Glycoproteomics QTL (gpQTL) Resources#

Glycoproteomics quantitative trait loci (gpQTL) identify genetic variants that influence protein glycosylation levels. Glycosylation is a key post-translational modification affecting protein folding, stability, and function, and is particularly relevant to neurodegeneration and amyloid processing in Alzheimer’s disease. This resource provides gpQTL summary statistics, fine-mapping results, TWAS/qTWAS pre-trained models, and trans-gpQTL analyses.

Overview#

gpQTL mapping was performed on glycopeptide-level mass spectrometry data from brain tissue, using the FunGen-xQTL pipeline. Glycopeptide abundances were normalized and regressed on covariates before tensorQTL-based association testing. Fine-mapping was performed using SuSiE-RSS. This represents one of the first large-scale brain gpQTL resources integrated with AD genetics.

Available Datasets#

Brain Tissue#

Analyses Performed#

Single-Context Fine-Mapping#

Fine-mapping using SuSiE-RSS per glycopeptide, yielding:

• Posterior inclusion probabilities (PIPs)
• 95% credible sets
• Effect sizes on glycosylation levels

Multi-Gene Fine-Mapping#

Multi-gene fine-mapping for gpQTLs jointly models co-regulated glycoproteins to resolve shared signals. Results available at syn69670592.

Trans-gpQTL Fine-Mapping#

Genome-wide trans-gpQTL analyses identify distant genetic influences on glycosylation patterns. Trans-gpQTL fine-mapping results for ROSMAP DLPFC are provided at ROSMAP DLPFC.

TWAS / qTWAS Models#

Pre-trained gpQTL-based TWAS models:

• TWAS weight models (syn69670600): ROSMAP DLPFC
• qTWAS models (syn69670611): quantile regression gpQTL models
• cTWAS inputs (syn70095142): causal TWAS formatted inputs

Colocalization#

AD GWAS–gpQTL colocalization analyses identify glycosylation-level mechanisms of AD risk variants. Results at syn69865816 and syn69670630.

Data Access#