MultiSuSiE

Summary

MultiSuSiE is an open-source Python tool developed for multi-ancestry statistical fine-mapping of GWAS loci. It extends the single-ancestry Sum of Single Effects (SuSiE) model, allowing causal effect sizes to vary across ancestries based on a multivariate normal prior informed by empirical data. Optimized for whole-genome sequencing datasets such as the All of Us Research Program, MultiSuSiE provides increased fine-mapping power and computational efficiency compared to other multi-ancestry methods.

Method and Capabilities

MultiSuSiE's design and features include: - Variable Effect Sizes: It models shared causal variants across multiple populations while allowing per-allele effect sizes to vary across ancestries. - WGS Compatibility: Specifically evaluated and optimized to handle large-scale whole-genome sequencing datasets, resolving complex multi-signal loci. - Scalable Summaries: Operates on both individual-level data and summary statistics using in-sample LD. It is substantially faster than alternative multi-ancestry extensions.

Independent Benchmark

A head-to-head benchmark of multi-ancestry fine-mapping methods on real TOPMed MESA pQTL data found MultiSuSiE produced the broadest credible sets among the multi-ancestry methods tested, with higher recall against UK Biobank replication than SuSiEx but lower precision — see Multi-Ancestry Proteome-Wide Association Studies for the full comparison against SuSiEx and SuShiE.

Citations

• Rossen, J., Shi, H., Strober, B. J., Zhang, M. J., Kanai, M., McCaw, Z. R., Liang, L., Weissbrod, O., & Price, A. L. (2026). MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data. Nature Genetics, 58(1), 67–76. Source paper: s41588-025-02450-5.pdf