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• W4387496665 abstract "Many drugs show promise in animal models but fail in human clinical trials due to lack of efficacy. Retrospective studies have found that proteins are more likely to make successful drug targets if they have been linked to the relevant disease by human genetic studies. We have published genome-wide association studies (GWASes) for Parkinson's disease (PD) and schizophrenia (SCZ), testing for an association between disease status and millions of single nucleotide polymorphisms (SNPs). Although >300 genomic regions were identified for these diseases, the causal genes in many of these regions remain unknown. We propose using state-of-the-art statistical genetics tools to identify these causal genes and explore their potential as drug targets. Most GWASes attempt to define the smallest possible set of SNPs that is likely to contain the causal variant (the credible set) in each identified region. However, they typically do not take advantage of information regarding the function of these SNPs. PolyFun, a well-established machine learning method, will be used to identify the features of SNPs (187 tested) that are associated with PD and SCZ risk (e.g., degree of evolutionary conservation). This information will then be used as a Bayesian prior to generate improved credible sets and map putative causal SNPs to putative causal genes (e.g., SNPs located in promoters). In addition to this SNP-centric approach, a similar gene-centric approach will be employed. PoPS, a recently-published machine learning method, will be used to identify the features of genes (>57,000 tested) that are associated with PD and SCZ risk (e.g., expression in brain). The genes that are most likely to affect disease risk will be identified based on their features. By combining these SNP-centric and gene-centric methods we will pinpoint causal genes in as-of-yet unresolved GWAS regions. As a positive control, we will present PolyFun and PoPS results for PD and SCZ loci that have previously been successfully fine-mapped. In addition, we will present results for loci that have not been successfully fine-mapped, focusing on loci where a single putative causal gene is nominated with high confidence. Here we propose using cutting edge computational techniques to identify causal variants and genes in GWAS loci for PD and SCZ. Our findings may help identify novel drug targets for these diseases and may lead to the development of new pharmacological approaches." @default.
• W4387496665 created "2023-10-11" @default.
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• W4387496665 date "2023-10-01" @default.
• W4387496665 modified "2023-10-12" @default.
• W4387496665 title "T80. IDENTIFICATION OF CAUSAL GENES AND PUTATIVE DRUG TARGETS FOR PARKINSON'S DISEASE AND SCHIZOPHRENIA USING STATISTICAL GENETICS TECHNIQUES" @default.
• W4387496665 doi "https://doi.org/10.1016/j.euroneuro.2023.08.364" @default.
• W4387496665 hasPublicationYear "2023" @default.
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