Abstract
We present two new methods, xTADA and VBASS (Variational inference Bayesian ASSociation), that integrate expression data to improve power of rare variants association analysis. Optimized for bulk RNA-seq and single-cell transcriptomics data respectively, xTADA and VBASS model the association of disease risk as a function of expression profiles of relevant tissue or cell types in Bayesian frameworks. On simulated data, both methods show proper error rate control and better power than extTADA, the state-of-the-art Bayesian method. We applied the methods to published datasets and identified more candidate risk genes than extTADA with supports from literature or data from independent cohorts.
Competing Interest Statement
The authors have declared no competing interest.
Copyright
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Posted May 15, 2022.
Integration of gene expression data in Bayesian association analysis of rare variants
