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Network-Based Multiple Sclerosis Pathway Analysis with GWAS Data from 15,000 Cases and 30,000 Controls.

Authors :
Baranzini, Sergio?E.
Khankhanian, Pouya
Patsopoulos, Nikolaos?A.
Li, Michael
Stankovich, Jim
Cotsapas, Chris
Søndergaard, Helle?Bach
Ban, Maria
Barizzone, Nadia
Bergamaschi, Laura
Booth, David
Buck, Dorothea
Cavalla, Paola
Celius, Elisabeth?G.
Comabella, Manuel
Comi, Giancarlo
Compston, Alastair
Cournu-Rebeix, Isabelle
D’alfonso, Sandra
Damotte, Vincent
Source :
American Journal of Human Genetics. Jun2013, Vol. 92 Issue 6, p854-865. 12p.
Publication Year :
2013

Abstract

Multiple sclerosis (MS) is an inflammatory CNS disease with a substantial genetic component, originally mapped to only the human leukocyte antigen (HLA) region. In the last 5 years, a total of seven genome-wide association studies and one meta-analysis successfully identified 57 non-HLA susceptibility loci. Here, we merged nominal statistical evidence of association and physical evidence of interaction to conduct a protein-interaction-network-based pathway analysis (PINBPA) on two large genetic MS studies comprising a total of 15,317 cases and 29,529 controls. The distribution of nominally significant loci at the gene level matched the patterns of extended linkage disequilibrium in regions of interest. We found that products of genome-wide significantly associated genes are more likely to interact physically and belong to the same or related pathways. We next searched for subnetworks (modules) of genes (and their encoded proteins) enriched with nominally associated loci within each study and identified those modules in common between the two studies. We demonstrate that these modules are more likely to contain genes with bona fide susceptibility variants and, in addition, identify several high-confidence candidates (including BCL10, CD48, REL, TRAF3, and TEC). PINBPA is a powerful approach to gaining further insights into the biology of associated genes and to prioritizing candidates for subsequent genetic studies of complex traits. [Copyright &y& Elsevier]

Details

Language :
English
ISSN :
00029297
Volume :
92
Issue :
6
Database :
Academic Search Index
Journal :
American Journal of Human Genetics
Publication Type :
Academic Journal
Accession number :
89119885
Full Text :
https://doi.org/10.1016/j.ajhg.2013.04.019