A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility
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A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility. / Damotte, V; Guillot-Noel, L; Patsopoulos, N A; Madireddy, L; El Behi, M; De Jager, P L; Baranzini, S E; Cournu-Rebeix, I; Fontaine, B; International Multiple Sclerosis Genetics Consortium ; Sørensen, Per Soelberg.
In: Genes and Immunity, Vol. 15, No. 2, 03.2014, p. 126-132.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A gene pathway analysis highlights the role of cellular adhesion molecules in multiple sclerosis susceptibility
AU - Damotte, V
AU - Guillot-Noel, L
AU - Patsopoulos, N A
AU - Madireddy, L
AU - El Behi, M
AU - De Jager, P L
AU - Baranzini, S E
AU - Cournu-Rebeix, I
AU - Fontaine, B
AU - International Multiple Sclerosis Genetics Consortium
AU - Sørensen, Per Soelberg
PY - 2014/3
Y1 - 2014/3
N2 - Genome-wide association studies (GWASs) perform per-SNP association tests to identify variants involved in disease or trait susceptibility. However, such an approach is not powerful enough to unravel genes that are not individually contributing to the disease/trait, but that may have a role in interaction with other genes as a group. Pathway analysis is an alternative way to highlight such group of genes. Using SNP association P-values from eight multiple sclerosis (MS) GWAS data sets, we performed a candidate pathway analysis for MS susceptibility by considering genes interacting in the cell adhesion molecule (CAMs) biological pathway using Cytoscape software. This network is a strong candidate, as it is involved in the crossing of the blood-brain barrier by the T cells, an early event in MS pathophysiology, and is used as an efficient therapeutic target. We drew up a list of 76 genes belonging to the CAM network. We highlighted 64 networks enriched with CAM genes with low P-values. Filtering by a percentage of CAM genes up to 50% and rejecting enriched signals mainly driven by transcription factors, we highlighted five networks associated with MS susceptibility. One of them, constituted of ITGAL, ICAM1 and ICAM3 genes, could be of interest to develop novel therapeutic targets.
AB - Genome-wide association studies (GWASs) perform per-SNP association tests to identify variants involved in disease or trait susceptibility. However, such an approach is not powerful enough to unravel genes that are not individually contributing to the disease/trait, but that may have a role in interaction with other genes as a group. Pathway analysis is an alternative way to highlight such group of genes. Using SNP association P-values from eight multiple sclerosis (MS) GWAS data sets, we performed a candidate pathway analysis for MS susceptibility by considering genes interacting in the cell adhesion molecule (CAMs) biological pathway using Cytoscape software. This network is a strong candidate, as it is involved in the crossing of the blood-brain barrier by the T cells, an early event in MS pathophysiology, and is used as an efficient therapeutic target. We drew up a list of 76 genes belonging to the CAM network. We highlighted 64 networks enriched with CAM genes with low P-values. Filtering by a percentage of CAM genes up to 50% and rejecting enriched signals mainly driven by transcription factors, we highlighted five networks associated with MS susceptibility. One of them, constituted of ITGAL, ICAM1 and ICAM3 genes, could be of interest to develop novel therapeutic targets.
KW - Antigens, CD
KW - Blood-Brain Barrier
KW - Cell Adhesion Molecules
KW - Genetic Predisposition to Disease
KW - Genome-Wide Association Study
KW - Humans
KW - Integrin alpha Chains
KW - Intercellular Adhesion Molecule-1
KW - Multiple Sclerosis
KW - Polymorphism, Single Nucleotide
KW - Signal Transduction
KW - T-Lymphocytes
U2 - 10.1038/gene.2013.70
DO - 10.1038/gene.2013.70
M3 - Journal article
C2 - 24430173
VL - 15
SP - 126
EP - 132
JO - Genes and Immunity
JF - Genes and Immunity
SN - 1466-4879
IS - 2
ER -
ID: 138177216