Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data.

ABSTRACT: Hundreds of autism risk genes have been reported recently, mainly based on genetic studies where these risk genes have more de novo mutations in autism subjects than healthy controls. However, as a complex disease, autism is likely associated with more risk genes and many of them may not be identifiable through de novo mutations. We hypothesize that more autism risk genes can be identified through their connections with known autism risk genes in personalized gene-gene interaction graphs. We estimate such personalized graphs using single cell RNA sequencing (scRNA-seq) while appropriately modeling the cell dependence and possible zero-inflation in the scRNA-seq data. The sample size, which is the number of cells per individual, ranges from 891 to 1,241 in our case study using scRNA-seq dat

SUBMITTER: Liu J 

PROVIDER: S-EPMC9070996 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature