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))

predict expression

Visual summary of predixcan runs

• 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 = TRUE
  )
## Remove the decimal points from the gene_id's
predicted_expression$gene_id = gsub("\\..*","",predicted_expression$gene_id)
head(predicted_expression)

## 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)

## merge with GEUVADIS expression data

## calculate Spearman correlation

## select a few genes and plot predicted vs observed expression

run association with a phenotype

export PHENO="sim.infinitesimal_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

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

export PHENO="sim.spike_n_slab_0.1_pve0.05"

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

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"))
truebetas = (predixcan_association %>% 
               inner_join(truebetas,by=c("gene"="gene_id")) %>%
               select(c('predicted_beta'='effect', 'true_beta'='effect_size','pvalue')))

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

Exercise

• show top genes
• compare with true effect sizes
• interpret

# truebetas %>% head()

#suppressPackageStartupMessages(library(qqman))

Exercise

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

## do you see examples of LD contamination?

——-

run s-predixcan

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

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)

export MODEL=$PRE/models
export DATA=$PRE/data

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

##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.pip
cd $PRE/data/fastenloc
gzip CAD.gwas.pip
cd $PRE 

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

The inputs have columns SNP_ID, LOCUS_ID, ZVAL, and the outputs have columns SNP_ID, LOCUS_ID, PVAL, PIP (???)

cd $PRE/data/fastenloc
zless Height.torus.zval.gz

zless Height.gwas.pip

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

export TWMR=$PRE/repos/TWMR-master
export OUTPUT=$PRE/results
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