Multiple sclerosis (MS) is an autoimmune disease of the central nervous system; genetic studies have identified more than 200 MS risk loci, but the functional mechanisms remain unclear; indeed, the identification of genetic factors associated with MS phenotypes and disease progression is crucial. The first phase of this PhD focused on collecting a large dataset of genetic and environmental data to explore gene-environment interactions, with specific aims including: to study autoimmune comorbidities in MS patients; to assess genotype-clinical outcome correlations, including post-attack sequelae, relapse frequency and MS severity in relation to smoking. MS patients in Piedmont were identified using validated algorithms. Autoimmune comorbidities were assessed by linkage with medical records, taking into account 14 autoimmune diseases. Genetic associations were analysed in 637 genotyped MS patients. A polygenic risk score (PRS) for autoimmune disease susceptibility was calculated. Further analyses were performed in 1,748 MS patients for genotype-clinical correlations and in 370 genotyped patients for relapse-frequency associations, with replication in 739 additional patients. MS patients had a higher prevalence of autoimmune diseases, with significant associations for CIDP, Behçet's syndrome, myasthenia gravis, celiac disease and others; MS patients with autoimmune comorbidities had a significantly higher PRS. GWAS for sequelae identified one suggestive SNP (rs6471090, chr8). The rs11871306 variant (WNT9B) was not replicated, but the rs79719335 variant (DMXL2) was associated with a shorter time to second relapse, highlighting its potential prognostic role. MS patients have a higher autoimmune comorbidity, probably influenced by common genetic factors. Considering sequelae, we have collected one of the largest Italian MS cohorts with deep clinical phenotyping and identified a suggestive genetic association with partial or incomplete recovery after first relapse.
GWAS era in multiple sclerosis: an integrative approach to explore genotype/phenotype interaction
PIZZINO, ALESSANDRO
2025
Abstract
Multiple sclerosis (MS) is an autoimmune disease of the central nervous system; genetic studies have identified more than 200 MS risk loci, but the functional mechanisms remain unclear; indeed, the identification of genetic factors associated with MS phenotypes and disease progression is crucial. The first phase of this PhD focused on collecting a large dataset of genetic and environmental data to explore gene-environment interactions, with specific aims including: to study autoimmune comorbidities in MS patients; to assess genotype-clinical outcome correlations, including post-attack sequelae, relapse frequency and MS severity in relation to smoking. MS patients in Piedmont were identified using validated algorithms. Autoimmune comorbidities were assessed by linkage with medical records, taking into account 14 autoimmune diseases. Genetic associations were analysed in 637 genotyped MS patients. A polygenic risk score (PRS) for autoimmune disease susceptibility was calculated. Further analyses were performed in 1,748 MS patients for genotype-clinical correlations and in 370 genotyped patients for relapse-frequency associations, with replication in 739 additional patients. MS patients had a higher prevalence of autoimmune diseases, with significant associations for CIDP, Behçet's syndrome, myasthenia gravis, celiac disease and others; MS patients with autoimmune comorbidities had a significantly higher PRS. GWAS for sequelae identified one suggestive SNP (rs6471090, chr8). The rs11871306 variant (WNT9B) was not replicated, but the rs79719335 variant (DMXL2) was associated with a shorter time to second relapse, highlighting its potential prognostic role. MS patients have a higher autoimmune comorbidity, probably influenced by common genetic factors. Considering sequelae, we have collected one of the largest Italian MS cohorts with deep clinical phenotyping and identified a suggestive genetic association with partial or incomplete recovery after first relapse.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/202912
URN:NBN:IT:UNIUPO-202912