DOI: 10.1093/bioinformatics/bty147 year: 2019 study: CMC title: A Bayesian framework for multiple trait colocalization from summary association statistics. grants: U01MH116442 R01MH110921 R01MH109677 authors: Giambartolomei C, Zhenli Liu J, Zhang W, Hauberg M, Shi H, Boocock J, Pickrell J, Jaffe AE, Pasaniuc B, Roussos P journal: Bioinformatics (Oxford England) abstract: Most genetic variants implicated in complex diseases by genome-wide association studies (GWAS) are non-coding, making it challenging to understand the causative genes involved in disease. Integrating external information such as quantitative trait locus (QTL) mapping of molecular traits (e.g. expression, methylation) is a powerful approach to identify the subset of GWAS signals explained by regulatory effects. In particular, expression QTLs (eQTLs) help pinpoint the responsible gene among the GW pubmedId: 29579179 entity_name: Giambartolomei Bioinformatics (Oxford England) 2019 (Pubmed ID 29579179)
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