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

2013; Elsevier BV; Volume: 92; Issue: 6 Linguagem: Inglês

10.1016/j.ajhg.2013.04.019

1537-6605

Sergio E. Baranzini, Pouya Khankhanian, Nikolaos A. Patsopoulos, Michael Li, Jim Stankovich, Chris Cotsapas, Helle Bach Søndergaard, Maria Ban, Nadia Barizzone, Laura Bergamaschi, David R. Booth, Dorothea Buck, Paola Cavalla, Elisabeth Gulowsen Celius, Manuel Comabella, Gıancarlo Comı, Alastair Compston, Isabelle Cournu‐Rebeix, Sandra D’Alfonso, Vincent Damotte, Lennox Din, Bénédicte Dubois, Irina Elovaara, Federica Esposito, Bertrand Fontaine, André Franke, An Goris, Pierre‐Antoine Gourraud, Christiane Graetz, Franca Rosa Guerini, Léna Guillot‐Noël, D Hafler, Hákon Hákonarson, Per Hall, Anders Hamsten, Hanne F. Harbo, Bernhard Hemmer, Jan Hillert, Anu Kemppinen, Ingrid Kockum, Keijo Koivisto, Malin Larsson, Mark Lathrop, Maurizio Leone, Christina M. Lill, Fabìo Macciardi, Roland Martinꝉ, Vittorio Martinelli, Filippo Martinelli Boneschi, Jacob L. McCauley, Kjell‐Morten Myhr, Paola Naldi, Tomas Olsson, Annette Oturai, Margaret A. Pericak‐Vance, Franco Perla, Mauri Reunanen, Janna Saarela, Safa Saker-Delye, Marco Salvetti, Finn Sellebjerg, Per Soelberg Sørensen, Anne Spurkland, Graeme J. Stewart, Bruce Taylor, Pentti J. Tienari, Juliane Winkelmann, Frauke Zipp, Adrian J. Ivinson, Jonathan L. Haines, Stephen Sawcer, Philip L. DeJager, Stephen L. Hauser, Jorge R. Oksenberg,

Genetic Associations and Epidemiology

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. 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.