Leveraging Gene Co-expression Patterns to Infer Trait-Relevant Tissues in Genome-wide Association Studies

Abstract

<jats:title>Abstract</jats:title><jats:p>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 sizes 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. Our results also provide empirical evidence supporting one hypothesis of the omnigenic model: that trait-relevant gene co-expression networks underlie disease etiology.</jats:p>

Pinpointing miRNA and genes enrichment over trait-relevant tissue network in Genome-Wide Association Studies

“…We employed MIGWAS [ 7 ], CoCoNet [ 9 ], and ANNOVAR [ 10 ] software packages to infer trait-relevant tissues based on next-generation sequencing omics data (e.g. GTEx data [ 16 ]) and annotated the variants obtained from GWAS and harbored by miRNAs targeted genes associated with traits.…”

“…Composite likelihood-based Covariance regression Network model (CoConet) is a network method for identification of trait-relevant tissues or cell types by incorporating tissue-specific gene co-expression networks [ 9 ]. CoCoNet further understands data from GWAS by demonstrating gene co-expression sub-networks which helps predict gene-level association effect sizes on and GWAS traits and diseases.…”

“…CoCoNet further understands data from GWAS by demonstrating gene co-expression sub-networks which helps predict gene-level association effect sizes on and GWAS traits and diseases. Moreover, CoConet infers trait-relevant tissue based on tissue-specific gene co-expression patterns and proves that tissue-specific gene networks underlie disease etiology [ 9 ].…”

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

“…Furthermore, we intersected the SNVs participating in scReQTLs with SNVs significantly associated with phenotypes by GWAS [38]. This analysis showed that 18 (out of the 408 unique scReQTL SNVs, 4.4%) were present in GWAS; these 18 SNVs participated in 84 scReQTL correlations (Supplementary Table 5).…”

Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges

“…Third, the potential mediators in genomics are often correlated with each other, sometimes quite strongly. For example, methylation measurements on proximal CpG sites are generally similar to each other and genes in the same pathway also show coordinated co-expression pattern [110] . However, existing univariate and multivariate mediation methods do not explicitly model correlation among potential mediators.…”