Two new gene-based association analysis methods, called PrediXcan and TWAS for GWAS individual-level and summary data, respectively, were recently 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 PrediXcan and TWAS, we propose a more powerful gene-based association test to integrate single set or multiple sets of eQTL data with GWAS individual-level data or summary statistics. The proposed test was applied to several GWAS datasets, including two lipid summary association datasets based on ~ 100; 000 and ~ 189; 000 samples, respectively, and uncovered more known or novel trait-associated genes, showcasing much improved performance of our proposed method. The software implementing the proposed method is freely available as an R package.
Bibliographical noteFunding Information:
The authors are grateful to the reviewers and the editor for helpful and constructive comments. The authors thank Sasha Gusev for providing and helping with the use of the TWAS database. This research was supported by National Institutes of Health grants R21AG057038, R01HL116720, R01GM113250, and R01HL105397, and by the Minnesota Supercomputing Institute; Z.X. was supported by a University of Minnesota MnDRIVE Fellowship and C.W. by a University of Minnesota Dissertation Fellowship.
- ASPU test
- Statistical power
- Sum test
- Transcriptome-wide association study (TWAS)
- Weighted association testing