A gene-based association method called PrediXcan was developed to identify genes associated with complex traits by leveraging reference transcriptome data. This method estimates genetically regulated gene expression (GReX) using whole-genome tissue-dependent prediction models trained on reference transcriptome datasets. It then correlates the imputed GReX with the phenotype of interest to detect genes involved in the etiology of the trait. PrediXcan offers advantages such as reduced multiple testing burden and a principled approach to follow-up experiments. It was tested on various datasets, including the WTCCC, and identified genes associated with diseases like type 1 diabetes, Crohn's disease, and rheumatoid arthritis. The method also provided insights into the mechanisms underlying these associations. PrediXcan outperformed traditional SNP-based GWAS and known gene-based tests in detecting associations, particularly in cases where the genetic signal was weak. The method was applied to multiple tissues and demonstrated robust performance across different datasets. It also showed potential for identifying novel disease-associated genes and provided directionality of effect, which is crucial for therapeutic development. The results highlight the importance of integrating regulatory information from transcriptome studies to improve the understanding of complex traits. PrediXcan is a valuable tool for combining results from rare and common variant association tests within whole-genome sequencing studies.A gene-based association method called PrediXcan was developed to identify genes associated with complex traits by leveraging reference transcriptome data. This method estimates genetically regulated gene expression (GReX) using whole-genome tissue-dependent prediction models trained on reference transcriptome datasets. It then correlates the imputed GReX with the phenotype of interest to detect genes involved in the etiology of the trait. PrediXcan offers advantages such as reduced multiple testing burden and a principled approach to follow-up experiments. It was tested on various datasets, including the WTCCC, and identified genes associated with diseases like type 1 diabetes, Crohn's disease, and rheumatoid arthritis. The method also provided insights into the mechanisms underlying these associations. PrediXcan outperformed traditional SNP-based GWAS and known gene-based tests in detecting associations, particularly in cases where the genetic signal was weak. The method was applied to multiple tissues and demonstrated robust performance across different datasets. It also showed potential for identifying novel disease-associated genes and provided directionality of effect, which is crucial for therapeutic development. The results highlight the importance of integrating regulatory information from transcriptome studies to improve the understanding of complex traits. PrediXcan is a valuable tool for combining results from rare and common variant association tests within whole-genome sequencing studies.