QGT-Columbia-analysis-plan
Hae Kyung Im
2020-06-03
Last updated: 2020-06-05
Checks: 6 1
Knit directory: QGT-Columbia-HKI/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 from Box data and software
- install anaconda/miniconda
- define imlabtools conda environment
- download the folder from Box here -[ ] TODO: add box link with updated folder this will have copies of all the software repositories and the models
- download metaxcan repo
- download torus repo
- download fastenloc repo
- download TMWR repo
- download prediction models from predictdb.org
workaround: in the experimental Rstudio server (http://104.154.186.85) For the terminal to work properly we need to run > source /home/rstudio/.bashrc > conda activate imlabtools
analysis plan
- predict whole blood expression
- 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 3.2.1 ✓ purrr 0.3.3
✓ tibble 2.1.3 ✓ dplyr 0.8.3
✓ tidyr 1.0.2 ✓ stringr 1.4.0
✓ readr 1.3.1 ✓ forcats 0.4.0── Conflicts ──────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()Preliminary information
Data and copies of repositories can be downloaded from Box here
The latest version of the analysis plan that generated this page is on github here
Transcriptome-wide association methods
print(getwd())[1] "/Users/yanyul/Documents/repo/github/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")predict expression
Visual summary of predixcan runs
conda activate imlabtools
export PRE="/home/student/QGT-Columbia-HKI"
export DATA=$PRE/data/predixcan
export MODEL=$PRE/models
export METAXCAN=$PRE/repos/MetaXcan-master/software
export RESULTS=$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/genotype/ALL.chr22.shapeit2_integrated_snvindels_v2a_27022019.GRCh38.phased.vcf.gz \
--vcf_mode genotyped \
--variant_mapping $DATA/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 $RESULTS/predixcan/Whole_Blood__summary.txt \
--verbosity 9 \
--throwassess prediction performance (optional)
predicted_expression = read_tsv(glue::glue("{results.dir}/predixcan/Whole_Blood__predict.txt"))
dim(predicted_expression)
head(predicted_expression[,1:5])
prediction_summary = read_tsv(glue::glue("{results.dir}/predixcan/Whole_Blood__summary.txt"))
dim(prediction_summary)
head(prediction_summary)
## merge with GEUVADIS expression data
## calculate spearman correlation
## select a few genes and plot predicted vs observed expressionrun association with phenotype
export PRE="/home/student/QGT-Columbia-HKI"
export DATA=$PRE/data/predixcan
export MODEL=$PRE/models
export METAXCAN=$PRE/repos/MetaXcan-master/software
export RESULTS=$PRE/results
printf "association\n\n"
python3 $METAXCAN/PrediXcanAssociation.py \
--expression_file $RESULTS/predixcan/Whole_Blood__predict.txt \
--input_phenos_file $DATA/phenotype/random_pheno_1000G_hg38.txt \
--input_phenos_column pheno \
--output $RESULTS/predixcan/random_pheno/Whole_Blood__association.txt \
--verbosity 9 \
--throw
read results
predixcan_association = read_tsv(glue::glue("{results.dir}/predixcan/random_pheno/Whole_Blood__association.txt"))
dim(predixcan_association)
predixcan_association %>% arrange(pvalue) %>% head
predixcan_association %>% arrange(pvalue) %>% ggplot(aes(pvalue)) + geom_histogram(bins=20)Exercise
-[ ] Run association with another phenotype in $PRE/predixcan/data/phenotype/ALL.chr22.shapeit2_integrated_snvindels_v2a_27022019.GRCh38_x_en_Whole_Blood.simulated_phenotype.spike_n_slab_0.5_x_pve0.6.txt
——-
Summary PrediXcan
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 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
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] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
[5] readr_1.3.1 tidyr_1.0.2 tibble_2.1.3 ggplot2_3.2.1
[9] tidyverse_1.3.0 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] tidyselect_1.0.0 xfun_0.12 haven_2.2.0 lattice_0.20-38
[5] colorspace_1.4-1 vctrs_0.2.2 generics_0.0.2 htmltools_0.4.0
[9] yaml_2.2.0 rlang_0.4.3 later_1.0.0 pillar_1.4.3
[13] withr_2.1.2 glue_1.3.1 DBI_1.1.0 dbplyr_1.4.2
[17] modelr_0.1.5 readxl_1.3.1 lifecycle_0.1.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.5 evaluate_0.14
[25] knitr_1.27 httpuv_1.5.2 fansi_0.4.1 broom_0.5.4
[29] Rcpp_1.0.3 promises_1.1.0 backports_1.1.5 scales_1.1.0
[33] jsonlite_1.6 fs_1.3.1 hms_0.5.3 digest_0.6.23
[37] stringi_1.4.5 grid_3.6.2 rprojroot_1.3-2 cli_2.0.1
[41] tools_3.6.2 magrittr_1.5 lazyeval_0.2.2 crayon_1.3.4
[45] whisker_0.4 pkgconfig_2.0.3 xml2_1.2.2 reprex_0.3.0
[49] lubridate_1.7.4 rstudioapi_0.10 assertthat_0.2.1 rmarkdown_2.1
[53] httr_1.4.1 R6_2.4.1 nlme_3.1-143 git2r_0.26.1
[57] compiler_3.6.2