Effect heterogeneity reveals complex pleiotropic effects of rare coding variants

Abstract

Recent expansion of large-scale biobank resources has enabled rare-variant association studies (RVAS) and systematic investigation of rare variant pleiotropy across thousands of phenotypes simultaneously. However, existing statistical frameworks for dissecting pleiotropy were largely developed for common variants and are not well suited to gene-based rare variant signals, limiting the interpretation of cross-phenotype associations. Here, we develop ALLSPICE, a likelihood-based method that tests whether rare variant effects in genes exhibiting cross-phenotype associations are proportional or heterogeneous across continuous traits while accounting for phenotypic correlation using summary statistics. ALLSPICE is well-calibrated in simulations and is implemented as an R package for scalable analysis of gene-level rare-variant burden associations. We applied ALLSPICE to RVAS of 359 continuous traits in the UK Biobank and identified 124 significant heterogeneous events among 11,810 pairs of gene-trait associations. By identifying effect heterogeneity within genes associated with multiple phenotypes, ALLSPICE clarifies shared and heterogeneous rare variant architectures underlying cross-phenotype associations and provides insight into rare variant pleiotropy.