Integration of single cell gene expression data in Bayesian association analysis of rare variants
Zhong, G.; Choi, Y. A.; Shen, Y.
Show abstract
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.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Incorporating family disease history and controlling case-control imbalance for population based genetic association studies 95%
- dsMTL - a computational framework for privacy-preserving, distributed multi-task machine learning 94%
- acmgscaler: An R package and Colab for standardised gene-level variant effect score calibration within the ACMG/AMP framework 94%
Similar papers in this journal
- SnapHiC-G: identifying long-range enhancer-promoter interactions from single-cell Hi-C data via a global background model 95%
- Accelerate the discovery of genetic variants in mitochondrial diseases with VIOLA: Variant PrIOritization using Latent space 94%
- Deciphering signatures of natural selection via deep learning 94%
Similar papers in this journal
- MIRAGE: a Bayesian rare variant association analysis method incorporating functional information of variants 97%
- UK-Biobank Whole Exome Sequence Binary Phenome Analysis with Robust Region-based Rare Variant Test 96%
- Accounting for age-of-onset and family history improves power in genome-wide association studies 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.