Imperial College London

Professor Paul M. Matthews

Faculty of MedicineDepartment of Medicine

Edmond and Lily Safra Chair and Head of Brain Sciences
 
 
 
//

Contact

 

+44 (0)20 7594 2855p.matthews

 
 
//

Assistant

 

Ms Siobhan Dillon +44 (0)20 7594 2855

 
//

Location

 

E502Burlington DanesHammersmith Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Wang:2011:10.1186/gm217,
author = {Wang, JH and Pappas, D and De, Jager PL and Pelletier, D and de, Bakker PIW and Kappos, L and Polman, CH and Chibnik, LB and Hafler, DA and Matthews, PM and Hauser, SL and Baranzini, SE and Oksenberg, JR},
doi = {10.1186/gm217},
journal = {GENOME MEDICINE},
title = {Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data},
url = {http://dx.doi.org/10.1186/gm217},
volume = {3},
year = {2011}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Background:Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early tomiddle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list ofdisease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance.Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed.Methods:We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logisticregression protocol to identify novel genetic associations. The emerging genetic profile included 350 independentmarkers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals withvarious degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functionalannotation tool, the GO Tree Machine, and the Pathway-Express profiling tool.Results:In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case andcontrol groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profileshows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, whichhave been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, themedian cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classificationsensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observedamong four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, asignifican
AU - Wang,JH
AU - Pappas,D
AU - De,Jager PL
AU - Pelletier,D
AU - de,Bakker PIW
AU - Kappos,L
AU - Polman,CH
AU - Chibnik,LB
AU - Hafler,DA
AU - Matthews,PM
AU - Hauser,SL
AU - Baranzini,SE
AU - Oksenberg,JR
DO - 10.1186/gm217
PY - 2011///
SN - 1756-994X
TI - Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data
T2 - GENOME MEDICINE
UR - http://dx.doi.org/10.1186/gm217
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000208627400003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/57705
VL - 3
ER -