HIRV-TB_eQTL

Overview

This repository contains scripts used for eQTL mapping and related analyses for the paper:

Common genetic determinants of dynamic human in vivo immune response to Mycobacterium tuberculosis
_{Ping Zhang, Carolin T Turner, Aneesh Chandran, Tom Parks, Joshua Rosenheim, Jana Jiang, Lucy K Bell, Santino Capocci, Marc Lipman, Heinke Kunst, Stefan Lozewicz, Gillian S Tomlinson, Julian C Knight and Mahdad Noursadeghi}

See also carolinturner/tst_eqtl for code used to generate figure panels and source data.

Pipeline

---

** directory layout:**

.
├── README.md
├── notebooks
│   ├── 1.TB_DESeq2_424.TB.Rmd
│   ├── 1.TB_DESeq2_424.TB.html
│   ├── 10.DeepSEA-Sei_LMM_results.Rmd
│   ├── 10.DeepSEA-Sei_LMM_results.html
│   ├── 2.Crosscheck_ALL.Rmd
│   ├── 2.Crosscheck_ALL.html
│   ├── 3.1KG_PCAs_267-freeze.TB.html
│   ├── 3.1KG_PCAs_267-freeze.TBs.Rmd
│   ├── 4.individual_eQTLs.plot_lmm_interaction.Rmd
│   ├── 4.individual_eQTLs.plot_lmm_interaction.html
│   ├── 5.Manhattan_LMM.Rmd
│   ├── 5.Manhattan_LMM.html
│   ├── 6.eGene.venn.and.XGR.pathway.Rmd
│   ├── 6.eGene.venn.and.XGR.pathway.html
│   ├── 7.coloc_TB.results.Rmd
│   ├── 7.coloc_TB.results.html
│   ├── 8.circular.plot.Rmd
│   ├── 8.circular.plot.html
│   ├── 9.ldsc.results.plot-opti.Rmd
│   └── 9.ldsc.results.plot-opti.html
└── scripts
    ├── 1.1.split.vcf.for.imputation.sh
    ├── 1.2.filter.and.vcf.to.plink.format_MIS_imputed.sh
    ├── 1.3.pca.with.1KG.data.sh
    ├── 2.1.hisat2.mapping.sh
    ├── 2.2.featureCounts.sh
    ├── 3.1.Crosscheck.RG.and.sort.sh
    ├── 3.2.Crosscheck.DNA.RNA.sh
    ├── 4.1.MatrixeQTL_cis.r
    ├── 4.2.trans-day7.PC25_subset.SNPs.r
    ├── 5.1.run.fastQTL.sh
    ├── 6.1.lmm.r
    ├── 6.2.eigenMT-input.r
    ├── 6.3.eigenMT_python3.py
    ├── 6.4.run_eigenMT_array.sh
    ├── 6.5.eigenMT-qvalue-significance.threshold.r
    ├── 6.6.conditional.analysis_lmerTest-array-ALL.pairs.r
    ├── 7.1.Coloc_SGE_array_LMM.resul_eQTL.R
    ├── 7.2.Coloc_SGE_array_LMM.resul_with.GWAS_opti.R
    ├── 8.1.LD Score (ldsc) regression analysis.txt
    ├── 9.1.interaction.analysis-Day7vsDay2.r
    └── 9.2.interaction.analysis_cell.module.r
3 directories, 42 files

---

Step	Script	Description
1.1	vcf_for_imputation.sh	Prepare VCF for imputation by filtering and splitting per chromosome
1.2	for.PCA.sh	Filter imputed VCFs and convert to PLINK format for PCA
1.3	pca.sh	Perform PCA on merged genotype data with 1000 Genomes reference data
2.1	hisat2_mapping.sh	RNA-seq read mapping using HISAT2
2.2	featurecount.sh	Generate read counts using featureCounts
3.1	dedup_rg_reorder.sh	Remove duplicates, add read groups, and reorder BAM files
3.2	crosscheck.sh	Perform cross-checking of DNA and RNA samples using Picard’s CrosscheckFingerprints
4.1	MatrixeQTL_cis.R	Run MatrixEQTL using different expression PCs
4.2	trans-day7.PC25_subset.SNPs.R	trans-eQTL mapping using day7 lead cis-eQTLs
5.1	run_fastqtl.sh	Run FastQTL for permutations and nominal passes
6.1	lmm.R	LMM model for cis-eQTL mapping
6.2	eigenMT-input.r	Prepare eigenMT input
6.3	eigenMT_python3.py	Python eigenMT script
6.4	run_eigenMT_array.sh	SLURM array script to run eigenMT per chromosome
6.5	eigenMT-qvalue-significance.threshold.r	Compute q-values for eigenMT results and define significance thresholds
6.6	conditional.analysis_lmerTest-array-ALL.pairs.r	Stepwise forward/backward conditional mapping using lmerTest
7.1	Coloc_run_arrayR-eQTL.r	Colocalisation analysis for eQTL Catalogue vs TB summary statistics
7.2	Coloc_SGE_array_LMM.resul_with.GWAS_opti.r	Colocalisation between TB eQTLs and GWAS summary statistics
8.1	LD Score (ldsc) regression analysis	Partitioned heritability estimation
9.1	interaction.analysis-Day7vsDay2.r	SNP × TB_Status (Day7 vs Day2) interaction analysis
9.2	interaction.analysis_cell.module.r	SNP x cell module interaction analysis

---

Environment & dependencies

R (≥ 4.2.1)

Package	Purpose
argparse	Command-line argument parsing for R scripts
data.table	Fast file I/O and manipulation
tidyverse	Core data wrangling (dplyr, tidyr, ggplot2, readr)
reshape	Parse variant IDs (colsplit)
MatrixEQTL	cis/trans-eQTL mapping
lme4, lmerTest	Linear mixed models and p-values
foreach, doMC	Parallel loops for SNP/gene scans
coloc	Bayesian colocalisation
seqminer	Tabix-based read of summary statistics
GenomicRanges	Interval operations for eQTLs
ggplot2, circlize, scales	Visualisation

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Python & command-line tools

Tool	Version (example)	Purpose
FastQTL	≥ 2.184	cis/trans eQTL discovery
HISAT2	≥ 2.1.0	RNA-seq alignment
featureCounts	≥ 1.6.4	Gene quantification
samtools	≥ 1.9	BAM processing
bcftools	≥ 1.10	VCF filtering / annotation
htslib (bgzip, tabix)	≥ 1.8	Compression / indexing
Picard Tools	≥ 2.21.1	BAM deduplication, read-group assignment
eigenMT	Python 3.12.4	Conditional cis-eQTL fine-mapping
ldsc	Python 2.7.15	Estimating heritability and genetic correlation from GWAS summary statistics
conda	v23.7.2	Package & environment management

---

HPC environment

Tool	Purpose
SBATCH (SLURM)	Array & batch job submission
module load / purge	HPC environment setup

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✉️ Contact

For any questions related to the code, please contact:
Ping Zhang (ping.zhang@well.ox.ac.uk)