Cost-effective non-additive GWAS across 2329 diseases in 500,349 individuals

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genetic evidence enhances drug candidate success in clinical trials.
• Genome-wide association studies (GWAS) are crucial for identifying genetic associations.
• Standard GWAS assume additive allele effects, but non-additive models offer broader insights.

Purpose of the Study:

• To develop a computationally efficient method for large-scale non-additive GWAS.
• To overcome the high computational burden of applying non-additive models across numerous phenotypes in biobanks.
• To prioritize genetic variants for genome-wide significance in non-additive analyses.

Main Methods:

• Leveraging the correlation between additive and non-additive p-values to prioritize variants.
• Applying the novel method to the FinnGen dataset (500,349 individuals, 2329 phenotypes).
• Reducing computational costs by three orders of magnitude while maintaining high sensitivity for non-additive associations.

Main Results:

• The method successfully reduced computational costs significantly.
• Nearly all true non-additive associations were retained.
• Identified 781 novel genetic loci missed by traditional additive GWAS.
• Fine-mapping and colocalization analyses revealed likely causal variants and biological insights for novel loci.

Conclusions:

• The developed method provides a computationally efficient approach for large-scale non-additive GWAS.
• This facilitates the discovery of novel genetic associations and potential drug targets.
• The findings enhance our understanding of genetic architectures underlying complex traits.