There are 78 loci associated with Parkinson\u2019s disease in the most recent genome-wide association study (GWAS), yet the specific genes driving these associations are mostly unknown. Herein, we aimed to nominate the top candidate gene from each Parkinson\u2019s disease locus and identify variants and pathways potentially involved in Parkinson\u2019s dis ease. We trained a machine learning model to predict Parkinson\u2019s disease-associated genes from GWAS loci using genomic, transcriptomic and epigenomic data from brain tissues and dopaminergic neurons. We nominated candidate genes in each locus and identified novel pathways potentially involved in Parkinson\u2019s disease, such as the inositol phosphate biosynthetic pathway (INPP5F, IP6K2, ITPKB and PPIP5K2). Specific common coding variants in SPNS1 and MLX may be involved in Parkinson\u2019s disease, and burden tests of rare variants further support that CNIP3, LSM7, NUCKS1 and the polyol\/inositol phosphate biosynthetic pathway are associated with the disease. Functional studies are needed to further analyse the involvements of these genes and pathways in Parkinson\u2019s disease.<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false},"studies-relation":[],"class_list":["post-12011","ccna_publication","type-ccna_publication","status-publish","hentry"],"acf":[],"yoast_head":"\n