Choice of phenotype scale is critical in biobank-based GxE tests

The importance of gene-environment interactions (GxE) for complex human traits is heavily debated. Recently, biobank-based GWAS have revealed many statistically significant GxE signals, though most lack clear evidence of biological significance. Here, we partly explain this discrepancy by showing that many GxE signals simplify to additive effects on a different phenotype scale, a classical concern that is currently underappreciated. Our results clearly distinguish GxSex effects on height, which vanish on the log scale, from GxSex effects on testosterone, where the log scale uncovers biologically meaningful female-specific effects. Across 32 phenotypes in UK Biobank, we find that scaling by a power transformation can explain 46% of PGSxSex interactions, and that simple log transformation can explain 23%, with similar results for other environments. We also show that phenotype scale can substantially impact GWAS discovery and the construction and evaluation of polygenic scores. Finally, we provide a set of guidelines to consider and choose phenotype scale in modern genetic studies.