Transcriptome QGT Training 2023

Author

Haky Im

Published

June 10, 2022

Modified

February 8, 2024

Goals of Lab

Predict whole blood expression from genotype
Check how well the prediction works with GEUVADIS expression data
Run association between predicted expression and a 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 cTWAS (fine-map SNPs and genes jointly)

Inital Remarks

We ask you to actively participate in today’s hands on activities.

Note

Notice that we may ask you to share your screen for pedagogic purposes.

Send Haky a private message if you really don’t want to be called to share your screen.
If you have any concerns about this, please ask me or one of the TAs for assistance. We are here to help you learn.
Open the questionnaire forms at the beginning of each section and answer the questions as you go along
- Name is a mandatory field. You can use your name or a pseudonymn. Just keep using the same name for each submission
- We will ask you to submit partial set of answers so that we can get a sense of where people are
Through out the lab you will see code chucks that are written with bash, eval=FALSE in place of R. These chunks are meant to be run in your Rstudio terminal and not in the Rmarkdown file itself.
R code chunks also have “eval=FALSE”. When you click on the green triangle, the code will run in the R console but not when we knit the document. This just happened to be more convenient for us to generate a knitted html.

Set Up

Questionnaire 01

Open and start filling questionnaire 01 Preliminary questionnaire https://forms.gle/fhNJAyjx7MJTy3yt8
Install packages as needed

# List of packages you want to install
packages <- c("tidyverse", "data.table", "BEDMatrix", "Rfast", "susieR", "coloc")

# Function to check and install any missing packages
check_and_install <- function(pkg){
  if (!require(pkg, character.only = TRUE)) {
    install.packages(pkg, dependencies = TRUE)
    library(pkg, character.only = TRUE)
  }
}

# Use the function to check and install packages
sapply(packages, check_and_install)

Load Rstudio Libraries

library(tidyverse)

## packages needed for susie+coloc
library(data.table)
library(BEDMatrix)
library(Rfast)
library(susieR)
library(coloc)
##library(tidyverse)

Navigate to starting directory

cd "/cloud/project/"

activate the the imlabtools environment, which will make sure that the right version of python modules are available

conda activate imlabtools

To define some variables to access the data more easily within the R session, run the following r chunk

print(getwd())

lab="/cloud/project/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"))

PRE="/cloud/project/QGT-Columbia-HKI-repo/box_files"
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")

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

check the values of the variables you just defined in R

MODEL

DATA

define some variables to access the data more easily in the terminal. Remember we are running R code in the R console and command line code in the terminal.

export PRE="/cloud/project/QGT-Columbia-HKI-repo/box_files"
export LAB="/cloud/project/QGT-Columbia-HKI-repo/"
export CODE=$LAB/code
export DATA=$PRE/data
export MODEL=$PRE/models
export RESULTS=$PRE/results
export METAXCAN=$PRE/repos/MetaXcan-master/software

check the values of the variables you just defined


echo $CODE
echo $RESULTS

Transcriptome-wide association (Review)

Now we will perform a transcriptome-wide association analysis using the PrediXcan suite of tools.

We start by predicting the expression levels of genes using the genotype data and the prediction weights and then perform an association between the predicted expression and the phenotype (denoted trait in the figure below).

Questionnaire 02

Open and start filling questionnaire 02 Prediction https://forms.gle/T6kAHvFTxYfcQguW7

Predict Expression from genotype

In this section we will predict expression of genes in whole blood using the Predict.py code in the METAXCAN folder.

Prediction models (weights) are located in the MODEL folder. Additional models for different tissues and transcriptome studies can be downloaded from predictdb.org.
This run should take about one minute.
run the following code in the terminal.

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

Note we are only predicting chromosome 22 here (check by running “predicted_expression %>% count(chromosome)” in the console)

run following code in the console to get information on reported prediction performance.

Find additional information in this wiki

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)

## how many unique genes were predicted?
predicted_expression %>% .[["gene_id"]] %>% unique() %>% length()

## gene expression were predicted for how many people?
predicted_expression %>% .[["IID"]] %>% unique() %>% length()

print("distribution of prediction performance r2")
summary(prediction_summary$pred_perf_r2)
hist(prediction_summary$pred_perf_r2)
## Note: this is what the prediction trainer reported as prediction performance

(Optional) Assess Actual Prediction Performance

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

obs_exp<- read_csv(glue::glue("{DATA}/predixcan/GEUVADIS.observed_df.csv.gz"))

## Note that the version of the ensemble id of the gene was removed
head(predicted_expression)

## Q: how many genes were predicted?
length(unique(predicted_expression$gene_id))

## inner join predicted expression with observed expression data (by IID and gene)
## common errors occur when ensemble id's have versions in one set and not the other set
fullset=inner_join(predicted_expression, obs_exp, by = c("gene_id","IID"))

## calculate spearman correlation for all genes
genelist = unique(predicted_expression$gene_id)
corvec = rep(NA,length(genelist))
names(corvec) = genelist
for(gg in 1:length(genelist))
{
  ind = fullset$gene_id==genelist[gg]
  corvec[gg] = cor(fullset$predicted_expression[ind], fullset$observed_expression[ind])
}

## what's the best performing gene?

## plot the histogram of the prediction performance
hist(corvec)

## list the top 10 best performing genes
head(sort(corvec,decreasing = TRUE),2)
tail(sort(corvec,decreasing = TRUE),2)

## plot the correlation of the top 2 best performing genes bottom 2

geneid = "ENSG00000100376"
genename = gencode_df %>% filter(gene_id==geneid) %>% .[["gene_name"]]
fullset %>% filter(gene_id==geneid) %>% ggplot(aes(observed_expression, predicted_expression))+geom_point()+ggtitle(paste(genename, "-",geneid))

geneid = "ENSG00000075234"
genename = gencode_df %>% filter(gene_id==geneid) %>% .[["gene_name"]]
fullset %>% filter(gene_id==geneid) %>% ggplot(aes(observed_expression, predicted_expression))+geom_point()+ggtitle(paste(genename, "-", geneid))

geneid = "ENSG00000070371"
genename = gencode_df %>% filter(gene_id==geneid) %>% .[["gene_name"]]
fullset %>% filter(gene_id==geneid) %>% ggplot(aes(observed_expression, predicted_expression))+geom_point()+ggtitle(paste(genename, "-", geneid))

geneid = "ENSG00000184164"
genename = gencode_df %>% filter(gene_id==geneid) %>% .[["gene_name"]]
fullset %>% filter(gene_id==geneid) %>% ggplot(aes(observed_expression, predicted_expression))+geom_point()+ggtitle(paste(genename, "-", geneid))

Run PrediXcan association

Questionnaire 03

Open and start filling questionnaire 03 PrediXcan https://forms.gle/3H319knWbLgnynNs9

We are going to use a simulated phenotype for which only UPK3A has an effect on the phenotype ($\beta=-0.9887378$)

$Y = \sum_k T_k \beta_k + \epsilon$

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 found in $DATA/predixcan/phenotype/. The naming of the phenotypes provides information about the genetic 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 π (i.e. prob β≠0)

Looking at 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) %>% select(gene_name,effect,se,pvalue,gene) %>% head
predixcan_association %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=10)
## compare distribution against the null (uniform)
gg_qqplot(predixcan_association$pvalue, max_yval = 40)

Comparing the Estimated Effect Size with True Effect Size

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) %>% select(gene_name,estimated_beta,true_beta,pvalue) %>% head
## do you see examples of potential LD contamination?
betas %>% mutate(causal= true_beta!=0) %>% ggplot(aes(estimated_beta, true_beta,col=causal))+geom_point(alpha=0.6,size=5)+geom_abline()+theme_bw()

UPK3A is the causal gene and has the most significant pvalue. RIBC2 is also significantly associated but has no causal role (we know because we simulated the phenotype that way). Why?

Hint: correlation between the genes

Run Summary PrediXcan

Now we will use the summary results from a GWAS of coronary artery disease to calculate the association between the genetic component of the expression of genes and coronary artery disease risk. We will use the SPrediXcan.py.

The GWAS results (harmonized and imputed) for coronary artery disease are available in $PRE/spredixcan/data/

Questionnaire 04

Open and start filling questionnaire 04 S-PrediXcan https://forms.gle/xJs2U66cnrqdb5cj6

Running 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

We can run the full genome because the summary statistics based PrediXcan is much faster than individual level one.

Plot and Interpret 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)

Question: SORT1, considered to be a causal gene for LDL cholesterol and as a consequence of coronary artery disease, is not found here. Why?
check whether SORT1 is expressed in whole blood GTEx portal
check whether SORT1 has eQTL in whole blood GTEx portal

Run S-PrediXcan using gene expression predicted in liver

-[] Run s-predixcan with liver model, do you find SORT1? Is it significant?

#loction Liver models 
#/cloud/project/QGT-Columbia-HKI-repo/box_files/models/gtex_v8_mashr/
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_Liver.db \
--covariance $MODEL/gtex_v8_mashr/mashr_Liver.txt.gz \
--keep_non_rsid \
--additional_output \
--model_db_snp_key varID \
--throw \
--output_file $RESULTS/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Liver.csv

spredixcan_association_L= load_spredixcan_association(glue::glue("{RESULTS}/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Liver.csv"), gencode_df)
dim(spredixcan_association_L)
spredixcan_association_L %>% arrange(pvalue) %>% head
spredixcan_association_L %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=20)

gg_qqplot(spredixcan_association_L$pvalue)
col_order= c("gene_name","gene","zscore","effect_size","pvalue","var_g","pred_perf_r2", "pred_perf_pval","pred_perf_qval", "n_snps_used", "n_snps_in_cov", "n_snps_in_model","best_gwas_p","largest_weight")
spredixcan_association_L <- spredixcan_association_L[, col_order]
filter(spredixcan_association_L, gene_name=="SORT1")

(Optional) Compare zscores in liver and whole blood.

Recall that zscore is the effect size divided by the standard error

spredixcan_association_L=rename(spredixcan_association_L, zscore_liver = "zscore")
head(spredixcan_association_L)
test=left_join(spredixcan_association, spredixcan_association_L, by="gene_name")
test=select(test,"gene_name","zscore","zscore_liver")
test %>% arrange(zscore_liver) %>% head

test %>% mutate(zscore_WB=zscore) %>% ggplot(aes(zscore_WB,zscore_liver)) + geom_point(size=3,alpha=.6) + geom_abline()

## S-PrediXcan association in liver and whole blood are significantly correlated
cor.test(test$zscore,test$zscore_liver)

(Optional) Run MultiXcan

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.
before you run multixcan ensure you have run s-predixcan for all the tissues you want to multixcan. In this tutorial we have two tissues (liver and whole blood), ensure you have run s-predixcan with the two tissues before running multixcan.
One thing to note is to ensure similar naming pattern for the output files. This is to ensure the files are captured correctly when running multixcan’s filter.


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 9 \
--throw \
--output $RESULTS/smultixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE_smultixcan.txt

Colocalization Methods (Review)

Colocalization methods seek to estimate the probability that the complex trait and expression causal variants are the same. We favor methods that calculate the probability of causality for each trait (posterior inclusion probability), called fine-mapping methods. Here we use susie for fine-mapping and coloc for colocalization.

Questionnaire 5

Open and start filling questionnaire 05 Colocalization https://forms.gle/NfH2MSdy4UyJzGAp7

Run colocalization

When you use coloc on your own data, you may want to check out coloc’s documentation, with good advice and tips on avoiding common mistakes https://cran.r-project.org/web/packages/coloc/vignettes

Due to time constraints, we will run one region and one gene only

Finemap GWAS of CAD
Finemap eQTL of SORT1

For finemapping, we need the summary statistics (effect size, standard errors, etc) and the correlation between SNPs (LD matrix)

load the genotype to calculate the ld matrix

# load the genotype to calculate the ld matrix
X_mat <- BEDMatrix(glue::glue("{DATA}/colocalization/geuvadis_chr1"))
colnames(X_mat) <- gsub("\\_.*", "",colnames(X_mat))
colnames(X_mat) <- str_replace_all(colnames(X_mat),":","_")
snp_info <- fread(glue::glue("{DATA}/colocalization/geuvadis_chr1.bim")) %>% 
  setnames(., colnames(.), c("chr", "snp", "cm", "pos", "alt", "ref")) 

snp_info$snp <- str_replace_all(snp_info$snp,":","_")

Load the eqtl and gwas effect size files

# load the eqtl effect sizes
gene_ss <- fread(glue::glue("{DATA}/colocalization/Liver_chr1.txt"))

# load gwas effect sizes
gwas <- data.table::fread(glue::glue("{DATA}/spredixcan/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz"))

# filter to select genome wide significant snps at 5 × 10−8
filtered_regions <- gwas %>% dplyr::filter(pvalue < 5e-8)

# load the ld block
ldblocks <-read_tsv(glue::glue("{DATA}/spredixcan/eur_ld.hg38.txt.gz"))

Find regions with the strongest signal in the gwas

# get the loci where the significant snps are located
for (n in 1:nrow(filtered_regions)){
  # extract genename, start and end
  variant_id <- as.character(filtered_regions[n,"variant_id"])
  variant_chr <- as.character(filtered_regions[n,"chromosome"])
  variant_pos <- as.numeric(filtered_regions[n,"position"])
  #gene_end <- as.numeric(genes[n,"end"])

  locus <- ldblocks %>%
    dplyr::filter(chr == variant_chr) %>%
    filter(variant_pos >= start & variant_pos < stop) %>%
    mutate(locus_name = paste0(chr,"_",start,"_",stop)) %>%
    dplyr::rename(locus_start=start,locus_end=stop) %>%
    mutate(variant_id = variant_id, position=variant_pos)

  # create a data frame with info
  if (exists('all_loci') && is.data.frame(get('all_loci'))) {
    all_loci <- rbind(all_loci,locus)
  } else {
    all_loci <- locus
  }
}

# select uniq loci
d_loci <- all_loci %>%
  dplyr::select(locus_name,chr,locus_start,locus_end) %>%
  dplyr::distinct()

select a region to run coloc

# select regions to fine map. we are going to use regions in chromosome 1
uniq_loci <- d_loci %>% dplyr::filter("chr1" == chr)
n = 3 # chr1_107867043_109761309 region

# extract information
l_chr = as.numeric(str_remove(uniq_loci[n,"chr"],"chr"))
s_chr = uniq_loci[n,]$chr
l_start = uniq_loci[n,]$locus_start
l_stop = uniq_loci[n,]$locus_end
l_name = uniq_loci[n,]$locus_name

Prepare gwas data for coloc

# select snps for the region from the summary stats
ss <- gwas %>% 
  dplyr::filter(chromosome == s_chr) %>%
  dplyr::filter(position >= l_start & position <= l_stop) %>% 
  dplyr::filter(! is.na(effect_size))

# find the snps in the genotype to calculate the correlation
g.snps <- ss %>% inner_join(snp_info %>% mutate(chr = glue::glue("chr{chr}")), 
                            by=c("chromosome" = "chr","panel_variant_id" = "snp"))


# select genotype to calculate correlation
#f_mat <- X_mat[,g.snps$snp]
f_mat <- X_mat[,g.snps$panel_variant_id]

# calculate corr
R = cora(f_mat) # the package is for speed

## clean up
rm(f_mat)

ff <- g.snps %>% dplyr::filter(! is.na(effect_size)) %>% #select(-snp) %>% 
  dplyr::rename(snp=panel_variant_id,beta=effect_size) %>% 
  mutate(varbeta = standard_error^2) %>% 
  dplyr::select(beta,varbeta,snp,position) %>% as.list()

ff$type <- "cc"
ff$sdY <- 1

ff$LD = R
ff$N = 184305

## check the data (NULL means it's fine)
check_dataset(ff)

Prepare eqtl data for coloc

#Using SORT1 gene and liver tissue
gene <- gene_ss %>% dplyr::filter(gene_id == "ENSG00000134243.11") %>% 
  dplyr::rename(snp = variant_id,beta = slope, MAF = maf,
                pvalue = pval_nominal) %>% 
  mutate(varbeta = slope_se^2, name = snp) %>% 
  filter(! is.na(varbeta)) %>% 
  separate(name, into = c("chr", "position","ref","alt","build"),sep = "_")

## calculate the ld matrix
### get the snps
gene.snps <- gene %>% mutate(position = as.integer(position)) %>% 
  inner_join(snp_info %>% mutate(chr = glue::glue("chr{chr}")), 
                              by=c("chr" = "chr","snp" = "snp"))

# select genotype to calculate correlation
g_mat <- X_mat[,gene.snps$snp]
# calculate corr
g.R = cora(g_mat)

# clean up
rm(g_mat)

# format data for coloc
gg <- gene %>% dplyr::filter(snp %in% gene.snps$snp) %>%
  mutate(position = as.integer(position)) %>% 
  dplyr::select(beta,varbeta,snp,position,MAF, pvalue) %>% as.list()

gg$type <- "quant"
gg$LD = g.R
gg$N = 208 # 670 for blood

## check the data
check_dataset(gg)

run older version of coloc which assumes single causal variant

coloc.abf makes the simplifying assumption that each trait has at most one causal variant in the region under consideration

my.res <- coloc.abf(dataset1=ff, dataset2=gg)
sensitivity(my.res,"H4 > 0.9")

run coloc allowing multiple causal variants

Multiple causal variants, using SuSiE to separate the signals

# Run susie fine maooing
S3 = runsusie(ff)
S4 = runsusie(gg)
#summary(S3)

# Run coloc
susie.res=coloc.susie(S3,S4)
print(susie.res$summary)

SuSiE can take a while to run on larger datasets, so it is best to run once per dataset with the =runsusie= function, store the results and feed those into subsequent analyses.

plot the coloc result with the sensitivity function because weird effects are much easier to understand visually

sensitivity(susie.res,"H4 > 0.9",row=1,dataset1=ff,dataset2=gg,)

cTWAS

cTWAS stands for causal TWAS and seeks to calculate the posterior inclusion probability of genes and SNPs in a given region to be causal. It’s a modification of SUSIER that adds predicted gene expression levels in addition to SNPs in the analysis. It is not yet published, but the authors (Siming Zhao, Wesley Crouse, Matthew Stephens et al) have shared a vignette with us to try it out.

Thank you, Wes, for creating this portion of the lab!

Questionnaire 06

Open and start filling out questionnaire 06 cTWAS https://forms.gle/A4evWkbhR7cXLy36A

#install.packages("R.utils")

#install.packages("remotes")
#remotes::install_github("simingz/ctwas", ref = "develop")

library(ctwas)

#get positions for region of interest (SORT1/PSRC1 locus)
region <- unlist(strsplit(spredixcan_association$region_id[spredixcan_association$gene_name=="PSRC1"], "_"))
chr <- region[2]
start <- as.numeric(region[3])
end <- as.numeric(region[4])

#format summary statistics (and subset to variants in region to save memory)
z_snp <- data.table::fread(glue::glue("{DATA}/spredixcan/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz"), select=c("chromosome", "position", "variant_id", "effect_allele", "non_effect_allele", "zscore", "sample_size"))
z_snp <- z_snp[z_snp$chromosome==chr & z_snp$position >= start & z_snp$position <= end,]
z_snp <- z_snp[!is.na(z_snp$variant_id),-(1:2)]
colnames(z_snp) <- c("id", "A1", "A2", "z", "ss")

#specify directories for LD matrices and weights
ld_R_dir <- glue::glue("{DATA}/cTWAS/LD_matrices")
weight <-  glue::glue("{MODEL}/gtex_v8_mashr/mashr_Liver.db")

#specify output locations and names for cTWAS
outputdir <- glue::glue("{DATA}/cTWAS/results/")
outname.e <- "CARDIoGRAM_Liver_expr"
outname <- "CARDIoGRAM_Liver_ctwas"

#impute gene z scores using cTWAS and save the results
##################################
# NOTE: we are skipping this step and using the precomputed values 
## takes ~10 minutes
##################################
# ctwas_imputation <- impute_expr_z(z_snp=z_snp, weight=weight, ld_R_dir=ld_R_dir, outputdir=outputdir, outname=outname.e, harmonize_z=F, harmonize_wgt=F)
# save(ctwas_imputation, file = paste0(outputdir, outname.e, "_output.Rd"))
load(paste0(outputdir, outname.e, "_output.Rd"))

z_gene <- ctwas_imputation$z_gene
ld_exprfs <- ctwas_imputation$ld_exprfs
z_snp <- ctwas_imputation$z_snp

#make custom region file for single region
ld_regions_custom <- data.frame("chr" = chr, "start" = start, "stop" = end)

write.table(ld_regions_custom, 
            file= paste0(outputdir, "ld_regions_custom.txt"),
            row.names=F, col.names=T, sep="\t", quote = F)
    
ld_regions_custom <- paste0(outputdir, "ld_regions_custom.txt")

#run cTWAS with pre-specified prior parameters at a single locus
#estimating prior requires genome-wide data, too slow for demonstration
#prior is 1% inclusion for genes and is 100x more likely than SNPs
#prior assumes genes have larger effect size than SNPs, in reasonable range for data we've looked at
ctwas_rss(z_gene=z_gene, z_snp=z_snp, ld_exprfs=ld_exprfs, ld_R_dir = ld_R_dir, ld_regions_custom=ld_regions_custom, outputdir = outputdir, outname = outname, thin = 0.01,
          estimate_group_prior = F,
          estimate_group_prior_var = F,
          group_prior=c(0.01, 0.0001),
          group_prior_var=c(50, 25))

#load results
ctwas_results <- data.table::fread(paste0(outputdir,outname,".susieIrss.txt"))

#merge gene names into the results
sqlite <- RSQLite::dbDriver("SQLite")
db = RSQLite::dbConnect(sqlite, weight)
query <- function(...) RSQLite::dbGetQuery(db, ...)
extra_table <- query("select * from extra")
RSQLite::dbDisconnect(db)

ctwas_results$genename <- extra_table$genename[match(ctwas_results$id, extra_table$gene)]

#show results with highest PIPs
col_order_2= c("genename", "chrom", "id", "pos", "type", "region_tag1", "region_tag2", "cs_index", "susie_pip" , "mu2")
ctwas_results <- ctwas_results[, ..col_order_2]
head(ctwas_results[order(-ctwas_results$susie_pip),])

FAQ and frequent errors

conda environment may not load correctly after resuming the project in Rstudio cloud
when inner joining by ensemble id of genes, make sure that both datasets have either the same versions or remove versions

Contributors

Festus Nyasimi
Margaret Perry
Wes Crouse
Owen Melia

--- title: Transcriptome QGT Training 2023 author: Haky Im date: '2022-06-10' date-modified: last-modified format: html: code-fold: false code-summary: "Show the code" code-tools: true code-overflow: wrap --- # Goals of Lab - Predict whole blood expression from genotype - Check how well the prediction works with GEUVADIS expression data - Run association between predicted expression and a 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 cTWAS (fine-map SNPs and genes jointly) # Inital Remarks - We ask you to actively participate in today's hands on activities. ::: callout-note Notice that we may ask you to share your screen for pedagogic purposes. ::: - Send Haky a private message if you really don't want to be called to share your screen. - If you have any concerns about this, please ask me or one of the TAs for assistance. We are here to help you learn. - Open the questionnaire forms at the beginning of each section and answer the questions as you go along - Name is a mandatory field. You can use your name or a pseudonymn. Just keep using the same name for each submission - We will ask you to submit partial set of answers so that we can get a sense of where people are - Through out the lab you will see code chucks that are written with bash, eval=FALSE in place of R. These chunks are meant to be run in your Rstudio terminal and not in the Rmarkdown file itself. - R code chunks also have "eval=FALSE". When you click on the green triangle, the code will run in the R console but not when we knit the document. This just happened to be more convenient for us to generate a knitted html. # Set Up ## Questionnaire 01 - [ ] Open and start filling questionnaire 01 Preliminary questionnaire https://forms.gle/fhNJAyjx7MJTy3yt8 - [ ] Install packages as needed ```{r, eval=FALSE} # List of packages you want to install packages <- c("tidyverse", "data.table", "BEDMatrix", "Rfast", "susieR", "coloc") # Function to check and install any missing packages check_and_install <- function(pkg){ if (!require(pkg, character.only = TRUE)) { install.packages(pkg, dependencies = TRUE) library(pkg, character.only = TRUE) } } # Use the function to check and install packages sapply(packages, check_and_install) ``` - [ ] Load Rstudio Libraries ```{r,eval=FALSE} library(tidyverse) ## packages needed for susie+coloc library(data.table) library(BEDMatrix) library(Rfast) library(susieR) library(coloc) ##library(tidyverse) ``` - [ ] Navigate to starting directory ```{bash, eval=FALSE} cd "/cloud/project/" ``` - [ ] activate the the imlabtools environment, which will make sure that the right version of python modules are available ```{bash,eval=FALSE} conda activate imlabtools ``` - [ ] To define some variables to access the data more easily within the R session, run the following r chunk ```{r,eval=FALSE} print(getwd()) lab="/cloud/project/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")) PRE="/cloud/project/QGT-Columbia-HKI-repo/box_files" 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") # This is a reference table we'll use a lot throughout the lab. It contains information about the genes. gencode_df = load_gencode_df() ``` - [ ] check the values of the variables you just defined in R ```{r, eval=FALSE} MODEL DATA ``` - [ ] define some variables to access the data more easily in the terminal. Remember we are running R code in the R console and command line code in the terminal. ```{bash, eval=FALSE} export PRE="/cloud/project/QGT-Columbia-HKI-repo/box_files" export LAB="/cloud/project/QGT-Columbia-HKI-repo/" export CODE=$LAB/code export DATA=$PRE/data export MODEL=$PRE/models export RESULTS=$PRE/results export METAXCAN=$PRE/repos/MetaXcan-master/software ``` - [ ] check the values of the variables you just defined ```{bash, eval=FALSE} echo $CODE echo $RESULTS ``` # Transcriptome-wide association (Review) Now we will perform a transcriptome-wide association analysis using the PrediXcan suite of tools. ![Transcriptome-wide association methods](https://raw.githubusercontent.com/hakyimlab/QGT-Columbia-HKI/master/extras/figures/Association-Methods.png) We start by predicting the expression levels of genes using the genotype data and the prediction weights and then perform an association between the predicted expression and the phenotype (denoted trait in the figure below). ![](https://raw.githubusercontent.com/hakyimlab/QGT-Columbia-HKI/master/extras/figures/PrediXcan-run.png) ## Questionnaire 02 - [ ] Open and start filling questionnaire 02 Prediction https://forms.gle/T6kAHvFTxYfcQguW7 # Predict Expression from genotype In this section we will predict expression of genes in whole blood using the Predict.py code in the METAXCAN folder. - Prediction models (weights) are located in the MODEL folder. Additional models for different tissues and transcriptome studies can be downloaded from [predictdb.org](http://predictdb.org). - This run should take about one minute. - [ ] run the following code in the terminal. ```{bash, eval=FALSE} 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 $RESULTS/predixcan/Whole_Blood__summary.txt \ --verbosity 9 \ --throw ``` > Note we are only predicting chromosome 22 here (check by running "predicted_expression %\>% count(chromosome)" in the console) - [ ] run following code in the console to get information on reported prediction performance. > Find additional information [in this wiki](https://github.com/hakyimlab/MetaXcan/wiki/Individual-level-PrediXcan:-introduction,-tutorials-and-manual) ```{r,eval=FALSE} prediction_fp = glue::glue("{RESULTS}/predixcan/Whole_Blood__predict.txt") ## Read the Predict.py output into a dataframe. This function reorganizes the data and adds gene names. predicted_expression = load_predicted_expression(prediction_fp, gencode_df) head(predicted_expression) ## read summary of prediction, number of SNPs per gene, cross validated prediction performance prediction_summary = load_prediction_summary(glue::glue("{RESULTS}/predixcan/Whole_Blood__summary.txt"), gencode_df) ## number of genes with a prediction model dim(prediction_summary) head(prediction_summary) ## how many unique genes were predicted? predicted_expression %>% .[["gene_id"]] %>% unique() %>% length() ## gene expression were predicted for how many people? predicted_expression %>% .[["IID"]] %>% unique() %>% length() print("distribution of prediction performance r2") summary(prediction_summary$pred_perf_r2) hist(prediction_summary$pred_perf_r2) ## Note: this is what the prediction trainer reported as prediction performance ``` ## (Optional) Assess Actual Prediction Performance ```{r,eval=FALSE} ## download and read observed expression data from GEUVADIS ## from https://uchicago.box.com/s/4y7xle5l0pnq9d1fwmthe2ewhogrnlrv obs_exp<- read_csv(glue::glue("{DATA}/predixcan/GEUVADIS.observed_df.csv.gz")) ## Note that the version of the ensemble id of the gene was removed head(predicted_expression) ## Q: how many genes were predicted? length(unique(predicted_expression$gene_id)) ## inner join predicted expression with observed expression data (by IID and gene) ## common errors occur when ensemble id's have versions in one set and not the other set fullset=inner_join(predicted_expression, obs_exp, by = c("gene_id","IID")) ## calculate spearman correlation for all genes genelist = unique(predicted_expression$gene_id) corvec = rep(NA,length(genelist)) names(corvec) = genelist for(gg in 1:length(genelist)) { ind = fullset$gene_id==genelist[gg] corvec[gg] = cor(fullset$predicted_expression[ind], fullset$observed_expression[ind]) } ## what's the best performing gene? ## plot the histogram of the prediction performance hist(corvec) ## list the top 10 best performing genes head(sort(corvec,decreasing = TRUE),2) tail(sort(corvec,decreasing = TRUE),2) ## plot the correlation of the top 2 best performing genes bottom 2 geneid = "ENSG00000100376" genename = gencode_df %>% filter(gene_id==geneid) %>% .[["gene_name"]] fullset %>% filter(gene_id==geneid) %>% ggplot(aes(observed_expression, predicted_expression))+geom_point()+ggtitle(paste(genename, "-",geneid)) geneid = "ENSG00000075234" genename = gencode_df %>% filter(gene_id==geneid) %>% .[["gene_name"]] fullset %>% filter(gene_id==geneid) %>% ggplot(aes(observed_expression, predicted_expression))+geom_point()+ggtitle(paste(genename, "-", geneid)) geneid = "ENSG00000070371" genename = gencode_df %>% filter(gene_id==geneid) %>% .[["gene_name"]] fullset %>% filter(gene_id==geneid) %>% ggplot(aes(observed_expression, predicted_expression))+geom_point()+ggtitle(paste(genename, "-", geneid)) geneid = "ENSG00000184164" genename = gencode_df %>% filter(gene_id==geneid) %>% .[["gene_name"]] fullset %>% filter(gene_id==geneid) %>% ggplot(aes(observed_expression, predicted_expression))+geom_point()+ggtitle(paste(genename, "-", geneid)) ``` # Run PrediXcan association ## Questionnaire 03 - [ ] Open and start filling questionnaire 03 PrediXcan https://forms.gle/3H319knWbLgnynNs9 We are going to use a simulated phenotype for which only UPK3A has an effect on the phenotype ($\beta=-0.9887378$) $Y = \sum_k T_k \beta_k + \epsilon$ with random effects $\beta_k \sim (1-\pi)\cdot \delta_0 + \pi\cdot N(0,1)$ ```{bash, eval=FALSE} 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 found in \$DATA/predixcan/phenotype/. The naming of the phenotypes provides information about the genetic 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 π (i.e. prob β≠0) ## Looking at Association Results ```{r,eval=FALSE} ## 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) %>% select(gene_name,effect,se,pvalue,gene) %>% head predixcan_association %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=10) ## compare distribution against the null (uniform) gg_qqplot(predixcan_association$pvalue, max_yval = 40) ``` ## Comparing the Estimated Effect Size with True Effect Size ```{r, eval=FALSE} 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) %>% select(gene_name,estimated_beta,true_beta,pvalue) %>% head ## do you see examples of potential LD contamination? betas %>% mutate(causal= true_beta!=0) %>% ggplot(aes(estimated_beta, true_beta,col=causal))+geom_point(alpha=0.6,size=5)+geom_abline()+theme_bw() ``` > UPK3A is the causal gene and has the most significant pvalue. RIBC2 is also significantly associated but has no causal role (we know because we simulated the phenotype that way). Why? Hint: correlation between the genes # Run Summary PrediXcan Now we will use the summary results from a GWAS of coronary artery disease to calculate the association between the genetic component of the expression of genes and coronary artery disease risk. We will use the SPrediXcan.py. ![](https://uchicago.box.com/shared/static/m45nnkeskzh88ifnv8td5e3fntdfzhjs.png) The GWAS results (harmonized and imputed) for coronary artery disease are available in \$PRE/spredixcan/data/ ## Questionnaire 04 - [ ] Open and start filling questionnaire 04 S-PrediXcan https://forms.gle/xJs2U66cnrqdb5cj6 ## Running S-PrediXcan ```{bash, eval=FALSE} 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 $RESULTS/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Whole_Blood.csv ``` > We can run the full genome because the summary statistics based PrediXcan is much faster than individual level one. ## Plot and Interpret Results ```{r,eval=FALSE} spredixcan_association = load_spredixcan_association(glue::glue("{RESULTS}/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Whole_Blood.csv"), gencode_df) dim(spredixcan_association) spredixcan_association %>% arrange(pvalue) %>% head spredixcan_association %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=20) gg_qqplot(spredixcan_association$pvalue) ``` - *Question:* SORT1, considered to be a causal gene for LDL cholesterol and as a consequence of coronary artery disease, is not found here. Why? - [ ] check whether SORT1 is expressed in whole blood [GTEx portal](https://gtexportal.org/home/gene/SORT1) - [ ] check whether SORT1 has eQTL in whole blood [GTEx portal](https://gtexportal.org/home/gene/SORT1) ## Run S-PrediXcan using gene expression predicted in liver -\[\] Run s-predixcan with liver model, do you find SORT1? Is it significant? ```{bash, eval=FALSE} #loction Liver models #/cloud/project/QGT-Columbia-HKI-repo/box_files/models/gtex_v8_mashr/ 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_Liver.db \ --covariance $MODEL/gtex_v8_mashr/mashr_Liver.txt.gz \ --keep_non_rsid \ --additional_output \ --model_db_snp_key varID \ --throw \ --output_file $RESULTS/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Liver.csv ``` ```{r,eval=FALSE} spredixcan_association_L= load_spredixcan_association(glue::glue("{RESULTS}/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Liver.csv"), gencode_df) dim(spredixcan_association_L) spredixcan_association_L %>% arrange(pvalue) %>% head spredixcan_association_L %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=20) gg_qqplot(spredixcan_association_L$pvalue) col_order= c("gene_name","gene","zscore","effect_size","pvalue","var_g","pred_perf_r2", "pred_perf_pval","pred_perf_qval", "n_snps_used", "n_snps_in_cov", "n_snps_in_model","best_gwas_p","largest_weight") spredixcan_association_L <- spredixcan_association_L[, col_order] filter(spredixcan_association_L, gene_name=="SORT1") ``` ## (Optional) Compare zscores in liver and whole blood. > Recall that zscore is the effect size divided by the standard error ```{r, eval=FALSE} spredixcan_association_L=rename(spredixcan_association_L, zscore_liver = "zscore") head(spredixcan_association_L) test=left_join(spredixcan_association, spredixcan_association_L, by="gene_name") test=select(test,"gene_name","zscore","zscore_liver") test %>% arrange(zscore_liver) %>% head test %>% mutate(zscore_WB=zscore) %>% ggplot(aes(zscore_WB,zscore_liver)) + geom_point(size=3,alpha=.6) + geom_abline() ## S-PrediXcan association in liver and whole blood are significantly correlated cor.test(test$zscore,test$zscore_liver) ``` ## (Optional) Run MultiXcan - 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. - before you run multixcan ensure you have run s-predixcan for all the tissues you want to multixcan. In this tutorial we have two tissues (liver and whole blood), ensure you have run s-predixcan with the two tissues before running multixcan. - One thing to note is to ensure similar naming pattern for the output files. This is to ensure the files are captured correctly when running multixcan's filter. ```{bash, eval=FALSE} 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 9 \ --throw \ --output $RESULTS/smultixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE_smultixcan.txt ``` # Colocalization Methods (Review) Colocalization methods seek to estimate the probability that the complex trait and expression causal variants are the same. We favor methods that calculate the probability of causality for each trait (posterior inclusion probability), called fine-mapping methods. Here we use susie for fine-mapping and coloc for colocalization. ![](https://raw.githubusercontent.com/hakyimlab/QGT-Columbia-HKI/master/extras/figures/colocalization-run.png) ## Questionnaire 5 - [ ] Open and start filling questionnaire 05 Colocalization https://forms.gle/NfH2MSdy4UyJzGAp7 ## Run colocalization When you use coloc on your own data, you may want to check out coloc's documentation, with good advice and tips on avoiding common mistakes https://cran.r-project.org/web/packages/coloc/vignettes Due to time constraints, we will run one region and one gene only 1. Finemap GWAS of CAD 2. Finemap eQTL of SORT1 For finemapping, we need the summary statistics (effect size, standard errors, etc) and the correlation between SNPs (LD matrix) ## load the genotype to calculate the ld matrix ```{r, eval=FALSE} # load the genotype to calculate the ld matrix X_mat <- BEDMatrix(glue::glue("{DATA}/colocalization/geuvadis_chr1")) colnames(X_mat) <- gsub("\\_.*", "",colnames(X_mat)) colnames(X_mat) <- str_replace_all(colnames(X_mat),":","_") snp_info <- fread(glue::glue("{DATA}/colocalization/geuvadis_chr1.bim")) %>% setnames(., colnames(.), c("chr", "snp", "cm", "pos", "alt", "ref")) snp_info$snp <- str_replace_all(snp_info$snp,":","_") ``` ## Load the eqtl and gwas effect size files ```{r, eval=FALSE} # load the eqtl effect sizes gene_ss <- fread(glue::glue("{DATA}/colocalization/Liver_chr1.txt")) # load gwas effect sizes gwas <- data.table::fread(glue::glue("{DATA}/spredixcan/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz")) # filter to select genome wide significant snps at 5 × 10−8 filtered_regions <- gwas %>% dplyr::filter(pvalue < 5e-8) # load the ld block ldblocks <-read_tsv(glue::glue("{DATA}/spredixcan/eur_ld.hg38.txt.gz")) ``` ## Find regions with the strongest signal in the gwas ```{r, eval=FALSE} # get the loci where the significant snps are located for (n in 1:nrow(filtered_regions)){ # extract genename, start and end variant_id <- as.character(filtered_regions[n,"variant_id"]) variant_chr <- as.character(filtered_regions[n,"chromosome"]) variant_pos <- as.numeric(filtered_regions[n,"position"]) #gene_end <- as.numeric(genes[n,"end"]) locus <- ldblocks %>% dplyr::filter(chr == variant_chr) %>% filter(variant_pos >= start & variant_pos < stop) %>% mutate(locus_name = paste0(chr,"_",start,"_",stop)) %>% dplyr::rename(locus_start=start,locus_end=stop) %>% mutate(variant_id = variant_id, position=variant_pos) # create a data frame with info if (exists('all_loci') && is.data.frame(get('all_loci'))) { all_loci <- rbind(all_loci,locus) } else { all_loci <- locus } } # select uniq loci d_loci <- all_loci %>% dplyr::select(locus_name,chr,locus_start,locus_end) %>% dplyr::distinct() ``` ## select a region to run coloc ```{r, eval=FALSE} # select regions to fine map. we are going to use regions in chromosome 1 uniq_loci <- d_loci %>% dplyr::filter("chr1" == chr) n = 3 # chr1_107867043_109761309 region # extract information l_chr = as.numeric(str_remove(uniq_loci[n,"chr"],"chr")) s_chr = uniq_loci[n,]$chr l_start = uniq_loci[n,]$locus_start l_stop = uniq_loci[n,]$locus_end l_name = uniq_loci[n,]$locus_name ``` ## Prepare gwas data for coloc ```{r, eval=FALSE} # select snps for the region from the summary stats ss <- gwas %>% dplyr::filter(chromosome == s_chr) %>% dplyr::filter(position >= l_start & position <= l_stop) %>% dplyr::filter(! is.na(effect_size)) # find the snps in the genotype to calculate the correlation g.snps <- ss %>% inner_join(snp_info %>% mutate(chr = glue::glue("chr{chr}")), by=c("chromosome" = "chr","panel_variant_id" = "snp")) # select genotype to calculate correlation #f_mat <- X_mat[,g.snps$snp] f_mat <- X_mat[,g.snps$panel_variant_id] # calculate corr R = cora(f_mat) # the package is for speed ## clean up rm(f_mat) ff <- g.snps %>% dplyr::filter(! is.na(effect_size)) %>% #select(-snp) %>% dplyr::rename(snp=panel_variant_id,beta=effect_size) %>% mutate(varbeta = standard_error^2) %>% dplyr::select(beta,varbeta,snp,position) %>% as.list() ff$type <- "cc" ff$sdY <- 1 ff$LD = R ff$N = 184305 ## check the data (NULL means it's fine) check_dataset(ff) ``` ## Prepare eqtl data for coloc ```{r, eval=FALSE} #Using SORT1 gene and liver tissue gene <- gene_ss %>% dplyr::filter(gene_id == "ENSG00000134243.11") %>% dplyr::rename(snp = variant_id,beta = slope, MAF = maf, pvalue = pval_nominal) %>% mutate(varbeta = slope_se^2, name = snp) %>% filter(! is.na(varbeta)) %>% separate(name, into = c("chr", "position","ref","alt","build"),sep = "_") ## calculate the ld matrix ### get the snps gene.snps <- gene %>% mutate(position = as.integer(position)) %>% inner_join(snp_info %>% mutate(chr = glue::glue("chr{chr}")), by=c("chr" = "chr","snp" = "snp")) # select genotype to calculate correlation g_mat <- X_mat[,gene.snps$snp] # calculate corr g.R = cora(g_mat) # clean up rm(g_mat) # format data for coloc gg <- gene %>% dplyr::filter(snp %in% gene.snps$snp) %>% mutate(position = as.integer(position)) %>% dplyr::select(beta,varbeta,snp,position,MAF, pvalue) %>% as.list() gg$type <- "quant" gg$LD = g.R gg$N = 208 # 670 for blood ## check the data check_dataset(gg) ``` ## run older version of coloc which assumes single causal variant *coloc.abf* makes the simplifying assumption that each trait has at most one causal variant in the region under consideration ```{r, eval=FALSE} my.res <- coloc.abf(dataset1=ff, dataset2=gg) sensitivity(my.res,"H4 > 0.9") ``` ## run coloc allowing multiple causal variants Multiple causal variants, using SuSiE to separate the signals ```{r, eval=FALSE} # Run susie fine maooing S3 = runsusie(ff) S4 = runsusie(gg) #summary(S3) # Run coloc susie.res=coloc.susie(S3,S4) print(susie.res$summary) ``` SuSiE can take a while to run on larger datasets, so it is best to run once per dataset with the =runsusie= function, store the results and feed those into subsequent analyses. plot the coloc result with the sensitivity function because weird effects are much easier to understand visually ```{r, eval=FALSE} sensitivity(susie.res,"H4 > 0.9",row=1,dataset1=ff,dataset2=gg,) ``` # cTWAS cTWAS stands for causal TWAS and seeks to calculate the posterior inclusion probability of genes and SNPs in a given region to be causal. It's a modification of SUSIER that adds predicted gene expression levels in addition to SNPs in the analysis. It is not yet published, but the authors (Siming Zhao, Wesley Crouse, Matthew Stephens et al) have shared a vignette with us to try it out. > Thank you, Wes, for creating this portion of the lab! ## Questionnaire 06 - [ ] Open and start filling out questionnaire 06 cTWAS https://forms.gle/A4evWkbhR7cXLy36A ```{r,eval=FALSE} #install.packages("R.utils") #install.packages("remotes") #remotes::install_github("simingz/ctwas", ref = "develop") library(ctwas) #get positions for region of interest (SORT1/PSRC1 locus) region <- unlist(strsplit(spredixcan_association$region_id[spredixcan_association$gene_name=="PSRC1"], "_")) chr <- region[2] start <- as.numeric(region[3]) end <- as.numeric(region[4]) #format summary statistics (and subset to variants in region to save memory) z_snp <- data.table::fread(glue::glue("{DATA}/spredixcan/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz"), select=c("chromosome", "position", "variant_id", "effect_allele", "non_effect_allele", "zscore", "sample_size")) z_snp <- z_snp[z_snp$chromosome==chr & z_snp$position >= start & z_snp$position <= end,] z_snp <- z_snp[!is.na(z_snp$variant_id),-(1:2)] colnames(z_snp) <- c("id", "A1", "A2", "z", "ss") #specify directories for LD matrices and weights ld_R_dir <- glue::glue("{DATA}/cTWAS/LD_matrices") weight <- glue::glue("{MODEL}/gtex_v8_mashr/mashr_Liver.db") #specify output locations and names for cTWAS outputdir <- glue::glue("{DATA}/cTWAS/results/") outname.e <- "CARDIoGRAM_Liver_expr" outname <- "CARDIoGRAM_Liver_ctwas" #impute gene z scores using cTWAS and save the results ################################## # NOTE: we are skipping this step and using the precomputed values ## takes ~10 minutes ################################## # ctwas_imputation <- impute_expr_z(z_snp=z_snp, weight=weight, ld_R_dir=ld_R_dir, outputdir=outputdir, outname=outname.e, harmonize_z=F, harmonize_wgt=F) # save(ctwas_imputation, file = paste0(outputdir, outname.e, "_output.Rd")) load(paste0(outputdir, outname.e, "_output.Rd")) z_gene <- ctwas_imputation$z_gene ld_exprfs <- ctwas_imputation$ld_exprfs z_snp <- ctwas_imputation$z_snp #make custom region file for single region ld_regions_custom <- data.frame("chr" = chr, "start" = start, "stop" = end) write.table(ld_regions_custom, file= paste0(outputdir, "ld_regions_custom.txt"), row.names=F, col.names=T, sep="\t", quote = F) ld_regions_custom <- paste0(outputdir, "ld_regions_custom.txt") #run cTWAS with pre-specified prior parameters at a single locus #estimating prior requires genome-wide data, too slow for demonstration #prior is 1% inclusion for genes and is 100x more likely than SNPs #prior assumes genes have larger effect size than SNPs, in reasonable range for data we've looked at ctwas_rss(z_gene=z_gene, z_snp=z_snp, ld_exprfs=ld_exprfs, ld_R_dir = ld_R_dir, ld_regions_custom=ld_regions_custom, outputdir = outputdir, outname = outname, thin = 0.01, estimate_group_prior = F, estimate_group_prior_var = F, group_prior=c(0.01, 0.0001), group_prior_var=c(50, 25)) #load results ctwas_results <- data.table::fread(paste0(outputdir,outname,".susieIrss.txt")) #merge gene names into the results sqlite <- RSQLite::dbDriver("SQLite") db = RSQLite::dbConnect(sqlite, weight) query <- function(...) RSQLite::dbGetQuery(db, ...) extra_table <- query("select * from extra") RSQLite::dbDisconnect(db) ctwas_results$genename <- extra_table$genename[match(ctwas_results$id, extra_table$gene)] #show results with highest PIPs col_order_2= c("genename", "chrom", "id", "pos", "type", "region_tag1", "region_tag2", "cs_index", "susie_pip" , "mu2") ctwas_results <- ctwas_results[, ..col_order_2] head(ctwas_results[order(-ctwas_results$susie_pip),]) ``` # FAQ and frequent errors - conda environment may not load correctly after resuming the project in Rstudio cloud - when inner joining by ensemble id of genes, make sure that both datasets have either the same versions or remove versions # Contributors - Festus Nyasimi - Margaret Perry - Wes Crouse - Owen Melia