Abstract
LD score regression (LDSC) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. The key concept underlying the LDSC framework is that there is a positive linear relationship between the magnitude of GWAS allelic effect estimates and linkage disequilibrium (LD) when complex traits are generated under the infinitesimal model — that is, causal variants are uniformly distributed along the genome and each have the same expected contribution to phenotypic variation. We present interaction-LD score (i-LDSC) regression: an extension of the original LDSC framework that accounts for non-additive genetic effects. 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 a cis-interaction score (i.e., interactions between a focal variant and nearby variants) significantly recovers substantial non-additive heritability that is not captured by LDSC. For each of the 25 traits analyzed in the UK Biobank and 23 of the 25 traits analyzed in BioBank Japan, i-LDSC detects a significant amount of variation contributed by genetic interactions. The i-LDSC 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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This version of the manuscript has been revised in categorical updates to the entire manuscript.