Fine-mapping from summary data with the “Sum of Single Effects” model

Abstract

AbstractIn recent work, Wang et al introduced the “Sum of Single Effects” (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To achieve this we introduce a simple strategy that could be used to extend any individual-level data method to deal with summary data. In essence, this strategy replaces the usual regression likelihood with an analogous likelihood based on summary data, exploiting the close connection between the two. Our strategy also has the benefit of dealing automatically with non-invertible LD matrices, which arise frequently in fine-mapping applications, and can complicate inference. We highlight other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Simulation studies show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.