PT - JOURNAL ARTICLE AU - Zhong, Guojie AU - Choi, Yoolim A. AU - Shen, Yufeng TI - Integration of single cell gene expression data in Bayesian association analysis of rare variants AID - 10.1101/2022.05.13.491893 DP - 2022 Jan 01 TA - bioRxiv PG - 2022.05.13.491893 4099 - http://biorxiv.org/content/early/2022/07/14/2022.05.13.491893.short 4100 - http://biorxiv.org/content/early/2022/07/14/2022.05.13.491893.full AB - We present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts.Competing Interest StatementThe authors have declared no competing interest.