Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression

Area of Science:

• Genetics
• Genomics
• Molecular Biology

Background:

• Genome-wide association studies (GWASs) identify genetic loci associated with various traits and diseases.
• Determining the specific genes affected by these loci and the impact on gene function remains a challenge.

Purpose of the Study:

• To integrate GWAS data with expression quantitative trait loci (eQTL) data to identify gene expression changes associated with traits.
• To investigate tissue-specific effects of genetic variants on gene expression and explore pleiotropy.

Main Methods:

• Integrated 57 GWAS datasets with 24 eQTL studies using a Mendelian randomization approach.
• Analyzed associations between common genetic variants and gene expression across a wide range of tissues.
• Utilized a false-discovery rate < 0.05 for significance.

Main Results:

• Identified 3,484 instances of gene-trait-associated expression changes.
• Discovered that affected genes are not always the closest to the genetic variant and are often found in pathophysiologically relevant tissues (e.g., liver for lipid traits, arterial tissue for cardiovascular disease).
• Highlighted the interleukin-27 pathway in rheumatoid arthritis and identified instances of agonistic and antagonistic pleiotropy, such as SNX19 and ABCB9 affecting schizophrenia risk and educational attainment.

Conclusions:

• Developed the GWAS2Genes database, a lexicon linking trait-associated genetic variants to gene expression changes in various tissues.
• Demonstrated that genetic variants can alter gene expression in non-obvious ways and in relevant tissues.
• Provided insights into pleiotropy and its role in complex traits and diseases.