Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative neuromuscular disease. Although genome-wide association studies (GWAS) have successfully identified many variants significantly associated with ALS, it is still difficult to characterize the underlying biological mechanisms inducing ALS. In this study, we performed a transcriptome-wide association study (TWAS) to identify disease-specific genes in ALS. Using the largest ALS GWAS summary statistic (n = 80,610), we identified seven novel genes using 19 tissue reference panels. We conducted a conditional analysis to verify the genes’ independence and to confirm that they are driven by genetically regulated expressions. Furthermore, we performed a TWAS-based enrichment analysis to highlight the association of important biological pathways, one in each of the four tissue reference panels. Finally, utilizing a connectivity map, a database of human cell expression profiles cultured with bioactive small molecules, we discovered functional associations between genes and drugs to identify 15 bioactive small molecules as potential drug candidates for ALS. We believe that, by integrating the largest ALS GWAS summary statistic with gene expression to identify new risk loci and causal genes, our study provides strong candidates for molecular basis experiments in ALS.
Highlights
Conditional analyses were conducted on the significant transcriptome-wide association study (TWAS) genes to determine whether they were detected as independently significant associations
TWAS-GSEA was performed using significant TWAS genes to investigate the biological effects derived from the TWAS results and to explore biological pathways involved in the pathogenesis of Amyotrophic lateral sclerosis (ALS)
Drug repositioning analysis was conducted in connectivity map (CMap) using significant TWAS genes to discover drug candidates for ALS
Summary
Amyotrophic lateral sclerosis (ALS), known as Lou Gehrig’s disease, affects nerve cells in the brain and spinal cord, causing motor neurons to mutate and disappear selectively and progressively. The annual incidence of ALS is about 2 per. Many studies have been conducted to identify the genetic mutations causing ALS, to understand the disease mechanisms and to facilitate the design of disease modeling and treatment [3,4]. Genome-wide association studies (GWAS) have successfully identified many variants significantly associated with ALS, it is often difficult to characterize the underlying biological mechanisms inducing ALS. In order to understand the underlying biology, researchers have used expression quantitative trait loci (eQTL)
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