A scalable framework for identifying allelic series from summary statistics

Genes where a dose-response relationship exists between functionality and phenotypic impact are appealing therapeutic targets, as the effects of pharmacological modulation can be predicted from natural genetic variation. We refer to such genes as harboring allelic series and have introduced the rare coding-variant allelic series test (COAST) for their identification. The original COAST required access to individual-level data. However, these data are often unavailable due to privacy or logistical constraints. Meanwhile, single-variant summary statistics of the type produced by genome-wide association studies are plentiful. Here, we introduce COAST-SS, an extension of COAST that accepts standard summary statistics as input. As a running example, we consider identifying allelic series for circulating lipid traits, drawing on data from the UK Biobank, the Million Veteran Program, and the Trans-Omics of Precision Medicine Program. Through extensive analyses of real and simulated data, we demonstrate that COAST-SS provides p values effectively equivalent to those from the original COAST. Interestingly, we find that when linkage disequilibrium (LD) is low, as is expected among rare variants, COAST-SS is robust to misspecification of the LD matrix. We explore several strategies for annotating the pathogenicity of variants supplied to COAST-SS, finding that they often yield similar power for detecting candidate allelic series. Lastly, we employ COAST-SS to screen for lipid-trait allelic series in a meta-analyzed cohort of up to 840,000 subjects. COAST-SS has been incorporated into the publicly available AllelicSeries R package.