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Assessment of the genetic contribution to brain magnetic resonance imaging lesion load and atrophy measures in multiple sclerosis patients
Author(s) -
Clarelli Ferdinando,
Rocca Maria Assunta,
Santoro Silvia,
De Meo Ermelinda,
Ferrè Laura,
Sorosina Melissa,
Martinelli Boneschi Filippo,
Esposito Federica,
Filippi Massimo
Publication year - 2021
Publication title -
european journal of neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.881
H-Index - 124
eISSN - 1468-1331
pISSN - 1351-5101
DOI - 10.1111/ene.14872
Subject(s) - grey matter , white matter , medicine , genome wide association study , multiple sclerosis , single nucleotide polymorphism , magnetic resonance imaging , atrophy , brain size , neuroimaging , pathology , neuroscience , genetics , biology , gene , genotype , radiology , immunology , psychiatry
Background and purpose Multiple sclerosis (MS) susceptibility is influenced by genetics; however, little is known about genetic determinants of disease expression. We aimed at assessing genetic factors influencing quantitative neuroimaging measures in two cohorts of progressive MS (PMS) and relapsing–remitting MS (RRMS) patients. Methods Ninety‐nine PMS and 214 RRMS patients underwent a 3‐T brain magnetic resonance imaging (MRI) scan, with the measurement of five MRI metrics including T2 lesion volumes and measures of white matter, grey matter, deep grey matter, and hippocampal volumes. A candidate pathway strategy was adopted; gene set analysis was carried out to estimate cumulative contribution of genes to MRI phenotypes, adjusting for relevant confounders, followed by single nucleotide polymorphism (SNP) regression analysis. Results Seventeen Kyoto Encyclopedia of Genes and Genomes pathways and 42 Gene Ontology (GO) terms were tested. We additionally included in the analysis genes with enriched expression in brain cells. Gene set analysis revealed a differential pattern of association across the two cohorts, with processes related to sodium homeostasis being associated with grey matter volume in PMS ( p = 0.002), whereas inflammatory‐related GO terms such as adaptive immune response and regulation of inflammatory response appeared to be associated with T2 lesion volume in RRMS ( p = 0.004 and p = 0.008, respectively). As for SNPs, the rs7104613 T mapping to SPON1 gene was associated with reduced deep grey matter volume (β = −0.731, p = 3.2*10 −7 ) in PMS, whereas we found evidence of association between white matter volume and rs740948 A mapping to SEMA3A gene (β = 22.04, p = 5.5*10 −6 ) in RRMS. Conclusions Our data suggest a different pattern of associations between MRI metrics and functional processes across MS disease courses, suggesting different phenomena implicated in MS.