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VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants.

Rare or de novo variants have substantial contribution to human diseases,
but the statistical power to identify risk genes by rare variants is
generally low due to rarity of genotype data. Previous studies have shown
that risk genes usually have high expression in relevant cell types,
although for many conditions the identity of these cell types are largely
unknown. Recent efforts in single cell atlas in human and model organisms
produced large amount of gene expression data. Here 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 Gamma-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. VBASS can be
generalized to integrate other types of functional genomics data in
statistical genetics analysis.

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