FGMB Atlas Manuscript Resources

FGMB Atlas Manuscript Resources#

FunGen-xQTL Multi-Brain (FGMB) Atlas is a multi-context, multi-modal, multi-prediction-method regulome-wide association studies (RWAS) resource, tailored to support studies of genetic regulation in aging-brain-related disease traits.

FGMB Atlas integrates molecular QTL resources generated by the ADSP Functional Genomics (FunGen-xQTL) Consortium across contexts (brain regions, cell types), study cohorts (ROSMAP, MSBB, KNIGHT ADRC), and molecular modalities (gene expression, protein abundance, and splicing). By combining diverse aging-brain molecular datasets with a broad panel of prediction methods, FGMB provides a harmonized set of molecular trait expression prediction models designed to capture both shared and context-specific regulatory effects.

Overview of Structure#

  • FGMB resource overview: a high-level description of the FGMB resource and the modeling strategy used across molecular contexts.

  • Modeling strategy: Description of the multi-method prediction framework used to train FGMB weights, evaluate model performance, definition of imputable models, and selection of best-performing weights for downstream analyses.

  • Workflow examples: reproducible illustrations for building expression predictors, running RWAS associations, and performing multi-group causal TWAS fine-mapping.

  • Manuscript figures: code-oriented pages for generating manuscript and supplementary visualizations.

  • Data access and citation: data access information for FGMB prediction weights, downstream analysis results, and causal TWAS (cTWAS) results.

Workflow Examples#

We provide workflow examples illustrating three main components of the FGMB analysis framework:

  1. Training expression prediction model to generate FGMB resource using competitive modeling strategy.

  2. Application of FGMB weights with GWAS summary statistics for RWAS gene–trait association testing.

  3. Integration of FGMB weights with RWAS results for causal fine-mapping via M-cTWAS.

Pipeline Implementation#

The analytical pipeline was implemented using the xqtl-protocol workflow framework, with model fitting, prediction-weight processing, and RWAS-related utilities supported by the pecotmr R package.

Citation#

If you use FGMB in your research, please cite:

Liu C, Wang A, Sun H, Luo K, Qian S, Li Y, He X, De Jager PL, Bennett DA, Wang M, Cruchaga C, The Alzheimer’s Disease Functional Genomics Consortium, Wang G, Morgante F. (2026). A multi-context regulome-wide association atlas for genetic studies of aging brain disorders. medRxiv. https://doi.org/10.64898/2026.05.15.26353329

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