FunGen-xQTL Resource File Format Description#

Overview#

This repository provides Quantitative Trait Loci (QTL) analysis and GWAS integration results from the FunGen-xQTL project, across diverse aging human brain regions and cell types. The analyses span multiple cohorts including ROSMAP, Knight ADRC, Mount Sinai Brain Bank, MiGA, MetaBrain, and STARNET.

For comprehensive metadata of all analyses with harmonized context definitions, see our analysis meta-data table.

Fine-mapping and integrative results for xQTL and AD GWAS#

The following table catalogs all fine-mapped QTL and AD GWAS integration results, organized by molecular modality, cohort, tissue context, and analytical method. Each entry represents fine-mapped genetic loci for corresponding molecular phenotypes, with top loci and variants exported in standardized BED format.

• AD_xQTL_colocalization results include “AD_Bellenguez_2022”, “AD_Wightman_Excluding23andMe_2021”, “AD_Wightman_ExcludingUKBand23andME_2021”, “AD_Kunkle_Stage1_2019”, “AD_Wightman_Full_2021”

Data Format Specifications#

Each exported file follows a standardized BED format with compressed storage (.gz), containing top loci from respective QTL analyses. Files are optimized for downstream genomic and transcriptomic research applications.

SuSiE/mvSuSiE fine-mapping columns#

ColocBoost-specific Columns#

TWAS-Specific Columns#

cTWAS-Specific Columns#

For detailed documentation, please refer to cTWAS (causal-TWAS)

fSuSiE-Specific Columns#

SuSiE-COLOC Colocalization Columns#

Between xQTL and AD, pairwise.

Consolidated single context xQTL loci

• FunGen_xQTL_single_context_top_loci_integrated.rds : FIXME

• FunGen_xQTL_AD_loci_xQTL_context_method_summary.fsusie.rds : FIXME

Usage Guidelines#

Prioritization Strategies#

• Causal variant identification: Filter by cs_coverage_0.95 > 0 or PIP > 0.1 to identify high-confidence causal variants
• Significant results only: Restrict analysis to rows with non-missing event_ID values for significance-filtered datasets
• Gene-level visualization: Group results by context and gene_ID to examine credible sets across studies
• Multi-context analysis: Rows with missing event_ID from multi-context analyses represent variants not passing LFSR threshold but retained for completeness

Integration Notes#

• All files use standardized variant naming convention for cross-dataset integration
• Credible set columns (cs_coverage_*) indicate membership probability thresholds with varying purity requirements
• For epigenetic marks in fSuSiE results, note that epi_mark_effects are interpolated from neighboring grid effects rather than direct measurements

xQTL Association Results Summary#

The following table provides paths to marginal QTL association results, including linear regression (LR), quantile regression (QR), and interaction analyses testing for genotype-by-sex, genotype-by-APOE, and other context-dependent effects.

For detailed methodology and implementation, refer to our analysis protocol documentation.