ROSMAP AC alternative splicing QTL
Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP) AC alternative splicing.
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 | syn69670592 | Controlled | Results described below |
Other Synapse IDs on this page syn69670600 syn69670611 syn69670597 syn69865816 syn69670630
pip install synapseclient
synapse login
synapse get -r syn69670592Contact
Ru Feng
Data Descriptions
Verifying current avaliable location for the dataset now
Samples were extracted using Qiagen’s miRNeasy mini kit (cat. no. 217004) and the RNase free DNase Set (cat. no. 79254), and quantified by Nanodrop and quality was evaluated by Agilent Bioanalyzer.
| Brain | Description |
|---|---|
| Batch 1 | 638 |
| Batch 2 | 252 (117 for PolyA selection & 135 for rRNA depletion) |
| Batch 3 | 251 |
| Total | 1141 |
Notes regarding sample naming: Sequencing samples that were sequenced more than once include the sequencing batch number as the last 2 digits of the id. For example, sample 123_456789_02 is sample “123_456789”, from batch 2. The sample IDs are otherwise randomly-assigned identifiers. Some samples were re-sequenced due to poor quality or low output, they may have “redo” in the sample identifier.
Links to QTL analysis notebooks for LeafCutter2
See notebooks in:
The notebooks in this folder contain the commands and data wrangling codes for analysis of the eQTL and sQTL data in ROSMAP. (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). Since the sQTL phenotype pre-processing used eQTL STAR alignment output, some of the early stage code are shared.
Association data preprocessing
LeafCutter2_QC shows the commands used for quality control of the LeafCutter2 results.
genotype_preprocessing.ipynb shows the commands used for genotype processing and preparation steps.All QTLs in one cohort have the same genotype_preprocessing procedure.
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_sQTL_LeafCutter2_commands provides information about the input files for AC/DLPFC/PCC TensorQTL cis association in the base_params variable in [generate_command_1].
Analysis Description
Brain multi-region RNA-seq data using LeafCutter2 was proccessed with the xQTL-pipeline, for analysis detial please check records here: sQTL_LeafCutter2
Results
In this analysis we performed two methods: leafcutter2 and psichomics.
leafcutter2
Output files: see the Synapse IDs listed on this page.
psichomics
- data generation using psichomics still in progress
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
Read from the methods: map in this page's metadata header, the same data as the analysis coverage grid.