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Abstract:
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Transcriptome-wide association studies (TWAS) have been proposed to integrate GWAS with eQTL data, alleviating two common problems in GWAS by boosting statistical power and facilitating biological interpretation of GWAS discoveries. Based on a novel reformulation of TWAS, we propose a more general gene-based association testing framework to integrate GWAS individual-level or summary data with other genomic data of intermediate molecular phenotypes, such as gene expression or DNA methylation. The proposed method was applied to several large GWAS datasets to demonstrate its flexibility and effectiveness.
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