Sage Journals: Discover world-class research

Abstract

Background:

Several genetic markers have been associated with multiple sclerosis (MS) susceptibility; however, uncovering the genetic aetiology of the complex phenotypic expression of MS has been more difficult so far. The most common approach in imaging genetics is based on mass-univariate linear modelling (MULM), which faces several limitations.

Objective:

Here we apply a novel multivariate statistical model, sparse reduced-rank regression (sRRR), to identify possible associations of glutamate related single nucleotide polymorphisms (SNPs) and multiple MRI-derived phenotypes in MS.

Methods:

Seven phenotypes related to brain and lesion volumes for a total number of 326 relapsing–remitting and secondary-progressive MS patients and a total of 3809 glutamate related and control SNPs were analysed with sRRR, which resulted in a ranking of SNPs in decreasing order of importance (‘selection probability’). Lasso regression and MULM were used as comparative statistical techniques to assess consistency of the most important associations over different statistical models.

Results:

Five SNPs within the NMDA-receptor-2A-subunit (GRIN2A) domain were identified by sRRR in association with normalized brain volume (NBV), normalized grey matter volume and normalized white matter volume (NMWM). The association between GRIN2A and both NBV and NWMV was confirmed in MULM and Lasso analysis.

Conclusions:

Using a novel, multivariate regression model confirmed by two other statistical approaches we show associations between GRIN2A SNPs and phenotypic variation in NBV and NWMV in this first exploratory study. Replications in independent datasets are now necessary to validate these findings.

Keywords

Multiple sclerosis genetics MRI single nucleotide polymorphisms glutamate endophenotyping atrophy white matter

Get full access to this article

View all access options for this article.

References

IMSGC; WTCCC2, Sawcer

. Genetic risk and a primary role for cell-mediate immune mechanisms in multiple sclerosis. Nature 2011; 476: 214–219.

De Jager

Jia

Wang

. Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet 2009; 41: 776–782.

Baranzini

Wang

Gibson

. Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Hum Mol Genet 2009; 18: 767–778.

Sawcer

. The complex genetics of multiple sclerosis: Pitfalls and prospects. Brain 2008; 131: 3118–3131.

Ayers

Cordell

. SNP selection in genome-wide and candidate gene studies via penalized logistic regression. Genet Epidemiol 2010; 34: 879–891.

Shi

Boerwinkle

Morrison

. Mining gold dust under the genome wide significance level: A two-stage approach to analysis of GWAS. Genet Epidemiol 2011; 35: 111–118.

Vounou

Nichols

Montana

. Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach. Neuroimage 2010; 53: 1147–1159.

Geurts

Wolswijk

. Altered expression patterns of group I and II metabotropic glutamate receptors in multiple sclerosis. Brain 2003; 126: 1755–1766.

Geurts

Wolswijk

. Expression patterns of Group III metabotropic glutamate receptors mGluR4 and mGluR8 in multiple sclerosis lesions. J Neuroimmunol 2005; 158: 182–190.

10.

Baranzini

Srinivasan

Khankhanian

. Genetic variation influences glutamate concentrations in brains of patients with multiple sclerosis. Brain 2010; 133: 2603–2611.

11.

Polman

Reingold

Edan

. Diagnostic criteria for multiple sclerosis: 2005 revisions to the ‘McDonald Criteria’. Ann Neurol 2005; 58: 840–846.

12.

Kurtzke

. Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 1983; 33: 1444–1452.

13.

Smith

Zhang

Jenkinson

. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002; 17: 479–489.

14.

Purcell

Neale

Todd-Brown

. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

15.

Okuda

Srinivasan

Oksenberg

. Genotype– phenotype correlations in multiple sclerosis: HLA genes influence disease severity inferred by 1HMR spectroscopy and MRI measures. Brain 2009; 132: 250–259.

16.

Benjamini

Hochberg

. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Royal Statist Soc B 1995; 57: 289–300.

17.

Benjamini

Yekutieli

. The control of the false discovery rate in multiple testing under dependency. Ann Statist 2001; 29: 1165–1188.

18.

Breiman

. Stacked regressions. Machine Learn 1996; 24: 49–64.

19.

Breiman

Friedman

. Predicting multivariate responses in multiple linear regression. J Royal Statist Soc B (Methodological) 1997; 59: 3–54.

20.

Tibshirani

. Regression shrinkage and selection via the Lasso. J Royal Statist Soc B 1996; 58: 267–288.

21.

Urdinguio

Sanchez-Mut

Esteller

. Epigenetic mechanisms in neurological diseases: Genes, syndromes, and therapies. Lancet Neurol 2009; 8: 1056–1072.

22.

Handunnetthi

Ramagopalan

Ebers

. Multiple sclerosis, vitamin D, and HLA-DRB1*15. Neurology 2010; 74: 1905–1910.

23.

De Jager

Chibnik

Cui

. Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: A weighted genetic risk score. Lancet Neurol 2009; 8: 1111–1119.

24.

Van Walderveen

Lycklama

ANG

Ader

. Hypointense lesions on T1-weighted spin-echo magnetic resonance imaging: Relation to clinical characteristics in subgroups of patients with multiple sclerosis. Arch Neurol 2001; 58: 76–81.

25.

Barkhof

Bruck

de Groot

. Remyelinated lesions in multiple sclerosis: Magnetic resonance image appearance. Arch Neurol 2003; 60: 1073–1081.

26.

Fisher

Lee

Nakamura

Rudick

. Gray matter atrophy in multiple sclerosis: A longitudinal study. Ann Neurol 2008; 64: 255–265.

27.

Zivadinov

Uxa

Bratina

. HLA-DRB1*1501, -DQB1*0301, -DQB1*0302, -DQB1*0602, and -DQB1*0603 alleles are associated with more severe disease outcome on MRI in patients with multiple sclerosis. Int Rev Neurobiol 2007; 79: 521–535.

28.

Sombekke

Lukas

Crusius

. HLA-DRB1*1501 and spinal cord magnetic resonance imaging lesions in multiple sclerosis. Arch Neurol 2009; 66: 1531–1536.

29.

Baranzini

Galwey

Wang

. Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet 2009; 18: 2078–2090.

30.

Vounou

Janousova

Wolz

. Alzheimer’s Disease Neuroimaging Initiative. Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease. Neuroimage 2012; 60: 700–716.

31.

Lundström

Greiner

Lundmark

. No influence on disease progression of non-HLA susceptibility genes in MS. J Neuroimmunol 2011; 237: 98–100.

32.

Choi

. Glutamate neurotoxicity and diseases of the nervous system. Neuron 1988; 1: 623–634.

33.

Matute

Domercq

Sanchez-Gomez

MV.

Glutamate-mediated glial injury: Mechanisms and clinical importance. Glia 2006; 53: 212–224.

34.

Domercq

Etxebarria

Perez-Samartin

. Excitotoxic oligodendrocyte death and axonal damage induced by glutamate transporter inhibition. Glia 2005; 52: 36–46.

35.

Barkhatova

Zavalishin

Askarova

. Changes in neurotransmitters in multiple sclerosis. Neurosci Behav Physiol 1998; 28: 341–344.

36.

Srinivasan

Sailasuta

Hurd

. Evidence of elevated glutamate in multiple sclerosis using magnetic resonance spectroscopy at 3 T. Brain 2005; 128: 1016–1025.

37.

Vallejo-Illarramendi

Domercq

Perez-Cerda

. Increased expression and function of glutamate transporters in multiple sclerosis. Neurobiol Dis 2006; 21: 154–164.

38.

Pampliega

Domercq

Villoslada

. Association of an EAAT2 polymorphism with higher glutamate concentration in relapsing multiple sclerosis. J Neuroimmunol 2008; 195: 194–198.

39.

Werner

Pitt

Raine

. Multiple sclerosis: Altered glutamate homeostasis in lesions correlates with oligodendrocyte and axonal damage. Ann Neurol 2001; 50: 169–180.

40.

Pina-Crespo

Talantova

Micu

. Excitatory glycine responses of CNS myelin mediated by NR1/NR3 ‘NMDA’ receptor subunits. J Neurosci 2010; 30: 11501–11505.

41.

Zamponi

Stys

. Role of prions in neuroprotection and neurodegeneration: A mechanism involving glutamate receptors? Prion 2009; 3: 187–189.

42.

Micu

Jiang

Coderre

. NMDA receptors mediate calcium accumulation in myelin during chemical ischaemia. Nature 2006; 439: 988–992.

43.

Trapp

Stys

. Virtual hypoxia and chronic necrosis of demyelinated axons in multiple sclerosis. Lancet Neurol 2009; 8: 280–291.

44.

Salter

Fern

. NMDA receptors are expressed in developing oligodendrocyte processes and mediate injury. Nature 2005; 438: 1167–1171.

45.

Salta

de Strooper

. Non-coding RNAs with essential roles in neurodegenerative disorders. Lancet Neurol 2012; 11: 189–200.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB

0.09 MB

Glutamate gene polymorphisms predict brain volumes in multiple sclerosis

Abstract

Background:

Objective:

Methods:

Results:

Conclusions:

Keywords

Get full access to this article

References

Supplementary Material