Transcript specific regulation of expression influences susceptibility to multiple sclerosis

Genome-wide association studies (GWAS) have identified over 100 loci containing single nucleotide variants (SNVs) that influence the risk of developing multiple sclerosis (MS). Most of these loci lie in non-coding regulatory regions of the genome that are active in immune cells and are therefore tho...

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Veröffentlicht in:European journal of human genetics : EJHG 2020-06, Vol.28 (6), p.826-834
Hauptverfasser: Ban, Maria, Liao, Wenjia, Baker, Amie, Compston, Alastair, Thorpe, John, Molyneux, Paul, Fraser, Mary, Khadake, Jyoti, Jones, Joanne, Coles, Alasdair, Sawcer, Stephen
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Sprache:eng
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Zusammenfassung:Genome-wide association studies (GWAS) have identified over 100 loci containing single nucleotide variants (SNVs) that influence the risk of developing multiple sclerosis (MS). Most of these loci lie in non-coding regulatory regions of the genome that are active in immune cells and are therefore thought to modify risk by altering the expression of key immune genes. To explore this hypothesis we screened genes flanking MS-associated variants for evidence of allele specific expression (ASE) by quantifying the transcription of coding variants in linkage disequilibrium with MS-associated SNVs. In total, we were able to identify and successfully analyse 200 such coding variants (from 112 genes) in both CD4+ and CD8+ T cells from 106 MS patients and 105 controls. Fifty-six of these coding variants (from 43 genes) showed statistically significant evidence of ASE in one or both cell types. In the Lck interacting transmembrane adaptor 1 gene (LIME1), for example, we were able to show that in both cell types, the MS-associated variant rs2256814 increased the expression of some transcripts while simultaneously reducing the expression of other transcripts. In CD4+ cells from an additional independent set of 96 cases and 93 controls we were able to replicate the effect of this SNV on the balance of alternate LIME1 transcripts using qPCR (p = 5 × 10 ). Our data thus indicate that some of the MS-associated SNVs identified by GWAS likely exert their effects on risk by distorting the balance of alternate transcripts rather than by changing the overall level of gene expression.
ISSN:1018-4813
1476-5438
DOI:10.1038/s41431-019-0569-0