A bcftools extension to call mosaic chromosomal alterations starting from phased VCF files with either B Allele Frequency (BAF) and Log R Ratio (LRR) or allelic depth (AD). If you use this tool in your publication, please cite the following papers from 2018 and 2020:
Loh P., Genovese G., McCarroll S., Price A. et al. Insights about clonal expansions from 8,342 mosaic
chromosomal alterations. Nature 559, 350–355 (2018). [PMID: 29995854] [DOI: 10.1038/s41586-018-0321-x]
Loh P., Genovese G., McCarroll S., Monogenic and polygenic inheritance become
instruments for clonal selection (2020). [PMID: 32581363] [DOI: 10.1038/s41586-020-2430-6]
and this website. For any feedback or questions, contact the author
- Usage
- Installation
- Download resources for GRCh37
- Download resources for GRCh38
- Data preparation
- Phasing pipeline
- Chromosomal alterations pipeline
- Allelic imbalance pipeline
- Plot results
- Acknowledgements
NOTICE: Starting from July 2020 a WDL pipeline is available to run the entire MoChA pipeline from raw intensity files to final calls
Usage: bcftools +mocha [OPTIONS] <in.vcf>
Required options:
-r, --rules <assembly>[?] predefined genome reference rules, 'list' to print available settings, append '?' for details
-R, --rules-file <file> genome reference rules, space/tab-delimited CHROM:FROM-TO,TYPE
General Options:
-x, --sex <file> file including information about the gender of the samples
--call-rate <file> file including information about the call_rate of the samples
-s, --samples [^]<list> comma separated list of samples to include (or exclude with "^" prefix)
-S, --samples-file [^]<file> file of samples to include (or exclude with "^" prefix)
--force-samples only warn about unknown subset samples
-v, --variants [^]<file> tabix-indexed [compressed] VCF/BCF file containing variants
-t, --targets [^]<region> restrict to comma-separated list of regions. Exclude regions with "^" prefix
-T, --targets-file [^]<file> restrict to regions listed in a file. Exclude regions with "^" prefix
-f, --apply-filters <list> require at least one of the listed FILTER strings (e.g. "PASS,.")
to include (or exclude with "^" prefix) in the analysis
-p --cnp <file> list of regions to genotype in BED format
--mhc <region> MHC region to exclude from analysis (will be retained in the output)
--kir <region> KIR region to exclude from analysis (will be retained in the output)
--threads <int> number of extra output compression threads [0]
Output Options:
-o, --output <file> write output to a file [no output]
-O, --output-type <b|u|z|v> b: compressed BCF, u: uncompressed BCF, z: compressed VCF, v: uncompressed VCF [v]
--no-version do not append version and command line to the header
-a --no-annotations omit Ldev and Bdev FORMAT from output VCF (requires --output)
--no-log suppress progress report on standard error
-l --log <file> write log to file [standard error]
-m, --mosaic-calls <file> write mosaic chromosomal alterations to a file [standard output]
-g, --genome-stats <file> write sample genome-wide statistics to a file [no output]
-u, --ucsc-bed <file> write UCSC bed track to a file [no output]
HMM Options:
--bdev-LRR-BAF <list> comma separated list of inverse BAF deviations for LRR+BAF model [-2.0,-4.0,-6.0,10.0,6.0,4.0]
--bdev-BAF-phase <list> comma separated list of inverse BAF deviations for BAF+phase model
[6.0,8.0,10.0,15.0,20.0,30.0,50.0,80.0,100.0,150.0,200.0]
--min-dist <int> minimum base pair distance between consecutive sites for WGS data [400]
--adjust-BAF-LRR <int> minimum number of genotypes for a cluster to median adjust BAF and LRR (-1 for no adjustment) [5]
--regress-BAF-LRR <int> minimum number of genotypes for a cluster to regress BAF against LRR (-1 for no regression) [15]
--LRR-GC-order <int> order of polynomial to regress LRR against local GC content (-1 for no regression) [2]
--xy-prob <float> transition probability [1e-06]
--err-prob <float> uniform error probability [1e-02]
--flip-prob <float> phase flip probability [1e-02]
--centromere-loss <float> penalty to avoid calls spanning centromeres [1e-04]
--telomere-gain <float> telomere advantage to prioritize CN-LOHs [1e-02]
--x-telomere-gain <float> X telomere advantage to prioritize mLOX [1e-03]
--y-telomere-gain <float> Y telomere advantage to prioritize mLOY [1e-04]
--short-arm-chrs <list> list of chromosomes with short arms [13,14,15,21,22,chr13,chr14,chr15,chr21,chr22]
--use-short-arms use variants in short arms [FALSE]
--use-centromeres use variants in centromeres [FALSE]
--use-no-rules-chrs use chromosomes without centromere rules [FALSE]
--LRR-weight <float> relative contribution from LRR for LRR+BAF model [0.2]
--LRR-hap2dip <float> difference between LRR for haploid and diploid [0.45]
--LRR-cutoff <float> cutoff between LRR for haploid and diploid used to infer gender [estimated from X nonPAR]
Examples:
bcftools +mocha -r GRCh37 input.bcf -v ^exclude.bcf -g stats.tsv -m mocha.tsv -p cnp.grch37.bed
bcftools +mocha -r GRCh38 input.bcf -Ob -o output.bcf -g stats.tsv -m mocha.tsv --LRR-weight 0.5
Install basic tools (Debian/Ubuntu specific if you have admin privileges):
sudo apt install wget git g++ zlib1g-dev unzip samtools bedtools bcftools libgomp1
Optionally, you can install these libraries to activate further HTSlib features:
sudo apt install libbz2-dev libssl-dev liblzma-dev libgsl0-dev
Preparation steps
mkdir -p $HOME/bin $HOME/res && cd /tmp
We recommend compiling the source code but, wherever this is not possible, Linux x86_64 pre-compiled binaries are available for download here. However, notice that you will require a copy of BCFtools 1.10 or newer (available with Ubuntu 20.04)
Download latest version of HTSlib and BCFtools (if not downloaded already)
git clone --branch=develop git://github.com/samtools/htslib.git
git clone --branch=develop git://github.com/samtools/bcftools.git
Download plugins code
/bin/rm -f bcftools/plugins/{{mocha,beta_binom,genome_rules}.h,{mocha,trio-phase,mochatools,extendFMT}.c}
wget -P bcftools/plugins https://raw.githubusercontent.com/freeseek/mocha/master/{{mocha,beta_binom,genome_rules}.h,{mocha,trio-phase,mochatools,extendFMT}.c}
Compile latest version of HTSlib (optionally disable bz2, gcs, and lzma) and BCFtools (make sure you are using gcc version 5 or newer)
cd htslib && autoheader && (autoconf || autoconf) && ./configure --disable-bz2 --disable-gcs --disable-lzma && make && cd ..
cd bcftools && make && cd ..
/bin/cp bcftools/{bcftools,plugins/{fill-tags,fixploidy,mocha,trio-phase,mochatools,extendFMT}.so} $HOME/bin/
Notice that you will need some functionalities missing from the base version of bcftools to run the pipeline
Make sure the directory with the plugins is available to bcftools
export PATH="$HOME/bin:$PATH"
export BCFTOOLS_PLUGINS="$HOME/bin"
Install latest version of Eagle
wget -O $HOME/bin/eagle https://data.broadinstitute.org/alkesgroup/Eagle/downloads/dev/eagle_v2.4.1
chmod a+x $HOME/bin/eagle
Install Minimac3 (optional for array data)
git clone git://github.com/statgen/Minimac3.git
sed -i 's/USER_WARNINGS ?= -Werror/USER_WARNINGS ?= -Wno-format-truncation/' Minimac3/Library/libStatGenForMinimac3/general/Makefile
sed -i 's/bool legacy_count = 0/int legacy_count = 0/' Minimac3/Library/libStatGenForMinimac3/general/Parameters.cpp
sed -i 's/"\([0-9][0-9]*\)"/"\1","chr\1"/g;s/,"X"/,"X","chrX"/;s/finChromosome=="X"/(finChromosome=="X" || finChromosome=="chrX")/;s/finChromosome!="X"/(finChromosome!="X" \&\& finChromosome!="chrX")/' Minimac3/src/HaplotypeSet.cpp
sed -i 's/rHap.finChromosome!="X"/rHap.finChromosome!="X" \&\& rHap.finChromosome!="chrX"/' Minimac3/src/Imputation.cpp
cd Minimac3 && make && cd ..
/bin/cp Minimac3/bin/Minimac3{,-omp} $HOME/bin/
Human genome reference
wget -O- ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz | \
gzip -d > $HOME/res/human_g1k_v37.fasta
samtools faidx $HOME/res/human_g1k_v37.fasta
Genetic map
wget -P $HOME/res https://data.broadinstitute.org/alkesgroup/Eagle/downloads/tables/genetic_map_hg19_withX.txt.gz
1000 Genomes project phase 3
cd $HOME/res
wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.chr{{1..22}.phase3_shapeit2_mvncall_integrated_v5a,X.phase3_shapeit2_mvncall_integrated_v1b,Y.phase3_integrated_v2a}.20130502.genotypes.vcf.gz{,.tbi}
for chr in {1..22} X Y; do
bcftools view --no-version -Ou -c 2 ALL.chr${chr}.phase3*integrated_v[125][ab].20130502.genotypes.vcf.gz | \
bcftools norm --no-version -Ou -m -any | \
bcftools norm --no-version -Ob -o ALL.chr${chr}.phase3_integrated.20130502.genotypes.bcf -d none -f $HOME/res/human_g1k_v37.fasta && \
bcftools index -f ALL.chr${chr}.phase3_integrated.20130502.genotypes.bcf
done
List of common germline duplications and deletions
wget -P $HOME/res ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/integrated_sv_map/ALL.wgs.mergedSV.v8.20130502.svs.genotypes.vcf.gz{,.tbi}
bcftools query -i 'AC>1 && END-POS+1>10000 && SVTYPE!="INDEL" && (SVTYPE=="CNV" || SVTYPE=="DEL" || SVTYPE=="DUP")' \
-f "%CHROM\t%POS0\t%END\t%SVTYPE\n" $HOME/res/ALL.wgs.mergedSV.v8.20130502.svs.genotypes.vcf.gz > $HOME/res/cnp.grch37.bed
Minimal divergence intervals from segmental duplications (make sure your bedtools version is not affected by the groupby bug)
wget -O- http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/genomicSuperDups.txt.gz | gzip -d |
awk '!($2=="chrX" && $8=="chrY" || $2=="chrY" && $8=="chrX") {print $2"\t"$3"\t"$4"\t"$30}' > genomicSuperDups.bed
awk '{print $1,$2; print $1,$3}' genomicSuperDups.bed | \
sort -k1,1 -k2,2n | uniq | \
awk 'chrom==$1 {print chrom"\t"pos"\t"$2} {chrom=$1; pos=$2}' | \
bedtools intersect -a genomicSuperDups.bed -b - | \
bedtools sort | \
bedtools groupby -c 4 -o min | \
awk 'BEGIN {i=0; s[0]="+"; s[1]="-"} {if ($4!=x) i=(i+1)%2; x=$4; print $0"\t0\t"s[i]}' | \
bedtools merge -s -c 4 -o distinct | \
sed 's/^chr//' | grep -v gl | bgzip > $HOME/res/dup.grch37.bed.gz && \
tabix -f -p bed $HOME/res/dup.grch37.bed.gz
1000 Genomes project phase 3 imputation panel for Minimac3 (optional for array data)
cd $HOME/res
for chr in {1..22} X; do
if [ $chr == "X" ]; then
bcftools +fixploidy --no-version ALL.chr$chr.phase3_integrated.20130502.genotypes.bcf -- -f 2 | \
sed -e 's/\t0\/0/\t0|0/' -e 's/\t1\/1/\t1|1/' > tmp.chr$chr.GRCh37.vcf
else
bcftools view --no-version ALL.chr$chr.phase3_integrated.20130502.genotypes.bcf -o tmp.chr$chr.GRCh37.vcf
fi
Minimac3-omp \
--refHaps tmp.chr$chr.GRCh37.vcf \
--processReference \
--myChromosome $chr \
--prefix ALL.chr$chr.phase3_integrated.20130502.genotypes \
--noPhoneHome
/bin/rm tmp.chr$chr.GRCh37.vcf
done
Notice that this conversion takes ~26GB of RAM to complete and ~4 days of CPU time
Download cytoband file
wget -O $HOME/res/cytoBand.hg19.txt.gz http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/cytoBand.txt.gz
Setup variables
ref="$HOME/res/human_g1k_v37.fasta"
mhc_reg="6:27486711-33448264"
kir_reg="19:54574747-55504099"
map="$HOME/res/genetic_map_hg19_withX.txt.gz"
kgp_pfx="$HOME/res/ALL.chr"
kgp_sfx=".phase3_integrated.20130502.genotypes"
rule="GRCh37"
cnp="$HOME/res/cnp.grch37.bed"
dup="$HOME/res/dup.grch37.bed.gz"
cyto="$HOME/res/cytoBand.hg19.txt.gz"
Human genome reference (following the suggestion from Heng Li)
wget -O- ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz | \
gzip -d > $HOME/res/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna
samtools faidx $HOME/res/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna
Genetic map
wget -P $HOME/res https://data.broadinstitute.org/alkesgroup/Eagle/downloads/tables/genetic_map_hg38_withX.txt.gz
1000 Genomes project phase 3 (fixing contig names, removing duplicate variants, removing incomplete variants)
cd $HOME/res
wget http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/GRCh38_positions/ALL.chr{{1..22},X,Y}_GRCh38.genotypes.20170504.vcf.gz{,.tbi}
ref="$HOME/res/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna"
for chr in {1..22} X Y; do
(bcftools view --no-version -h ALL.chr${chr}_GRCh38.genotypes.20170504.vcf.gz | \
grep -v "^##contig=<ID=[GNh]" | sed 's/^##contig=<ID=MT/##contig=<ID=chrM/;s/^##contig=<ID=\([0-9XY]\)/##contig=<ID=chr\1/'; \
bcftools annotate --no-version -x INFO/END ALL.chr${chr}_GRCh38.genotypes.20170504.vcf.gz | \
bcftools view --no-version -H -c 2 | \
grep -v "[0-9]|\.\|\.|[0-9]" | sed 's/^/chr/') | \
bcftools norm --no-version -Ou -m -any | \
bcftools norm --no-version -Ob -o ALL.chr${chr}_GRCh38.genotypes.20170504.bcf -d none -f $ref && \
bcftools index -f ALL.chr${chr}_GRCh38.genotypes.20170504.bcf
done
Do notice though that the 1000 Genomes project team incorrectly lifted over chromosome X genotypes over PAR1 and PAR2 regions, despite this issue not being reported in the README. This will directly affect the ability to detect chromosome Y loss events
List of common germline duplications and deletions
wget -P $HOME/res ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/integrated_sv_map/supporting/GRCh38_positions/ALL.wgs.mergedSV.v8.20130502.svs.genotypes.GRCh38.vcf.gz{,.tbi}
bcftools query -i 'AC>1 && END-POS+1>10000 && SVTYPE!="INDEL" && (SVTYPE=="CNV" || SVTYPE=="DEL" || SVTYPE=="DUP")' \
-f "chr%CHROM\t%POS0\t%END\t%SVTYPE\n" $HOME/res/ALL.wgs.mergedSV.v8.20130502.svs.genotypes.GRCh38.vcf.gz > $HOME/res/cnp.grch38.bed
Minimal divergence intervals from segmental duplications (make sure your bedtools version is not affected by the groupby bug)
wget -O- http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/genomicSuperDups.txt.gz | gzip -d |
awk '!($2=="chrX" && $8=="chrY" || $2=="chrY" && $8=="chrX") {print $2"\t"$3"\t"$4"\t"$30}' > genomicSuperDups.bed
awk '{print $1,$2; print $1,$3}' genomicSuperDups.bed | \
sort -k1,1 -k2,2n | uniq | \
awk 'chrom==$1 {print chrom"\t"pos"\t"$2} {chrom=$1; pos=$2}' | \
bedtools intersect -a genomicSuperDups.bed -b - | \
bedtools sort | \
bedtools groupby -c 4 -o min | \
awk 'BEGIN {i=0; s[0]="+"; s[1]="-"} {if ($4!=x) i=(i+1)%2; x=$4; print $0"\t0\t"s[i]}' | \
bedtools merge -s -c 4 -o distinct | \
grep -v "GL\|KI" | bgzip > $HOME/res/dup.grch38.bed.gz && \
tabix -f -p bed $HOME/res/dup.grch38.bed.gz
1000 Genomes project phase 3 imputation panel for Minimac3 (optional for array data)
cd $HOME/res
for chr in {1..22} X; do
if [ $chr == "X" ]; then
bcftools +fixploidy --no-version ALL.chr${chr}_GRCh38.genotypes.20170504.bcf -- -f 2 | \
sed -e 's/\t0\/0/\t0|0/' -e 's/\t1\/1/\t1|1/' > tmp.chr$chr.GRCh38.vcf
else
bcftools view --no-version ALL.chr${chr}_GRCh38.genotypes.20170504.bcf -o tmp.chr$chr.GRCh38.vcf
fi
Minimac3-omp \
--refHaps tmp.chr$chr.GRCh38.vcf \
--processReference \
--myChromosome chr$chr \
--prefix ALL.chr${chr}_GRCh38.genotypes.20170504 \
--noPhoneHome
/bin/rm tmp.chr$chr.GRCh38.vcf
done
Notice that this conversion takes ~26GB of RAM to complete and ~4 days of CPU time
Download cytoband file
wget -O $HOME/res/cytoBand.hg38.txt.gz http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/cytoBand.txt.gz
Setup variables
ref="$HOME/res/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna"
mhc_reg="chr6:27518932-33480487"
kir_reg="chr19:54071493-54992731"
map="$HOME/res/genetic_map_hg38_withX.txt.gz"
kgp_pfx="$HOME/res/ALL.chr"
kgp_sfx="_GRCh38.genotypes.20170504"
rule="GRCh38"
cnp="$HOME/res/cnp.grch38.bed"
dup="$HOME/res/dup.grch38.bed.gz"
cyto="$HOME/res/cytoBand.hg38.txt.gz"
Preparation steps
vcf="..." # input VCF file with phased GT, LRR, and BAF
pfx="..." # output prefix
thr="..." # number of threads to use
crt="..." # file with call rate information (first column sample ID, second column call rate)
sex="..." # file with computed gender information (first column sample ID, second column gender: 1=male; 2=female)
xcl="..." # VCF file with additional list of variants to exclude (optional)
ped="..." # pedigree file to use if parent child duos are present
dir="..." # directory where output files will be generated
mkdir -p $dir
If you want to process genotype array data you need a VCF file with ALLELE_A, ALLELE_B, GC, GT, BAF, and LRR information:
##fileformat=VCFv4.2
##INFO=<ID=ALLELE_A,Number=1,Type=Integer,Description="A allele">
##INFO=<ID=ALLELE_B,Number=1,Type=Integer,Description="B allele">
##INFO=<ID=GC,Number=1,Type=Float,Description="GC ratio content around the variant">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=BAF,Number=1,Type=Float,Description="B Allele Frequency">
##FORMAT=<ID=LRR,Number=1,Type=Float,Description="Log R Ratio">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878
1 752566 rs3094315 G A . . ALLELE_A=1;ALLELE_B=0;GC=0.3675 GT:BAF:LRR 1|1:0.0111:-0.0798
1 776546 rs12124819 A G . . ALLELE_A=0;ALLELE_B=1;GC=0.435 GT:BAF:LRR 0|1:0.5441:0.4959
1 798959 rs11240777 G A . . ALLELE_A=1;ALLELE_B=0;GC=0.4075 GT:BAF:LRR 0|0:0.9712:0.2276
1 932457 rs1891910 G A . . ALLELE_A=1;ALLELE_B=0;GC=0.6425 GT:BAF:LRR 1|0:0.5460:-0.1653
Making sure that BAF refers to the allele frequency of the reference allele if ALLELE_B=0 and of the alternate allele if ALLELE_B=1
If you do not already have a VCF file but you have Illumina or Affymetrix genotype array data, you can use the gtc2vcf tools to convert the data to VCF and you can use the mochatools plugin to fill the ALLELE_A/ALLELE_B/GC info fields. Alternatively you can use your own scripts
Create a minimal binary VCF
bcftools annotate --no-version -Ob -o $dir/$pfx.unphased.bcf $vcf \
-x ID,QUAL,^INFO/ALLELE_A,^INFO/ALLELE_B,^INFO/GC,^FMT/GT,^FMT/BAF,^FMT/LRR && \
bcftools index -f $dir/$pfx.unphased.bcf
If you want to process whole-genome sequence data you need a VCF file with GC, GT and AD information:
##fileformat=VCFv4.2
##INFO=<ID=GC,Number=1,Type=Float,Description="GC ratio content around the variant">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=AD,Number=R,Type=Integer,Description="Allelic depths for the ref and alt alleles in the order listed">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878
1 752566 rs3094315 G A . . GC=0.3675 GT:AD 1|1:0,31
1 776546 rs12124819 A G . . GC=0.435 GT:AD 0|1:21,23
1 798959 rs11240777 G A . . GC=0.4075 GT:AD 0|0:31,0
1 932457 rs1891910 G A . . GC=0.6425 GT:AD 1|0:18,14
Make sure that AD is a "Number=R" format field (this was introduced in version 4.2 of the VCF) or multi-allelic variants will not split properly
Create a minimal binary VCF (notice that you will need a version newer than BCFtools 1.10.2 with implemented the --keep-sum option)
bcftools view --no-version -h $vcf | sed 's/^\(##FORMAT=<ID=AD,Number=\)\./\1R/' | \
bcftools reheader -h /dev/stdin $vcf | \
bcftools filter --no-version -Ou -e "FMT/DP<10 | FMT/GQ<20" --set-GT . | \
bcftools annotate --no-version -Ou -x ID,QUAL,^INFO/GC,^FMT/GT,^FMT/AD | \
bcftools norm --no-version -Ou -m -any --keep-sum AD | \
bcftools norm --no-version -Ob -o $dir/$pfx.unphased.bcf -f $ref && \
bcftools index $dir/$pfx.unphased.bcf
This will set to missing all genotypes that have low coverage or low genotyping quality, as these can cause issues
Generate a list of variants that will be excluded from modeling by both eagle and mocha (notice that you will need a version newer than BCFtools 1.10.2 with implemented the F_MISSING option, or else you should drop that filter)
awk -F"\t" '$2<.97 {print $1}' $crt > samples_xcl_list.txt; \
echo '##INFO=<ID=JK,Number=1,Type=Float,Description="Jukes Cantor">' | \
bcftools annotate --no-version -Ou -a $dup -c CHROM,FROM,TO,JK -h /dev/stdin $dir/$pfx.unphased.bcf | \
bcftools view --no-version -Ou -S ^samples_xcl_list.txt | \
bcftools +fill-tags --no-version -Ou -t ^Y,MT,chrY,chrM -- -t ExcHet,F_MISSING | \
bcftools +mochatools --no-version -Ou -- -x $sex -G | \
bcftools annotate --no-version -Ob -o $dir/$pfx.xcl.bcf \
-i 'FILTER!="." && FILTER!="PASS" || INFO/JK<.02 || INFO/ExcHet<1e-6 || INFO/F_MISSING>1-.97 ||
INFO/AC_Sex_Test<1e-6 && CHROM!="X" && CHROM!="chrX" && CHROM!="Y" && CHROM!="chrY"' \
-x ^INFO/JK,^INFO/ExcHet,^INFO/F_MISSING,^INFO/AC_Sex_Test && \
bcftools index -f $dir/$pfx.xcl.bcf;
/bin/rm samples_xcl_list.txt
This command will create a list of variants falling within segmental duplications with low divergence (<2%), high levels of missingness (>2%), variants with excess heterozygosity (p<1e-6), and variants that correlate with sex in an unexpected way (p<1e-6). If you are using WGS data and you don't have a file with sex information, you can skip the quality control line using this information. When later running MoChA, sex will be imputed and a sex file can be computed from MoChA's output
If a file with additional variants to be excluded is available, further merge it with the generated list
/bin/mv $dir/$pfx.xcl.bcf $dir/$pfx.xcl.tmp.bcf && \
/bin/mv $dir/$pfx.xcl.bcf.csi $dir/$pfx.xcl.tmp.bcf.csi && \
bcftools merge --no-version -Ob -o $dir/$pfx.xcl.bcf -m none $dir/$pfx.xcl.tmp.bcf $xcl && \
bcftools index -f $dir/$pfx.xcl.bcf
Phase VCF file by chromosome with Eagle
for chr in {1..22} X; do
eagle \
--geneticMapFile $map \
--outPrefix $dir/$pfx.chr$chr \
--numThreads $thr \
--vcfRef $kgp_pfx${chr}$kgp_sfx.bcf \
--vcfTarget $dir/$pfx.unphased.bcf \
--vcfOutFormat b \
--noImpMissing \
--outputUnphased \
--vcfExclude $dir/$pfx.xcl.bcf \
--chrom $chr \
--pbwtIters 3 && \
bcftools index -f $dir/$pfx.chr$chr.bcf
done
Eagle's memory requirements will depend on the number of samples in the target (Nt) and in the reference panel (Nr=2504), and the number of variants (M) in the largest contig, and will amount to 1.5(Nt+Nr)M bytes. The running time will be ~1 minute of CPU time per genome for reference-based phasing with a small target and reference panel (see Supplementary Tables 2,3) and ~5 minutes of CPU time per genome for non-reference-based phasing with a large cohort (see Supplementary Tables 7,8). Also, by default, if the option --pbwtIters is not used, Eagle will perform one phasing iteration if Nt<Nr/2=1252, two if 1252=Nr/2<Nt<2Nr=5008, and three if 5008=2Nr<Nt and in the second and third iterations both target and reference panel haplotypes will be used as references for phasing (see <a href="https://www.nature.com/articles/ng.3679#Sec10"here).
Notice that you can also use alternative phasing methods that might be more effective, such as using HRC (use the Sanger Imputation Service, as the Michigan Imputations Server does not work with binary VCFs, does not work with VCFs with multiple chromosomes, does not work with chromosome X, and has no option for phasing without imputation). This might provide better phasing and therefore better ability to detect large events at lower cell fractions. Notice also that phasing can also be performed across overlapping windows rather than entire chromosomes to achieve better parallelization
Extract chromosomes that do not require phasing
bcftools view --no-version -Ob -o $dir/$pfx.other.bcf $dir/$pfx.unphased.bcf \
-t ^$(seq -s, 1 22),X,$(seq -f chr%.0f -s, 1 22),chrX && \
bcftools index -f $dir/$pfx.other.bcf
Concatenate eagle output into a single VCF file and add GC/CpG content information
bcftools concat --no-version -Ob -o $dir/$pfx.bcf $dir/$pfx.{chr{{1..22},X},other}.bcf && \
bcftools index -f $dir/$pfx.bcf
Notice that if the phasing was made in overlapping windows rather than chromosomes, the overlapping windows should be concatenated using the --ligate option in bcftools concat
If pedigree information with duos or trios is available, you can improve the phased haplotypes from eagle by running the following command instead of the previous one:
bcftools concat --no-version -Ou $dir/$pfx.{chr{{1..22},X},other}.bcf | \
bcftools +trio-phase --no-version -Ob -o $dir/$pfx.bcf -- -p $ped && \
bcftools index -f $dir/$pfx.bcf
(it requires a ped file)
Impute variants using Minimac3 (optional for array data)
for chr in {1..22} X; do
bcftools annotate --no-version -Ou $dir/$pfx.bcf -a $dir/$pfx.xcl.bcf -m +XCL -r $chr,chr$chr | \
bcftools view --no-version -Ov -o $dir/$pfx.chr$chr.vcf -e "XCL==1"
Minimac3-omp \
--cpus $thr \
--refHaps $kgp_pfx${chr}$kgp_sfx.m3vcf.gz \
--format GT,GP \
--haps $dir/$pfx.chr$chr.vcf \
--prefix $dir/$pfx.chr$chr \
--lowMemory \
--noPhoneHome
bcftools query -l $dir/$pfx.bcf | \
bcftools view --no-version -Ob -o $dir/$pfx.chr$chr.dose.bcf -S /dev/stdin $dir/$pfx.chr$chr.dose.vcf.gz
/bin/rm $dir/$pfx.chr$chr.vcf $dir/$pfx.chr$chr.dose.vcf.gz
done
Concatenate imputed genotypes into a single VCF file (optional for array data)
bcftools concat --no-version -Ob -o $dir/$pfx.dose.bcf $dir/$pfx.chr{{1..22},X}.dose.vcf.gz && \
bcftools index -f $dir/$pfx.dose.bcf
Remove unphased VCF and single chromosome files (optional)
/bin/rm $dir/$pfx.{unphased,chr{{1..22},X},other}.bcf{,.csi} $dir/$pfx.chr{{1..22},X}.dose.bcf
Preparation steps
pfx="..." # output prefix
sex="..." # file with computed gender information (first column sample ID, second column gender: 1=male; 2=female)
crt="..." # file with call rate information (first column sample ID, second column call rate)
lst="..." # file with list of samples to analyze for asymmetries (e.g. samples with 1p CN-LOH)
cnp="..." # file with list of regions to genotype in BED format
mhc_reg="..." # MHC region to skip
kir_reg="..." # KIR region to skip
Call mosaic chromosomal alterations with MoChA
bcftools +mocha \
--rules $rule \
--sex $sex \
--call-rate $crt \
--no-version \
--output-type b \
--output $dir/$pfx.mocha.bcf \
--variants ^$dir/$pfx.xcl.bcf \
--mosaic-calls $dir/$pfx.calls.tsv \
--genome-stats $dir/$pfx.stats.tsv \
--ucsc-bed $dir/$pfx.ucsc.bed \
--cnp $cnp \
--mhc $mhc_reg \
--kir $kir_reg \
$dir/$pfx.bcf && \
bcftools index -f $dir/$pfx.mocha.bcf
Notice that MoChA will read input computed gender and call rate if provided, otherwise these will be estimated from the VCF. For array data these statistics are usually available from the output of the Illumina's GenCall or Affymetrix's Axiom genotyping algorithms
The genome statistics file contains information for each sample analyzed in the VCF and it includes the following columns:
sample_id - sample ID
computed_gender - estimated sample gender from X nonPAR region (not heterozygous sites count)
XXX_median - median LRR or sequencing coverage across autosomes
XXX_sd - standard deviation for LRR or sequencing coverage
XXX_auto - auto correlation across consecutive sites for LRR or sequencing coverage (after GC correction)
baf_sd/_cor - BAF standard deviation or beta-binomial overdispersion for read counts
baf_conc - BAF phase concordance across phased heterozygous sites (see Vattathil et al. 2012)
baf_auto - phased BAF auto correlation across consecutive phased heterozygous sites
n_sites - number of sites across the genome for model based on LRR and BAF
n_hets - number of heterozygous sites across the genome for model based on BAF and genotype phase
x_nonpar_n_hets - number of heterozygous sites in the X nonPAR region
x_nonpar_baf_sd/_corr - BAF standard deviation or beta-binomial overdispersion for read counts in the X nonPAR region
x_nonpar_XXX_median - median LRR or sequencing coverage over the X nonPAR region
y_nonpar_XXX_median - median LRR or sequencing coverage over the Y nonPAR region
mt_XXX_median - median LRR or sequencing coverage over the mitochondrial genome
lrr_gc_rel_ess - LRR or sequencing coverage explained sum of squares fraction using local GC content
lrr_gc_X - coefficient X for polynomial in GC content fitting LRR estimates
The mosaic calls file contains information about each mosaic and germline chromosomal alteration called and it includes the following columns:
sample_id - sample ID
computed_gender - inferred sample gender
chrom - chromosome
beg_XXXXXX - beginning base pair position for the call (according to XXXXXX genome reference)
end_XXXXXX - end base pair position for the call (according to XXXXXX genome reference)
length - base pair length of the call
p_arm - whether the call extends to the small arm and whether it reaches the telomere
q_arm - whether the call extends to the long arm and whether it reaches the telomere
n_sites - number of sites used for the call
n_hets - number of heterozygous sites used for the call
n50_gets - N50 value for consecutive heterozygous sites distances
bdev - BAF deviation estimate from 0.5
bdev_se - standard deviation estimate for BAF deviation
rel_cov - relative coverage estimate from LRR or sequencing coverage
rel_cov_se - standard deviation estimate for relative coverage
lod_lrr_baf - LOD score for model based on LRR and BAF
lod_baf_phase - LOD score for model based on BAF and genotype phase
n_flips - number of phase flips for calls based on BAF and genotype phase model (-1 if LRR and BAF model used)
baf_conc - BAF phase concordance across phased heterozygous sites underlying the call (see Vattathil et al. 2012)
lod_baf_conc - LOD score for model based on BAF phase concordance (genome-wide corrected)
type - Type of call based on LRR / relative coverage
cf - estimated cell fraction based on BDEV and TYPE, or LDEV and TYPE if either BDEV or BDEV_SE are missing
The output VCF will contain the following extra FORMAT fields:
Ldev - LRR deviation estimate
Bdev - BAF deviation estimate
Bdev_Phase - for heterozygous calls: 1/-1 if the alternate allele is over/under represented
For array data, MoChA's memory requirements will depend on the number of samples (N) and the number of variants (M) in the largest contig and will amount to 9NM bytes. For example, if you are running 4,000 samples and chromosome 1 has ~80K variants, you will need approximately 2-3GB to run MoChA. It will take ~1/3 second of CPU time per genome to process samples genotyped on the Illumina GSA DNA microarray. For whole genome sequence data, MoChA's memory requirements will depend on the number of samples (N), the --min-dist parameter (D, 400 by default) and the length of the longest contig (L) and will amount to no more than 9NL/D, but could be significantly less, depending on how many variants you have in the VCF. If you are running 1,000 samples with default parameter --min-dist 400 and chromosome 1 is ~250Mbp long, you might need up to 5-6GB to run MoChA. For whole genome sequence data there is no need to batch too many samples together, as batching will not affect the calls made by MoChA (it will for array data unless you use options --adjust-BAF-LRR -1 and --regress-BAF-LRR -1). Notice that the CPU requirements for MoChA will be negligible compared to the CPU requirements for phasing with Eagle
Depending on your application, you might want to filter the calls from MoChA. For example, the following code:
awk -F "\t" 'NR==FNR && FNR==1 {for (i=1; i<=NF; i++) f[$i] = i}
NR==FNR && FNR>1 {sample_id=$(f["sample_id"]); call_rate=$(f["call_rate"]); baf_auto=$(f["baf_auto"])}
NR==FNR && FNR>1 && (call_rate<.97 || baf_auto>.03) {xcl[sample_id]++}
NR>FNR && FNR==1 {for (i=1; i<=NF; i++) g[$i] = i; print}
NR>FNR && FNR>1 {sample_id=$(g["sample_id"]); len=$(g["length"]); p_arm=$(g["p_arm"]); q_arm=$(g["q_arm"]);
bdev=$(g["bdev"]); rel_cov=$(g["rel_cov"]); lod_baf_phase=$(g["lod_baf_phase"]); type=$(g["type"]);
if (lod_baf_phase=="nan") lod_baf_phase=0}
NR>FNR && FNR>1 && !(sample_id in xcl) && rel_cov>0.5 && type!~"^CNP" &&
( len>5e6 + 5e6 * (p_arm!="N" && q_arm!="N") ||
len>5e5 && bdev<1/10 && rel_cov<2.5 && lod_baf_phase>10 ||
rel_cov<2.1 && lod_baf_phase>10 )' $pfx.stats.tsv $pfx.calls.tsv > $pfx.calls.filtered.tsv
awk 'NR==FNR {x[$1"_"$3"_"$4"_"$5]++} NR>FNR && ($0~"^track" || $4"_"$1"_"$2"_"$3 in x)' \
$pfx.calls.filtered.tsv $pfx.ucsc.bed > $pfx.ucsc.filtered.bed
will generate a new table after removing samples with call_rate lower than 0.97 baf_auto greater than 0.03, removing calls with less than a lod_baf_phase score of 10 unless they are larger than 5Mbp (or 10Mbp if they span the centromere) for the model based on BAF and genotype phase, removing calls flagged as germline copy number polymorphisms (CNPs), and removing calls that are likely germline duplications similarly to how it was done in the UKBB
import results from MoChA into VCF file with imputed genotypes (optional for array data)
bcftools annotate \
--no-version -Ob \
--columns FMT/Ldev,FMT/Bdev,FMT/Bdev_Phase \
$dir/$pfx.dose.bcf \
--annotations $dir/$pfx.mocha.bcf \
-o $dir/$pfx.dose.mocha.bcf && \
bcftools index $dir/$pfx.dose.mocha.bcf
Run asymmetry analyses (subset cohort, run binomial test, discard genotypes)
bcftools +extendFMT \
--no-version -Ou \
--format Bdev_Phase \
--phase \
--dist 500000 \
--regions $reg \
--samples $lst \
$dir/$pfx.dose.mocha.bcf | \
bcftools +mochatools \
--no-version \
--output-type b \
--output $dir/$pfx.bal.bcf \
-- --balance Bdev_Phase \
--drop-genotypes && \
bcftools index -f $dir/$pfx.bal.bcf
Observe results for asymmetry analyses in table format
fmt="%CHROM\t%POS\t%ID\t%Bal{0}\t%Bal{1}\t%Bal_Test\n"
bcftools query \
--include "Bal_Test>6" \
--format "$fmt" \
$dir/$pfx.bal.bcf | \
column -ts $'\t'
Install basic tools (Debian/Ubuntu specific if you have admin privileges):
sudo apt install r-cran-optparse r-cran-ggplot2 r-cran-data.table
Download R scripts
/bin/rm -f $HOME/bin/{summary,pileup,mocha}_plot.R
wget -P $HOME/bin https://raw.githubusercontent.com/freeseek/mocha/master/{summary,pileup,mocha}_plot.R
chmod a+x $HOME/bin/{summary,pileup,mocha}_plot.R
Generate summary plot
summary_plot.R \
--pdf $dir/$pfx.pdf \
--stats $dir/$pfx.stats.tsv \
--calls $dir/$pfx.calls.tsv
Generate pileup plot
pileup_plot.R \
--pdf $dir/$pfx.pdf \
--stats $dir/$pfx.stats.tsv \
--calls $dir/$pfx.calls.tsv \
--cytoband $HOME/res/cytoBand.hg19.txt.gz
Plot mosaic chromosomal alterations (for array data)
mocha_plot.R \
--mocha \
--stats $dir/$pfx.stats.tsv \
--vcf $dir/$pfx.mocha.bcf \
--png MH0145622.png \
--samples MH0145622 \
--regions 11:81098129-115077367 \
--cytoband $HOME/res/cytoBand.hg19.txt.gz
Notice that by default MoChA will perform internal BAF (for array data) and LRR adjustments that will not be performed by the plotter so the visualized data might actually look noisier than what was actually processed
Mosaic deletion from array data overlapping the ATM gene (GRCh37 coordinates). The deletion signal can be observed across LRR, BAF and phased BAF, although it is the most clear with the latter. Furthermore, evidence of three phase switch errors can be observed in the shifted phased BAF signal
Plot mosaic chromosomal alterations (for WGS data)
mocha_plot.R \
--wgs \
--mocha \
--stats $dir/$pfx.stats.tsv
--vcf $dir/$pfx.mocha.bcf \
--png CSES15_P26_140611.png \
--samples CSES15_P26_140611 \
--regions 1:202236354-211793505 \
--cytoband $HOME/res/cytoBand.hg19.txt.gz
Complex duplication overlapping the MDM4 gene (GRCh37 coordinates). Signal over heterozygous sites colored in blue shows evidence of a triplication event and signal over heterozygous sites colored in red shows evidence of a duplication event. Multiple phase switch errors can be observed in the shifted phased BAF signal
This work is supported by NIH grant R01 HG006855, NIH grant R01 MH104964, US Department of Defense Breast Cancer Research Breakthrough Award W81XWH-16-1-0316 (project BC151244), and the Stanley Center for Psychiatric Research. This work would have not been possible without the efforts of Heng Li [email protected], Petr Danecek [email protected], John Marshall [email protected], James Bonfield [email protected], and Shane McCarthy [email protected] in building HTSlib and BCFtools and Po-Ru Loh [email protected] in building the Eagle phasing software
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent