Abstract
Genome-wide association studies (GWAS) have identified tens of genetic variants associated with Parkinson’s disease (PD). Nevertheless, the genes or DNA elements that affect traits through these genetic variations are usually undiscovered. This study was the first to combine meta-analysis GWAS data and expression data to identify PD risk genes. Four known genes, CRHR1, KANSL1, NSF and LRRC37A, and two new risk genes, STX4 and BST1, were identified. Among them, CRHR1 is a known drug target, indicating that hydrocortisone may become a potential drug for the treatment of PD. Furthermore, the potential pathogenesis of CRHR1 and LRRC37A was explored by applying DNA methylation (DNAm) data, indicating a pathogenesis whereby the effect of a genetic variant on PD is mediated by genetic regulation of transcription through DNAm. Overall, this research identified the risk genes and pathogenesis that affect PD through genetic variants, which has significance for the diagnosis and treatment of PD.
Highlights
Parkinson’s disease (PD) is called the “undead cancer” and is the second most common progressive neurodegenerative disease affecting the elderly population [1]
The determination of genotype–phenotype causality based on Genome-wide association studies (GWAS) still faces many difficulties: (i) most of the mutation sites are located in noncoding regions of the genome and are not protein-coding sequences [5]; (ii) the linkage disequilibrium (LD) effect of adjacent single nucleotide polymorphisms (SNPs) will produce a noncausality between genotype and phenotype [6]; and (iii) the limitation of sample size reduces the statistical effect of GWAS
There were 48 SNPs significantly related to PD
Summary
Parkinson’s disease (PD) is called the “undead cancer” and is the second most common progressive neurodegenerative disease affecting the elderly population [1]. Genome-wide association studies (GWAS) identified tens of genetic variants associated with PD, most of which are from patients of European ancestry, and there is relatively little knowledge about PD in other populations [3]. Nalls et al examined the risk relationships between PD and other phenotypes based on these genetic variants [4]. The determination of genotype–phenotype causality based on GWAS still faces many difficulties: (i) most of the mutation sites are located in noncoding regions of the genome and are not protein-coding sequences [5]; (ii) the linkage disequilibrium (LD) effect of adjacent single nucleotide polymorphisms (SNPs) will produce a noncausality between genotype and phenotype [6]; and (iii) the limitation of sample size reduces the statistical effect of GWAS. Rare and low frequency variations may not reach the statistical threshold [7,8]
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