ROSMAP PCC alternative splicing QTL
Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP) PCC 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
Frank Grenn
Study Overview
In progress
- Sample information:
####/####_sample_attributes.csv. - Lab protocol:
####/####_lab_protocol.csv. - Computational protocol:
####/####_computational_protocol.csv. - QTL summary statistics output:
####/####.qtl_results.csv. - Fine-mapping results individual level data model:
####/####.susie.csv. - Fine-mapping results summary statistics model:
####/####.susie_rss.csv. - Colocalization with AD GWAS:
####/####.susie_coloc.csv.
Analysis Status
TransQTL association: Need to be performed.
Dataset Details
Path(s) to omics-data matrix
$ ls -lh batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.txt
-rw-r--r-- 1 fgrennjr casa 3.1G Mar 6 14:06 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.txtPath(s) to QTL results
$ ls -lh | head
total 27G
-rw-r--r-- 1 fgrennjr casa 374M Mar 24 11:42 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.formated.bed.per_chrom_leafcutter.pcc_batch_all.ROSMAP_covariates.ROSMAP_NIA_WGS.pca.PEER.txt.1.cis_qtl_pairs.1.parquet
-rw-r--r-- 1 fgrennjr casa 285 Mar 24 22:12 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.formated.bed.per_chrom_leafcutter.pcc_batch_all.ROSMAP_covariates.ROSMAP_NIA_WGS.pca.PEER.txt.1.cis_qtl_pairs.1.parquet.stderr
-rw-r--r-- 1 fgrennjr casa 138K Mar 24 22:12 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.formated.bed.per_chrom_leafcutter.pcc_batch_all.ROSMAP_covariates.ROSMAP_NIA_WGS.pca.PEER.txt.1.cis_qtl_pairs.1.parquet.stdout
-rw-r--r-- 1 fgrennjr casa 433K Mar 24 22:12 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.formated.bed.per_chrom_leafcutter.pcc_batch_all.ROSMAP_covariates.ROSMAP_NIA_WGS.pca.PEER.txt.1.emprical.cis_sumstats.txt
-rw-r--r-- 1 fgrennjr casa 2.2G Mar 24 11:44 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.formated.bed.per_chrom_leafcutter.pcc_batch_all.ROSMAP_covariates.ROSMAP_NIA_WGS.pca.PEER.txt.1.norminal.cis_long_table.txt
-rw-r--r-- 1 fgrennjr casa 180M Mar 24 11:28 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.formated.bed.per_chrom_leafcutter.pcc_batch_all.ROSMAP_covariates.ROSMAP_NIA_WGS.pca.PEER.txt.10.cis_qtl_pairs.10.parquet
-rw-r--r-- 1 fgrennjr casa 285 Mar 24 16:48 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.formated.bed.per_chrom_leafcutter.pcc_batch_all.ROSMAP_covariates.ROSMAP_NIA_WGS.pca.PEER.txt.10.cis_qtl_pairs.10.parquet.stderr
-rw-r--r-- 1 fgrennjr casa 58K Mar 24 16:48 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.formated.bed.per_chrom_leafcutter.pcc_batch_all.ROSMAP_covariates.ROSMAP_NIA_WGS.pca.PEER.txt.10.cis_qtl_pairs.10.parquet.stdout
-rw-r--r-- 1 fgrennjr casa 187K Mar 24 16:48 batch_all_bam_no_ext_no_outlier_intron_usage_perind.counts.gz_raw_data.qqnorm.formated.bed.per_chrom_leafcutter.pcc_batch_all.ROSMAP_covariates.ROSMAP_NIA_WGS.pca.PEER.txt.10.emprical.cis_sumstats.txtLinks 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].
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