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Related Experiment Video

Updated: Jun 19, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Interactions with polygenic background impact quantitative traits in the UK Biobank.

Lino A F Ferreira^1,2, Sile Hu^1,2, Simon R Myers^2,3

¹Centre for Human Genetics, University of Oxford.

Medrxiv : the Preprint Server for Health Sciences

|December 3, 2025

Summary

We developed a powerful new method to detect genetic interactions, uncovering 144 independent interactions across 52 traits in UK Biobank data. This approach reveals complex biological networks influencing human phenotypes.

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Area of Science:

Human Genetics
Statistical Genomics
Bioinformatics

Background:

Association studies identify genetic variants linked to phenotypes, but biological mechanisms remain challenging to elucidate.
Genes function in complex networks, suggesting genetic interactions (epistasis) are expected but difficult to detect due to vast search spaces and small effect sizes.

Purpose of the Study:

To develop a powerful statistical method for detecting interactions between single-nucleotide polymorphisms (SNPs) and groups of variants aggregated in polygenic scores (PGS).
To identify novel genetic interaction networks influencing quantitative human traits.
To explore functional partitioning of PGSs based on transcription factor binding sites to uncover regulatory interactions.

Main Methods:

Developed a novel statistical test for SNP-by-PGS interactions applicable to any quantitative trait.
Applied the method to 97 quantitative phenotypes in UK Biobank European samples.
Developed methods for refining signals and detecting pairwise SNP-SNP interactions, including functional partitioning of PGSs using the HOCOMOCO database.

Main Results:

Identified 144 independent interactions affecting 52 traits, including known disease risk variants in genes like APOE, FTO, and TCF7L2.
Detected 38 pairwise SNP interactions, including known interactions for alkaline phosphatase levels and a novel interaction for eosinophil levels.
Identified 12 interactions involving functionally partitioned PGSs, including a regulatory interaction between TCF7L2 and KDM2A affecting glycated haemoglobin.

Conclusions:

The developed method significantly increases power to detect genetic interaction networks.
The study substantially expands the repertoire of known epistatic effects for human phenotypes.
Statistical interactions effectively reflect underlying biological interdependencies between genes and regulatory elements.