Summary of analysis plan

• predict whole blood expression
• check how well the prediction works with GEUVADIS expression data
• run association between predicted expression and a simulated phenotype
• calculate association between expression levels and coronary artery disease risk using s-predixcan
• fine-map the coronary artery disease gwas results using torus
• calculate colocalization probability using fastenloc
• run transcriptome-wide mendelian randomization in one locus of interest

Preliminary definitions

• log into Midway2 and run the following commands. Don’t forget to replace username with your user name.

# define environmental variables for convenience
USERNAME="WRITE YOUR USER NAME HERE"
PRE="/project2/bios25328/Data/Lab-QGT/"
WORK="/project2/bios25328/username/$USERNAME/lab-QGT/"
SOFTWARE="/project2/bios25328/software/"

## create the working directory if you don't have it 
mkdir $WORK
cd $WORK

LAB="/project2/bios25328/Data/Lab-QGT/QGT-Columbia-HKI-repo/"
CODE=$LAB/code
DATA=$PRE/data
MODEL=$PRE/models
RESULTS=$PRE/results
METAXCAN=$PRE/repos/MetaXcan-master/software
TWMR=$PRE/repos/TWMR-master

# load python
module load python
# load conda
module load python/anaconda-2020.02

• activate the the imlabtools environment, which will make sure all the necessary python modules are available to the software we will be running.

conda activate $SOFTWARE/MetaXcan/env/imlabtools/

Reminder: the bash chunks need to be copy-pasted to the terminal, not performed within the chunk.

• Get some helper scripts

## git clone https://github.com/hakyimlab/QGT-Columbia-HKI-repo/

This should have generated a folder /project2/bios25328/Data/Lab-QGT/QGT-Columbia-HKI-repo

• Load and open R

module load R
R

• Within R, load the tidyverse package

suppressPackageStartupMessages(library(tidyverse))

• define some variables to access the data more easily within the R session. Run the following r chunk

print(getwd())

PRE="/project2/bios25328/Data/Lab-QGT"
MODEL=glue::glue("{PRE}/models")
DATA=glue::glue("{PRE}/data")
RESULTS=glue::glue("{PRE}/results")
METAXCAN=glue::glue("{PRE}/repos/MetaXcan-master/software")
FASTENLOC=glue::glue("{PRE}/repos/fastenloc-master")
TORUS=glue::glue("{PRE}/repos/torus-master")
TWMR=glue::glue("{PRE}/repos/TWMR-master")

lab="/project2/bios25328/Data/Lab-QGT/QGT-Columbia-HKI-repo"
CODE=glue::glue("{lab}/code")
source(glue::glue("{CODE}/load_data_functions.R"))
source(glue::glue("{CODE}/plotting_utils_functions.R"))



# This is a reference table we'll use a lot throughout the lab. It contains information about the genes.
gencode_df = load_gencode_df()

predict expression

• We will predict expression of genes in whole blood using the Predict.py code in the METAXCAN folder.

• Prediction models are located in the MODEL folder. Additional models for different tissues and transcriptome studies can be downloaded from predictdb.org

• Remember you need to copy and paste this code chunk into the terminal to run it. Also make sure you activated the imlabtools environment which has all the necessary python modules.

• Make sure all the paths and file names are correct

• This run should take about one minute.

• copy and paste the following block to the terminal and hit enter

printf "Predict expression\n\n"
python3 $METAXCAN/Predict.py \
--model_db_path $PRE/models/gtex_v8_en/en_Whole_Blood.db \
--vcf_genotypes $DATA/predixcan/genotype/filtered.vcf.gz \
--vcf_mode genotyped \
--variant_mapping $DATA/predixcan/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

• check predicted values

prediction_fp = glue::glue("{RESULTS}/predixcan/Whole_Blood__predict.txt")

## Read the Predict.py output into a dataframe. This function reorganizes the data and adds gene names.
predicted_expression = load_predicted_expression(prediction_fp, gencode_df)

head(predicted_expression)

## read summary of prediction, number of SNPs per gene, cross validated prediction performance
prediction_summary = load_prediction_summary(glue::glue("{RESULTS}/predixcan/Whole_Blood__summary.txt"), gencode_df)
## number of genes with a prediction model
dim(prediction_summary)
head(prediction_summary)

print("distribution of prediction performance r2")
summary(prediction_summary$pred_perf_r2)

assess prediction performance (optional)

## download and read observed expression data from GEUVADIS 
## from https://uchicago.box.com/s/4y7xle5l0pnq9d1fwmthe2ewhogrnlrv

## Remove the version number from the gene_id's (ENSG000XXX.ver)
head(predicted_expression)

## merge predicted expression with observed expression data (by IID and gene)

## plot observes vs predicted expressioni for 
## ERAP1 (ENSG00000164307)
## PEX6 (ENSG00000124587)

## calculate spearman correlation for all genes

## what's the best performing gene?

run association with a simulated phenotype

\(Y = \sum_k T_k \beta_k + \epsilon\)

with random effects \(\beta_k \sim (1-\pi)\cdot \delta_0 + \pi\cdot N(0,1)\)

sPHENO="sim.spike_n_slab_0.01_pve0.1"

printf "association\n\n"
python3 $METAXCAN/PrediXcanAssociation.py \
--expression_file $RESULTS/predixcan/Whole_Blood__predict.txt \
--input_phenos_file $DATA/predixcan/phenotype/$PHENO.txt \
--input_phenos_column pheno \
--output $RESULTS/predixcan/$PHENO/Whole_Blood__association.txt \
--verbosity 9 \
--throw

More predicted phenotypes can be found in $DATA/predixcan/phenotype/. The naming of the phenotypes provides information about the genic architecture: the number after pve is the proportion of variance of Y explained by the genetic component of expression. The number after spike_n_slab represents the probability that a gene is causal \(\pi\)(i.e. prob \(\beta \ne 0\))

read association results

## read association results
PHENO="sim.spike_n_slab_0.01_pve0.1"

predixcan_association = load_predixcan_association(glue::glue("{RESULTS}/predixcan/{PHENO}/Whole_Blood__association.txt"),
                                                   gencode_df)

## take a look at the results
dim(predixcan_association)
predixcan_association %>% arrange(pvalue) %>% head
predixcan_association %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=20)
## compare distribution against the null (uniform)
gg_qqplot(predixcan_association$pvalue, max_yval = 40)

compare estimated effects with true effect sizes

truebetas = load_truebetas(glue::glue("{DATA}/predixcan/phenotype/gene-effects/{PHENO}.txt"), gencode_df)
betas = (predixcan_association %>% 
               inner_join(truebetas,by=c("gene"="gene_id")) %>%
               select(c('estimated_beta'='effect', 
                        'true_beta'='effect_size',
                        'pvalue', 
                        'gene_id'='gene', 
                        'gene_name'='gene_name.x', 
                        'region_id'='region_id.x')))
betas %>% arrange(pvalue) %>% head
## do you see examples of potential LD contamination?
betas %>% ggplot(aes(estimated_beta, true_beta))+geom_point()+geom_abline()

run s-predixcan

python $METAXCAN/SPrediXcan.py \
--gwas_file  $DATA/spredixcan/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

spredixcan_association = load_spredixcan_association(glue::glue("{RESULTS}/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Whole_Blood.csv"), gencode_df)
dim(spredixcan_association)
spredixcan_association %>% arrange(pvalue) %>% head
spredixcan_association %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=20)

gg_qqplot(spredixcan_association$pvalue)

• SORT1, considered to be a causal gene for LDL cholesterol and as a consequence of coronary artery disease, is not found here. Why? (tissue)

Exercise

• run s-predixcan with liver model, do you find SORT1? Is it significant?

• compare zscores in liver and whole blood.

run multixcan (optional)

• multixcan aggregates information across multiple tissues to boost the power to detect association. It was developed movivated by the fact that eQTLs are shared across multiple tissues, i.e. many genetic variants that regulate expression are common across tissues.

python $METAXCAN/SMulTiXcan.py \
--models_folder $MODEL/gtex_v8_mashr \
--models_name_pattern "mashr_(.*).db" \
--snp_covariance $MODEL/gtex_v8_expression_mashr_snp_smultixcan_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 $DATA/spredixcan/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

GWAS summary statistics to torus format

• the following code will format GWAS summary statistics into a format that the fine-mapping method torus can understand.

• we precalculated this for you so there is no need to recalculate

## We ran this formatting for you because it takes over 10 minutes.
python $CODE/gwas_to_torus_zscore.py \
-input_gwas $DATA/spredixcan/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz \
-input_ld_regions $DATA/spredixcan/eur_ld_hg38.txt.gz \
-output_fp $DATA/fastenloc/CARDIoGRAM_C4D_CAD_ADDITIVE.zval.gz

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, such as DAP-G or SusieR.

• torus has been precompiled and placed within the PATH

TORUSOFT=torus

$TORUSOFT -d $PRE/data/fastenloc/CARDIoGRAM_C4D_CAD_ADDITIVE.zval.gz --load_zval -dump_pip $PRE/data/fastenloc/CARDIoGRAM_C4D_CAD_ADDITIVE.gwas.pip
cd $PRE/data/fastenloc
gzip CARDIoGRAM_C4D_CAD_ADDITIVE.gwas.pip
cd $PRE 

We can take a quick look at the z-values and finemapping PIPs:

cd $PRE/data/fastenloc
zless CARDIoGRAM_C4D_CAD_ADDITIVE.zval.gz
zless CARDIoGRAM_C4D_CAD_ADDITIVE.gwas.pip.gz

calculate colocalization with fastENLOC

## check out tutorial https://github.com/xqwen/fastenloc/tree/master/tutorial

eqtl_annotation_gzipped=$PRE/data/fastenloc/FASTENLOC-gtex_v8.eqtl_annot.vcf.gz
gwas_data_gzipped=$PRE/data/fastenloc/CARDIoGRAM_C4D_CAD_ADDITIVE.gwas.pip.gz
TISSUE=Whole_Blood
FASTENLOCSOFT=fastenloc
##FASTENLOCSOFT=/Users/owenmelia/projects/finemapping_bin/src/fastenloc/src/fastenloc

mkdir $RESULTS/fastenloc/
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

## optional - compare with s-predixcan results

fastenloc_results = load_fastenloc_coloc_result(glue::glue("{RESULTS}/fastenloc/enloc.sig.out"))
spredixcan_and_fastenloc = inner_join(spredixcan_association, fastenloc_results, by=c('gene'='Signal'))
ggplot(spredixcan_and_fastenloc, aes(RCP, -log10(pvalue))) + geom_point()

## which genes are both colocalized (rcp>0.10) and significantly associated (pvalue<0.05/number of tests)

---

run TWMR (for a locus)

# Load the 'analyseMR' function
source(glue::glue("{CODE}/TWMR_script.R"))

# Collect the list of genes available to run
gene_lst <- list.files(TWMR)
gene_lst <- gene_lst[str_detect(gene_lst, "ENS.*")]
gene_lst <- (gsub("\\..*", "", gene_lst) %>% unique)

# Set the gene and run. The function writes output to a file.
for (gene in gene_lst) {
  analyseMR(gene, TWMR)
}

twmr_results <- load_twmr_results(TWMR, gencode_df)