STARNET macrophage gene expression QTL
Access
Controlled access. Request data access for STARNET on the AD Knowledge Portal; you need a Synapse account. See How to get the data.
| Where | ID | Access | What you get |
|---|---|---|---|
| Synapse | syn69670592 | Controlled | Results described below |
Other Synapse IDs on this page syn69670600 syn69670611 syn69670597 syn69865816 syn69670630
pip install synapseclient
synapse login
synapse get -r syn69670592STARNET is an RNA expression study of various disease-relevant tissues obtained from living patients with cardiovascular disease (CVD). The inclusion criterion for patients was eligibility for coronary artery by-pass graft (CABG) surgery. Please refer to this document for an overview of the STARNET project.
Contact
- Contact Name: Travyse Edwards
- Contact Email: travyse.edwards@icahn.mssm.edu
- Contact Affiliation: Icahn School of Medicine at Mount Sinai
- Contact Role: Travyse performed QC & QTL analysis
Study Overview
- Study name : STARNET Macrophage eQTL
- Study Description : Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) macrophage eQTL analysis summary statistics generated using the FGC xQTL pipeline.
Analysis Details
Sample Size Tracking Through Analysis Workflow
| Step | Sample Size |
|---|---|
| Raw Expression Data | 480 |
| QC’d Expression Data | 479 |
| After overlapping with STARNET genotype data | 471 |
Quality Control and Normalization Details for Phenotype Data
For quality control performed for the macrophage gene expression dataset, please refer to this markdown document.
Genotype Parameters
| Parameter | Value |
|---|---|
| Minimum Minor Allele Freq | 0.01 |
| Maximum Minor Allele Freq | 0.99 |
| Kinship Coefficient for Related Individuals | 0.0625 |
| Maximum Missingness Per Variant | 0.1 |
| Maximum Missingness Per Sample | 0.1 |
| Hardy-Weinberg Equilibrium Filter | 1e-15 |
Covariate Parameters
PCA Analysis:
| Parameter | Value |
|---|---|
| Minor Allele Freq for PCA | 0.01 |
| LD Pruning: Window | 0.1 |
| LD Pruning: Shift | 0.1 |
| LD Pruning: Window | 0.1 |
| Number of PCs | 13 |
PEER Analysis:
| Parameter | Value |
|---|---|
| Max Iterations | 1000 |
Prior Parameter: Alpha_a | 0.001 |
Prior Parameter: Alpha_b | 0.1 |
Prior Parameter: Eps_a | 0.1 |
Prior Parameter: Eps_b | 10.0 |
Tolerance Parameter: tol | 0.001 |
Tolerance Parameter: var_tol | 1e-5 |
| Convergence Mode | “fast” |
Final Covariates: Sex, Age, PCs (13), PEER Factors (60)
PCA Method for Latent Factor Identification (Using Marchenko-Pastur Method):
TBD
TensorQTL Parameters
| Parameter | Value |
|---|---|
| Cis Window | 1,000,000 |
| Minor Allele Count Cutoff | 0 |
| Minor Allele Freq Cutoff | 0 |
Links to QTL analysis notebooks
Currently rearranging this section and below
The notebooks in this folder,
https://github.com/cumc/fungen-xqtl-analysis/tree/main/analysis/Marcora_MSSM/STARNET
contain the commands and some of the results visualizations produced by the pipeline when analyzing the STARNET macrophage RNA-seq expression data.
STARNET_Analysis_Complete.ipynbprovides information about the imputation of genotype, summary from other preprocessing steps, and a look into the cisQTL association scan.genotype_preprocessing.ipynbshows the commands used for genotype processing and preparation steps.- Mostly contains the commands used to run the pipeline, but includes some pre-pipeline data wrangling.
- This includes QC, PCA, and GRM steps
phenotype_preprocessing.ipynbshows the commands used for the phenotype data processing and preparation steps.- As above, it mostly contains the commands used to run the pipeline, but includes some pre-pipeline data wrangling.
- This includes quantification steps, QC, annotation, and residual expression steps
covariate_preprocessing.ipynbshows the commands used for the covariate data processing and preparation steps.- This includes factor analysis steps
association_scan_cis.ipynbprovides information about the TensorQTL cis association scan.
Wang lab:
- TensorQTL.ipynb provides the pipeline to generate TensorQTL cis association results for all QTLs.
- STARNET_eQTL provides information about the input files for TensorQTL cis association in the base_params variable in [generate_command_1].
Dataset Details
- Number of cases :
- Number of controls :
- Annotation information :
- Genotype level imputation :
- Imputation Panel :
Path(s) to fine-mapping with SuSiE RSS model
Fine-mapping model objects (SuSiE-RSS, .rds) are available in the finemapping models folder (syn69670592).
Path(s) to TWAS models
Pre-trained TWAS weight models for this context: syn69670600
Quantile TWAS (qTWAS) models: syn69670611
Path(s) to colocalization with SuSiE-coloc / ColocBoost
Multi-context colocalization models (ColocBoost): syn69670597
AD GWAS–xQTL colocalization results: syn69865816
AD GWAS–xQTL colocalization models: syn69670630
Analyses
- cis-QTLDone
- SuSiEDone
- TWASDone
Read from the methods: map in this page's metadata header, the same data as the analysis coverage grid.