Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association...

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Large-scale biobanks integrate genotype and phenotype data, enabling the study of genetic associations across diverse traits.
• Gene-level association tests offer biologically interpretable insights into the genetic architecture of phenotypes.
• Identifying shared genetic architecture across multiple phenotypes is crucial for understanding complex diseases and prioritizing molecular targets.

Purpose of the Study:

• To introduce Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), a novel computational method.
• To identify groups of phenotypes that share a core set of genes enriched for mutations in disease cases.
• To leverage large-scale biobank data for discovering shared genetic architecture among numerous phenotypes.

Main Methods:

• Development and simulation-based validation of the WINGS algorithm.
• Application of gene-level association tests combined with WINGS.
• Analysis of 81 case-control and seven quantitative phenotypes from 349,468 European-ancestry individuals in the UK Biobank.

Main Results:

• WINGS successfully identifies clusters of phenotypes sharing common genetic underpinnings.
• Eight distinct phenotype clusters with shared genetic architecture were identified.
• Previously established gene-level associations were successfully recovered within the prioritized clusters.

Conclusions:

• WINGS is an effective method for uncovering shared genetic architecture among multiple phenotypes.
• The identified phenotype clusters and associated genes provide valuable insights for future molecular validation and research.
• This approach enhances the utility of large biobanks for complex trait genetics.