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• W2400221198 abstract "With the increasing availability of functional genomic data, incorporating genomic annotations into genetic association analysis has become a standard procedure. However, the existing methods often lack rigor and/or computational efficiency and consequently do not maximize the utility of functional annotations. In this paper, we propose a rigorous inference procedure to perform integrative association analysis incorporating genomic annotations for both traditional GWASs and emerging molecular QTL mapping studies. In particular, we propose an algorithm, named deterministic approximation of posteriors (DAP), which enables highly efficient and accurate joint enrichment analysis and identification of multiple causal variants. We use a series of simulation studies to highlight the power and computational efficiency of our proposed approach and further demonstrate it by analyzing the cross-population eQTL data from the GEUVADIS project and the multi-tissue eQTL data from the GTEx project. In particular, we find that genetic variants predicted to disrupt transcription factor binding sites are enriched in cis-eQTLs across all tissues. Moreover, the enrichment estimates obtained across the tissues are correlated with the cell types for which the annotations are derived." @default.
• W2400221198 created "2016-06-24" @default.
• W2400221198 creator A5035820223 @default.
• W2400221198 creator A5045777431 @default.
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• W2400221198 creator A5089187929 @default.
• W2400221198 date "2016-06-01" @default.
• W2400221198 modified "2023-10-14" @default.
• W2400221198 title "Efficient Integrative Multi-SNP Association Analysis via Deterministic Approximation of Posteriors" @default.
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• W2400221198 doi "https://doi.org/10.1016/j.ajhg.2016.03.029" @default.
• W2400221198 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4908152" @default.
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