ROSMAP snRNA-seq pseudo-bulk gene expression QTL #

Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP) snRNA-seq from different cells in Dorsolateral Prefrontal Cortex (DLPFC). Please refer to this document for an overview of the ROSMAP project.

Contact #

Hao Sun (eQTL), Masashi Fujita (eQTL), Haochen Sun (fine-mapping), Jiajun Tao (replication)

Study Overview #

Sample information: ROSMAP/ROSMAP_Pseudo_Bulk_sample_attributes.csv.
Lab protocol: ROSMAP/ROSMAP_Pseudo_Bulk_lab_protocol.csv.
Computational protocol: ROSMAP/ROSMAP_Pseudo_Bulk_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.

Analysis Status #

TransQTL association: Finished.

Dataset Description #

Path(s) to genotype matrix #

Using `MatrixQTL` pipeline (by Masashi) #

genotype is an all-chromosome, all-samples vcf collection
The original gz vcf is gzipped but not bgzipped, thus cannot tabix -p
The vcf is not imputed.

Dosage file. The number of ALT allele were counted per donor. /mnt/mfs/ctcn/team/masashi/snuc-eqtl/genotype/get-dosage.ALL.dosage
SNP position file in GRCh38 /mnt/mfs/ctcn/team/masashi/snuc-eqtl/genotype/get-dosage.ALL.snppos
VCF file used to generate above files. This is a subset of ROSMAP WGS VCF. /mnt/mfs/ctcn/team/masashi/snuc-eqtl/genotype/get-dosage.ALL.vcf.gz
The original VCF files of ROS/MAP WGS is here (N = 1,196; GRCh37): /mnt/mfs/ctcn/datasets/rosmap/wgs/ampad/variants/snvCombined/
A summary of quality control is here: /mnt/mfs/ctcn/datasets/rosmap/wgs/ampad/qualityControl/sampleSheetQc.csv
Liftover of the above VCFs from GRCh37 to GRCh38. /mnt/mfs/hgrcgrid/shared/MenonLab/snRNAseq/rosmap_mastervcf/GRCh38_liftedover_sorted_all.vcf.gz
Sorted positions of SNPs, added rsID in dbSNP154, and renamed chromosomes (e.g. 1 to chr1). /mnt/mfs/ctcn/resources/snRNAseq/rosmap_mastervcf/GRCh38_liftedover_re-sorted_dbSNP154_chr-renamed_all.bcf
424 donors extracted for snRNAseq and applied filtering of MAF, HWE, etc. /mnt/mfs/ctcn/team/masashi/snuc-eqtl/genotype/get-dosage.ALL.vcf.gz

Path(s) to omics-data matrix #

Path(s) to covariate data matrix #

Using `MatrixQTL` pipeline (by Masashi) #

Here, I use astrocytes as an example. But all other cell types have the same folder structure. Covariates of eQTL analysis are sex, age, PMI, study, total genes detected, top 3 genotype PCs, and up to 30 expression PCs.

De Jager Lab: /mnt/mfs/ctcn/team/masashi/snuc-eqtl/v20211109.celltypes/Ast/covariates-20211118.tsv.

Using `TenorQTL` pipeline (by Hao) #

Path(s) to QTL results #

Using `MatrixQTL` pipeline (by Masashi) #

De Jager Lab: /mnt/mfs/ctcn/team/masashi/snuc-eqtl Take astrocytes as an example,
De Jager Lab: /mnt/mfs/ctcn/team/masashi/snuc-eqtl/v20211109.celltypes/Ast/matrix-eqtl/covariates-20211118/matrix-eqtl.rds.

df <- readRDS("matrix-eqtl.rds")$cis$eqtl

Using `TenorQTL` pipeline (by Hao) #

Wang Lab: /ftp_fgc_xqtl/projects/single-cell-rna-seq/pseudo_bulk/eight_celltypes_sumstat
Wang Lab(CU Server): /mnt/vast/hpc/csg/wanggroup/fungen-xqtl-analysis/analysis/Wang_Columbia/ROSMAP/pseudo_bulk_eqtl

ROSMAP snRNA-seq pseudo-bulk gene expression QTL #

Contact #

Study Overview #

Analysis Status #

Dataset Description #

Path(s) to genotype matrix #

Using `MatrixQTL` pipeline (by Masashi) #

Path(s) to omics-data matrix #

Path(s) to covariate data matrix #

Using `MatrixQTL` pipeline (by Masashi) #

Using `TenorQTL` pipeline (by Hao) #

Path(s) to QTL results #

Using `MatrixQTL` pipeline (by Masashi) #

Using `TenorQTL` pipeline (by Hao) #

Path(s) to fine-mapping with SuSiE model #

Path(s) to fine-mapping with SuSiE RSS model #

Links to QTL analysis notebooks #

ROSMAP snRNA-seq pseudo-bulk gene expression QTL #

Contact #

Study Overview #

Analysis Status #

Dataset Description #

Path(s) to genotype matrix #

Using MatrixQTL pipeline (by Masashi) #

Path(s) to omics-data matrix #

Path(s) to covariate data matrix #

Using MatrixQTL pipeline (by Masashi) #

Using TenorQTL pipeline (by Hao) #

Path(s) to QTL results #

Using MatrixQTL pipeline (by Masashi) #

Using TenorQTL pipeline (by Hao) #

Path(s) to fine-mapping with SuSiE model #

Path(s) to fine-mapping with SuSiE RSS model #

Links to QTL analysis notebooks #

Using `MatrixQTL` pipeline (by Masashi) #

Using `MatrixQTL` pipeline (by Masashi) #

Using `TenorQTL` pipeline (by Hao) #

Using `MatrixQTL` pipeline (by Masashi) #

Using `TenorQTL` pipeline (by Hao) #