Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.
Highlights
Parkinson’s disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations
We analysed 290 PD-associated Genome wide association studies (GWAS) single nucleotide polymorphisms (SNPs) (Supplementary Table S1) for spatial expression quantitative trait loci (eQTL) interactions (Ramani et al, 2016; Fadason et al, 2017; Pal et al, 2019) across 49 Genotype-Tissue Expression (GTEx) tissues (Aguet et al, 2017). 231 of the 290 (79.7%) PD SNPs tested were involved in 18,041 tissue-specific eQTL associations (Benjamini–Hochberg FDR < 0.05 (Benjamini and Hochberg, 1995); Supplementary Table S2), regulating 1,334 eGenes across the 49 GTEx tissues
This resulted in the generation of a PD-SNP derived weighted WTCCC PD genotype eQTL effect matrix containing 17,829 tissue-specific eQTL-eGene pairs (227 SNPs, 1,310 eGenes, 49 tissues) and 54 SNPs that had no known eQTL effects following our CoDeS3D analysis
Summary
Parkinson’s disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Genome wide association studies (GWAS) have identified human genetic variants that are associated. Machine Learning Parkinson’s Disease Risk with the risk of developing PD (Spencer et al, 2011; Nalls et al, 2019). In the most recent PD GWAS meta-analysis, Nalls et al (2019) identified 90 independent single nucleotide polymorphisms (SNPs) that are significantly associated with PD risk. There are an additional 290 PD-associated GWAS SNPs (279 in non-coding and 11 in coding regions) listed in the GWAS catalog. It is difficult to understand how these variants confer PD risk because the majority of the PD SNPs are located in non-coding regions of the genome (Visscher et al, 2012, 2017; Farrow et al, 2021)
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