Focus on single-gene effects limits discovery and interpretation of complex-trait-associated variants

Area of Science:

• Genetics
• Genomics
• Molecular Biology

Background:

• Standard quantitative trait locus (QTL) mapping typically analyzes single gene effects, overlooking allelic pleiotropy where one variant impacts multiple genes.
• Allelic pleiotropy includes effects on local/distal genes or mixed molecular impacts on a single gene.
• This study focuses on 'proxitropy,' where a single variant influences the expression of multiple neighboring genes.

Purpose of the Study:

• To introduce a novel multi-gene expression QTL (eQTL) mapping framework called cis-principal-component eQTL (pcQTL).
• To identify genetic variants associated with shared expression variation across clusters of neighboring genes.
• To uncover novel genetic loci and improve understanding of gene regulation and complex trait associations.

Main Methods:

• Developed and applied a multi-gene eQTL mapping framework: cis-principal-component eQTL (pcQTL).
• Performed pcQTL mapping across 13 human tissues using GTEx data.
• Identified variants associated with shared expression variation across neighboring gene clusters.

Main Results:

• Discovered novel genetic loci missed by traditional single-gene approaches.
• Identified an average of 1,396 pcQTLs per tissue, with 27% being novel findings.
• Novel pcQTLs colocalized with 176 additional genome-wide association study (GWAS) trait-associated variants, a 33% increase over single-gene methods.

Conclusions:

• Moving beyond single-gene analyses to multi-gene approaches provides a more comprehensive view of gene regulation.
• The pcQTL framework effectively identifies variants influencing multiple neighboring genes, enhancing the discovery of trait-associated loci.
• This work advances the understanding of genetic architecture underlying complex traits by accounting for multi-gene regulatory effects.