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)#

  1. genotype is an all-chromosome, all-samples vcf collection
  2. The original gz vcf is gzipped but not bgzipped, thus cannot tabix -p
  3. 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

Association scan using TensorQTL and summary statistics standardization#

  • TensorQTL.ipynb provides the pipeline to generate TensorQTL cis association results for all QTLs.
  • ROSMAP_DeJager_snuc_eQTL provides information about the input files for TensorQTL cis association in the base_params variable in [generate_command_1].
  • ROSMAP_Kellis_eQTL provides information about the input files for TensorQTL cis association in the base_params variable in [generate_command_1].
  • ROSMAP_mega_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

  • s3://statfungen/ftp_fgc_xqtl/analysis_result/cis_association/ROSMAP/eQTL/snuc_DeJager/
  • s3://statfungen/ftp_fgc_xqtl/analysis_result/cis_association/ROSMAP/eQTL/snuc_Kellis/
  • s3://statfungen/ftp_fgc_xqtl/analysis_result/cis_association/ROSMAP/eQTL/snuc_mega/

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

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

pseudo_bulk_eQTL_DeJager: Preprocess_bundle provides commands to preprocess genotype, phenotype and covariate data all at once. Phenotype_preprocessing shows the commands used for the phenotype data processing and preparation steps for all cell types. Cell-specific phenotype preprocessing are listed here in different folders.

pseudo_bulk_eQTL_Kellis: Preprocess_bundle provides commands to preprocess genotype, phenotype and covariate data all at once. Genotype_pca provides steps for PCA analysis for genotype data. Phenotype_preprocessing shows the commands used for the phenotype data processing and preparation steps. Covariates_preprocessing shows the commands used for the covariate data processing and preparation steps.

pseudo_bulk_eQTL_mega: Genotype_pca provides steps for PCA analysis for genotype data. Phenotype_preprocessing shows the commands used for the phenotype data processing and preparation steps. Covariates_preprocessing shows the commands used for the covariate data processing and preparation steps.