Discovering non-additive heritability using additive GWAS summary statistics

LD score regression (<monospace>LDSC</monospace>) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. In this work, we present interaction-LD score (<monospace>i-LDSC</monospace>) regression: an extension of the original<monospace>LDSC</monospace>framework that accounts for interactions between genetic variants. By studying a wide range of generative models in simulations, and by re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals in the UK Biobank and up to 159,095 individuals in BioBank Japan, we show that the inclusion of acis-interaction score (i.e., interactions between a focal variant and proximal variants) recovers genetic variance that is not captured by<monospace>LDSC</monospace>. For each of the 25 traits analyzed in the UK Biobank and BioBank Japan,<monospace>i-LDSC</monospace>detects additional variation contributed by genetic interactions. The<monospace>i-LDSC</monospace>software and its application to these biobanks represent a step towards resolving further genetic contributions of sources of non-additive genetic effects to complex trait variation.