Modeling tissue co-regulation estimates tissue-specific contributions to disease

Area of Science:

• Genetics
• Systems Biology
• Computational Biology

Background:

• Genome-wide association studies (GWAS) and gene expression data integration implicate numerous disease-critical tissues.
• Distinguishing biologically causal tissues from tagging tissues is challenging due to co-regulation of genetic effects across tissues.

Purpose of the Study:

• To develop and validate a novel statistical method, tissue co-regulation score regression (TCSC), for disentangling causal from tagging tissues in disease association studies.
• To enhance the precision of identifying specific tissues and their co-regulation patterns linked to various diseases and traits.

Main Methods:

• Tissue co-regulation score regression (TCSC) was developed, regressing gene-disease association statistics from transcriptome-wide association studies (TWAS) against tissue co-regulation scores.
• The method was applied to analyze 78 diseases/traits using gene expression prediction models from 48 Genotype-Tissue Expression (GTEx) tissues.

Main Results:

• TCSC identified 21 significant causal tissue-trait pairs at a 5% false discovery rate (FDR).
• Identified novel plausible associations (e.g., aorta artery and glaucoma) and refined known associations (e.g., subcutaneous adipose for high-density lipoprotein).
• Discovered 17 significant causal tissue-trait covariance pairs at 5% FDR.

Conclusions:

• TCSC provides a precise computational approach to differentiate causal tissues from tagging tissues in genetic studies.
• The method advances our understanding of tissue-specific genetic contributions to complex diseases and traits.