ROSMAP RNA-seq monocyte QTL
Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP) monocyte data-set.
Please refer to this document for an overview of the ROSMAP project.
Access
Controlled access. Request data access for ROSMAP on the AD Knowledge Portal; you need a Synapse account. See How to get the data.
| Where | ID | Access | What you get |
|---|---|---|---|
| Synapse | syn22024496 | Controlled | Results described below |
Other Synapse IDs on this page syn69670592 syn69670600 syn69670611 syn69670597 syn69865816 syn69670630
pip install synapseclient
synapse login
synapse get -r syn22024496Contact
Travyse Edwards
Data descriptions
The ROSMAP monocyte data on MSSM were downloaded from syn22024496 on July 11th, 2022. (path on MSSM to be added)
Preview Monocyte Gene Expression Data
Over all 615 samples in the dataset, 302 samples have diagnosis meta data, 1 sample missing associated sex meta data.
These samplesa ll share the same library preparation method and are not multi-specimen samples. However, there is variety in read length, sex, and platform. It may be worthwhile to use these as covariates in the downstream analyses. I will remove the two samples that are missing sex information (though I can just impute the sex).
The biospecimen file identifies the individuals that have SNP array data, Whole Genome Sequencing data, or both available. An overlapping check confirmed the monocyte gene expression contains a mix of SNP array and whole genome sequencing data (with a lot of overlap between the two). With in the 614 samples for which we have the expression data for, 246 are overlapped with WGS, 313 are overlapped with SNP array and 239 are overlapped with joint-call WGS.
Among the 613 samples in the bed.gz file,
- 56 of the samples are duplicates, there are 557 unique samples in total
- 237 of the samples are properly mapped to the samples. 320 samples remaining.
- 154 out of the 320 samples are in the AD Knowledge Portal metadata, but only 3 has wgs, whose genotype ID cannot be found
- 166 out of the 320 is not in any of metadata, nor in any covariates data. Please see this notebook for details.
Links to QTL analysis notebooks
See notebooks in:
The notebooks in this folder contain the commands and data wrangling codes for analysis of the expression data in KnightADRC. (data wrangling exist because not all data are processed using the xqtl-pipeline from the beginning and need to be reformatted to fit one intermediate step of the pipeline).
Association data preprocessing
genotype_preprocessing.ipynb shows the commands used for genotype processing and preparation steps.
phenotype_preprocessing.ipynb shows the commands used for the phenotype data processing and preparation steps.
covariate_preprocessing.ipynb shows the commands used for the covariate data processing and preparation steps.
Association scan using TensorQTL and summary statistics standardization
- TensorQTL.ipynb provides the pipeline to generate TensorQTL cis association results for all QTLs.
- ROSMAP_CUIMC1_monocyte_eQTL provides information about the input files for TensorQTL cis association in the base_params variable in [generate_command_1].
Path(s) to cis-QTL association testing
output of TensorQTL.ipynb
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
- cTWASDone
Read from the methods: map in this page's metadata header, the same data as the analysis coverage grid.