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analysis_plan.Rmd
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analysis_plan.Rmd
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---
title: "QGT-Columbia-analysis-plan"
author: "Hae Kyung Im"
date: "2020-06-03"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
## set up
- install anaconda/miniconda
- define imlabtools conda environment
- download metaxcan repo
- download torus repo
- download fastenloc repo
- download TMWR repo
workaround: in the Rstudio server (http://104.154.186.85:8787/) For the terminal to work properly we need to run
> source /home/rstudio/.bashrc
> conda activate imlabtools
```{r preliminary definitions}
library(tidyverse)
```
# Preliminary information
Data and copies of repositories can be downloaded from [Box here](https://uchicago.box.com/s/zhapf2zfxcpj7thvq4sjnqale3emleum)
The latest version of the analysis plan that generated this page is on [github here](https://github.com/hakyimlab/QGT-Columbia-HKI/blob/master/analysis/analysis_plan.Rmd)
# Transcriptome-wide association methods
```{r preliminaries}
print(getwd())
pre="/home/rstudio/QGT-Columbia-HKI"
model.dir=glue::glue("{pre}/models")
metaxcan.dir=glue::glue("{pre}/repos/MetaXcan-master/software")
fastenloc.dir=glue::glue("{pre}/repos/fastenloc-master")
torus.dir=glue::glue("{pre}/repos/torus-master")
twmr.dir=glue::glue("{pre}/repos/TWMR-master")
results.dir=glue::glue("{pre}/results")
```
## predict expression
```{bash predict genetic component of expression,eval=FALSE}
export PRE="/home/rstudio/QGT-Columbia-HKI"
export DATA=$PRE/predixcan/data
export MODEL=$PRE/models
export METAXCAN=$PRE/repos/MetaXcan-master/software
export RESULTS=$PRE/results
printf "Predict expression\n\n"
python3 $METAXCAN/Predict.py \
--model_db_path $PRE/models/gtex_v8_en/en_Whole_Blood.db \
--vcf_genotypes $DATA/genotype/ALL.chr22.shapeit2_integrated_snvindels_v2a_27022019.GRCh38.phased.vcf.gz \
--vcf_mode genotyped \
--variant_mapping $DATA/gtex_v8_eur_filtered_maf0.01_monoallelic_variants.txt.gz id rsid \
--on_the_fly_mapping METADATA "chr{}_{}_{}_{}_b38" \
--prediction_output $RESULTS/predixcan/Whole_Blood__predict.txt \
--prediction_summary_output $RESULTS/predixcan/Whole_Blood__summary.txt \
--verbosity 9 \
--throw
```
## assess prediction performance (optional)
```{r}
predicted_expression = read_tsv(glue::glue("{results.dir}/predixcan/Whole_Blood__predict.txt"))
dim(predicted_expression)
head(predicted_expression[,1:5])
prediction_summary = read_tsv(glue::glue("{results.dir}/predixcan/Whole_Blood__summary.txt"))
dim(prediction_summary)
head(prediction_summary)
## merge with GEUVADIS expression data
## calculate spearman correlation
## select a few genes and plot predicted vs observed expression
```
## run association with phenotype
```{bash, eval=FALSE}
export PRE="/home/rstudio/QGT-Columbia-HKI"
export DATA=$PRE/predixcan/data
export MODEL=$PRE/models
export METAXCAN=$PRE/repos/MetaXcan-master/software
export RESULTS=$PRE/results
printf "association\n\n"
python3 $METAXCAN/PrediXcanAssociation.py \
--expression_file $RESULTS/predixcan/Whole_Blood__predict.txt \
--input_phenos_file $DATA/phenotype/random_pheno_1000G_hg38.txt \
--input_phenos_column pheno \
--output $RESULTS/predixcan/random_pheno/Whole_Blood__association.txt \
--verbosity 9 \
--throw
```
## read results
```{r, eval=FALSE}
predixcan_association = read_tsv(glue::glue("{results.dir}/predixcan/random_pheno/Whole_Blood__association.txt"))
dim(predixcan_association)
predixcan_association %>% arrange(pvalue) %>% head
predixcan_association %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=20)
```
## Exercise
-[ ] Run association with another phenotype
in $PRE/predixcan/data/phenotype/ALL.chr22.shapeit2_integrated_snvindels_v2a_27022019.GRCh38_x_en_Whole_Blood.simulated_phenotype.spike_n_slab_0.5_x_pve0.6.txt
```{bash, eval=FALSE}
```
```{r}
```
-------
-------
# Summary PrediXcan
```{r}
## harmonized and imputed GWAS result for coronary artery disease is available in
# $PRE/s-predixcan/data/
```
## run s-predixcan
```{bash, eval=FALSE}
export PRE="/home/rstudio/QGT-Columbia-HKI"
export DATA=$PRE/s-predixcan/data
export MODEL=$PRE/models
export METAXCAN=$PRE/repos/MetaXcan-master/software
export RESULTS=$PRE/results
python $METAXCAN/SPrediXcan.py \
--gwas_file $DATA/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz \
--snp_column panel_variant_id --effect_allele_column effect_allele --non_effect_allele_column non_effect_allele --zscore_column zscore \
--model_db_path $MODEL/gtex_v8_mashr/mashr_Whole_Blood.db \
--covariance $MODEL/gtex_v8_mashr/mashr_Whole_Blood.txt.gz \
--keep_non_rsid --additional_output --model_db_snp_key varID \
--throw \
--output_file $RESULTS/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Whole_Blood.csv
```
## plot and interpret s-predixcan results
```{r, eval=FALSE}
spredixcan_association = read_csv(glue::glue("{results.dir}/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Whole_Blood.csv"))
dim(spredixcan_association)
spredixcan_association %>% arrange(pvalue) %>% head
spredixcan_association %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=20)
```
SORT1, considered to be a causal gene for LDL cholesterol and as a consequence of coronary artery disease, is not found here. Why? (tissue)
## run multixcan (optional)
```{bash, eval=FALSE}
python $METAXCAN/SMulTiXcan.py \
--models_folder $DATA/models/eqtl/mashr \
--models_name_pattern "mashr_(.*).db" \
--snp_covariance $DATA/models/gtex_v8_expression_mashr_snp_covariance.txt.gz \
--metaxcan_folder $RESULTS/spredixcan/eqtl/ \
--metaxcan_filter "CARDIoGRAM_C4D_CAD_ADDITIVE__PM__(.*).csv" \
--metaxcan_file_name_parse_pattern "(.*)__PM__(.*).csv" \
--gwas_file $RESULTS/processed_summary_imputation/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz \
--snp_column panel_variant_id --effect_allele_column effect_allele --non_effect_allele_column non_effect_allele --zscore_column zscore --keep_non_rsid --model_db_snp_key varID \
--cutoff_condition_number 30 \
--verbosity 7 \
--throw \
--output $RESULTS/smultixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE_smultixcan.txt
```
# Colocalization methods
## fine-map GWAS results
We will run torus due to time limitation but ideally we would like to run a method that allows multiple causal variants per locus.
```{bash, eval=FALSE}
#torus -d Height.torus.zval.gz --load_zval -dump_pip Height.gwas.pip
#gzip Height.gwas.pip
TORUSOFT=$PRE/repos/torus-master/src/torus.static
$TORUSOFT -d $PRE/fastenloc/data/Height.torus.zval.gz --load_zval -dump_pip $PRE/fastenloc/data/Height.gwas.pip
cd $PRE/fastenloc/data
gzip Height.gwas.pip
cd $PRE
```
## calculate colocalization with fastENLOC
```{bash, eval=FALSE}
## check out tutorial https://github.com/xqwen/fastenloc/tree/master/tutorial
export eqtl_annotation_gzipped=$PRE/fastenloc/data/FASTENLOC-gtex_v8.eqtl_annot.vcf.gz
export gwas_data_gzipped=$PRE/fastenloc/data/Height.gwas.pip.gz
export TISSUE=Whole_Blood
export FASTENLOCSOFT=$PRE/repos/fastenloc-master/src/fastenloc.static
cd $RESULTS/fastenloc/
$FASTENLOCSOFT -eqtl $eqtl_annotation_gzipped -gwas $gwas_data_gzipped -t $TISSUE
#[-total_variants total_snp] [-thread n] [-prefix prefix_name] [-s shrinkage]
```
## analyze results
```{r}
## optional - compare with s-predixcan results
```
-[] prepare
----------
# Mendelian randomization methods
## run SMR (optional)
```{bash, eval=FALSE}
```
## run TWMR (for a locus)
```{bash, eval=FALSE}
cd $TWMR
export PRE=/Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI
export TWMR=$PRE/repos/TWMR-master
export OUTPUT=$PRE/results
GENE=ENSG00000002919
R < $TWMR/MR.R --no-save $GENE
cd $PRE
```