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Source: vignettes/announcements.Rmd
announcements.Rmd

Release notes

Version 2.0.0 - Major Release

Release date: 2026-03-29

Release Overview

This major release introduces susieR 2.0, a complete architectural redesign that addresses the code duplication and fragmented architecture of the original SuSiE implementation as a result of years of continued development, while adding new features and various performance optimizations, all while maintaining backward compatibility.

susieR 2.0 eliminates the duplicative architecture of the original implementation through a unified framework built on modular design principles. The user-facing interface remains largely unchanged, but the implementation now uses generic functions with data-type specific backends through R’s S3 dispatch system. A key design insight is that most SuSiE extensions reduce to customizations in either Bayes factor computation (per single-effect regression) or residual variance estimation (on the model objective), and the S3 dispatch architecture exploits this separation so that each extension is a local change without modifying the core IBSS algorithm. This architecture has enabled the integration of multiple SuSiE extensions, and serves as the backbone for the refactored mvsusieR package for multi-trait fine-mapping (Zou et al. 2026). Beyond architectural improvements, this release introduces new features including unmappable effects modeling, stochastic LD reference with mixture panel support, enhanced computational speed, and improved IBSS refinement procedures.

New Features

  • SuSiE-ash Model: New adaptive shrinkage framework (Stephens 2017) for unmappable effects, extending SuSiE to handle moderately strong but unmappable effects. SuSiE-ash achieves calibration intermediate between standard SuSiE and the more conservative SuSiE-inf under polygenic architectures while maintaining comparable power.
  • SuSiE-inf Integration: Implementation of SuSiE-inf (Cui et al. 2024), which models infinitesimal effects alongside sparse causal signals to improve fine-mapping calibration when genetic architecture includes polygenic backgrounds.
  • Servin-Stephens Prior Integration: New prior option (Servin and Stephens, 2007) on residual variance estimates in single effect regression (SER) to improve credible set coverage and calibration, particularly useful for small sample studies (Denault et al. 2025). Implemented for both individual-level and summary statistics backends.
  • Stochastic LD Reference: Support for using genotype matrices directly, including stochastic genotypes in place of LD correlation matrices in summary statistics models (Sun et al., in preparation), with linear algebra optimizations that cache decompositions and exploit the LD structure for speedup.
  • Mixture of LD Reference Panels: Support for combining multiple LD reference panels with adaptive weight optimization within the regularized LD (RSS-lambda) model framework (Sun et al., in preparation), enabling more robust fine-mapping with diverse or mismatched reference populations.
  • mvsusieR Backbone: The S3 dispatch architecture serves as the shared backbone for mvsusieR, enabling our multi-trait fine-mapping methods to build directly on the susieR 2.0 framework with their own S3 dispatch class.

Enhancements

  • Performance improvements for RSS model: algebraic optimizations improves RSS models, with further gains on large genomic regions when reference genotype matrices replace LD correlation matrices.
  • Convergence Criteria: PIP-based convergence option alongside traditional ELBO convergence, particularly useful for methods where ELBO computation is expensive.
  • Residual Variance Estimation: Method of Moments option alongside Maximum Likelihood for improved stability.
  • Block Coordinate Ascent Refinement: Improved refinement procedure that escapes local optima through alternative initializations and enables residual variance estimation with missing data in multi-trait fine-mapping via mvsusieR.
  • Verbose Diagnostics: Detailed diagnostic output for monitoring model fitting behavior.
  • Attainable Coverage: Post-hoc credible set coverage adjustment as a rough alternative when LD matrices are unavailable for purity filtering.
  • Unit Test Coverage: Comprehensive unit tests covering 100% of code.