QGT-Columbia-analysis-plan
Hae Kyung Im
2020-06-03
Last updated: 2020-06-08
Checks: 6 1
Knit directory: QGT-Columbia-lab/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a2b9918 | Hae Kyung Im | 2020-06-07 | testing, reducing size of genotype file |
| html | a2b9918 | Hae Kyung Im | 2020-06-07 | testing, reducing size of genotype file |
| Rmd | 2257a0f | Hae Kyung Im | 2020-06-06 | raw figure links |
| html | 2257a0f | Hae Kyung Im | 2020-06-06 | raw figure links |
| Rmd | efbfceb | Hae Kyung Im | 2020-06-06 | raw urls for figures |
| Rmd | 973fc2b | Hae Kyung Im | 2020-06-05 | prelim notes |
| Rmd | 409558f | Yanyu Liang | 2020-06-05 | updated some paths |
| html | 409558f | Yanyu Liang | 2020-06-05 | updated some paths |
| Rmd | 0deee3c | Hae Kyung Im | 2020-06-05 | added TWMR |
| html | 0deee3c | Hae Kyung Im | 2020-06-05 | added TWMR |
| Rmd | 064f6ee | Hae Kyung Im | 2020-06-05 | added figures and slides under extras |
| html | 064f6ee | Hae Kyung Im | 2020-06-05 | added figures and slides under extras |
| Rmd | 1b70eb7 | Hae Kyung Im | 2020-06-05 | updated prerequisites |
| html | 1b70eb7 | Hae Kyung Im | 2020-06-05 | updated prerequisites |
| Rmd | 339f40c | Hae Kyung Im | 2020-06-05 | added plan |
| html | 339f40c | Hae Kyung Im | 2020-06-05 | added plan |
| Rmd | a3aa03e | Hae Kyung Im | 2020-06-05 | prerequisites added |
| Rmd | 09f5dae | Hae Kyung Im | 2020-06-05 | fastenloc |
| Rmd | ccb1167 | Hae Kyung Im | 2020-06-05 | knit |
| html | ccb1167 | Hae Kyung Im | 2020-06-05 | knit |
| Rmd | d427aee | Hae Kyung Im | 2020-06-05 | minor comment sort1 |
| Rmd | 4ca65b9 | Hae Kyung Im | 2020-06-05 | edits 2 |
| Rmd | f59cd02 | Hae Kyung Im | 2020-06-04 | edits |
| html | f59cd02 | Hae Kyung Im | 2020-06-04 | edits |
| Rmd | d65c555 | Hae Kyung Im | 2020-06-04 | twmr |
| html | d65c555 | Hae Kyung Im | 2020-06-04 | twmr |
| html | 682b6e2 | Hae Kyung Im | 2020-06-04 | Build site. |
| Rmd | d3502e8 | Hae Kyung Im | 2020-06-04 | wflow_publish(“analysis/analysis_plan.Rmd”) |
| Rmd | 4420fc7 | Hae Kyung Im | 2020-06-04 | wflow_rename(“analysis/predixcan_analysis.Rmd”, “analysis/analysis_plan.Rmd”) |
| html | 4420fc7 | Hae Kyung Im | 2020-06-04 | wflow_rename(“analysis/predixcan_analysis.Rmd”, “analysis/analysis_plan.Rmd”) |
Set up
This information is also on the slides
- download data and software from Box. This will have copies of all the software repositories and the models
Linux is the operating system of choice to run bioinformatics software. Here are offering two options
- Option 1: full setup, recommended for the linux-savvy with full setup
- OPtion 2: pre-installed RStudio in Google cloud, recommended for people less familiar with linux
The latest version of the analysis plan markdown document that generated this page is on github here rendered here as an html page
Option 1
- install anaconda/miniconda
- define imlabtools conda environment how to here, which will install all the python modules needed for this analysis session
-
download software (copies of the repos are already included in the course folder QCT-Columbia-HKI/repos/)
- download metaxcan repo
- download torus repo
- download fastenloc repo
- download TMWR repo
- download prediction models from predictdb.org (a few models are included in the course folder QCT-Columbia-HKI/repos/)
- install R/RStudio/tidyverse package
- (optional) install workflowr package in R
- git clone https://github.com/hakyimlab/QGT-Columbia-HKI.git
- start Rstudio (if you installed workflowr, you can just open the QGT-Columbia-HKI.Rproj)
Option 2
- claim your Rstudio server IP address ()
- connect to the Rstudio server using the url you claimed (http://xxx.xxx.xxx.xxx:8787)
Both options
- update the analysis document
PRE="/home/student/"
cd $PRE/../lab/
git pull - activate the the imlabtools environment
conda activate imlabtools** Notice that the bash chunks need to be copy-pasted to the terminal, not performed within the chunk.
Summary of analysis plan
- predict whole blood expression
- check how well the prediction works with GEUVADIS expression data
- run association between predicted expression and a simulated phenotype
- calculate association between expression levels and coronary artery disease risk using s-predixcan
- fine-map the coronary artery disease gwas results using torus (need some preformatting)
- calculate colocalization probability using fastenloc
- run transcriptome-wide mendelian randomization in one locus of interest
library(tidyverse)── Attaching packages ──────────────────────────────────────────────── tidyverse 1.3.0 ──✓ ggplot2 3.3.0 ✓ purrr 0.3.4
✓ tibble 3.0.0 ✓ dplyr 0.8.5
✓ tidyr 1.0.2 ✓ stringr 1.4.0
✓ readr 1.3.1 ✓ forcats 0.5.0── Conflicts ─────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()Transcriptome-wide association methods
print(getwd())[1] "/Users/haekyungim/Github/QGT-Columbia-lab"pre="~/Box/LargeFiles/QGT-Columbia-HKI"
#pre="/home/student/QGT-Columbia-HKI"
model.dir=glue::glue("{pre}/models")
metaxcan.dir=glue::glue("{pre}/repos/MetaXcan-master/software")
fastenloc.dir=glue::glue("{pre}/repos/fastenloc-master")
torus.dir=glue::glue("{pre}/repos/torus-master")
twmr.dir=glue::glue("{pre}/repos/TWMR-master")
results.dir=glue::glue("{pre}/results")
MODEL=glue::glue("{pre}/models")
DATA=glue::glue("{pre}/data")
RESULTS=glue::glue("{pre}/results")
METAXCAN=glue::glue("{pre}/repos/MetaXcan-master/software")
FASTENLOC=glue::glue("{pre}/repos/fastenloc-master")
TORUS=glue::glue("{pre}/repos/torus-master")
TWMR=glue::glue("{pre}/repos/TWMR-master")export PRE="/home/student/QGT-Columbia-HKI"
export DATA=$PRE/data
export MODEL=$PRE/models
export RESULTS=$PRE/results
export METAXCAN=$PRE/repos/MetaXcan-master/softwarepredict expression
Visual summary of predixcan runs
Remember you need to copy and paste this code chunk into the terminal to run it. Also make sure you activated the imlabtools environment which has all the necessary python modules.
Make sure all the paths and file names are correct. This run should take about one minute.
<!-- PRE="/home/student/QGT-Columbia-HKI" -->
<!-- DATA=$PRE/data -->
<!-- MODEL=$PRE/models -->
<!-- METAXCAN=$PRE/repos/MetaXcan-master/software -->
<!-- RESULTS=$PRE/results -->
printf "Predict expression\n\n"
python3 $METAXCAN/Predict.py \
--model_db_path $PRE/models/gtex_v8_en/en_Whole_Blood.db \
--vcf_genotypes $DATA/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
assess prediction performance (optional)
predicted_expression = read_tsv(glue::glue("{results.dir}/predixcan/Whole_Blood__predict.txt"))
dim(predicted_expression)
head(predicted_expression[,1:5])
prediction_summary = read_tsv(glue::glue("{results.dir}/predixcan/Whole_Blood__summary.txt"))
dim(prediction_summary)
head(prediction_summary)
## merge with GEUVADIS expression data
## calculate spearman correlation
## select a few genes and plot predicted vs observed expressionrun association with a phenotype
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
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
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.dir}/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)
qq(predixcan_association$pvalue)
truebetas = read_tsv(glue::glue("{DATA}/predixcan/phenotype/gene-effects/{PHENO}.txt"))
predixcan_association %>% inner_join(truebetas,by=c("gene"="gene_id")) %>% ggplot(aes(effect,effect_size))+geom_point()+geom_abline()Exercise
-[ ] show top genes -[ ] compare with true effect sizes
library(qqman)For example usage please run: vignette('qqman')Citation appreciated but not required:Turner, S.D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. biorXiv DOI: 10.1101/005165 (2014).——-
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.
Visual summary of s-predixcan
## harmonized and imputed GWAS result for coronary artery disease is available in
# $PRE/s-predixcan/data/run s-predixcan
export PRE="/home/student/QGT-Columbia-HKI"
export DATA=$PRE/data/s-predixcan
export MODEL=$PRE/models
export METAXCAN=$PRE/repos/MetaXcan-master/software
export RESULTS=$PRE/results
python $METAXCAN/SPrediXcan.py \
--gwas_file $DATA/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz \
--snp_column panel_variant_id --effect_allele_column effect_allele --non_effect_allele_column non_effect_allele --zscore_column zscore \
--model_db_path $MODEL/gtex_v8_mashr/mashr_Whole_Blood.db \
--covariance $MODEL/gtex_v8_mashr/mashr_Whole_Blood.txt.gz \
--keep_non_rsid --additional_output --model_db_snp_key varID \
--throw \
--output_file $RESULTS/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Whole_Blood.csv
plot and interpret s-predixcan results
spredixcan_association = read_csv(glue::glue("{results.dir}/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Whole_Blood.csv"))
dim(spredixcan_association)
spredixcan_association %>% arrange(pvalue) %>% head
spredixcan_association %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=20)SORT1, considered to be a causal gene for LDL cholesterol and as a consequence of coronary artery disease, is not found here. Why? (tissue)
run multixcan (optional)
export MODEL=$PRE/models
export DATA=$PRE/data/s-predixcan
python $METAXCAN/SMulTiXcan.py \
--models_folder $MODEL/gtex_v8_mashr \
--models_name_pattern "mashr_(.*).db" \
--snp_covariance $MODEL/gtex_v8_expression_mashr_snp_covariance.txt.gz \
--metaxcan_folder $RESULTS/spredixcan/eqtl/ \
--metaxcan_filter "CARDIoGRAM_C4D_CAD_ADDITIVE__PM__(.*).csv" \
--metaxcan_file_name_parse_pattern "(.*)__PM__(.*).csv" \
--gwas_file $DATA/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz \
--snp_column panel_variant_id --effect_allele_column effect_allele --non_effect_allele_column non_effect_allele --zscore_column zscore --keep_non_rsid --model_db_snp_key varID \
--cutoff_condition_number 30 \
--verbosity 7 \
--throw \
--output $RESULTS/smultixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE_smultixcan.txt
Colocalization methods
Visual summary of colocalization
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.
#torus -d Height.torus.zval.gz --load_zval -dump_pip Height.gwas.pip
#gzip Height.gwas.pip
TORUSOFT=torus
$TORUSOFT -d $PRE/data/fastenloc/Height.torus.zval.gz --load_zval -dump_pip $PRE/data/fastenloc/Height.gwas.pip
cd $PRE/data/fastenloc
gzip Height.gwas.pip
cd $PRE
calculate colocalization with fastENLOC
## check out tutorial https://github.com/xqwen/fastenloc/tree/master/tutorial
export eqtl_annotation_gzipped=$PRE/data/fastenloc/FASTENLOC-gtex_v8.eqtl_annot.vcf.gz
export gwas_data_gzipped=$PRE/data/fastenloc/Height.gwas.pip.gz
export TISSUE=Whole_Blood
export FASTENLOCSOFT=fastenloc
mkdir $RESULTS/fastenloc/
cd $RESULTS/fastenloc/
$FASTENLOCSOFT -eqtl $eqtl_annotation_gzipped -gwas $gwas_data_gzipped -t $TISSUE
#[-total_variants total_snp] [-thread n] [-prefix prefix_name] [-s shrinkage]
analyze results
## optional - compare with s-predixcan results-[] prepare
Mendelian randomization methods
run SMR (optional)
run TWMR (for a locus)
TWMR
export TWMR=$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
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
other attached packages:
[1] qqman_0.1.4 forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5
[5] purrr_0.3.4 readr_1.3.1 tidyr_1.0.2 tibble_3.0.0
[9] ggplot2_3.3.0 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] tidyselect_1.0.0 xfun_0.13 haven_2.2.0 lattice_0.20-41
[5] colorspace_1.4-1 vctrs_0.2.4 generics_0.0.2 htmltools_0.4.0
[9] yaml_2.2.1 rlang_0.4.5 later_1.0.0 pillar_1.4.3
[13] withr_2.1.2 glue_1.4.1 DBI_1.1.0 dbplyr_1.4.2
[17] calibrate_1.7.5 readxl_1.3.1 modelr_0.1.6 lifecycle_0.2.0
[21] cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0 workflowr_1.6.2
[25] rvest_0.3.5 evaluate_0.14 knitr_1.28 httpuv_1.5.2
[29] fansi_0.4.1 broom_0.5.5 Rcpp_1.0.4.6 promises_1.1.0
[33] backports_1.1.6 scales_1.1.0 jsonlite_1.6.1 fs_1.4.1
[37] hms_0.5.3 digest_0.6.25 stringi_1.4.6 rprojroot_1.3-2
[41] grid_3.6.3 cli_2.0.2 tools_3.6.3 magrittr_1.5
[45] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3 MASS_7.3-51.5
[49] ellipsis_0.3.0 xml2_1.3.1 reprex_0.3.0 lubridate_1.7.8
[53] rstudioapi_0.11 assertthat_0.2.1 rmarkdown_2.1 httr_1.4.1
[57] R6_2.4.1 nlme_3.1-147 git2r_0.26.1 compiler_3.6.3