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 interestgi

Initial remarks

• We ask you to actively participate in today’s hands on activities. Notice that we may ask you to share your screen for pedagogic purposes.

• As you run the analysis and programs, we ask you to respond the questions in this document. Find the tab with your name and fill out the questions as you go along.

• You are welcome to check other people’s answers as guidelines but please make sure you write down your own answers.

• If you have any concerns about this, please ask me or one of the TAs for assistance. We are here to help you learn.

Preliminary definitions

• Go to the terminal tab on the RStudio server and update the analysis document to the most recent version. The commands are shown below. Copy the text (without the lines with apostrophes: ```), paste them to the terminal, and hit enter.

PRE="/home/student/"
cd $PRE/lab/
git pull 

• 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 imlabtools

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

• execute the following chunk (you can use the green arrow below to the right)

suppressPackageStartupMessages(library(tidyverse))

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

print(getwd())

lab="/home/student/lab"
CODE=glue::glue("{lab}/code")
source(glue::glue("{CODE}/extra_functions.R"))
#source(glue::glue("code/extra_functions.R"))

PRE="/home/student/QGT-Columbia-HKI"
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")

• define some variables to access the data more easily in the terminal. Run the following bash chunk. You will need to copy and paste the following chunk in the terminal

export PRE="/home/student/QGT-Columbia-HKI"
export LAB="/home/student/lab"
export CODE=$LAB/code
export DATA=$PRE/data
export MODEL=$PRE/models
export RESULTS=$PRE/results
export METAXCAN=$PRE/repos/MetaXcan-master/software
export TWMR=$PRE/repos/TWMR-master

predict expression

Visual summary of predixcan runs

• 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.

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
predicted_expression = read.table(file=prediction_fp, sep="\t", quote="", comment.char="", skip = 1, header = TRUE)
# Retain the column names
cols = read.table(file=prediction_fp, sep="\t", quote="", comment.char="", nrows = 1)
# Fill the column names
colnames(predicted_expression) = unname(unlist(cols[1,]))
## Melt the data so each row is FID, IID, gene_id, predicted_expression
predicted_expression = predicted_expression %>%
  pivot_longer(
    cols = starts_with("ENSG"),
    names_to = "gene_id",
    values_to = "predicted_expression",
    values_drop_na = 
  )

## read summary of prediction, number of SNPs per gene, cross validated prediction performance
prediction_summary = read_tsv(glue::glue("{RESULTS}/predixcan/Whole_Blood__summary.txt"))
## 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)
predicted_expression$gene_id = gsub("\\..*","",predicted_expression$gene_id)
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 $\beta_k \sim (1-\pi)\cdot \delta_0 + \pi\cdot N(0,1)$

export PHENO="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 downloaded from here. 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 = read_tsv(glue::glue("{RESULTS}/predixcan/{PHENO}/Whole_Blood__association.txt"))

## 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)

truebetas = read_tsv(glue::glue("{DATA}/predixcan/phenotype/gene-effects/{PHENO}.txt"))
betas = (predixcan_association %>% 
               inner_join(truebetas,by=c("gene"="gene_id")) %>%
               select(c('predicted_beta'='effect', 'true_beta'='effect_size','pvalue')))

betas %>% ggplot(aes(predicted_beta, true_beta))+geom_point()+geom_abline()

## get chr and position of genes (transcription start site)

## do you see examples of potential LD contamination?

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 = read_csv(glue::glue("{RESULTS}/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)

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)

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

##TODO CAD GWAS is in hg38

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

export 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

export eqtl_annotation_gzipped=$PRE/data/fastenloc/FASTENLOC-gtex_v8.eqtl_annot.vcf.gz
export gwas_data_gzipped=$PRE/data/fastenloc/CARDIoGRAM_C4D_CAD_ADDITIVE.gwas.pip.gz
export TISSUE=Whole_Blood
export FASTENLOCSOFT=fastenloc
##export 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()

run TWMR (for a locus)

TWMR

GENE=ENSG00000002919

cd $TWMR

R < $TWMR/MR.R --no-save $GENE

cd $PRE

## output: /home/student/QGT-Columbia-HKI/repos/TWMR-master/ENSG00000002919.alpha

Session information

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.6.2 Rcpp_1.0.4.6    rprojroot_1.3-2 digest_0.6.25  
 [5] later_1.0.0     R6_2.4.1        backports_1.1.6 git2r_0.26.1   
 [9] magrittr_1.5    evaluate_0.14   stringi_1.4.6   rlang_0.4.5    
[13] fs_1.4.1        promises_1.1.0  whisker_0.4     rmarkdown_2.1  
[17] tools_3.6.3     stringr_1.4.0   glue_1.4.1      httpuv_1.5.2   
[21] xfun_0.13       yaml_2.2.1      compiler_3.6.3  htmltools_0.4.0
[25] knitr_1.28