Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits

Area of Science:

• Genetics
• Molecular Biology
• Biostatistics

Background:

• Genome-wide association studies (GWASs) identify genetic risk loci but often fail to pinpoint causal variants and genes.
• Understanding the genetic architecture of complex traits requires integrating diverse data types.

Purpose of the Study:

• To develop and apply a method for estimating genetic correlation between gene expression and complex traits.
• To identify novel genes and pathways influencing complex traits through gene expression.
• To investigate causal relationships between metabolic traits using genetic data.

Main Methods:

• Developed a method to estimate local genetic correlation between predicted gene expression and complex traits.
• Integrated gene expression data from 45 panels with GWAS summary statistics for 30 transcriptome-wide association studies (TWASs).
• Employed bi-directional regression to infer causal relationships between body mass index (BMI), triglyceride levels, and low-density lipoprotein (LDL).

Main Results:

• Identified 1,196 genes with expression associated with complex traits, including 168 novel genes distant from known GWAS loci.
• Discovered 43 pairs of traits with significant genetic correlation based on predicted expression, with 8 novel correlations not found at the SNP level.
• Provided evidence for a causal influence of BMI on triglyceride levels and triglyceride levels on LDL cholesterol.

Conclusions:

• The developed method effectively links gene expression to complex traits, uncovering novel genetic associations.
• Gene expression plays a significant role in the susceptibility to complex diseases.
• Identified potential causal pathways in metabolic regulation, offering targets for future research.