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Table 1 Summary of features used in the prioritization of MS-PRGs and validation

From: Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment

Feature Description
Bayesian framework (refined version of iRIGS) features
Genetic variants in MS 200 genetic variants from the currently largest MS GWAS with genome-wide significance
Functional Annotation of the Mammalian Genome 5 (FANTOM5) Annotations of mammalian regulatory components, such as promoters, enhancers lncRNAs and miRNAs (provided by original iRIGS analysis)
Genome-scale chromosome conformation capture (Hi-C) Brain Hi-C data including both short- and long-range interactions among genomic loci (provided by original iRIGS analysis)
Expression in MS brain tissue Differentially expressed genes in MS brain tissue from 21 post-mortem brain samples (11 MS cases and 10 control subjects)
DNA methylation in MS brain tissue Differentially methylated genes from epigenome-wide changes in DNA methylation levels of 28 MS cases and 19 control subjects
Gene–gene relationships Gene interactions from Gene Ontology (GO) network (provided by original iRIGS analysis)
Two-sample Mendelian randomization (2SMR) feature
Expression quantitative trait loci (eQTL) Top cis-eQTL of 19 tissues based on tissue expression data from the Genotype-Tissue Expression (GTEx) portal
Genetic features used in validation
Probability of loss of function (LoF) intolerant (pLI) scores A high pLI score indicates the gene is more likely to be intolerant towards protein-truncating variant(s)
Evolutionary rate The ratio of nonsynonymous over synonymous substitution rate (dN/dS)
Genes of human diseases From OMIM and ClinVar databases
Cell type features used in validation
Single-nuclei RNA-sequencing (snRNA-seq) Cell type-specificity enrichment analysis using a snRNA-seq dataset from the brain tissue of 4 progressive MS patients and 5 non-neurological controls
Drug features used in validation
MS drug targets 32 MS drug targets were collected from the DrugBank database, followed by the enrichment analysis
Connectivity Map (CMap) drug signatures The co-expressed gene-set enrichment analysis (Cogena) R package was performed for the downregulated and upregulated 100 CMap gene sets