Extended the cTWAS framework to incorporate prediction models from multiple molecular trait types across diverse biological contexts.
Redesigned the software interface to improve modularity, implementing key M-cTWAS tasks as standalone components. Users may run the full M-cTWAS analysis via the main function or execute individual steps independently.
Implemented a “no-LD” mode that enables M-cTWAS analyses without requiring an LD reference panel.
Other important updates
Input processing
Supports multiple prediction model formats, including PredictDB, FUSION, and top-QTL.
Provides streamlined preprocessing tools to harmonize GWAS summary statistics, prediction models, and LD reference.
Offers utility functions for constructing LD matrices from individual-level genotype data and for loading LD matrices of various formats, with flexible support for user-defined custom formats.
Computation
Introduces new parameter estimation procedures that enable sharing of prior effect variance parameters across multiple groups of molecular traits. Several schemes of parameter sharing are supported, including: shared variance for all groups of variables (including SNPs); shared variance for all molecular traits; shared variance for all molecular traits of the same type/modality.
Compute standard errors and p-values of enrichment parameters.
Implements new methods to screen genomic regions with likely causal molecular trait signals. This speeds up the computation, as only such regions will be subject to full fine-mapping (allowing more than one causal signal).
Supports fine-mapping of single or multiple regions, with or without LD information.
Re-implements a simplified single effect regression (SER) model from SuSiE, incorporating the new implementation of null configuration. It is used in EM to speed up computation.
Improves computational efficiency by more than ten-fold, enabling large-scale analyses across many molecular traits. A typical M-cTWAS analysis using molQTLs from multiple groups could be completed within 1–3 hours.
Creating the output
Introduces new methods to combine evidence from all molecular traits targeting the same genes.
Enhances visualization of M-cTWAS results, including updated locus plots with integrated gene tracks and fine-mapping result panels.
Incorporates LD-mismatch diagnosis from SuSiE-RSS in post-processing.
Introduces a region-merging step to resolve the “cross-region” challenge, where the genetic variants contributing to a molecular trait spans two regions.
Note: this updated cTWAS version currently only works for GWAS summary statistics. To use individual level data, please refer to the old cTWAS version for now. We will release a new version that will work with individual level data later.