GTExV6PRareVariation

Repository to reproduce analyses from the GTEx V6P Rare Variation Manuscript

To run the code

Install the dependencies

R packages

cowplot
data.table
doMC
ggplot2
matrixStats
peer
plotrix
plyr
pROC
RColorBrewer
reshape2
scales

Python modules

numpy
pybedtools
pysam
scipy

Unix packages

GNU parallel
tabix

External software

bedtools v2.26.0 or later
vcftools
samtools
bwa
PICARD

Download required files

Download from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz:

processed data directory (referred to below as <processed_data>)

Download from http://www.gtexportal.org/home/datasets:

gencode.v19.genes.v6p_model.patched_contigs.gtf.gz
GTEx_Analysis_v6p_RNA-seq_RNA-SeQCv1.1.8_gene_reads.gct.gz (gunzip it)
GTEx_Analysis_v6p_RNA-seq_RNA-SeQCv1.1.8_gene_rpkm.gct.gz (gunzip it)
GTEx_Analysis_v6p_RNA-seq_RNA-SeQCv1.1.8_gene_median_rpkm.gct (gunzip it)
the covariates used during eQTL discovery
the eGene and significant variant-gene associations based on permutations for each tissue (GTEx_Analysis_v6p_eQTL.tar on the portal)

Download from Gencode (http://www.gencodegenes.org/releases/19.html; Comprehensive gene annotation gtf):

gencode.v19.annotation.gtf.gz (gunzip it)

Download from dbGaP:
(It is possible some of the file names may be different in the final release.)

GTEx_Analysis_2015-01-12_WholeGenomeSeq_148Indiv_GATK_HaplotypeCaller.vcf.gz
ASE files
GTEx_Data_V6_Annotations_SampleAttributesDS.txt
GTEx_Analysis_2015-01-12_Annotations_SubjectPhenotypesDS.txt

Download from http://krishna.gs.washington.edu/download/CADD/v1.2/:

whole_genome_SNVs.tsv.gz
whole_genome_SNVs.tsv.gz.tbi
whole_genome_SNVs_inclAnno.tsv.gz
whole_genome_SNVs_inclAnno.tsv.gz.tbi

Download from ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502/:

ALL.chr*.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz

Download from http://compbio.mit.edu/encode-motifs/:

matches.txt.gz

Download Epigenomics Roadmap files from http://www.broadinstitute.org/~meuleman/reg2map/HoneyBadger2_release/DNase/p2/:

prom/BED_files_per_sample/regions_prom_E*.bed
enh/BED_files_per_sample/regions_enh_E*.bed
dyadic/BED_files_per_sample/regions_dyadic_E*.bed

Download ExAC constraint data from ftp://ftp.broadinstitute.org/pub/ExAC_release/release0.3/functional_gene_constraint/:

forweb_cleaned_exac_r03_march16_z_data_pLI.txt

Download RNA-Seq data for K562 cell lines from ENCODE at https://www.encodeproject.org/search/?type=Experiment&biosample_term_name=K562&assay_title=RNA-seq

ENCFF104VTJ_CSHL_1.tsv
ENCFF201HGA_CSHL_2.tsv
ENCFF387ZRA_Caltech_1.tsv
ENCFF870QAL_Caltech_2.tsv
ENCFF553DDU_UConn_1.tsv
ENCFF811VBA_UConn_2.tsv
gencode.v24.tRNAs.gtf.gz

Download the hg19 human reference genome in FASTA format from http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz. Combine FASTA files for each chromosome into a single reference FASTA.

Download VCFs from the UK10K dataset for two studies

Other files:

gtex.lumpy.gs.svscore.low_conf.vcf.gz (Obtained directly from the Hall Lab.)

Download annotation resources to generate genomic features for running RIVER

Setup

The code relies on an assumed directory structure. Everything is under an upper level directory. If you are using the processed files available online, set this upper-level directory to be the path to <processed_data>.
Otherwise, set this to be any path where you want scripts to output.

Either run or add the following to your .bashrc (omit trailing slashes for all directories):

export RAREVARDIR=<the path to the upper-level directory>
export CADD_DIR=<the path to the CADD files>
export KG_DIR=<the path to the 1000 genomes files>
export ENCODE_MOTIF_DIR=<the path to matches.txt.gz (excluding the file name)>
export ER_DIR=<the path to the Epigenomics Roadmap directory (sub directories are prom, enh, and dyadic)>
export EXAC_DIR=<the path to forweb_cleaned_exac_r03_march16_z_data_pLI.txt (excluding the file name)>
export GTEXCISDIR=<the path to the eGene and significant variant-gene pairs for each tissue from the GTEx v6p release>
export HG19=<the path to the HG19 reference genome in FASTA format>
export PICARDPATH=<the path to the PICARD executable directory>

If you are going to run everything from scratch, first make the directories that are assumed to exist.
You can skip this step if you will use the processed data files downloaded from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.

mkdir ${RAREVARDIR}/logs
mkdir ${RAREVARDIR}/preprocessing
mkdir ${RAREVARDIR}/preprocessing/PEER
mkdir ${RAREVARDIR}/reference
mkdir ${RAREVARDIR}/data
mkdir ${RAREVARDIR}/data/medz
mkdir ${RAREVARDIR}/data/singlez
mkdir ${RAREVARDIR}/data/metasoft
mkdir ${RAREVARDIR}/features
mkdir ${RAREVARDIR}/features/annotations
mkdir ${RAREVARDIR}/features/annotations/ACMG
mkdir ${RAREVARDIR}/features/annotations/ClinVar
mkdir ${RAREVARDIR}/features/annotations/GWAS
mkdir ${RAREVARDIR}/features/annotations/OMIM
mkdir ${RAREVARDIR}/features/annotations/Orphanet
mkdir ${RAREVARDIR}/features/annotations/Other
mkdir ${RAREVARDIR}/RIVER
mkdir ${RAREVARDIR}/RIVER/code
mkdir ${RAREVARDIR}/RIVER/code/mfiles
mkdir ${RAREVARDIR}/RIVER/data
mkdir ${RAREVARDIR}/RIVER/data/expression
mkdir ${RAREVARDIR}/RIVER/data/score
mkdir ${RAREVARDIR}/RIVER/data/score/indiv
mkdir ${RAREVARDIR}/RIVER/data/score/feature
mkdir ${RAREVARDIR}/RIVER/data/rvsite
mkdir ${RAREVARDIR}/data/wgs
mkdir ${RAREVARDIR}/data/wgs/1KG

These directories are required to hold the results of scripts for which we do not provide processed data.

mkdir ${RAREVARDIR}/paper_figures
mkdir ${RAREVARDIR}/features/variantBeds
mkdir ${RAREVARDIR}/data/CRISPR
mkdir ${RAREVARDIR}/data/CRISPR/bams
mkdir ${RAREVARDIR}/data/K562

Then you can run the code below.
Some of the steps reproduce processed files available on https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz and are marked as such.

Pipeline

Expression data correction and normalization

Generates processed data that can be downloaded from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.

Generate rpkm and read count matrices from GTEx V6P combined file

cat <path to downloaded file>/GTEx_Data_V6_Annotations_SampleAttributesDS.txt | \
	cut -f1,14 | sed 's/ - /_/' | sed 's/ /_/g' | sed 's/(//' | sed 's/)//' | sed 's/c-1/c1/' | grep -v NA12878 > \
	${RAREVARDIR}/preprocessing/gtex_2015-01-12_samples_tissues.txt

python preprocessing/split_by_tissues.py \
    --GTEX <path to downloaded file>/GTEx_Analysis_v6p_RNA-seq_RNA-SeQCv1.1.8_gene_rpkm.gct \
    --SAMPLE ${RAREVARDIR}/preprocessing/gtex_2015-01-12_samples_tissues.txt \
    --OUT ${RAREVARDIR}/preprocessing/PEER \
    --END .rpkm.txt <br>

python preprocessing/split_by_tissues.py \
    --GTEX <path to downloaded file>/GTEx_Analysis_v6p_RNA-seq_RNA-SeQCv1.1.8_gene_reads.gct \
    --SAMPLE ${RAREVARDIR}/preprocessing/gtex_2015-1-12_samples_tissues.txt \
    --OUT ${RAREVARDIR}/preprocessing/PEER \
    --END .reads.txt

Run bash scripts to generate PEER corrected data (includes non-EAs) with covariates removed

(Uses multiple cores. Currently set to 10 cores. Can be altered by changing the number of cores
specified by parallel --jobs 10 in the scripts preprocessing/PEER/calc.PEER.factors.all.tissues.sh and
preprocessing/PEER/calc.residuals.sh)

bash preprocessing/PEER/PEER.pipeline.sh

Make list of individuals and tissues

bash preprocessing/get_tissue_by_individual.sh

Make flat files from raw RPKMs and PEER-corrected data for all tissues and individuals

python preprocessing/gather_filter_normalized_expression.py
python preprocessing/gather_filter_rpkm.py

Make list of expressed genes

cat preprocessing/PEER/*peer.ztrans.txt | cut -f1 | sort | uniq | grep -v Id > preprocessing/gtex.expressed.genes.txt

Make summary statistics for expressed genes

(Uses multiple cores. Can set the number at the top of the script. Currently set to 10 cores.)

Rscript preprocessing/rpkm.expression.analysis.R

Preparing reference files used later

Generates processed data that can be downloaded from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.

Copy or move the GTEx annotation file (gencode.v19.genes.v6p_model.patched_contigs.gtf.gz) to ${RAREVARDIR}/reference.

bash preprocessing/process.reference.files.sh <path to>/gencode.v19.genes.v6p_model.patched_contigs.gtf.gz <path to>/gencode.v19.annotation.gtf

(relies on pad.gtf.exons.py, gtf2TSS.sh, and gtf2genebed.sh)

Outlier calling

Generates processed data that can be downloaded from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.

Call multi-tissue outliers

(Uses 12 cores. Can set the number at the top of the script.)

Rscript call_outliers/call_outliers_medz.R

Call single-tissue outliers

python call_outliers/call_outliers_single_tissue.py

Compare single-tissue and multi-tissue outliers as well as get stats on each

Rscript call_outliers/compare_single_multi_outliers.R

Run replication for single-tissue and multi-tissue outliers

(The multi-tissue replication uses 12 cores. Can set the number at the top of the script.)

Rscript call_outliers/multi_tissue_replication.R
Rscript call_outliers/single_tissue_replication.R

Feature generation

Processing VCFs into bed files for each individual

bash feature_construction/vcf2bedfiles.sh \
	 <path to>/GTEx_Analysis_2015-01-12_WholeGenomeSeq_148Indiv_GATK_HaplotypeCaller.vcf.gz \
	 <path to>/gtex.lumpy.gs.svscore.low_conf.vcf.gz

(this script and some of its dependencies use multiple cores [set number at top of relevant scripts]; relies on :

vcf2bedfiles_helper_processVCF.sh
vcf2bedfiles_helper_processVCF_SV.sh
vcf2bedfiles_helper_processVCFtoolsOutput.sh
vcf2bedfiles_helper_processVCFtoolsOutput_CNV.sh
compileCADDscores.sh
extractCADDscores_ekt.py)

Extract features to be combined with the individual bed files

bash feature_construction/extract.1kg.AF.sh

(uses 15 cores, the number of which is set at the top of the script; relies on process.1kg.AF.py)

bash feature_construction/subset.CADD.features.sh

(uses 8 cores, set in the sort command)

bash feature_construction/TFBS_pipeline.sh

(relies on pouya.raw.summary.py)

bash feature_construction/ER_pipeline.sh

Add extracted features to individual bed files

bash run_add_features_variant_beds.sh

Important: Make sure to use bedtools version 2.26.0 or later. Memory leak in previous versions causes the memory for this script to blow up.
(uses 15+ cores. Set the number of processes at the top of the script. Relies on add_features_variant_beds.sh.)

Collapse site-level features created above into gene-level features

bash feature_construction/run_build_feature_count_summaries_all_genes.sh

(uses multiple cores; relies on :

build_count_summaries_all_genes.sh set number of processes at top of script
build_feature_summaries_all_genes.sh set number of processes at top of script
build_feature_set.py)

Compile features for outliers and controls

bash feature_construction/run_compile_features_outliers.sh

(uses up to 10 cores; relies on:

compile_features_outliers.sh set number of processes at top of script
compile_features_outliers_nothresh.sh
compile_features_outliers_singletissue.sh set number of processes at top of script
pick_outliers_controls_imbalanced.py)

Compute minor allele frequencies of rare variants segregating in GTEx using the UK10K dataset

bash feature_construction/calc.uk10k.freqs.sh

Disease gene annotations

We are providing the processed gene lists for the eight disease gene sets we analyzed for overlap with genes with multi-tissue outliers.
We are also providing, where applicable, the commands and raw files needed to generate these processed lists.

ACMG

Source: http://www.ncbi.nlm.nih.gov/clinvar/docs/acmg/
Raw file: acmg.csv
Download from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.
Move the downloaded file to ${RAREVARDIR}/features/annotations/ACMG/.

ClinVar

Source: ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/gene_condition_source_id
Raw file: gene_condition_source_id
Download from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.
Move the downloaded file to ${RAREVARDIR}/features/annotations/ClinVar/.

GWAS

Source: http://www.ebi.ac.uk/gwas/
Raw file: gwas_catalog_v1.0-downloaded_2015-11-30.tsv
Download from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.
Move the downloaded file to ${RAREVARDIR}/features/annotations/GWAS/.

OMIM

Source: http://www.omim.org/
Raw files: morbidmap.txt and mim2gene.txt
Processed file: omim.genes.txt
Download the raw and processed files from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.
Move these files to ${RAREVARDIR}/features/annotations/OMIM/.

To produce the processed file from the raw files:

grep '(3)' ${RAREVARDIR}/features/annotations/OMIM/morbidmap.txt | cut -f2 | sed 's/, /\n/g' | sort | uniq > ${RAREVARDIR}/features/annotations/OMIM/omim.genes.temp.txt

grep -wf ${RAREVARDIR}/features/annotations/OMIM/omim.genes.temp.txt ${RAREVARDIR}/features/annotations/OMIM/mim2gene.txt  > ${RAREVARDIR}/features/annotations/OMIM/temp.mim2gene.intersection.txt

cut -f4,5 ${RAREVARDIR}/features/annotations/OMIM/temp.mim2gene.intersection.txt | sort | uniq > ${RAREVARDIR}/features/annotations/OMIM/omim.genes.txt

rm ${RAREVARDIR}/features/annotations/OMIM/omim.genes.temp.txt ${RAREVARDIR}/features/annotations/OMIM/temp.mim2gene.intersection.txt

Orphanet

Source: http://www.orphadata.org/data/xml/en_product6.xml
Raw file: en_product6.xml
Processed file: orphanet.genes.txt
Download the raw and processed files from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.
Move these files to ${RAREVARDIR}/features/annotations/Orphanet/.

To produce the processed file from the raw file:

grep ENSG ${RAREVARDIR}/features/annotations/Orphanet/en_product6.xml | sort | uniq | grep -o 'ENSG[0-9]*' > ${RAREVARDIR}/features/annotations/Orphanet/orphanet.genes.txt

DDG2P

Source: http://www.ebi.ac.uk/gene2phenotype/downloads
Raw file: DDG2P_2_8_2017.csv.gz
Download the raw file from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.
Move this file to ${RAREVARDIR}/features/annotations/DDG2P/.

Other: Cardiovascular and Cancer disease genes

We assessed overlap of genes with multi-tissue outliers with two expert curated disease gene lists: one for heritable cancer predisposition and one for heritable cardiovascular disease. See the methods section of our manuscript for more information.
Raw files: cancer.genes.gold.standard.csv (Cancer), cardio.genes.gold.standard.csv (Cardio) Download the raw files from https://s3-us-west-2.amazonaws.com/gtex-v6p-rare-variation-data/GTExV6PRareVariationData.tar.gz.
Move these files to ${RAREVARDIR}/features/annotations/Other/.

Shared eQTLs defined by METASOFT

Process the METASOFT results choosing the single best variant tested per gene as determined by P-value from the RE2 model.
Also provide summary statistics regarding the number of tissues the eQTL is active in and the expression level for the gene across tissues.

python shared.eqtls/bf.metasoft.py --META ${RAREVARDIR}/data/metasoft/Metasoft_Output_v6p.txt \
    --TISS ${RAREVARDIR}/data/metasoft/Metasoft_tissue_order.txt \
    --OUT ${RAREVARDIR}/data/metasoft/gtex.metasoft.v6p.selected.txt
Rscript shared.eqtls/metasoft.summary.R

Validation of large-effect rare variants using CRISPR-Cas9 genome editing

Prioritize variants for validation with CRISPR

Rscript crispr/prioritize.for.crispr.R

Prune the list of prioritized CRISPR variants down to a manageable size (N ~ 12)

Rscript crispr/prune.crispr.variants.R

Add major/minor allele info

python crispr/add.major.minor.alleles.py --IN ${RAREVARDIR}/data/CRISPR/crispr.overexpression.candidates.pruned.vcf \
    --OUT ${RAREVARDIR}/data/CRISPR/crispr.overexpression.candidates.pruned.maf.alleles.vcf \
    --FRQ ${RAREVARDIR}/features/variantBeds/GTEx_Analysis_2015-01-12_WholeGenomeSeq_148Indiv_GATK_HaplotypeCaller_123EAonly_SNPs.frq

Extract 100 bp sequences from the hg19 reference centered on each variant position

out=${RAREVARDIR}/data/CRISPR/crispr.candidates.donor.seq.raw.fa
if [ -e $out ]; then
    rm $out
fi
ref=${HG19}/hg19.fa
positions=`tail -n +2 ${RAREVARDIR}/data/CRISPR/crispr.candidates.pruned.vep.loftee.parsed.vcf | awk '{print "chr"$1":"$2-49"-"$2+49}'`
for line in $positions; do
    samtools faidx $ref $line >> $out
done

Generate donor sequences. One sequence for wild-type and one for rare allele.

python crispr/process.crispr.donor.seq.py \
    --VCF ${RAREVARDIR}/data/CRISPR/crispr.candidates.pruned.vep.loftee.parsed.vcf \
    --FASTA ${RAREVARDIR}/data/CRISPR/crispr.candidates.donor.seq.raw.fa \
    --OUT ${RAREVARDIR}/data/CRISPR/crispr.candidates.donor.seq.fa

Index the reference FASTA files for each amplicon region in the cDNA and gDNA

bwa index -p crispr/crispr.outlier.cdna.ref.fa crispr/crispr.outlier.cdna.ref.fa
bwa index -p crispr/crispr.control.cdna.ref.fa crispr/crispr.control.cdna.ref.fa
bwa index -p crispr/crispr.outlier.gdna.ref.fa crispr/crispr.outlier.gdna.ref.fa
bwa index -p crispr/crispr.control.gdna.ref.fa crispr/crispr.control.gdna.ref.fa

Perform mapping with BWA, sort and output to BAM format using samtools.
Uses 10 cores. This is set at the top of the script.

bash crispr/crispr.bwa.aln.sort.sh

Summarize the CRISPR results

R CMD BATCH --no-save crispr/summarize.crispr.results.R

RIVER

Here is an entire procedure of generating genomic features and expression values for running RIVER. To run these codes, you might need to move gencode.v19.genes.v6p.patched_contigs.autosome.coding_linkRNA.gtf,gene_ensembl_ids.txt,tissue_names.txt into {RAREVARDIR}/reference and an original WGS vcf fileinto{RAREVARDIR}/data/wgs`. Note that you might need to change specified directory names into your own directories and revise some codes depending on which and how many features you would like to consider in your study. In order to run RIVER in bioconductor, you might also need to generate data following data structure and format in RIVER R package based upon final two files and a list of individual pairs having same rare variants within 10K from TSS of each gene.

[Step 1] Generate matlab annotation files with both indivs and genes considered in GTEx v6p and gene expression matrix used for calling outliers later

matlab -nodisplay -nodesktop -nosplash -nojvm -singleCompThread -r "run('${RAREVARDIR}/RIVER/code/generate_annotation_matlab.m')"
matlab -nodisplay -nodesktop -nosplash -nojvm -singleCompThread -r "run('${RAREVARDIR}/RIVER/code/generate_expmat_44tissues.m')"

[Step 2] In a WGS vcf file, all indels were removed, high quality variant calls (VQSLOD = PASS) were considered, only sites having <= 10 individuals in terms of missing genotypes were considered, and only autosomes were considered, and only European subjects were considered.

vcftools --gzvcf ${RAREVARDIR}/data/wgs/${an_original_GTEx_WGS_file} --keep ${RAREVARDIR}/preprocessing/gtex_2015-01-12_wgs_ids.txt --remove-indels --remove-filtered-all --max-missing-count 10 --not-chr X --recode --recode-INFO-all --stdout | bgzip -c > ${RAREVARDIR}/data/wgs/a_filtered_and_compressed_GTEx_WGS_vcf_file
tabix -p vcf ${RAREVARDIR}/data/wgs/filtered_and_compressed_GTEx_WGS_vcf_file

[Step 3] Compute GTEx allele frequencies with only subjects of interest

vcftools --gzvcf ${RAREVARDIR}/data/wgs/filtered_and_compressed_GTEx_WGS_vcf_file} --freq --out ${RAREVARDIR}/data/wgs/GTEx_af.vcf
bgzip -c ${RAREVARDIR}/data/wgs/GTEx_af.vcf > ${RAREVARDIR}/data/wgs/GTEx_af.vcf.gz
tabix -p vcf ${RAREVARDIR}/data/wgs/GTEx_af.vcf.gz

[Step 4] Extract a list of targeted regions for genes of interest

matlab -nodisplay -nodesktop -nosplash -nojvm -singleCompThread -r "run('${RAREVARDIR}/RIVER/code/generate_regions.m')"

[Step 5] In each subject of interest, extract a list of individual-specific rare variant sites based on AFs of both GTEx and EUR 1K population

count_ind=0
for ID in $(cat ${RAREVARDIR}/preprocessing/gtex_2015-01-12_wgs_ids_outlier_filtered.txt)
do
count_ind=$(( $count_ind + 1 ))
cat ${RAREVARDIR}/RIVER/data/rvsite/region.tss10k.txt | ${RAREVARDIR}/RIVER/code/extract_rvsites_ByInd.py -n $count_ind --id $ID --WGSvcf_in ${RAREVARDIR}/data/wgs/a_filtered_and_compressed_GTEx_WGS_vcf_file --GTExvcf_in ${RAREVARDIR}/data/wgs/a_compressed_GTEx_allele_frequency_file --EURvcf_in ${RAREVARDIR}/data/wgs/1KG/a_compressed_EUR_allele_freq_vcf_file --site_out ${RAREVARDIR}/RIVER/data/score/indiv/${ID}.${count_ind}.rvsites.txt
done

[Step 6] Extract all the features simulataneously (CADD, chromHMM, phylop, DANN).

count_ind=0
for ID in $(cat ${RAREVARDIR}/preprocessing/gtex_2015-01-12_wgs_ids_outlier_filtered.txt)
do
count_ind=$(( $count_ind + 1))
cat ${RAREVARDIR}/RIVER/data/score/indiv/${ID}.txt | ${RAREVARDIR}/RIVER/code/extract_scores_combined.py -n $count_ind --id $ID --af_in ${RAREVARDIR}/data/wgs/GTEx_af.vcf.gz --wgs_in filtered_and_compressed_GTEx_WGS_vcf_file --anno_in ${RAREVARDIR}/data/wgs/GTEx_vep.vcf.gz --cadd_in ${RAREVARDIR}/RIVER/data/whole_genome_SNVs_inclAnno.tsv.gz --dann_in ${RAREVARDIR}/RIVER/data/DANN_whole_genome_SNVs.tsv.bgz --chromHMM_in ${RAREVARDIR}/RIVER/data/wgEncodeBroadHmmGm12878HMM.sorted.hg19.bed.txt.gz --phylop_in ${RAREVARDIR}/RIVER/data/phyloP100way.txt.gz --score_out ${RAREVARDIR}/RIVER/data/score/indiv/${ID}.${count_ind}.score.nuc.txt
done

[Step 7] Extract quantitative values of genomic features from individual files generated by the previous script at gene level

count_ind=0
for ID in $(cat ${RAREVARDIR}/preprocessing/gtex_2015-01-12_wgs_ids_outlier_filtered.txt)
do
count_ind=$(( $count_ind + 1 ))
sed "s/order = 1;/order = $count_ind/" ${RAREVARDIR}/RIVER/code/extract_features_byInd.m > ${RAREVARDIR}/RIVER/code/mfiles/extract_features.$ID.$count_ind.m
matlab -nodisplay -nodesktop -nosplash -nojvm -singleCompThread -r "run('${RAREVARDIR}/RIVER/code/mfiles/extract_features.$ID.$count_ind.m')"
done

[Step 8] Compute normalized scores of genomic features

for order in {1..11}
do 
sed "s/order = 1;/order = $order/" ${RAREVARDIR}/RIVER/code/compute_scores.m > ${RAREVARDIR}/RIVER/code/mfiles/compute_scores.$order.m
matlab -nodisplay -nodesktop -nosplash -nojvm -singleCompThread -r "run('${RAREVARDIR}/RIVER/code/mfiles/compute_scores.$order.m')"
done

[Step 9] Generate both "genomic_features.txt" and "zscores.txt" for running RIVER

matlab -nodisplay -nodesktop -nosplash -nojvm -singleCompThread -r "run('${RAREVARDIR}/RIVER/code/generate_data_RIVER.m')"

Main figures

Figure 1

bash paper_figures/pick.cartoon.example.sh
Rscript paper_figures/figure1a.plot.cartoon.example.R
Rscript paper_figures/figure1b.outlier.sharing.R
Rscript paper_figures/figure1c.replication.rate.consistent.R

Figure 2

bash paper_figures/count_rarevars.sh
Rscript paper_figures/figure2a.count.enrichments.R
Rscript paper_figures/figure2b.threshold.enrichments.R
Rscript paper_figures/figure2c.ASE.enrichments.R
Rscript paper_figures/figure2.R

Figure 3

Rscript paper_figures/figure3a.rare.variant.class.enrichments.R
Rscript paper_figures/figure3b.feature.enrichments.R
Rscript paper_figures/figure3de.outlier.effect.size.R
Rscript paper_figures/figure3.R

Figure 4

Rscript paper_figures/figure4a.uk10k.R
Rscript paper_figures/figure4b.exac.enrichments.R
Rscript paper_figures/figure4c.gene.list.enrichments.R
Rscript paper_figures/figure4.R

Figure 5

Rscript getPosteriorsEval.R data/genomic_features.txt data/outliers.txt
Rscript paper_figures/figure5b.R
Rscript getPosteriorsApp.R data/genomic_features.txt data/outliers.txt data/postprobs_all.txt
Rscript paper_figures/figure5c.R

Supplemental figures

EDF 1 and Supplementary Tables 1 and 2

## Adjusted R-squared values between top 15 PEER factors and top 20 sample and subject covariates in skeletal muscle
Rscript paper_figures/muscle_covariates_peerfactors.R
Rscript paper_figures/superheat_peer_muscle.R /data/muscle_samples_covariates_peerFactors.RData muscle.sample.peer.pdf 0.7
Rscript paper_figures/superheat_peer_muscle.R /data/muscle_subject_covariates_peerFactors.RData muscle.subject.peer.pdf 0.7

## Adjusted R-squared values between the total expression component removed by PEER in each of 44 tissues and top 20 sample and subject covariates
Rscript paper_figures/pve_samples_pertiss.R
Rscript paper_figures/pve_subject_pertiss.R
Rscript paper_figures/process_results.R
Rscript paper_figures/superheat_expression.R /data/superheat.subject.RData rv.subject.expression.pdf 0.25
Rscript paper_figures/superheat_expression.R /data/superheat.sample.RData rv.sample.expression.pdf 0.2

## Improvement of rare variant enrichments at varying levels of PEER correction
#Generate expression residuals with the top 0 or 5 PEER factors removed in addition to sex and genotype PCs
bash preprocessing/PEER/calc_residuals_covs_peerless.sh ${RAREVARDIR}/preprocessing/PEER 0
bash preprocessing/PEER/calc_residuals_covs_peerless.sh ${RAREVARDIR}/preprocessing/PEER 5

# Generate flat files for these new PEER corrected datasets to use for outlier calling
python preprocessing/gather_filter_normalized_expression_peerless.py \
       ${RAREVARDIR}/preprocessing/gtex_2015-01-12_tissues_all_normalized_samples.txt \
       ${RAREVARDIR}/preprocessing/gtex_2015-01-12_individuals_all_normalized_samples.txt \
       .peer.top0.ztrans.txt \
       ${RAREVARDIR}/preprocessing/gtex_2015-01-12_normalized_expression.peer.top0.txt
python preprocessing/gather_filter_normalized_expression_peerless.py \
       ${RAREVARDIR}/preprocessing/gtex_2015-01-12_tissues_all_normalized_samples.txt \
       ${RAREVARDIR}/preprocessing/gtex_2015-01-12_individuals_all_normalized_samples.txt \
       .peer.top5.ztrans.txt \
       ${RAREVARDIR}/preprocessing/gtex_2015-01-12_normalized_expression.peer.top5.txt

# Call Median Z-score outliers on data with top 0/5 PEER factors removed and compile features
R -f call_outliers/call_outliers_medz_peerless.R --slave --vanilla --args .peer.top0.txt
R -f call_outliers/call_outliers_medz_peerless.R --slave --vanilla --args .peer.top5.txt
bash feature_construction/run_compile_features_outliers_peerless.sh

Panel c is produced in paper_figures/figure2a.count.enrichments.R

EDF 2

You need to set the path to the subject annotations in the script below.

Rscript paper_figures/suppfig.number.outliers.per.individual.R

EDF 3

Rscript paper_figures/suppfig.single.replication.compare.multi.R

EDF 4

You need to set the path to the downloaded expression covariates and subject annotations in the script below.

Rscript paper_figures/suppfig.number.rare.vars.pca.R

EDF 5

Rscript paper_figures/suppfig.count.enrichments.R

EDF 6

Before running, set path to RNA-seq RPKMs in preprocessing/get_median_rpkms.R correctly.

Rscript preprocessing/get_median_rpkms.R
Rscript paper_figures/suppfig.over.under.expression.R

EDF 7

Rscript paper_figures/EDF7.R

EDF 8

Relies on the eGene and singificant variant-gene associations as downloaded for the GTEx v6p release from the portal.

bash paper_figures/annotate.variants.by.gene.sh 
Rscript paper_figures/suppfig.rare.var.counts.disease.genes.gtex.cohort.R
Rscript paper_figures/suppfig.egene.enrichment.R

EDF 9

Rscript paper_figures/main_RIVER_VaryingThrds.R
Rscript paper_figures/Generate_figures_RIVER_VaryingThrds.R
Rscript paper_figures/main_RIVER_10CV.R
Rscript paper_figures/EDF9c.R
Rscript paper_figures/EDF9d.R
Rscript paper_figures/EDF9e.R

EDF 10

Rscript paper_figures/EDF10bd.R
Rscript paper_figures/EDF10ef.rpkm.R
Rscript paper_figures/EDF10ef.Zscores.R

EDF 11

EDF11c is generated in crispr/summarize.crispr.results.R, run above.

Name		Name	Last commit message	Last commit date
Latest commit History 160 Commits
RIVER		RIVER
call_outliers		call_outliers
crispr		crispr
feature_construction		feature_construction
paper_figures		paper_figures
preprocessing		preprocessing
shared.eqtls		shared.eqtls
README.md		README.md

vtwang/GTExV6PRareVariation

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GTExV6PRareVariation

To run the code

Install the dependencies

Download required files

Setup

Pipeline

Expression data correction and normalization

Generate rpkm and read count matrices from GTEx V6P combined file

Run bash scripts to generate PEER corrected data (includes non-EAs) with covariates removed

Make list of individuals and tissues

Make flat files from raw RPKMs and PEER-corrected data for all tissues and individuals

Make list of expressed genes

Make summary statistics for expressed genes

Preparing reference files used later

Outlier calling

Call multi-tissue outliers

Call single-tissue outliers

Compare single-tissue and multi-tissue outliers as well as get stats on each

Run replication for single-tissue and multi-tissue outliers

Feature generation

Processing VCFs into bed files for each individual

Extract features to be combined with the individual bed files

Add extracted features to individual bed files

Collapse site-level features created above into gene-level features

Compile features for outliers and controls

Compute minor allele frequencies of rare variants segregating in GTEx using the UK10K dataset

Disease gene annotations

ACMG

ClinVar

GWAS

OMIM

Orphanet

DDG2P

Other: Cardiovascular and Cancer disease genes

Shared eQTLs defined by METASOFT

Validation of large-effect rare variants using CRISPR-Cas9 genome editing

RIVER

[Step 1] Generate matlab annotation files with both indivs and genes considered in GTEx v6p and gene expression matrix used for calling outliers later

[Step 2] In a WGS vcf file, all indels were removed, high quality variant calls (VQSLOD = PASS) were considered, only sites having <= 10 individuals in terms of missing genotypes were considered, and only autosomes were considered, and only European subjects were considered.

[Step 3] Compute GTEx allele frequencies with only subjects of interest

[Step 4] Extract a list of targeted regions for genes of interest

[Step 5] In each subject of interest, extract a list of individual-specific rare variant sites based on AFs of both GTEx and EUR 1K population

[Step 6] Extract all the features simulataneously (CADD, chromHMM, phylop, DANN).

[Step 7] Extract quantitative values of genomic features from individual files generated by the previous script at gene level

[Step 8] Compute normalized scores of genomic features

[Step 9] Generate both "genomic_features.txt" and "zscores.txt" for running RIVER

Main figures

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Supplemental figures

EDF 1 and Supplementary Tables 1 and 2

EDF 2

EDF 3

EDF 4

EDF 5

EDF 6

EDF 7

EDF 8

EDF 9

EDF 10

EDF 11

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