Multi-context genetic modeling of transcriptional regulation resolves novel disease loci

Abstract

A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT—a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.

MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer

“…Several recent studies have explored context-specific TWAS [35][36][37] . Li et al proposed a tissue-specificity-aware TWAS framework that uses prior knowledge of trait-related tissue types for accurate detection of single-tissue and cross-tissue TWAS 35 .…”

“…Feng et al proposed to derive cross-tissue expression features using sparse canonical correlation analysis, and then combine expression-outcome associations across single-and cross-tissue features for powerful detection 36 . Thompson et al proposed CONTENT to go one step further and model both shared and tissue-specific components of gene expression in bulk multi-tissue data for model construction 37 . This approach can also be used for modeling shared and cell-type-specific components in singlecell RNAseq data 37 .…”

“…Thompson et al proposed CONTENT to go one step further and model both shared and tissue-specific components of gene expression in bulk multi-tissue data for model construction 37 . This approach can also be used for modeling shared and cell-type-specific components in singlecell RNAseq data 37 . These recently developed TWAS methods model associations at the same resolution of the data, such as modeling tissue-level associations for bulk profiling data and cell-type-level associations for single-cell data, and thus do not provide higherresolution (single cell) understanding of disease using lowerresolution (bulk tissue) data as does MiXcan.…”

STACCato: Supervised Tensor Analysis tool for studying Cell-cell Communication using scRNA-seq data across multiple samples and conditions

“…Furthermore, it facilitates the identification of activity patterns among cell types involved in CCC. We applied STACCato to analyze a SLE dataset with an extremely unbalanced design 10,11 and an ASD dataset with a balanced design 12 . Additionally, we conducted simulation studies to mimic real data with different study designs.…”

Transcriptome‐Wide Association Studies (TWAS): Methodologies, Applications, and Challenges

“…These technologies are helping to generate comprehensive reference maps of cell types and cell states (Rozenblatt‐Rosen et al., 2017), expanding our understanding of cellular identity, cell‐type‐specific disease‐relevant signatures, and the molecular function of genetic variation (Perez et al., 2022). Here, GReX modeling can shed light on the context‐specific and context‐shared components of gene expression (Thompson et al., 2022). Furthermore, genetic models of cell type‐specific and cell state‐adjusted gene expression from differentiating cell types can provide insights into the dynamics of gene regulation and enable the discovery of context‐specific disease associations not accessible via bulk sequencing derived models (Abe et al., 2023).…”