Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies

Autoři: Lulu Shang aff001;  Jennifer A. Smith aff002;  Xiang Zhou aff001
Působiště autorů: Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America aff001;  Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States of America aff002;  Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, United States of America aff003;  Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America aff004
Vyšlo v časopise: Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies. PLoS Genet 16(4): e32767. doi:10.1371/journal.pgen.1008734
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008734


Genome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect measurements for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases.

Klíčová slova:

Autoimmune diseases – Brain diseases – Colon – Covariance – Gene expression – Genetic networks – Genome-wide association studies – Neurons


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