CoCoNet

Composite likelihood-based Covariance regression Network model

About CoCoNet

Identifying trait-relevant tissues or cell types is important for understanding disease etiology. Several computational methods have been recently developed to integrate omics studies with genome-wide association studies (GWASs) in order to infer trait-relevant tissues or cell types. However, these previous methods have thus far ignored an important biological feature of gene expression data – genes are interconnected with each other and are co-regulated together. Such gene co-expression pattern occurs in a tissue specific or cell type specific fashion and may contain invaluable information for inferring trait-tissue relevance.

Here, we develop a network model to take advantage of the tissue-specific or cell type specific gene co-expression patterns inferred from bulk RNA sequencing or single cell RNA sequencing studies into GWASs. We illustrate the benefits of our method in identifying trait-relevant tissues or cell types through simulations and applications to real data sets.