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