Brain and behavior · 2025
Genome-Wide Meta-Analysis of Parkinson's Disease Associated Genetic Loci and Validation of Therapeutic Targets.
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Yang Shan, Wu Qinfen, Yang Xinling
Аннотация
BACKGROUND: To identify genetic loci associated with Parkinson's disease (PD) through a genome-wide meta-analysis, to screen for druggable genes significantly linked to PD risk, and evaluate their potential as therapeutic targets.
METHODS: Data from DGIdb, GeneCards, and the Finan genomic resource were integrated to identify candidate therapeutic genes associated with PD. Genome-wide meta-analysis was conducted using GWAS data from the International Parkinson's Disease Genomics Consortium and FinnGen, involving 1,851,374 participants. Mendelian randomization (MR), colocalization analysis, and phenome wide association studies (PheWAS) were conducted to validate the associations between the identified genes and PD. Furthermore, knockout mouse models from the Mouse Genome Informatics were analyzed to validate PD-related phenotypes, and DSigDB was utilized to predict potential therapeutic compounds.
RESULTS: We identified several therapeutic genes significantly associated with PD risk. Colocalization analysis suggested shared causal genetic variants between these genes and PD. PheWAS further revealed that GCLC and GFPT1 exhibit limited pleiotropic effects across other traits. Eight potential compounds were identified through DSigDB predictions.
CONCLUSION: Through genome-wide meta-analysis, MR, colocalization, and PheWAS, we identified genetic loci associated with PD and assessed GCLC and GFPT1 as potential therapeutic targets.
METHODS: Data from DGIdb, GeneCards, and the Finan genomic resource were integrated to identify candidate therapeutic genes associated with PD. Genome-wide meta-analysis was conducted using GWAS data from the International Parkinson's Disease Genomics Consortium and FinnGen, involving 1,851,374 participants. Mendelian randomization (MR), colocalization analysis, and phenome wide association studies (PheWAS) were conducted to validate the associations between the identified genes and PD. Furthermore, knockout mouse models from the Mouse Genome Informatics were analyzed to validate PD-related phenotypes, and DSigDB was utilized to predict potential therapeutic compounds.
RESULTS: We identified several therapeutic genes significantly associated with PD risk. Colocalization analysis suggested shared causal genetic variants between these genes and PD. PheWAS further revealed that GCLC and GFPT1 exhibit limited pleiotropic effects across other traits. Eight potential compounds were identified through DSigDB predictions.
CONCLUSION: Through genome-wide meta-analysis, MR, colocalization, and PheWAS, we identified genetic loci associated with PD and assessed GCLC and GFPT1 as potential therapeutic targets.