that are available for its relapsing-remitting phase
A brilliant example of how clinical practice can be informed by reworking genome-wide association studies (GWAS) data was given in a work on an MS-associated single nucleotide polymorphism (SNP) in the tumor necrosis factor (TNF) receptor gene region, where the authors demonstrated that the genic variant mimicked the effect of TNF-blocking drugs in increasing the risk of demyelinating disease [22]

Summary

Introduction

Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system causing neurological disability. A brilliant example of how clinical practice can be informed by reworking GWAS data was given in a work on an MS-associated SNP in the tumor necrosis factor (TNF) receptor gene region, where the authors demonstrated that the genic variant mimicked the effect of TNF-blocking drugs in increasing the risk of demyelinating disease [22] Another recent finding (overexpression of the B cell-promoting cytokine BAFF due to an MS-associated genetic variant; [23]) seems to be in line with the substantial impact of B cell-depleting therapies on MS course [24], but is yet to be reconciled with the worsening effects resulting from an anti-BAFF trial on disease activity [24,25]. A possible channel for the future development of similar approaches may lie in the bioinformatic reworking of GWAS data, considering the components of PI together with supposedly active, nonheritable factors which are known to interact with the genetic signals resulting from genome-scale data

Bioinformatic Reworking of GWAS Data

Results

Interactome-Based Approach

Future Perspectives