{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ho D"],"funding":["Ministry for Business Innovation and Employment","Wellcome Trust","Michael J. Fox Foundation for Parkinson’s Research"],"pagination":["785436"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8762216"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12"],"pubmed_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 a"],"journal":["Frontiers in genetics"],"pubmed_title":["Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity."],"pmcid":["PMC8762216"],"funding_grant_id":["085475","076113","090355"],"pubmed_authors":["Cooper AA","O'Sullivan JM","Ho D","Kempa-Liehr AW","Farrow SL","Schierding W"],"additional_accession":[]},"is_claimable":false,"name":"Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity.","description":"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 a","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021","modification":"2026-05-07T23:20:24.857Z","creation":"2022-02-11T16:12:35.824Z"},"accession":"S-EPMC8762216","cross_references":{"pubmed":["35047012"],"doi":["10.3389/fgene.2021.785436"]}}