ParseCNV takes CNV calls as input and creates probe based statistics for CNV occurrence in (cases and controls, families, or population with quantitative trait) then calls CNVRs based on neighboring SNPs of similar significance. CNV calls may be from aCGH, SNP array, Exome Sequencing, or Whole Genome Sequencing.

A brief introduction: DNA base call GWAS deals with 3 possible states at each SNP [AA,AB,BB] where A and B=[A,T,C,G] and A≠B.

CNV association deals with 5 possible states at each SNP [0,1,2,3,4] where 2 is typically high frequency as the normal diploid one maternal one paternal copy expectation. To leverage 3 state association, we divide out deletion [0,1,2] and duplication [2,3,4] association.

Pre and Post association quality control metrics are important to limit bias in overly stringent Pre association quality control.

CNVs are also called Copy Number Abberations, Changes, Differences or Polymorphisms (CNA, CNC, CND or CNP) depending on the discipline, context and the level of specificity. CNVs are the popularly assessed subset of Structural Variations, which include insertions and inversions.

See simple example input files: Cases.rawcnv Controls.rawcnv Cases.fam ChrSnp0Pos.map IDToPath.txt and try to run them as a test of the software on your system. If you have trouble with your input files, create similarly simple versions to quickly run the software and isolate the problem.

Run Command: perl ParseCNV.pl Cases.rawcnv Controls.rawcnv Cases.fam ChrSnp0Pos.map (optional --out <prefix>, --idToPath <file.txt>, --includePed, --probesBafLrr <file.txt>, --build <hg19>, --batch <file.txt>, --mergePVar <1>, --mergeDist <1000000>, --tdt, --maxPInclusion <0.05>, --includeAllRedFlags)

Read and cite the paper:

Glessner JT, Li J and Hakonarson H (2013) ParseCNV integrative copy number variation association software with quality tracking. Nucleic Acids Research doi: 10.1093/nar/gks1346

New Major Features (full version log at bottom):

NEW Feature! 2014-02-27 MakeRedFlagPlots.pl: derive continuous confidence score based on redFlagReasons column in ParseCNV --includeAllRedFlags

NEW Feature! 2014-02-22 PQN_Concord_Inh.pl: multiple CNV detection algorithm comparison and de novo, transmitted, and untransmitted CNV call based annotation

NEW Feature! 2013-12-13 Graphical Interface for ParseCNV.pl using wxPerl and Tk ParseCNV_GUI.pl

NEW Feature! 2013-11-25 --build bosTau7 (Cow 1-29 X) --build susScr3 (Pig 1-18 XY) now supported in addition to hg18 hg19

NEW Feature! 2013-11-21 Brief column .stats main folder output, ParseCNV_QC.pl QC Thresholds may be specified, basic non-human species support

NEW Feature! 2012-11-27 Sample quality control metrics and sample remove report now automated in ParseCNV_QC.pl

NEW Feature! 2012-11-25 Gene based association approach now available in ParseCNV_GeneBased.pl

NEW Feature! 2012-11-20 BAF/LRR plots of two samples with associated CNV and one sample with CN=2 with flanking diploid data for each CNVR with --idToPath

NEW Feature! 2012-08-25 Image created with BAF and LRR of specific association regions across contributing cases as part of --idToPath option.

NEW Feature! 2012-04-09 Plink p values can now be imported into ParseCNV using InsertPlinkPvalues.pl for CNVR boundary calling.

NEW Feature! 2012-02-06 UCSC review features are now automatically scored.

Input Files:

CNV Calls

CNV calls (.rawcnv) can be generated by any algorithm but should be in the PennCNV format:

CNVCallChromosome:Start-StopBoundaries NumProbes Size(BasePairs) CNState SampleID StartSNP EndSNP

Format conversion scripts for popular algorithms (QuantiSNP, XHMM, and GenomeStrip) are now included. Typically this reformatting is relatively easy in Excel. Calls for a given sample should be sorted together in subsequent rows. CNState values: state1,cn=0 state2,cn=1 state5,cn=3 state6,cn=4. These states are also referred to as homozygous deletion, hemizygous deletion, hemizygous duplication, and homozygous duplication respectively in literature. Be careful there is no blank line at end of file. PennCNV states [1,2,5,6] correspond to CN [0,1,3,4].

For example: “chr1:156783977-156788016 numsnp=6 length=4,040 state2,cn=1 sample1.txt startsnp=rs16840314 endsnp=rs10489835”

Alternatively: “chr1:156783977-156788016 numsnp=6 length=4,040 state2,cn=1 sample1.txt startsnp=rs16840314 endsnp=rs10489835 conf=20.659”

There are separate files for the study subjects (cases and related parents or siblings if available) and control subjects.

If you want to filter a .rawcnv with both cases and controls into separate input files you can use: GeneRef/RemoveSpecificBarcodes-Hash_Example_DefFile_ColRemKeep.pl (FilterCNV.pl)

state3,cn=2 on chrX is male duplication so should be recoded state5,cn=3

state4,cn=2 means copy neutral LOH i.e. ROH

Make sure you do upfront sample quality control!

PennCNV: compile_pfb.pl creates a .pfb and cal_gc_snp.pl creates a .gcmodel for your specific array. Use the same version of array for cases and controls.

Please make sure you use --log (or 2>>MySamples.log) and --conf command line arguments for quality assessment. Then:

grep summary MySamples.log > MySamples_QC.txt

awk ‘{print $5}’ MySamples.rawcnv | uniq --count > MySamples_CountCNVs.txt

If you want ChrX and LOH(copy neutral LOH/ROH) calls, you have to run separately --chrx and --loh --hmm hh550.hmm

Merging fragmented calls with clean_cnv.pl is useful, especially if you are interested in large CNVs: perl clean_cnv.pl combineseg file.rawcnv -signalfile file.pfb -out file_clean.rawcnv

PFB gives universal definition for snp to chr pos and snp set used

detect_cnv.pl --trio after detect_cnv.pl --test is strongly recommended if you have trios to limit the de novo rate.

Review LRR BAF of the parents for false negative CNVs by copying child CNVs and adding parent IDs and state3,cn=2 to run visualize_cnv.pl the run: convert Mom.JPG Dad.JPG +append Parents.JPG; convert Parents.JPG Child.JPG -append Trio.JPG

QuantiSNP: You have to download from here. The various other websites do not work. Make sure your files are: “SNPName Chromosome Position LRR BAF” the header is not read and if you switch LRR and BAF no error is given.

Plink: Run --missing to get call rates (or from GenomeStudio/GenotypingConsole), --genome to check relatedness (high PI_HAT), --mendel to verify families, --check-sex to verify gender, .mds (--genome then --read-genome --cluster --mds-plot) or Eigenstrat smartpca to create .pca.evec for ethnicity/stratification.

FAM

The family or FAM file has the family relationships in the study subjects to track inheritance of CNVs. Tab Delimited:

FamID IndividualID(InCNVCallFile) FatherID MotherID Gender(1=male,2=female) Affected(1=unaffected,2=affected,-9=exclude).

IndividualID and Affected columns are required others can be zeroed if not applicable.

For example: “1 1.txt 0 0 1 2”

If you have Cases.rawcnv and they are just unrelated cases, without any families (unrelated), or quantitative traits you can just run: awk '{print $5}' Cases.rawcnv | uniq | sed 's/^/0\t/' | sed 's/$/\t0\t0\t0\t2/' > Cases.fam

MAP

The SNP definition file is four columns:

Chromosome ProbeID PositionCentiMorgan PositionBasePair

similar to Plink map file (PositionCentiMorgan is fine just zeroed out). Sorted by position and chromosome Sorted.map created automatically which should be used with the resulting ped. Map could be an intersection or union of chip types if study is across platforms. Be sure that all SNPs used for CNV calling are provided.

For example: “1 rs1739822 0 16196820”

The full .map chr snp pos information should be available on the Illumina/Affymetrix website, baflrr "signal intensity files" PennCNV inputs, in Illumina GenomeStudio / Affymetrix Genotyping Console, or PennCNV .pfb file. You can use the linux awk command to rearrange the columns if needed.

Chr X should be X, not 23 as in some plink processed files.

For example if you have the PennCNV .pfb run: awk '{print $2"\t"$1"\t0\t"$3}' ChipX.pfb > ChipX.map

If you have a .vcf run: head -1 DATA.PCA_normalized.filtered.sample_zscores.RD.txt | sed 's/\s/\n/g' | grep -v Matrix | sed 's/:/\t/' | awk '{print $1"\t"$1"_"$2"\t0\t"$2}' > file.map

OR awk '{print $1" "$2}' file.vcf | sed 's/^chr//' | awk '{print $1"\t"$1"_"$2"\t0\t"$2}' | grep -v ^# > file.map

ParseCNV will automatically define probes missing in the map according to the .rawcnv definitions but this should only be utilized for a small number of probes to properly represent the actual data resolution.

Download Test Data 785 cases, 1110 controls, 561,308 probes

Try running per chr by “grep chr1: file.rawcnv” if runs out of memory indicated by percentage updating getting cutoff.

grep chr1: Cases.rawcnv > Cases.rawcnv_chr1

grep chr1: Controls.rawcnv > Controls.rawcnv_chr1

grep -P "^1\s" ChrSnp0Pos.map > ChrSnp0Pos.map_chr1 OR sed 's/^/chr/' ChrSnp0Pos.map | awk '{print $1":\t"$2"\t"$3"\t"$4}' | grep chr1: | sed 's/chr//' | sed 's/://' > ChrSnp0Pos.map_chr1

Cases.fam no edit needed

cat SSC_ParentsControlOnly_PCGC_chr1_Recluster_CNVR_ALL_ReviewedCNVRs.txt outputs when done

Optional Files and Features:

--out output file prefix.

--idToPath <file.txt> Additional SampleID to BafLrrFilePath file for Specific BAF LRR access of all associated samples for associated CNVRs. The input file should contain 2 columns: IDs as in column 5 of .rawcnv and full path to baf lrr file (signal intensity). In many cases, the 2 columns may be the same.

--probesBafLrr <file.txt> If Probe ID not first column of BAF LRR file, provide probe order reference file.

--includePed will write .ped file for usage in PLINK

--build <hg18> Specify the genome build used in CNV calling and .map file. Uses corresponding build files in GeneRef folder. See README.txt end for build specific download commands (example below).

--batch <file.txt> A subset of samples which form a discernible batch to monitor the contributions of such as a different chip type/version, processing lab, sample type/source, or reagent batch.

--mergePVar <1> Change the default of merging probes with p-values no more than one power of ten variation.

--mergeDist <1000000> Change the default of merging probes with proximal distance no more than 1000000 base pairs (1MB).

--tdt Specify a family based study with trios to allow TDT deletion and duplication p-value to drive CNVR boundary determination rather than case:control.

--maxPInclusion <0.05> The maximum p value of probe based statistics to include in CNVR determination and thus reported associations.

--includeAllRedFlags Include all red flag values in RedFlagReasons regardless of extreme values

File	Explanation
ArrayConcordance_InheritanceDenovo_AnnotateCNVCalls	PQN_Concord_Inh.pl: multiple CNV detection algorithm comparison and de novo, transmitted, and untransmitted CNV call based annotation
GeneRef	Genome definition files from UCSC Tables
PerlModules	Perl Modules and scripts used within the main folder scripts
1_A.baflrr	Example BafLrr SignalFile
2_A.baflrr	Example BafLrr SignalFile
3_A.baflrr	Example BafLrr SignalFile
4_A.baflrr	Example BafLrr SignalFile
5_A.baflrr	Example BafLrr SignalFile
AnnotateCNV.txt	Annotates many of the genomic feature red flags onto .rawcnv calls
AvgGCFromScanRegion.pl	Averages GC (probably not needed)
Batch.txt	Example Batch input file
Cases.fam	Example Fam File
Cases.rawcnv	Example Cases rawcnv file
ChrSnp0Pos.map	Example Map File
Controls.rawcnv	Example Controls rawcnv file
ConvertQuantiSNPToPennCNV.pl	Convert QuantiSNP format CNV Calls to PennCNV format
ConvertVCFToPennCNV_GenomeStrip.pl	Convert GenomeStrip VCF format CNV Calls to PennCNV format
ConvertVCFToPennCNV_XHMM.pl	Convert XHMM VCF format CNV Calls to PennCNV format
CountAvgScanRegion.pl	Counts DGV and SegDups and Averages GC as Part of AnnotateCNV.txt Commands
CountGenes.pl	Counts the number of values in a comma separated list such as genes (probably not needed)
extremevalues_2.2.tgz	Used by ParseCNV_QC R module to find extreme values (needs work, perhaps substitute median absolute deviation)
FilterCNV.pl	Handy utility to keep or remove a certain list of samples from a specified column, such as QC filtering the .rawcnv file
FindSnpsInRange.pl	Query a genomic region of interest in the ParseCNV .stats file
GetSnpsInSnpsRange.pl	Query Multiple genomic regions of interest in the ParseCNV .stats file
IDToPath.txt	Example rawcnv column 5 ID matched to BafLrr SignalFile path (in many cases will be the same)
InsertPlinkPvalues.pl	Use Plink or other software p-values to define ParseCNV CNVRs and RedFlag Annotations
MakeRedFlagPlots.pl	Derive continuous confidence score based on redFlagReasons column in ParseCNV --includeAllRedFlags
NullControl_ForQuantitativeTrait.rawcnv	Empty Controls rawcnv file if running quantitative trait where all subjects are in Cases.rawcnv
ParseCNV_Denovo.pl	Reformat files for de novo calling (probably not needed)
ParseCNV_GeneBased.pl	Gene based association approach counting any overlap of gene or gene exons together (collapsing association strategy)
ParseCNV_GUI_Tk.pl	Graphical user interface for ParseCNV.pl using perl Tk
ParseCNV_GUI_Wx.pl	Graphical user interface for ParseCNV.pl using perl Wx
ParseCNV.pl	THE MAIN SCRIPT.
ParseCNV_QC.pl	Sample Quality Control metrics plotting and sample remove report based on automatic or manual thresholds
PennCNV_MultSpaceToTab.pl	Used by ParseCNV_Denovo (probably not needed)
PercentSamples.pl	Showing contributing counts of batches in IDsCasesDel column.
Polishgc5BaseForScanRegion_KeepOnlyChrLines_5120Increments.pl	Make GC file format for scan_region.pl for non-human species in 5120 increments to be similar to the human definition
PolishRefGeneForScanRegion_KeepOnlyChrLines.pl	Make RefGene file format for scan_region.pl for non-human species keeping only "Chr Number" entries
probesBafLrr.txt	Example probesBafLrr input file in case BafLrr SignalFiles do not have SNP ID column but are just in a standard order
README.txt	Standard Readme file, most pertinent updated information will be on the website
TestConcordance.pl	compares pairs of CNV calls
WeightTEST.pheno	Example Quantitative Trait Phenotype File
wgetUcscPdfs.sh	Pulls UCSC Track high-quality images for review of significant CNVRs

Download hg19 definition files by pasting these commands (Included in standard download now):

cd GeneRef
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/knownGene.txt.gz
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/kgXref.txt.gz
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/bioCycPathway.txt.gz
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/keggPathway.txt.gz
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/keggMapDesc.txt.gz
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/cgapBiocPathway.txt.gz
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/cgapAlias.txt.gz
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/cgapBiocDesc.txt.gz
gunzip knownGene.txt.gz; mv knownGene.txt hg19_knownGene.txt
gunzip kgXref.txt.gz; mv kgXref.txt hg19_kgXref.txt
gunzip bioCycPathway.txt.gz; mv bioCycPathway.txt hg19_bioCycPathway.txt
gunzip keggPathway.txt.gz; mv keggPathway.txt hg19_keggPathway.txt
gunzip keggMapDesc.txt.gz; mv keggMapDesc.txt hg19_keggMapDesc.txt
gunzip cgapBiocPathway.txt.gz; mv cgapBiocPathway.txt hg19_cgapBiocPathway.txt
gunzip cgapAlias.txt.gz; mv cgapAlias.txt hg19_cgapAlias.txt
gunzip cgapBiocDesc.txt.gz; mv cgapBiocDesc.txt hg19_cgapBiocDesc.txt
perl MakeExonScanRegionDefFile.pl hg19_knownGene.txt
perl MakeExonScanRegionDefFile_wGene.pl hg19_knownGene.txt
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/cytoBand.txt.gz
gunzip cytoBand.txt.gz; mv cytoBand.txt hg19_cytoBand.txt
awk -F"\t" '{print $1$4"\t"$1"\t+\t"$2"\t"$3"\t \t \t \t \t \t \t "}' hg19_cytoBand.txt | sed 's/chr//' > hg19_cytoBand_SimFormat_AllCol.sorted
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/genomicSuperDups.txt.gz
gunzip genomicSuperDups.txt.gz
sort -k 2,2 -k 3,3n genomicSuperDups.txt > hg19_genomicSuperDups.sorted
awk '{print $27"\t"$2"\t"$7"\t"$3"\t"$4"\t \t \t \t \t \t \t "}' hg19_genomicSuperDups.sorted > hg19_genomicSuperDups_SimFormat_AllCol.sorted

rm genomicSuperDups.txt
rm hg19_genomicSuperDups.sorted
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/dgvMerged.txt.gz
gunzip dgvMerged.txt.gz; mv dgvMerged.txt hg19_dgv.txt
awk -F"\t" '{print $12"\t"$2"\t"$7"\t"$3"\t"$4"\t \t \t \t \t \t \t "}' hg19_dgv.txt> hg19_dgv_SimFormat_AllCol.sorted
rm hg19_dgv.txt
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/gc5Base.txt.gz
gunzip gc5Base.txt.gz
awk -F"\t" '{print $13/$12"\t"$2"\t+\t"$3"\t"$4"\t \t \t \t \t \t \t "}' gc5Base.txt> hg19_gc5Base_SimFormat_AllCol.sorted
rm gc5Base.txt

hg19_somaticRearrangement_SimFormat_AllCol.sorted now provided in download if error arises at that step.

Was: dgv.txt.gz, now:dgvMerged.txt.gz and dgvSupporting.txt.gz

Quantitative Trait Association

§ run ParseCNV with --includePed and --out <myPrefix> (Cases.rawcnv can have all samples and Controls.rawcnv can just be a blank file with no blank line at end NullControl_ForQuantitativeTrait.rawcnv)

§ run plink --ped CNVDEL.ped --map myMap_UseWithPed.map --make-bed --out CNVDEL_Bed and again with CNVDUP.ped (this will compress and allow .fam to be easily edited, just make sure you keep the sorted order of .fam the same)

§ run plink --bfile CNVDEL_Bed --pheno file.pheno --assoc --adjust --all-pheno --out CNVDEL_Bed_Qassoc --allow-no-sex and again with CNVDUP_Bed (see here and here)

§ perl InsertPlinkPvalues.pl --del <Plink deletion association result file .qassoc> --dup <Plink duplication association result file .qassoc> --outPrefix <ParseCNV out Prefix from first run with --includePed>

§ Note many SNPs do not have any CNV frequency with “NA” p-values or low/rare CNV frequency.

§ Be sure your plink input and output files are all in the ParseCNV folder. The .pheno file should be: FID IID QuantitativeTrait

§ InsertPlinkPvalues.pl will then generate the CNVR reports for quantitative trait association instead of ParseCNV.pl which generates CNVR reports for Case-Control and TDT.

§ Example:

§ Cases.rawcnv in this case contains all samples in your study since all subjects have a continuous trait like weight and are not easily separated into simple case and control cohorts.

§ perl ParseCNV.pl Cases.rawcnv NullControl_ForQuantitativeTrait.rawcnv Cases.fam ChrSnp0Pos.map --includePed --out TEST

§ plink --ped TESTCNVDEL.ped --map TESTChrSnp0PosSorted_UseWithPed.map --pheno WeightTEST.pheno --out DelWeightTEST --noweb --assoc

§ plink --ped TESTCNVDUP.ped --map TESTChrSnp0PosSorted_UseWithPed.map --pheno WeightTEST.pheno --out DupWeightTEST --noweb --assoc

§ perl InsertPlinkPvalues.pl --del DelWeightTEST.qassoc --dup DupWeightTEST.qassoc --outPrefix TEST --out TESTInsert

§ To correct for population variation, include “--covar myMDSResult.mds” or “--covar myPCAResult.pca.evec” to the plink command as detailed below. (Note –assoc and --tdt do not work with --covar so use --linear or --logistic instead.)

§ If you want to use other P-value generating tools such as GEMMA update the header to be “SNP”, “BETA” or “T” or “OR”, and “P”, just note InsertPlinkPvalues dependencies

§ REQUIRED DEPENDENCY:

§ TESTCNV_Verbose.stats

§ CAN MAKE FILES:

§ vim TESTParseCNV.log containing “Running Command: perl ParseCNV.pl Cases.rawcnv Controls.rawcnv Cases.fam ChrSnp0Pos.map --includePed --out TEST”

§ vim DelWeightTEST.log

§ vim DupWeightTEST.log

Population Stratification Correction

§ Use genotype .ped in Plink to create .mds result (--genome then --read-genome --cluster --mds-plot) or in Eigenstrat smartpca to create .pca.evec result.

§ run ParseCNV with --includePed

§ run Plink --ped CNVDEL.ped --map myMap_UseWithPed.map --covar myMDSResult.mds --logistic --adjust --allow-no-sex and again with CNVDUP.ped

§ egrep ‘SNP|ADD’ MDS.assoc.logistic > MDS.assocADDlogistic (important to name this way without extra “.” so Plink .log found, If you are using Affy “SNP_” IDs choose another word unique to the header)

§ InsertPlinkPvalues.pl --del <Plink deletion association result file> --dup <Plink duplication association result file> --outPrefix <ParseCNV out Prefix from first run with --includePed>

§ I recommend checking for CaseControl PCA overlap -> Majority Cluster (typically Caucasians) ensuring overlap -> Logistic -> then separately do African and Asian analysis since ethnicity false positives may occur for rare CNVs and direction of effect may differ

§ I support one quantitative trait specified in the .pheno. I do not currently support QuantitativeTrait in the .fam affected column or multiple quantitative traits in the .pheno.

§ You can also use LMM, which is agrued to be the best stratification correction method for rare variants such as CNVs

§ Gemma has this command to generate the relatedness/kinship matrix: ./gemma -bfile /work/1a/SCZ/chr1CNVDEL -gk 1 -o chr1CNVDELGemmaKinship

§ Then you run Gemma again to do the actual p-value calculations: ./gemma -bfile /work/1a/SCZ/chr1CNVDEL -k ./output/chr1CNVDELGemmaKinship.cXX.txt -lmm 4 -maf 0.000000005 -o chr1CNVDELGemma

Gene Based Association

Collapsing approach used to combine non-overlapping CNVs which all overlap the same gene.

Gene based association approach now available in ParseCNV_GeneBased.pl

perl ParseCNV_GeneBased.pl Cases.rawcnv Controls.rawcnv

sort by OR>1 for Case enriched significant Genes.

Burden

See Burden.txt output from ParseCNV.pl

We need to filter the CNV calls by PennCNV provided [numsnp, length, and conf], run PennCNV clean_cnv.pl --fraction 0.8 to merge fragmented calls, genomic features [seg dups, DGV, Telo/Centromere/GC/somatic rearrangements], array features[sparse coverage/low AB frequency in dups], cohort features[population frequency]. Finally do some spot confirmation by BAF/LRR review and PCR validation.

As always, check what chip versions make up the data and if the full probe set was used.

The total length burden doesn't take into account the number of CNVs or the number of genes overlapped by those CNVs.

Non-Human Species GeneRef

I have tested bosTau7 Cow and susScr3 Pig specifically to work. If you have a different species or build you can try replacing “bosTau7” in the commands below. Mouse, chimp, and fly should work. You may need to run the UCSC LiftOver web tool if definition files are not available in your build version.

Segmental Duplications annotations available through UWash Eichler website: http://eichlerlab.gs.washington.edu/database.html has: Mouse, chimp, rat, dog, fly, worm, monkey, fugu, chicken, cow, and stickleback.

Database of Genomic Variants (DGV) only has healthy human control CNVs.

dbVar is similar for more species ftp://ftp.ncbi.nlm.nih.gov/pub/dbVar/data/ has: Bos_taurus cow, Canis_lupus gray wolf, Danio_rerio Zebrafish, Drosophila_melanogaster fly, Equus_caballus Horse, Homo_sapiens Human, Macaca_mulatta Rhesus macaque, Mus_musculus Mouse, Pan_troglodytes chimpanzee, Sorghum_bicolor grass grain, and Sus_scrofa Pig.

Download bosTau7 Cow definition files by pasting these commands:

cd GeneRef

wget http://hgdownload.cse.ucsc.edu/goldenPath/bosTau7/database/refGene.txt.gz

wget http://hgdownload.cse.ucsc.edu/goldenPath/bosTau7/database/refLink.txt.gz

###wget http://hgdownload.cse.ucsc.edu/goldenPath/bosTau7/database/gc5BaseBw.txt.gz

wget http://hgdownload.cse.ucsc.edu/gbdb/bosTau7/bbi/gc5Base.bw

###Use hg19 Pathways files by querying TOUPPER of all genes

wget http://hgdownload.cse.ucsc.edu/goldenPath/bosTau7/database/cytoBandIdeo.txt.gz

wget http://cowparalogy.gs.washington.edu/data/cow4GenomicSuperDupgenomicSuperDup.tab ###NoHeader SeeHeader in http://paralogy.gs.washington.edu/build37/data/GRCh37GenomicSuperDup.tab

wget ftp://ftp.ncbi.nlm.nih.gov/pub/dbVar/data/Bos_taurus/by_assembly/Btau_4.6.1/gvf/Btau_4.6.1.remap.all.germline.ucsc.gvf.gz

gunzip refGene.txt.gz; mv refGene.txt bosTau7_refGene.txt

gunzip refLink.txt.gz; mv refLink.txt bosTau7_refLink.txt

###gunzip gc5BaseBw.txt.gz; mv gc5BaseBw.txt bosTau7_gc5BaseBw.txt

perl PolishRefGeneForScanRegion_KeepOnlyChrLines.pl bosTau7_refGene.txt ### to delete non “chr” lines

perl MakeExonScanRegionDefFile_refGene.pl bosTau7_refGene.txt_OnlyChr.txt
perl MakeExonScanRegionDefFile_wGene_refGene.pl bosTau7_refGene.txt_OnlyChr.txt

#cp PerlModules/scan_region.pl .

wget http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/bigWigToBedGraph

chmod 777 bigWigToBedGraph

### These steps will take considerable compute time

wget http://hgdownload.cse.ucsc.edu/gbdb/bosTau7/bbi/gc5Base.bw

mv gc5Base.bw bosTau7_gc5Base.bw

./bigWigToBedGraph bosTau7_gc5Base.bw bosTau7_gc5Base.bedGraph

perl Polishgc5BaseForScanRegion_KeepOnlyChrLines_5120Increments.pl bosTau7_gc5Base.bedGraph

### Illegal division by zero at Polishgc5BaseForScanRegion_KeepOnlyChrLines_5120Increments.pl line 30, <SAMPSHEET> line 358024431.# so no Y

awk -F"\t" '{print $4"\t"$1"\t+\t"$2"\t"$3"\t \t \t \t \t \t \t "}' bosTau7_gc5Base.bedGraph_OnlyChr_5120Intervals.txt > bosTau7_gc5Base_SimFormat_AllCol.sorted ### Now unique values, rand() was added before to $4 GC to allow duplicates in scan region since original 5 base pair interval values are only 20,40,60,80

###perl Polishgc5BaseForScanRegion_KeepOnlyChrLines.pl bosTau7_gc5Base_SimFormat_AllCol.sorted_randDec

rm bosTau7_gc5Base.bw; rm bosTau7_gc5Base.bedGraph; rm bosTau7_gc5Base.bedGraph_OnlyChr_5120Intervals.txt

gunzip cytoBandIdeo.txt.gz; mv cytoBandIdeo.txt bosTau7_cytoBandIdeo.txt

awk -F"\t" '{print $1$4"\t"$1"\t+\t"$2"\t"$3"\t \t \t \t \t \t \t "}' bosTau7_cytoBandIdeo.txt | sed 's/chr//' > bosTau7_cytoBand_SimFormat_AllCol.sorted

mv cow4GenomicSuperDupgenomicSuperDup.tab bosTau7_cow4GenomicSuperDupgenomicSuperDup.tab

sort -k 1,1 -k 2,2n bosTau7_cow4GenomicSuperDupgenomicSuperDup.tab > bosTau7_cow4GenomicSuperDupgenomicSuperDup.tab.sorted #Sort By Chr Pos
awk '{print $7”:”$8"\t"$1"\t"$6"\t"$2"\t"$3"\t \t \t \t \t \t \t "}' bosTau7_cow4GenomicSuperDupgenomicSuperDup.tab.sorted > bosTau7_genomicSuperDups_SimFormat_AllCol.sorted

gunzip Btau_4.6.1.remap.all.germline.ucsc.gvf.gz; mv Btau_4.6.1.remap.all.germline.ucsc.gvf bosTau7_Btau_4.6.1.remap.all.germline.ucsc.gvf

grep -v ^# bosTau7_Btau_4.6.1.remap.all.germline.ucsc.gvf | sed 's/ID=//' | sed 's/;Name/\t/' | sed 's/copy_number_/CN/’ | sed 's/variation/var/’ | awk '{print $9”:”$3"\t"$1"\t+\t"$4"\t"$5"\t \t \t \t \t \t \t "}' > bosTau7_dgv_SimFormat_AllCol.sorted

Download susScr3 Pig definition files by pasting these commands:

cd GeneRef

wget http://hgdownload.cse.ucsc.edu/goldenPath/susScr3/database/refGene.txt.gz

wget http://hgdownload.cse.ucsc.edu/goldenPath/susScr3/database/refLink.txt.gz

###wget http://hgdownload.cse.ucsc.edu/goldenPath/susScr3/database/gc5BaseBw.txt.gz### just has path below

wget http://hgdownload.cse.ucsc.edu/gbdb/susScr3/bbi/gc5Base.bw

###Use hg19 Pathways files by querying TOUPPER of all genes

wget http://hgdownload.cse.ucsc.edu/goldenPath/susScr3/database/cytoBandIdeo.txt.gz

wget http://www.nature.com/nature/journal/v491/n7424/extref/nature11622-s3.xls ### xls2csv and xls2txt could work but just include in downloads <susScr3_PigSegDups_GroenenMAetalNature2012SupTable16.txt>

wget ftp://ftp.ncbi.nlm.nih.gov/pub/dbVar/data/Sus_scrofa/by_assembly/Sscrofa3/gvf/Sscrofa3.submitted.all.germline.ucsc.gvf.gz ### < ftp://ftp.ncbi.nlm.nih.gov/pub/dbVar/data/Sus_scrofa/by_assembly/Sscrofa10.2/gvf/Sscrofa10.2.remap.all.germline.ucsc.gvf.gz >

gunzip refGene.txt.gz; mv refGene.txt susScr3_refGene.txt

gunzip refLink.txt.gz; mv refLink.txt susScr3_refLink.txt

###gunzip gc5BaseBw.txt.gz; mv gc5BaseBw.txt susScr3_gc5BaseBw.txt

perl PolishRefGeneForScanRegion_KeepOnlyChrLines.pl susScr3_refGene.txt ### to delete chromosome “GL892101-1”...”GL896661-1” and “JH118404-1”...”JH118996-1” (or add “chr” but input not accepted)

perl MakeExonScanRegionDefFile_refGene.pl susScr3_refGene.txt_OnlyChr.txt
perl MakeExonScanRegionDefFile_wGene_refGene.pl susScr3_refGene.txt_OnlyChr.txt

#cp PerlModules/scan_region.pl .

wget http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/bigWigToBedGraph

chmod 777 bigWigToBedGraph

### These steps will take considerable compute time

wget http://hgdownload.cse.ucsc.edu/gbdb/susScr3/bbi/gc5Base.bw

mv gc5Base.bw susScr3_gc5Base.bw

./bigWigToBedGraph susScr3_gc5Base.bw susScr3_gc5Base.bedGraph

perl Polishgc5BaseForScanRegion_KeepOnlyChrLines_5120Increments.pl susScr3_gc5Base.bedGraph

awk -F"\t" '{print $4"\t"$1"\t+\t"$2"\t"$3"\t \t \t \t \t \t \t "}' susScr3_gc5Base.bedGraph_OnlyChr_5120Intervals.txt > susScr3_gc5Base_SimFormat_AllCol.sorted ### Now unique values, rand() was added before to $4 GC to allow duplicates in scan region since original 5 base pair interval values are only 20,40,60,80

###perl Polishgc5BaseForScanRegion_KeepOnlyChrLines.pl susScr3_gc5Base_SimFormat_AllCol.sorted_randDec

rm susScr3_gc5Base.bw; rm susScr3_gc5Base.bedGraph; rm susScr3_gc5Base.bedGraph_OnlyChr_5120Intervals.txt

gunzip cytoBandIdeo.txt.gz; mv cytoBandIdeo.txt susScr3_cytoBandIdeo.txt

awk -F"\t" '{print $1$4"\t"$1"\t+\t"$2"\t"$3"\t \t \t \t \t \t \t "}' susScr3_cytoBandIdeo.txt | sed 's/chr//' | grep chr > susScr3_cytoBand_SimFormat_AllCol.sorted

grep -v ^# susScr3_PigSegDups_GroenenMAetalNature2012SupTable16.txt | sort -k 1,1 -k 2,2n > susScr3_PigSegDups_GroenenMAetalNature2012SupTable16.txt.sorted #Sort By Chr Pos
awk '{print $1”:”$2"\tchr"$1"\t+\t"$2"\t"$3"\t \t \t \t \t \t \t "}' susScr3_PigSegDups_GroenenMAetalNature2012SupTable16.txt.sorted > susScr3_genomicSuperDups_SimFormat_AllCol.sorted

gunzip Sscrofa3.submitted.all.germline.ucsc.gvf.gz; mv Sscrofa3.submitted.all.germline.ucsc.gvf susScr3_Sscrofa3.submitted.all.germline.ucsc.gvf

grep -v ^# susScr3_Sscrofa3.submitted.all.germline.ucsc.gvf | sed 's/ID=//' | sed 's/;Name/\t/' | sed 's/copy_number_/CN/’ | sed 's/variation/var/’ | sort -k 1,1 -k 4,4n | awk '{print $9”:”$3"\t"$1"\t+\t"$4"\t"$5"\t \t \t \t \t \t \t "}' > susScr3_dgv_SimFormat_AllCol.sorted

Compare CNV Calls

TestConcordance.pl compares pairs of CNV calls.

The pair can be PennCNV vs. Nexus of the same sample, Mother vs. Child, Tumor vs. Blood, Transplant Donor vs. Recipient, technical replicate 1 vs. technical replicate 2.

The input is calls in PennCNV format with confidence column and Algorithm column, sorted by ID and algorithm. You also need to provide a map file for all probes being considered.

Multiple CNV Detection Comparison and De Novo CNV Calls Annotation

ArrayConcordance_InheritanceDenovo_AnnotateCNVCalls Folder Scripts

COMMAND:

perl PQN_Concord_Inh.pl Cases_Denovo.rawcnv Cases_Denovo_Q.rawcnv Cases_Denovo_N.rawcnv Cases_Denovo.fam ChrSnp0Pos.map --cluster

You need a parallel compute cluster you can qsub to use --cluster option, otherwise run without this option to run in serial fashion.

INPUTS:

Make sure enter blank line at the end.

Cases_Denovo.rawcnv

chr1:1-3 numsnp=3 length=3 state1,cn=0 Mom.baflrr startsnp=rs1 endsnp=rs3 conf=10 PennCNV

chr1:4-7 numsnp=4 length=4 state5,cn=3 Dad.baflrr startsnp=rs4 endsnp=rs7 conf=10 PennCNV

chr1:1-3 numsnp=3 length=3 state1,cn=0 Pro.baflrr startsnp=rs1 endsnp=rs3 conf=10 PennCNV

chr1:4-7 numsnp=4 length=4 state5,cn=3 Pro.baflrr startsnp=rs4 endsnp=rs7 conf=10 PennCNV

chr1:8-10 numsnp=3 length=3 state5,cn=3 Pro.baflrr startsnp=rs8 endsnp=rs10 conf=10 PennCNV

Cases_Denovo_Q.rawcnv

chr1:1-3 numsnp=3 length=3 state1,cn=0 Mom.baflrr startsnp=rs1 endsnp=rs3 conf=10 QuantiSNP

chr1:4-7 numsnp=4 length=4 state5,cn=3 Dad.baflrr startsnp=rs4 endsnp=rs7 conf=10 QuantiSNP

chr1:1-3 numsnp=3 length=3 state1,cn=0 Pro.baflrr startsnp=rs1 endsnp=rs3 conf=10 QuantiSNP

chr1:4-7 numsnp=4 length=4 state5,cn=3 Pro.baflrr startsnp=rs4 endsnp=rs7 conf=10 QuantiSNP

chr1:8-9 numsnp=2 length=2 state5,cn=3 Pro.baflrr startsnp=rs8 endsnp=rs9 conf=10 QuantiSNP

Cases_Denovo_N.rawcnv

chr1:1-3 numsnp=3 length=3 state1,cn=0 Mom.baflrr startsnp=rs1 endsnp=rs3 conf=10 Nexus

chr1:4-7 numsnp=4 length=4 state5,cn=3 Dad.baflrr startsnp=rs4 endsnp=rs7 conf=10 Nexus

chr1:1-3 numsnp=3 length=3 state1,cn=0 Pro.baflrr startsnp=rs1 endsnp=rs3 conf=10 Nexus

chr1:4-7 numsnp=4 length=4 state5,cn=3 Pro.baflrr startsnp=rs4 endsnp=rs7 conf=10 Nexus

chr1:8-9 numsnp=2 length=2 state5,cn=3 Pro.baflrr startsnp=rs8 endsnp=rs9 conf=10 Nexus

Cases_Denovo.fam

FID IID FatID MotID Gend Aff

1 Mom.baflrr 0 0 0 1

1 Dad.baflrr 0 0 0 1

1 Pro.baflrr Dad.baflrr Mom.baflrr 0 2

ChrSnp0Pos.map

Chr Snp Cm Pos

1 rs1 0 1

1 rs2 0 2

1 rs3 0 3

1 rs4 0 4

1 rs5 0 5

1 rs6 0 6

1 rs7 0 7

1 rs8 0 8

1 rs9 0 9

1 rs10 0 10

OUTPUTS:

The program does not currently generate new overlapping segments consensus CNV calls from multiple algorithm comparison for further analysis. I have used PennCNV calls that had good overlap in QuantiSNP rather than generating an overlapping segments consensus call. Many CNV calling algorithms are conservative in the boundary calls, especially if you did not run the PennCNV clean_cnv.pl script to merge fragmented CNVs.

Make sure you look at either PvsQ or QvsP CallMatching and MatchSummary, looking at both will be confusing. For the QvsP output, the first 9 columns refer to the QuantiSNP calls. The next 6 columns refer to the overlap profile of the QuantiSNP call with the PennCNV call. The next 9 columns starting with RefCallMatchingCN_CNVBoundariesChrStartStop refer to the overlapping PennCNV call with the same CN contributing to the “hitsnp” column. The next 9 columns starting with RefCallDiffCN_CNVBoundariesChrStartStop refer to the overlapping PennCNV call with a different CN contributing to the “hitDiffCN” column.

The columns with numbers in the QvsP.txtCNVMatchSummary.txt file are the hitDiffCN mismatches. For example, 01 is the count of QuaniSNP calls that were CN=0 but PennCNV called as CN=1.

PennCNV_ConcordInh_NoROH.txt

We see full overlap of sample level rawcnv calls of the three algorithms PennCNV, QuanitSNP, and Nexus with reference to the PennCNV calls (indicated by “vsP”), except PennCNV chr1:8-10 where the other algorithms called chr1:8-9 but this still is a consistently detected CNV.

We see full inheritance reporting with inheritance from mother chr1:1-3, inheritance from father chr1:4-7, and de novo chr1:8-10 for the proband CNV calls. We see inheritance to proband chr1:1-3 for the mother CNV call. We see inheritance to proband chr1:4-7 for the father CNV call. In the case of a CNV overlapping but with a different CN state between family members, similar columns are reported with an additional column specifying the different CN state. Certainly CN 3 and 4 seems the most typical permissible different CN to allow as a match but others may indicate abnormal noise patterns.

CNV Boundaries Chr StartStop Hg19

numsnp

Length (bp)

ChipID

startsnp

endsnp

confidence

algorithm

50% PennCNV Overlap

Freq PennCNV Overlap

50% QuantiSNP Overlap

Freq QuantiSNP Overlap

50% Nexus Overlap

Freq Nexus Overlap

BlindedID

Inherit From Mother 50% Overlap

Freq Inherit From Mother Overlap

Inherit From Father 50% Overlap

Freq Inherit From Father Overlap

Inherit To Proband 50% Overlap

Freq Inherit To Proband Overlap

Inherit From Mother 50% Overlap Diff CN

Freq Inherit From Mother Overlap Diff CN

Inherit From Mother Diff CN

Inherit From Father 50% Overlap Diff CN

Freq Inherit From Father Overlap Diff CN

Inherit From Father Diff CN

Inherit To Proband 50% Overlap Diff CN

Freq Inherit To Proband Overlap Diff CN

Inherit To Proband Diff CN

MFP Trio State (D=Diploid, 2=LOH, N=NA)

Denovo Call

Gene

Distance

chr1:1-3

Pro.baflrr

rs1

rs3

PennCNV

Pro.baflrr

0D0

NotDenovo

DDX11L1,DDX11L9

11871

chr1:4-7

Pro.baflrr

rs4

rs7

PennCNV

Pro.baflrr

D33

NotDenovo

DDX11L1,DDX11L9

11867

chr1:8-10

Pro.baflrr

rs8

rs10

PennCNV

0.667

Pro.baflrr

DD3

Denovo

DDX11L1,DDX11L9

11864

chr1:1-3

Mom.baflrr

rs1

rs3

PennCNV

Mom.baflrr

NNN

NotDenovo

DDX11L1,DDX11L9

11871

chr1:4-7

Dad.baflrr

rs4

rs7

PennCNV

Dad.baflrr

NNN

NotDenovo

DDX11L1,DDX11L9

11867

changing Cases_Denovo.rawcnv

chr1:1-3 numsnp=3 length=3 state1,cn=0 Mom.baflrr startsnp=rs1 endsnp=rs3 conf=10 PennCNV

chr1:1-3 numsnp=3 length=3 state2,cn=1 Mom.baflrr startsnp=rs1 endsnp=rs3 conf=10 PennCNV

and reviewing the output PennCNV_ConcordInh_NoROH.txt

50%QuantiSNPOverlap and 50%NexusOverlap become N since the CN state is different.

InheritFromMother50%Overlap becomes N and InheritFromMother50%OverlapDiffCN becomes Y with InheritFromMotherDiffCN 1 for the proband CNV call.

InheritToProband50%Overlap becomes N and InheritToProband50%OverlapDiffCN becomes Y with InheritToProbandDiffCN 0 for the mother CNV call.

CNV Boundaries Chr StartStop Hg19

numsnp

Length (bp)

ChipID

startsnp

endsnp

confidence

algorithm

50% PennCNV Overlap

Freq PennCNV Overlap

50% QuantiSNP Overlap

Freq QuantiSNP Overlap

50% Nexus Overlap

Freq Nexus Overlap

BlindedID

Inherit From Mother 50% Overlap

Freq Inherit From Mother Overlap

Inherit From Father 50% Overlap

Freq Inherit From Father Overlap

Inherit To Proband 50% Overlap

Freq Inherit To Proband Overlap

Inherit From Mother 50% Overlap Diff CN

Freq Inherit From Mother Overlap Diff CN

Inherit From Mother Diff CN

Inherit From Father 50% Overlap Diff CN

Freq Inherit From Father Overlap Diff CN

Inherit From Father Diff CN

Inherit To Proband 50% Overlap Diff CN

Freq Inherit To Proband Overlap Diff CN

Inherit To Proband Diff CN

MFP Trio State (D=Diploid, 2=LOH, N=NA)

Denovo Call

Gene

Distance

chr1:1-3

Pro.baflrr

rs1

rs3

PennCNV

Pro.baflrr

1D0

NotDenovo

DDX11L1,DDX11L9

11871

chr1:4-7

Pro.baflrr

rs4

rs7

PennCNV

Pro.baflrr

D33

NotDenovo

DDX11L1,DDX11L9

11867

chr1:8-10

Pro.baflrr

rs8

rs10

PennCNV

0.667

Pro.baflrr

DD3

Denovo

DDX11L1,DDX11L9

11864

chr1:1-3

Mom.baflrr

rs1

rs3

PennCNV

Mom.baflrr

NNN

NotDenovo

DDX11L1,DDX11L9

11871

chr1:4-7

Dad.baflrr

rs4

rs7

PennCNV

Dad.baflrr

NNN

NotDenovo

DDX11L1,DDX11L9

11867

PennCNV_ConcordInh_NoROH_Summary.txt

Sample based counts

ChipID	BlindedID	Total Calls	PennCNV Overlap	QuantiSNP Overlap	Nexus Overlap	Three Overlap	Two Overlap	One Overlap	Inherit From Mother	Inherit From Father	Inherit To Proband	Denovo	Inherit From Mother And Father Same CN	Inherit From Mother And Father Diff CN	MFP=110	MFP=334	Three Overlap And Inh	Two Overlap And Inh	One Overlap And Inh	Three Overlap And Inh Under Fiftykb	Three Overlap And Inh Fifty To Two Hundred kb	Three Overlap And Inh Above Two Hundred kb	Two Overlap And Inh Under Fifty kb	Two Overlap And Inh Fifty To Two Hundred kb	Two Overlap And Inh Above Two Hundred kb	One Overlap And Inh Under Fifty kb	One Overlap And Inh Fifty To Two Hundred kb	One Overlap And Inh Above Two Hundred kb	Family Status
Pro.baflrr	Pro.baflrr	3	3	3	3	3	0	0	1	1	0	1	0	0	0	0	2	0	0	2	0	0	0	0	0	0	0	0	trio
Mom.baflrr	Mom.baflrr	1	1	1	1	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	trio
Dad.baflrr	Dad.baflrr	1	1	1	1	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	trio
		0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	duo
		0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	single

De novo Prioritization

Trio Recall PennCNV

At least 2 algorithms call de novo (P, Q, N) (do not exclude parent CNV called by 1 algorithm that is inherited to child CNV called by 2 algorithms as more de novos will result)

Parental Origin low p (if enough informative markers)

Greater than 5 SNPs

Low/absent untransmitted rates

Low/absent Control (DGV, SSC Healthy Trios, CHOP Control, Framingham)

BAF/LRR Review (full trio in case FN Parent)

ddPCR Validation

CNV Meta-analysis

For each study:

ParseCNV --includePed

Plink --covar .pca.evec

PennCNV scan_region SNP based .stats results cohort A→closest SNP results cohort B

METAL to generate meta-analysis p-value SNP based

InsertPlinkPValues Input is general tab delimited P, SNP, and one of BETA/T/OR header present.

You can run for each study to get study specific red flag confidence scores or run ParseCNV and Plink again on the combined data to use as input for combined study red flags

Convert CNV Calls Formats from Different Algorithms

PennCNV is the motivating input. The PennCNV input is very human readable, widely used and informative. CNV .vcf is widely used in sequencing applications, but not human readable. Therefore, the idea is to convert CNVs called by different algorithms into PennCNV format with a final column stating the algorithm for tracking. In most cases, the CNV calls file columns and coding can be edited relatively easily in Excel. The main exception is BirdSuite and GenomeStrip .vcf matrix formats. For these common yet very different formats, use ConvertVCFToPennCNV.pl. For the commonly used QuantiSNP conversion use ConvertQuantiSNPToPennCNV.pl.

PennCNV, QuantiSNP, Nexus, IlluminaGenomeStudio, TCGA, AgilentGenomicWorkbench/Cytogenomics, AffymetrixGenotypingConsole, BirdSuite, CGHscape, NimbleScan, R-GADA, Conifer, XHMM, Plink .cnv file cnvPartition, GenomeStrip .vcf DEL DUP INS INV CNV

Similar settings should be used between algorithms to ensure comparability such as using GC base content correction.

Nexus “Min Region” should be used not “Chromosome Region” since that is the average position between probes. AgilentGenomicWorkbench/Cytogenomics has the sample ID listed only once as a header of the CNV calls subsections.

ConvertQuantiSNPToPennCNV.pl

ConvertVCFToPennCNV_XHMM.pl

ConvertVCFToPennCNV_GenomeStrip.pl

Sample Quality Control:

Consider CallRate, LRR_SD, GCWF, CountCNV, PCA, and PI_HAT as the most important sample quality metrics. CallRate and LRR_SD are widely used and critical to evaluate. The problem is these metrics are derived from various sources, even if you use SNP arrays with PennCNV, only LRR_SD and GCWF are provided in the PennCNV .log. CountCNV is automatically figured out by the .rawcnv files. CallRate, PCA, and PI_HAT are based on genotyping A/T/C/G, not CNVs. For CallRate, you can provide Plink --missing .imiss, Illumina LIMs Project Detail Report .csv, or Illumina Genome Studio Samples Table. PCA population stratification components can be Eigenstrat smartPCA .pca.evec or Plink .mds. PI_HAT is from Plink --genome .genome file. PCA is not automatically used to create IDs to remove, which would be done for a study where almost all samples are tightly clustered and a few outliers are then removed. Typically the PCA shows admixture and various ethnicities and population stratification correction without sample removal based on PCA is the best approach.

For Illumina 550k data, key data quality metric thresholds we have observed are: call rate > 98%, SD LRR < 0.3, |GCWF| < 0.05, and count CNV< 100.

For Affymetrix 6.0 data, these measures include: call rate > 96%, SD LRR < 0.35, |GCWF| < 0.02, and count CNV < 80.

Inputs: (all are recommended but not required to run ParseCNV_QC.pl)

PennCNV .log

PennCNV .rawcnv

Plink --missing .imiss / Illumina LIMs Project Detail Report .csv / Illumina Genome Studio Samples Table / General format: SampleID CallRate

Eigenstrat smartPCA .pca.evec / Plink .mds

Plink --genome .genome

perl ParseCNV_QC.pl --log PennCNV_Omni25.log --rawcnv file.rawcnv --callrate PCGC_Omni25-8v1_BED_Miss.imiss --popstrat file.pca.evec --related PCGC_Omni25-8v1_BED_GenomeAll.genome

Outputs:

See QC_Plot.pdf sample QC distributions with outliers to remove in blue and corresponding QC_RemoveIDs.txt with ChipIDs failing the various QC metrics. You may delete some rows from this file if you want a more aggressive ChipID/Sample inclusion or if the threshold does not look correct on the plot. Remove thresholds for each quality metric are determined by the R package extremevalues function getOutliers. You can also use exclusions based on other PennCNV log metrics provided in QC_Plot_2.pdf and QC_RemoveIDs_LRRmean_BAFmean_BAFSD_BAFDRIFT_WF.txt.

Then run this code found in the GeneRef folder to create a "clean" .rawcnv file to use for ParseCNV association

perl RemoveSpecificBarcodes-Hash_Example_DefFile_ColRemKeep.pl QC_RemoveIDs.txt CNVCalls.rawcnv 5 remove

(FilterCNV.pl)

UPDATE: The R getOutliers is not very dynamic in selecting outliers based on a variety of possible data quality metric distributions. This typically results in too many samples being excluded. Again, look for linear mode for inclusion and exponential mode for exclusion. This can be easily done in R or Excel. Once an appropriate threshold is determined from reviewing the first run distributions or from a static know threshold for consistency, ParseCNV_QC.pl can be run again specifying thresholds to drive the plots and sample remove report. (hopefully for R13).

Run once with no thresholds specified and review automatically determined thresholds based on outliers in your dataset.

Then you may specify some adjusted threshold for a given quality metric based on your review of the plots.

For example add: --qccallrate 0.98 --qclrrsd 0.2 --qcgcwf 0.005 –qcnumcnv 100

popstrat is just for plotting, no exclusion is currently supported based on popstrat.

Example: running ParseCNV_QC again after reviewing plots. See 146 vs. 49 samples excluded based on automatic vs. manual quality metric thresholds (see bottom of plot “unique fail any QC”):

perl ParseCNV_QC_3_ProvideFilterThresholds.pl --log PennCNV_Omni1.log --rawcnv file.rawcnv --callrate PCGC_Omni25-8v1_BED_Miss.imiss --popstrat file.pca.evec

perl ParseCNV_QC_3_ProvideFilterThresholds.pl --log PennCNV_Omni1.log --rawcnv file.rawcnv --callrate PCGC_Omni25-8v1_BED_Miss.imiss --popstrat file.pca.evec --qcnumcnv 700 --qcgcwf 0.005 --qclrrsd 0.2 --qccallrate 0.98

Bimodal metrics indicate batches, typically of different chip versions

CNV Call Quality Control:

Removing CNV calls may introduce significant bias and there are a limited number of metrics to make an informed decision. Probably the best metric is the confidence score based on the cumulative probability from the HMM. ParseCNV annotates average numsnp, length, and confidence of each potential CNVR association which captures this quality/confidence issue with less bias than upfront CNV call quality control.

Step by Step Processing Description:

CNV calls are mapped into SNP based statistics which are then merged into CNVRs based on proximity (1Mb) and similar significance (power of ten P value) of neighboring SNPs.

The most significant subregion is presented in the case of multiple significant proximal CNVRs.

Deletion and Duplication p values are then calculated by pooling 0 and 1 copy for deletion and 3 and 4 for duplication.

The runtime log lists the processing steps:

scan_range2: Takes SNP based statistics and collapses into CNVRs based on distance 1Mb and p-value one power of ten in negative log.

scan_region: Takes CNVR and annotates nearest UCSC gene (more inclusive than RefSeq genes) and proximal distance until gene boundary (including introns, 0 is direct overlap). Definition files can be updated or different builds used by downloading from UCSC Tables.

scan_DescPathway: Takes CNVR with gene and gives full text description of gene name and pathway (many not_found in current version of definition file).

scan_rangeJustPos2: Takes SNP based statistics and gives SNP indexes based only on distance 1MB to limit redundancy of small regions with different significance levels.

CNVToPed: --includePed since large file. Del and Dup Ped files created for more statistics in Plink. In the Del Ped, state1cn=0 → 1 1 and state2cn=1 → 1 2 other → 2 2. In the Dup Ped, state5cn=3 → 1 2 state6cn=4 → 1 1 other → 2 2. This definition closely resembles the CNV state frequencies (1 1=rare, 1 2=common, 2 2=very common). Note many probes will be always diploid (no CNV calls) so NA will result in Plink. Sorted.map created automatically should be used with the resulting ped.

vlookup SNP to Region ID: Add column Region ID.

countBarcodeOCCURENCE_V2: Count Region ID occurrences.

vlookup Count Sig Regions: Add column Count Region ID.

Sort by p-value: Sort from Chr-Pos to low->high p-value.

Data-filter-advanced-unique records only: Include the first occurrence of each Region ID to filter out many significant regions close together, keeping the lowest p-value occurrence.

Create UCSC Custom Track For Review: UCSCGenomeBrowser-Select Assembly-add custom tracks-Browse-Submit

NUMLines: Adds Tab delimited Index Column to space delimited CNV calls (.rawcnv) files to access recorded contributing calls to each association for back referencing trackability ComponentDelCallsCases,ComponentDupCallsCases,ComponentDelCallsControls,ComponentDupCallsControls

PercentSamples-AverageNumsnpLenComponentCalls: Adds AverageNumsnps AverageLength CNVRange for cases and controls for screening purposes

SpecificBafLrrAccessMany: Assumes all BafLrr files have ProbeID as first column and have same probe sorted error, but can be different from map. If Probe ID not first column, provide probe order reference file --probesBafLrr <file.txt>. Needs additional SampleID to BafLrrFilePath file specified on command line by --idToPath <file.txt>. Highly recommended if BafLrr files available. Takes CNVRs and contributing sample IDs and creates files with BAF and LRR values for each CNVR with all samples and a master file with all CNVRs and signals in all samples with CNV contributing to association in BafLrr Folder. Used for review of specific association region across many samples.

Output:

Main output: CNVR_ALL_ReviewedCNVRs.txt Note results sorted by deletion p are followed by results sorted by duplication p (See CNVType column).

Copy and paste text into EXCEL to review output! Do not try viewing in shell terminal except perhaps *_brief.txt.

To view output files in OpenOfficeCalc (Linux vs Dos endlines could cause blank lines):

RightClick-OpenWith-OpenOffice.orgCalc-TextImport=Defaults

If blank lines: SelectRowsNotHeader-Data-Filter-StandardFilter-Value=Empty-RightClick-Delete Rows-Data-Filter-RemoveFilter

Output File Names	Description
CNVR_ALL_ReviewedCNVRs.txt	50 column significant case enriched CNVRs with Red Flag annotation for quality filtering
CNVR_ALL_ReviewedCNVRs_brief.txt	10 column significant case enriched CNVRs with Red Flag annotation for quality filtering
CNVR_ALL_ReviewedCNVRs_verbose.txt	100 column significant case enriched CNVRs with Red Flag annotation for quality filtering (need for TDT details)
CNVR_ALL_ReviewedCNVRs_ControlEnriched.txt	50 column significant control enriched CNVRs with Red Flag annotation for quality filtering
CNVR_ALL_ReviewedCNVRs_brief_ControlEnriched.txt	10 column significant control enriched CNVRs with Red Flag annotation for quality filtering
CNV_UCSC_bedfile_ForCustomTrackReview.txt	.rawcnv significant locus visualization of individual sample CNVs genome.ucsc.edu Be sure you select correct genome assembly→click manage custom tracks→Browse for UCSC bedfile→Paste CNVR coordinates in Genome Browser search box
CNV_UCSC_bedfile_ForCustomTrackReview_Probes.txt	Probe coverage significant locus visualization
CNV_ContributingCalls.txt	CNVRs contributing calls full boundaries as in the .rawcnv input
CNV_brief.stats	Probe based CNV association statistics file
Burden.txt	Cases and controls IDs, and TotalDelLengths, and TotalDupLengths
ParseCNV.log	ParseCNV log
ChrSnp0PosSorted_UseWithPed.map	Map Sorted to use with CNV ped

Deletion and Duplication significant CNVRs with counts in cases and control and p-value. Sample IDs, CN states, CNV calls contributing to each association and many other statistics and tracking data are provided (100 fields in total verbose output and 40 highly informative fields in brief output).

UCSC custom track bed file with CNV calls.

CNV_ContributingCalls.txt: CNV contributing calls for each CNVR

CNVDEL.ped CNVDUP.ped: .ped files for further statistics deletion and duplication are generated for use in Plink.

CNVR_ALL_ReviewedCNVRs: All significant CNVRs with CNVType, redFlagCount, and redFlagReasons.

Nomenclature:

CNVR: CNVRegion intersection region of overlapping CNVs with significant enrichment in cases vs. controls if overlap (Primary importance, refined CNV association region)

CNVRange: CNVRange union region of overlapping CNVs (Typical meaning of “CNVR” in literature)

People are often confused by the small range and value of the first 2 columns: CNVR and CountSNPs. This is the most significant overlapping region, not the entire span of contributing CNVs. CNVRange and AvgNumSnps columns reflect the entire span of contributing CNVs. CNVs at a particular locus do not have precisely the same boundaries, especially rare CNVs (see paper Figure 3).

temp/CNV_Verbose.stats full SNP based statistics for specific locus queries not among the significant loci or for querying replication based on TagSNP (i.e. GeneName → PositionRange → SNP IDs in PositionRange → egrep SNP IDs from temp/CNV_Verbose.stats → CNVFrequencyInGene). A cleaner way is: perl GeneRef/RemoveSpecificBarcodes-Hash_Example_DefFile_ColRemKeep.pl SNPIDsToReplicate.txt temp/CNV_Verbose.stats 1 keep

(FilterCNV.pl)

For querying replication based on genomic coordinate range use FindSnpsInRange.pl StatsFile Chr Start Stop (much better than grep cytoband since not specific enough).

AllRes.pdf: Image created with BAF and LRR of specific association regions across contributing cases with --idToPath option

If you get the error: “-bash: convert: command not found” run: “sudo apt-get install imagemagick”. This command combines the BAF LRR plots into a single image to scroll through CNVRs sorted by significance. Alternatively, the images can be flipped through in Powerpoint using Insert-PhotoAlbum or Open Office Impress Add On

Multiple Testing Correction

5x10^-4 is a conservative bar for CNV genome-wide significance surviving multiple testing correction based on analysis of Illumina and Affymetrix genome-wide SNP arrays. The typical bar of 5x10^-8 used in GWAS is not appropriate for CNV considering:

§ the number of probes with a nominal frequency of CNV occurrence (only probes with some CNV detected are informative)

§ the number of probes with enrichment in cases vs. controls and vice versa (evidence of more case enriched loci than control enriched loci)

§ probes with less than 1% population frequency of CNV (optionally)

§ the number of CNVRs (multiple probes are needed to detect a single CNV and should not count separately for multiple testing correction)

2x10^-5according to permutation studies.

Review Results:

UCSC Track Review for spurious association. (UCSCGenomeBrowser-Select Assembly-add custom tracks-Browse-Submit).

Recommended UCSC tracks:

Header:Mapping and Sequencing Tracks-Item: GC Percent (full) (exclude if low or high across window)

Header:Variation and Repeats-Item: DGV Struct Var (pack) and Item:Segmental Dups (pack) (exclude if 10+)

Header:Genes and Gene Prediction Tracks-Item: UCSC Genes (pack) (See redFlagCount and redFlagReasons columns in “CNVR_ALL_ReviewedCNVRs.txt” for automated scoring of relevant features originally visually scored).

Header:Phenotype and Literature-Item: ISCA, DECIPHER

BAF/LRR Review of specific association region across many samples using SpecificBafLrrAccess output AllRes.txt or CNVR specific file.

BAF/LRR Review of whole chromosome data of sample with associated CNV and SNP clusters using Illumina Genome Studio Genome Viewer or Affymetrix Genotyping Console Browser.

qPCR Wet Lab Review for confirmation of true positive and potentially true negatives.

These are done in order of increasing effort per locus but the number of loci will be filtered down by each step.

Red flag count <= 3 may be considered high confidence results. More important red flags to fail a CNVR as low confidence include: AvgProbes<5, DgvEntries>10, PenMaxP>0.5 and high frequency, SegDups>10, TeloCentro, Recurrent, FreqInflated , AvgConf<10, ABFreqLow.

Standard Filter for high significant and confident results for SpecificBafLrr Visual: Sort RF <=3, delP<5x10^-4 and ORDup>1 OR dupP<5x10^-4 and ORDup>1, (on exon)

RedFlag	Default Report Threshold	Explanation
SegDups (count, max, avg)	>10, >0.98 max Fraction Matching	Many segmental duplications (i.e., nearly identical DNA segments), representing genomic segments that are difficult to uniquely hybridize probes to, which could underlie false positive CNV detection. Segmental Duplications inform CNV breakpoints if flanking (include) and noisy regions if overlapping (exclude).
DgvEntries	>10	Overlapping multiple Database of Genomic Variants (DGV) entries, representing CNV signals observed in “healthy” individuals, suggesting that a potential association result in the study at hand may be false.
TeloCentro	any overlap	Residing at centromere and telomere proximal regions as they often have sparse probe coverage and only have a single flanking diploid reference to base CNV calls.
AvgGC	31>GC>60	Harboring high or low GC content regions that bias probe hybridization kinetics even after GC model correction is done by CNV calling algorithms, producing false CNV calling and biasing the result.
AvgProbes	<10	CNVs captured with low average number of probes, contributing to association with low confidence. If an association depends on a preponderance of small CNVs, the likelihood of false positive is high.
Recurrent	any overlap	Locus frequently found in multiple studies such as TCR, Ig, HLA, and OR genes. TCRs undergo somatic rearrangement due to VDJ recombination causing inter-individual differences in the clonality of T-cell populations and thus are not true CNVs, necessitating exclusion.
PopFreq	>0.01	CNV regions with high population frequency (for rare CNV focused studies) indicate that probe clustering is likely biased due to a high percentage of samples with CNV used in clustering definition thus biasing CNV detection.
PenMaxP_Freq_HighFreq	PenMaxP >0.5, Freq >0.5, HighFreq >0.05	CNV peninsula of common CNV (sparse probe coverage and nearby high frequency CNV) indicates that within the range of contributing CNV boundaries there is a non-significant (p>0.5) p-value which is notably different from the CNVR association typically due to random extension of common CNVs to neighboring sparse or noisy probes. PenMaxP is the worst p-value in the span of CNV calls contributing to the significant CNVR. Freq is the frequency of this PenMaxP worst p-value. HighFreq is the frequency any non-nominally significant p-value (P>0.05).
FreqInflated	>0.5 sids at this locus have >(maxInflatedSampleCount-2) occurrences in all significant results	The same inflated sample driving multiple CNV association signals. Certain samples have many noisy CNV calls arising in rare regions despite upfront sample quality filtering.
Sparse	>50kb	A large gap in probe coverage exists within the CNV calls indicating uncertainty in the continuity of a single CNV event, typically due to dense clusters of copy number (intensity only) probes with large intervening gaps.
ABFreq	<1% values (0.1,0.4) or (0.6,0.9)	For duplications, AB banding of BAF at 0.33 and 0.66 for CN=3 or 0.25 and 0.75 for CN=4 are very important observations given the relatively modest gain in intensity observed in duplications.
AvgConf	<10	The HMM confidence score in PennCNV is a superior indication of CNV call confidence compared to numsnps and length in studies comparing de novo vs. inherited CNV calls, giving an indication of the strength of the CNV signal or aggregate difference in probability between the called CN and the next highest probability CN. Other CNV calling algorithms give different range confidence scores or lower values might mean more confidence (i.e. call p value) so threshold may need modification. It is recommended to be in .rawcnv file as column 8 i.e. “conf=20.659” but not required.
AvgLength	<10kb	A classical confidence scoring parameter is the length of the CNV. If the CNV is too small, it is submicroscopic and even if many probes are tightly clustered, bias of local DNA regions and probe overlap make confidence difficult.

If you did the case:control (default), you want to look at “DelTwoTail” and “DupTwoTail”.

If you did the --tdt, you want to look at “TDTDel” and “TDTDup” for TDT P-value and “NormParDel” and “NormParDup” for the count de novo.

ParseCNV Published Studies:

Glessner JT, et al. (2009) Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature. 459, 569-73. Cited by 626

Glessner JT, et al. (2010) Strong synaptic transmission impact by copy number variations in schizophrenia. Proc Natl Acad Sci U S A.107, 10584-9. Cited by 80

Glessner JT, et al. (2010) A genome-wide study reveals copy number variants exclusive to childhood obesity cases. Am J Hum Genet. 87, 661-6. Cited by 43

Glessner JT, et al. (2010) Duplication of the SLIT3 locus on 5q35.1 predisposes to major depressive disorder. PLoS One. 5, e15463. ‎Cited by 20

Orange JS, Glessner JT, et al. (2011) Genome-wide association identifies diverse causes of common variable immunodeficiency. J Allergy Clin Immunol. 127, 1360-1367. Cited by 43

Elia J, Glessner JT, et al. (2011) Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder. Nat Genet. 44, 78-84. ‎Cited by 54

Glessner JT, et al. (2013) Copy number variations in alternative splicing gene networks impact lifespan. PLoS One. 8(1):e53846. Cited by 1

Xu L, Hou Y, Bickhart DM, Song J, Liu GE (2013) Comparative Analysis of CNV Calling Algorithms: Literature Survey and a Case Study Using Bovine High-Density SNP Data. Microarrays. 2(3), 171-185.

Fereshteh Jahaniani, Varsha Rao, Stephanie Nevins, Damek Spacek, Neal Bharadwaj, Jason Reuter, Michael Snyder (2013) Emerging Technologies to Study Long Non-coding RNAs Molecular Biology of Long Non-coding RNAs, pp 163-195

Fowler, Katie E., et al. "Genome wide analysis reveals single nucleotide polymorphisms associated with fatness and putative novel copy number variants in three pig breeds." BMC Genomics 14.1 (2013): 784.

JJ Connolly, JT Glessner, B Almoguera, DR Crosslin. Copy Number Variation Analysis in the Context of Electronic Medical Records & Large-Scale Frontiers in Genetics 2014

Araujo AN1, Moraes L, França MI, Hakonarson H, Li J, Pellegrino R, Maciel RM, Cerutti JM. Genome-Wide Copy Number Analysis in a Family with p.G533C RET Mutation and Medullary Thyroid Carcinoma Identified Regions Potentially Associated with a Higher Predisposition to Lymph Node Metastasis. J Clin Endocrinol Metab. 2014 Mar 6:jc20132993.

Hoffmann TJ, Shaw GM, Stevenson DK, Wang H, Quaintance CC, Oehlert J, Jelliffe-Pawlowski LL, Gould JB, Witte JS, O'Brodovich HM. 2014. Copy number variation in bronchopulmonary dysplasia. Am J Med Genet Part A 9999:1–4.

Sequencing

CNV Association Workflow

Significant CNVR Output Fields Description:

Column	Description
CNVR	CNV Region of greatest significance and overlap coordinates
CountSNPs	The number of probes available in the CNVR for this dataset In this case, contributing individual CNV calls may be larger
SNP	Tag SNP for ease and clarity of reporting and replication
DelTwoTailed	Two Tailed Fisher's Exact P-value based on the contingency table Cases Del/Cases Diploid/Controls Del/Controls Diploid as listed separately
DupTwoTailed	Two Tailed Fisher's Exact P-value based on the contingency table Cases Dup/Cases Diploid/Controls Dup/Controls Diploid as listed separately
ORDel	The Odds Ratio for deletion.
ORDup	The Odds Ratio for duplication.
Cases Del	The number of cases with a deletion detected in this region by PennCNV
Cases Diploid	The number of cases without a deletion or duplication detected in this region by PennCNV
Control Del	The number of controls with a deletion detected in this region by PennCNV
Control Diploid	The number of controls without a deletion or duplication detected in this region by PennCNV
Cases Dup	The number of cases with a duplication detected in this region by PennCNV
Cases Diploid	The number of cases without a deletion or duplication detected in this region by PennCNV
Control Dup	The number of controls with a duplication detected in this region by PennCNV
Control Diploid	The number of controls without a deletion or duplication detected in this region by PennCNV
IDsCasesDel	The sample IDs of cases corresponding to the Cases Del column for clinical data lookup. To convert to list in Excel: Data-TextToColumns-Delimited-Space then Copy-PasteSpecial-Transpose
IDsCasesDup	The sample IDs of cases corresponding to the Cases Dup column for clinical data lookup. To convert to list in Excel: Data-TextToColumns-Delimited-Space then Copy-PasteSpecial-Transpose
StatesCasesDel	CN states listed corresponding to IDsCasesDel (1(CN=0)/2(CN=1))
StatesCasesDup	CN states listed corresponding to IDsCasesDup (5(CN=3)/6(CN=4))
TotalStatesCases(1)	The number of cases in Cases Del with a homozygous deletion or both copies lost
TotalStatesCases(2)	The number of cases in Cases Del with a hemizygous deletion or one copy lost
TotalStatesCases(5)	The number of cases in Cases Dup with a hemizygous duplication or one copy gained
TotalStatesCases(6)	The number of cases in Cases Dup with a homozygous duplication or two copies gained
IDsDelControl	The sample IDs of controls corresponding to the Control Del column for clinical data lookup.
IDsDupControl	The sample IDs of controls corresponding to the Control Dup column for clinical data lookup.
StatesDelControl	CN states listed corresponding to IDsDelControl (1(CN=0)/2(CN=1))
StatesDupControl	CN states listed corresponding to IDsDupControl (5(CN=3)/6(CN=4))
TotalStates(1)	The number of Controls in Controls Del with a homozygous deletion or both copies lost
TotalStates(2)	The number of Controls in Controls Del with a hemizygous deletion or one copy lost
TotalStates(5)	The number of Controls in Controls Dup with a hemizygous duplication or one copy gained
TotalStates(6)	The number of Controls in Controls Dup with a homozygous duplication or two copies gained
ALLTwoTailed	All CNV states considered together p
ORALL	All CNV states considered together OR
ZeroTwoTailed	Only CN=0 CNV state considered together p
ORZero	Only CN=0 CNV state considered together OR
OneTwoTailed	Only CN=1 CNV state considered together p
OROne	Only CN=1 CNV state considered together OR
ThreeTwoTailed	Only CN=3 CNV state considered together p
ORThree	Only CN=3 CNV state considered together OR
FourTwoTailed	Only CN=4 CNV state considered together p
ORFour	Only CN=4 CNV state considered together OR
Gene	The closest proximal gene based on UCSC Genes which includes both RefSeq Genes and Hypothetical Gene transcripts
Distance	The distance from the CNVR to the closest proximal gene annotated. If the value is 0, the CNVR resides directly on the gene.
Description	The gene description delimited by "/" for multiple gene transcripts or multiple genes listed
Pathway	Annotated pathway membership of Gene with reference compiled from Gene Ontology database, BioCarta database and the KEGG database (definition files in GeneRef folder)
AverageNumsnpsCaseDel	The average numsnp of CNV calls contributing to Case Del CNVR. Allows for much more informative CNV size (confidence) filtering post-hoc.
AverageLengthCaseDel	The average length of CNV calls contributing to Case Del CNVR. Allows for much more informative CNV size (confidence) filtering post-hoc.
CNVRangeCaseDel	Alternative larger CNV Range Case Del definition compared to minimal common overlap definition of CNVR
AverageNumsnpsControlDel	The average numsnp of CNV calls contributing to Control Del CNVR. Allows for much more informative CNV size (confidence) filtering post-hoc.
AverageLengthControlDel	The average length of CNV calls contributing to Control Del CNVR. Allows for much more informative CNV size (confidence) filtering post-hoc.
CNVRangeControlDel	Alternative larger CNV Range Control Del definition compared to minimal common overlap definition of CNVR
CNVType	Deletion or duplication CNVR Significant in combined report
Cytoband	Cytoband genomic landmark designations
redFlagCount	Count red flag from association review of 9 (see text, briefly: SegDups, DGV, Centro/Telo, GC, ProbeCount, PopFreq, Peninsula, Inflated)
redFlagReasons	The failing metrics for association review and their values

Reformat CNV Calls from Different Algorithms/Sources into Standard PennCNV .rawcnv Format:

CNV Calling Algorithms Formats:

PennCNV

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 PennCNV

QuantiSNP

Sample Name Chromosome Start Position (bp) End Position (bp) Start Probe ID End Probe ID Length (bp) No. Probes Copy Number Max. Log BF Log BF: State 0 Log BF: State 1 Log BF: State 2 Log BF: State 3 Log BF: State 4 Log BF: State 5 Log BF: State 6

7500809055_R02C01 1 248740572 248795110 kgp8531759 kgp6234332 54539 19 0 137.858 137.858 -9.05444 0 1.18944 -7.74257 -9.26705 -44.1485

Nexus

Sample Chromosome Region Event Length Cytoband % of CNV Overlap Probe Median % Heterozygous Probes Min Size Min Region Max Size Max Region Locus IDs Call Pvalue

4363511659_R02C01 chr3:191,219,380-191,224,532 Homozygous Copy Loss 5153 q28 84.14208075 -4.471399784 40 5

GenomeStudio

SampleID BookmarkType Chr Start End Size Author CreatedDate Value Comment

K069 [735] CNV Bin: Min 0 To Max 0.5 1 17548878 17549783 905 2011-07-13 ì˜¤í›„ 2:15:11 0 CNV Confidence: 177.519

TCGA

barcode chromosome start stop num.mark seg.mean

TCGA-13-0891-01A-01D-0403-02 Y 22008576 22014412 -11.7734

GenomicWorkbench (also header with colons and footer with equals)

AberrationNo Chr Cytoband Start Stop #Probes Amplification Deletion pval Gene Names

AS_001

1620 chr5 q23.3 127363880 127364126 3 0 -5.862602 0

GenotypingConsole

Sample Copy Number State Loss/Gain Chr Cytoband_Start_Pos Cytoband_End_Pos Size(kb) #Markers Avg_DistBetweenMarkers(kb) %CNV_Overlap Start_Linear_Pos End_Linear_Position Start_Marker End_Marker CNV_Annotation

Genomewide6.0_120.CN5.CNCHP 0 Loss 4 q13.2 q13.2 112 61 2 100 69371991 69484097 CN_1109547 CN_1111668 Variation_1082 // chr4:69428222-69459530 // Mendelian inconsistencies //

BirdSuite

cnv_region_id 10893_INNED_g_Plate_No._6_GenomeWideSNP_6_E07_52176.CEL 11109_PERDU_g_Plate_No._30_GenomeWideSNP_6_G04_51146.CEL 11213_ARDOR_g_Plate_No._31_GenomeWideSNP_6_G10_51844.CEL 11888_JOYED_g_Plate_No._7_GenomeWideSNP_6_C10_46602.CEL 12233_CRAVE_g_Plate_No._25_GenomeWideSNP_6_F12_50312.CEL 12643_STAYS_g_Plate_No._1_GenomeWideSNP_6_H02_54228.CEL 12719_CRAVE_g_Plate_No._25_GenomeWideSNP_6_G02_50154.CEL 12864_SCHWA_g_Plate_No._3_GenomeWideSNP_6_F09_54144.CEL 13534_ARDOR_g_Plate_No._31_GenomeWideSNP_6_F12_51874.CEL #chrom chromStart chromEnd region

CNP10 2 2 2 2 2 2 2 2 2 chr1 755964 799636 CNP3

CGHscape

CNV No Gain/Loss Chr Size Start ID Start Pos End ID End Pos Length Score

1 Loss chr18 21011675 RP11-178F10 20300971 RP11-463D17 41312645 21 1.03776

Conifer

sampleID chromosome start stop state

1-00079_BEST.sorted.bam.rpkm chr1 104205278 104238261 dup

XHMM

SAMPLE CNV INTERVAL KB CHR MID_BP TARGETS NUM_TARG Q_EXACT Q_SOME Q_NON_DIPLOID Q_START Q_STOP MEAN_RD MEAN_ORIG_RD

1-00079 DEL chr3:100566432-100570801 4.37 chr3 100568616 39697..39701 5 24 36 36 17 20 -3.41 254.73

XHMM VCF

#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 1-00070

#chr1 1386051 chr1:1386051-1431197 <DIP> <DEL>,<DUP> . . AC=11,1;AF=0.04,0.00;AN=279;END=1431197;IMPRECISE;SVLEN=45147;SVTYPE=CNV;TPOS=1386051;TEND=1431197;NUMT=27;GQT=4 GT:NDQ:DQ:EQ:SQ:NQ:LQ:RQ:PL:RD:ORD:DSCVR 0:0:69:0,0:0,0:95,69:0,0:0,0:0,255,255:0.65:62.72:N

GenomeStrip VCF

#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT HG00096 HG00100 HG00103 HG00106 HG00108

1 738636 MERGED_DEL_2_47 A <DEL> . . CIEND=-17,33;CIPOS=-18,19;END=742073;SOURCE=GenomeStrip_738636_742073;SVTYPE=DEL GT:FT:GL:GQ 0/0:LowQual:-0.11,-0.65,-17.33:5.35 0/1:LowQual:-0.38,-0.24,-5.92:1.38 0/0:LowQual:-0.05,-0.94,-22.07:8.92 0/1:LowQual:-0.63,-0.12,-3.49:5.09 0/0:PASS:-0.00,-2.32,-36.68:23.15

Plink .cnv

FID IID CHR BP1 BP2 TYPE SCORE SITES

1 AU000103 10 46410734 47138713 4 5 24

Reformatted to PennCNV .rawcnv Example:

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 PennCNV

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 QuantiSNP

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 Nexus

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 GenomeStudio

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 TCGA

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 GenomicWorkbench

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 GenotypingConsole

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 BirdSuite

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 CGHscape

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 Conifer

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 XHMM

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 XHMM VCF

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 GenomeStrip VCF

chr1:248740572-248795110 numsnp=20 length=54,539 state1,cn=0 7500809055_R02C01 startsnp=kgp8531759 endsnp=kgp6234332 conf=82.372 Plink .cnv

Joe’s Development Thoughts:

If you really would like to see a specific feature or have an idea how to optimize please email me.

De novo Note: On 2012-09-12 I introduced TDT p-value driven CNVR definition to use alternatively to Case-Control. The third popular route of study is de novo but there is no de novo p-value, simply a count which is rarely real upon review at 2 or more cases on one locus, making de novo or large CNV Burden and Gene Set Enrichment Analysis by DAVID, DAPPLE, or Ingenuity more popular and fruitful lines of investigation. However, I feel these are not robust alternatives. Currently, see the --tdt CNVR result columns “NormParDel” and “NormParDup” for the count de novo (Now DenovoDel and DenovoDup). A true de novo p-value would be minor allele deletion/duplication (de novo + transmitted + cases incomplete trio) vs. (untransmitted + controls).

Do you prefer probabilistic CNV genotyping as in CNVTools rather than discrete as in PennCNV? Maybe you could run PennCNV -validate to get likelihoods and I could output a SNPTEST .geno file and InsertSnpTestPvalues to mark up red flags. perl detect_cnv.pl -validate -hmm example.hmm -pfb example.pfb -log ex5.log -out ex5.rawcnv -candlist ccnvr -list inputlist -valilog ex.valilog

ArrayConcordance_InheritanceDenovo_AnnotateCNVCalls/PQN_Concord_Inh.pl:

Generate new overlapping segments consensus CNV calls from multiple algorithm comparison for further analysis.

SKAT test statistic to increase sensitivity http://cran.r-project.org/web/packages/SKAT/

PathwayBased/GeneBased to increase sensitivity of multi-genic non-overlapping signals with RedFlags and ped output for specificity and stratification correction

Continuous RedFlag to increase specificity robustly correlating to validation and true association? Pass/Fail annotation based on RedFlags?

ParseCNV --includeAllRedFlags

plot R histogram: using MakeRedFlagPlots.pl CNVR_ALL_ReviewedCNVRs_brief.txt

plot R curve: using lines(density(a$a))

integrate observed value at significant CNVR in proper direction of red flag: dens2 <- density(a$SegDups, from=0, to=a$SegDups[i]) with(dens2, sum(y * diff(x)[1]))

average integration likelihoods: Not very well correlated with validation success by simple average (same weights).

Correlate/Weight with validation success: GLM weights assigned and correlation of 0.8 with validation success achieved with reasonable cutoff for GLMWeightedConfidence of 0.2. ROC curve looks solid and the AUC score is 0.983 using ROCR.

Combined UCSC, BAF LRR, SNP Clusters and PCA with CNV samples highlighted for passing RedFlag Confidence and Multiple Testing Correction Significance

Integrate TADA: Transmission And De novo Association Test

http://www.geneimprint.com/site/genes-by-species CNVs create problems especially for imprinted loci which only express the mother or father copy.

Mosaicism calls from R-GADA or BAFSegmentation.

Replace arrays with pointers, binary, or indexed memory/files for less RAM and disk space (ped vs. bed) footprint. Less RAM would allow genome wide runs using any array resolution and cohort size without by chromosome splitting. Advice on that would be much appreciated.

Wx Perl or Tk Perl Needed:

perl -MCPAN -e “install Wx” I had some trouble installing Wx on a machine but it is more portable and uses native GUI elements of the system when made into .exe.

perl -MCPAN -e "install Tk" Tk is more established and has more documentation than Wx.

Updated CNVRuler Supplementary Table 1. Comparison of the CNV-association analysis Tools

	CONAN	BirdSuite	Plink	CNVineta	CNVassoc	CNVTools	R-Gada	CNVRuler	HD-CNV	ParseCNV
Input Platform	Affymetrix	Affymetrix	ALL	Illumina Affymetrix	ALL	ALL	ALL	ALL	ALL	ALL
CNV Call Data	PennCNV	Array data	PED1)	APT1)	CGHcall	Text file1)	BeadStudio1)	Nexus	CSV	Nexus
	QuantiSNP		BirdSuite2)	QuantiSNP1)	Plink		Genotyping Console1)	PennCNV		PennCNV
	Genotyping Console				Text file1)		Text file1)	Genomic Workbench		Genomic Workbench
	Text file1)							TCGA		TCGA
	MS Exel1)							NimbleScan		NimbleScan
								APT		APT
								Genotyping Console		Genotyping Console
								Genome Studio		Genome Studio
								Text file		Text file
OS	ALL	Linux	ALL	ALL	ALL	ALL	ALL	ALL	ALL	ALL
Frequent CNV Region3)	CNVR	N/A	N/A	Fragment	CGHregions	N/A	N/A	CNVR	CNVR	CNVR
Frequent CNV Region3)								RO		RO
								Fragment		Fragment
GUI	Yes	No	Yes4)	No	No	No	No	Yes	Yes	Yes
Required	Oracle (Optional)	Matlab	No	R	R	R	R	R	Java Swing	R
	Annotation File	R							JGraphT
		Annotation File
Statistical Methods	Linear regression	Regression	CA Trend Test	Logistic regression	Logistic regression	Maximum likelihood	Logistic regression	Fisher’s exact test	Interval Graph	Fisher’s exact test
		(SNP ref)	Fisher’s exact test		Linear regression	EM	Likelihood Ratio Test	Chi-Square	Bron Kerbosch Clique Finder Algorithm	Chi-Square
			Stratified Test					Logistic regression	Gephi	CA Trend Test
			Multi-locus Test					Linear regression		Stratified Test
			Likelihood Ratio Test							Multi-locus Test
			Logistic regression							Likelihood Ratio Test
			Linear regression							Logistic regression
										Linear regression Confidence Score
Disadvantage / Limitation	Support Platform	Support Platform	Data conversion	Support Platform	Data conversion	Data conversion	User Interface	Graphical Report	P-value	Graphical Report
	Single Statistical Method	Large data handling	Region definition	Single Statistical Method	User Interface	Region definition			Confidence Scoring
		Region definition		User Interface		User Interface
		User Interface				No Covariates
						Limited data model