# 1 Introduction

Many tools for germline copy number variant (CNV) detection from NGS data have been developed. Usually, these tools were designed for different input data like WGS, WES or panel data, and their performance may depend on the CNV size. Available benchmarks show that all these tools obtain false positives, sometimes reaching a very high number of them.

With the aim of reducing the number of false positives, CNVfilteR identifies those germline CNVs that can be discarded. This task is performed by using the germline single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines. As VCF field interpretation is key when working with these files, CNVfilteR specifically supports VCFs produced by VarScan2, Strelka/Strelka2, freeBayes, HaplotypeCaller (GATK), and UnifiedGenotyper (GATK). Additionally, results can be plotted using the functions provided by the R/Bioconductor packages karyoploteR and CopyNumberPlots.

# 2 Installation

CNVfilteR is a Bioconductor package and to install it we have to use BiocManager.

  if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CNVfilteR")

We can also install the package from github to get the latest devel version, but beware that it might be incompatible with the release version of Bioconductor.

  BiocManager::install("jpuntomarcos/CNVfilteR")

# 3 Quick Start

Below we show a full example that covers the usual steps: CNVs data loading, variants loading, identifying false postives and plotting the results.

First, we can load some CNV tool results:

library(CNVfilteR)

cnvs.file <- system.file("extdata", "DECoN.CNVcalls.csv", package = "CNVfilteR", mustWork = TRUE)
cnvs.gr <- loadCNVcalls(cnvs.file = cnvs.file, chr.column = "Chromosome", start.column = "Start", end.column = "End", cnv.column = "CNV.type", sample.column = "Sample", genome = "hg19")

Then, we load the SNVs stored in a couple of VCF files.

vcf.files <- c(system.file("extdata", "variants.sample1.vcf.gz", package = "CNVfilteR", mustWork = TRUE),
system.file("extdata", "variants.sample2.vcf.gz", package = "CNVfilteR", mustWork = TRUE))
vcfs <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr)
## Scanning file /tmp/RtmpqbMEKO/Rinst32983005b074/CNVfilteR/extdata/variants.sample1.vcf.gz...
## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.
## Scanning file /tmp/RtmpuwuY98/variants.sample2.vcf.gz...
## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.

We observe that the function recognized VarScan2 as the source, so fields were selected and allele frequency consequently. Now we can call filterCNVs() to identify those CNVs that can be discarded.

results <- filterCNVs(cnvs.gr, vcfs)
names(results)
## [1] "cnvs"               "variantsForEachCNV" "filterParameters"

And we can check those CNVs that can be filtered out:

results$cnvs[results$cnvs$filter == TRUE] ## GRanges object with 5 ranges and 9 metadata columns: ## seqnames ranges strand | cnv sample filter ## <Rle> <IRanges> <Rle> | <character> <character> <character> ## 3 chr2 48025751-48028294 * | duplication sample1 TRUE ## 16 chr17 41243453-41247939 * | duplication sample1 TRUE ## 17 chr17 41251793-41256973 * | duplication sample1 TRUE ## 19 chr13 32900637-32929425 * | deletion sample2 TRUE ## 20 chr17 59870959-59938900 * | deletion sample2 TRUE ## n.total.variants n.hm.variants n.ht.discard.CNV n.ht.confirm.CNV ## <character> <character> <character> <character> ## 3 5 0 3 2 ## 16 2 0 2 0 ## 17 1 0 1 0 ## 19 10 4 6 ## 20 1 0 1 ## score cnv.id ## <character> <character> ## 3 2.539241834223 3 ## 16 1.99927691434002 16 ## 17 0.994339688718521 17 ## 19 19 ## 20 20 ## ------- ## seqinfo: 24 sequences from hg19 genome As an example, we can observe that the CNV with cnv.id=3 has 4 variants matching it: 2 in favor of discarding it, two against discarding it. If we want to know more about the variants matching a certain CNV we can do: results$variantsForEachCNV[["3"]]
##   seqnames    start      end width strand ref alt ref.support alt.support
## 1     chr2 48026019 48026019     1      *   G   C         516         521
## 2     chr2 48027019 48027019     1      *   G   C        1528         964
## 3     chr2 48027182 48027182     1      *   G   A        1506         971
## 4     chr2 48027434 48027434     1      *   A   T        1593        1462
## 5     chr2 48027763 48027763     1      *   G   A         900         863
##   alt.freq total.depth indel type      score
## 1  50.2411        1037 FALSE   ht  0.9999596
## 2  38.6838        2492 FALSE   ht -0.3169985
## 3  39.2006        2477 FALSE   ht -0.1417051
## 4  47.8560        3055 FALSE   ht  0.9981889
## 5  48.9507        1763 FALSE   ht  0.9997969

Two variants are close to the default expected heterozygous frequency, 0.5, so they obtain a positive score. The other two variants are not so clearly close to the default expected duplication value, 0.33, so they obtain a low negative score. All these default values and others can be modified in the filterCNVs() function.

Finally, we may be interested in plotting the results. For example, we can plot easily the duplication CNV with cnv.id=3 and all the variants matching it.

plotVariantsForCNV(results, "3")

We can do the same to plot the deletion CNV with cnv.id=19, where all variants discard the CNV except one homozygous variant that does not give us any information for supporting or discarding the CNV:

plotVariantsForCNV(results, "19")

On the opposite side, we can observe those CNVs that cannot be discarded:

results$cnvs[results$cnvs$filter != TRUE] ## GRanges object with 15 ranges and 9 metadata columns: ## seqnames ranges strand | cnv sample filter ## <Rle> <IRanges> <Rle> | <character> <character> <character> ## 1 chr2 47641409-47641557 * | duplication sample1 ## 2 chr2 47698105-47698201 * | duplication sample1 ## 4 chr3 10091059-10091189 * | duplication sample1 ## 5 chr3 14219967-14220068 * | duplication sample1 ## 6 chr3 37042447-37042544 * | duplication sample1 ## .. ... ... ... . ... ... ... ## 12 chr13 32906410-32907524 * | duplication sample1 ## 13 chr13 32945094-32945237 * | duplication sample1 ## 14 chr13 32953455-32969070 * | duplication sample1 ## 15 chr17 41209070-41215390 * | duplication sample1 ## 18 chr17 41267744-41276113 * | duplication sample1 ## n.total.variants n.hm.variants n.ht.discard.CNV n.ht.confirm.CNV ## <character> <character> <character> <character> ## 1 0 0 ## 2 0 0 ## 4 0 0 ## 5 0 0 ## 6 1 0 1 0 ## .. ... ... ... ... ## 12 0 0 ## 13 0 0 ## 14 1 0 0 1 ## 15 0 0 ## 18 0 0 ## score cnv.id ## <character> <character> ## 1 1 ## 2 2 ## 4 4 ## 5 5 ## 6 0.372384685846936 6 ## .. ... ... ## 12 12 ## 13 13 ## 14 -0.996334798387489 14 ## 15 15 ## 18 18 ## ------- ## seqinfo: 24 sequences from hg19 genome For example, the CNV with cnv.id=14 has one variant matching it. If we get the variant info, we see that the variant has an allele frequency very close to the default expected duplication value, 0.66. results$variantsForEachCNV[["14"]]
##   seqnames    start      end width strand ref alt ref.support alt.support
## 1    chr13 32968607 32968607     1      *   A   G          14          25
##   alt.freq total.depth indel type      score
## 1  64.1026          39 FALSE   ht -0.9963348

So, no evidence was found for discarding the CNV. We can also plot the CNV and the variant:

plotVariantsForCNV(results, "3")

CNVfilteR functions expect germline CNVs calls to be a GRanges object with a few specificic metadata columns:

• cnv for storing the CNV type: duplication or deletion.
• sample for storing the sample name of the CNV.

You can create this object yourself, but CNVfilter provides the proper function to do this, loadCNVcalls(). This function can interpret any tsv o csv file by indicating which columns store the information. For example, in the following code, the chr.column column is stored in the “Chromosome” column of the cnvs.file.

cnvs.file <- system.file("extdata", "DECoN.CNVcalls.csv", package = "CNVfilteR", mustWork = TRUE)
cnvs.gr <- loadCNVcalls(cnvs.file = cnvs.file, chr.column = "Chromosome", start.column = "Start", end.column = "End", cnv.column = "CNV.type", sample.column = "Sample", genome = "hg19")

loadCNVcalls() can interpret different types of CNVs. Among other options, separator can be selected using the sep parameter (defaults to \t), and first lines can be skipped using the skip parameter (defaults to 0). Also, the value used in cnv.column to store the CNV type can be modified using the deletion and duplication parameters (defaults to “deletion” and “duplication”, respectively). If, for example, our cnv.column uses “CN1” and “CN3” for deletion and duplication (respectively), we should indicate the function to use these values:

cnvs.gr.2 <- loadCNVcalls(cnvs.file = cnvs.file.2, deletion = "CN1", duplication = "CN3", chr.column = "Chromosome", start.column = "Start", end.column = "End", cnv.column = "CNV.type", sample.column = "Sample")

Some CNV tools generate results where the CNV location is stored in a single column with the format chr:start-end (i.e. 1:538001-540000). In this case, we can call loadCNVcalls() using the coord.column instead of the chr.column, start.column and end.column columns.

Common NGS pipelines produce germline variant calling (SNVs or INDELs) in a VCF file. However, VCF interpretation is challenging due to the flexibility provided by the VCF format definition. It is not straightforward to interpret correctly the fields in the VCF file and compute the allele frequency. loadVCFs() interprets automatically VCFs produced by VarScan2, Strelka/Strelka2, freeBayes, HaplotypeCaller (GATK), and UnifiedGenotyper (GATK).

In the following example the function recognizes VarScan2 as the source.

vcf.files <- c(system.file("extdata", "variants.sample1.vcf.gz", package = "CNVfilteR", mustWork = TRUE),
system.file("extdata", "variants.sample2.vcf.gz", package = "CNVfilteR", mustWork = TRUE))
vcfs <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr)
## Scanning file /tmp/RtmpqbMEKO/Rinst32983005b074/CNVfilteR/extdata/variants.sample1.vcf.gz...
## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.
## Scanning file /tmp/RtmpuwuY98/variants.sample2.vcf.gz...
## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.

We can also load the VCF file spicifying how to interpret it, which can be useful if the VCF was generated by a caller not supported by CNVfilteR. For example we can specify the ref/alt fields:

vcfs <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr, vcf.source = "MyCaller", ref.support.field = "RD", alt.support.field = "AD")
## Scanning file /tmp/RtmpqbMEKO/Rinst32983005b074/CNVfilteR/extdata/variants.sample1.vcf.gz...
## VCF source MyCaller is not supported, but ref.support.field/alt.support.field were provided.
## Scanning file /tmp/RtmpuwuY98/variants.sample2.vcf.gz...
## VCF source MyCaller is not supported, but ref.support.field/alt.support.field were provided.

Alternatively, we can set the list.support.field parameter so that field will be loaded assuming that it is a list in this order: reference allele, alternative allele. As an example:

vcf.file3 <- c(system.file("extdata", "variants.sample3.vcf", package = "CNVfilteR", mustWork = TRUE))
vcfs3 <- loadVCFs(vcf.file3, cnvs.gr = cnvs.gr, vcf.source = "MyCaller", list.support.field = "AD")
## Scanning file /tmp/RtmpuwuY98/variants.sample3.vcf.gz...
## VCF source MyCaller is not supported, but list.support.field was provided.

## 5.1 VCF free of artifacts

As VCF will be used for determining if a CNV can be filtered out, it is recommended to use a VCF file free of artifacts to improve the accuracy when identifying those CNVs. loadVCFs will load only variants with a minimum total depth greater than min.total.depth (defaults to 30). Additionally, we can exclude those regions that have already known artifacts with the parameter regions.to.exclude. In this example, we exclude PMS2, PRSS1, and FANCD2 genes because they are pseudogenes with alignments artifacts:

regions.to.exclude <- GRanges(seqnames = c("chr3","chr7", "chr7"), ranges = IRanges(c(10068098, 6012870, 142457319), c(10143614, 6048756, 142460923)))
vcfs4 <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr, regions.to.exclude = regions.to.exclude)
## Scanning file /tmp/RtmpqbMEKO/Rinst32983005b074/CNVfilteR/extdata/variants.sample1.vcf.gz...
## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.
## Scanning file /tmp/RtmpuwuY98/variants.sample2.vcf.gz...
## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.

Also, the parameter exclude.indels indicates whether to exclude INDELs when loading the variants. TRUE is the default and recommended value given that INDELs allele frequency varies differently than SNVs. Including INDELs may allow the algorithm to identify more CNVs to discard with a greater risk of identifying them wrongly.

## 5.2 Other settings

The function loadVCFs() also adapts to different needs. If sample.names parameter is not provided, the sample names included in the VCF itself will be used. Both single-sample and multi-sample VCFs are accepted, but when multi-sample VCFs are used, sample.names parameter must be NULL.

If VCF is not compressed with bgzip, the function compresses it and generates the .gz file. If .tbi file does not exist for a given VCF file, the function also generates it. All files are generated in a temporary folder.

See loadVCFs() documentation to see other parameters info.

# 6 Identifying false positives

The task of identifying false positives is performed by the filterCNVs() function. It checks all the variants (SNVs and optionally INDELs) matching each CNV present in cnvs.gr to identify those CNVs that can be filtered. It returns an S3 object with 3 elements: cnvs, variantsForEachCNV and filterParameters:

results <- filterCNVs(cnvs.gr, vcfs)
tail(resultscnvs) ## GRanges object with 6 ranges and 9 metadata columns: ## seqnames ranges strand | cnv sample filter ## <Rle> <IRanges> <Rle> | <character> <character> <character> ## 15 chr17 41209070-41215390 * | duplication sample1 ## 16 chr17 41243453-41247939 * | duplication sample1 TRUE ## 17 chr17 41251793-41256973 * | duplication sample1 TRUE ## 18 chr17 41267744-41276113 * | duplication sample1 ## 19 chr13 32900637-32929425 * | deletion sample2 TRUE ## 20 chr17 59870959-59938900 * | deletion sample2 TRUE ## n.total.variants n.hm.variants n.ht.discard.CNV n.ht.confirm.CNV ## <character> <character> <character> <character> ## 15 0 0 ## 16 2 0 2 0 ## 17 1 0 1 0 ## 18 0 0 ## 19 10 4 6 ## 20 1 0 1 ## score cnv.id ## <character> <character> ## 15 15 ## 16 1.99927691434002 16 ## 17 0.994339688718521 17 ## 18 18 ## 19 19 ## 20 20 ## ------- ## seqinfo: 24 sequences from hg19 genome Observe that those CNVs that can be filtered have the value TRUE in the column filter. CNVfilteR employs two different strategies for identifying those CNVs: • A deletion CNV can be filtered if there is at least ht.deletions.threshold% of heterozygous variants matching the CNV. Default ht.deletions.threshold value is 15, so 15% is required. • A duplication CNV can be filtered if the total score is >= dup.threshold.score after computing all heterozygous variants matching that CNV. Default dup.threshold.score value is 0.5. How the score is computed for each variant is explained in the next section. ## 6.1 Scoring model for duplication CNVs The scoring model for determining whether a certain duplication CNV can be discarded is based on the allele frequency for each heterozygous variant matching that CNV: • In common conditions with no presence of a duplication CNV, the allele frequency of a heterozygous variant is expected to be close to 50% (expected.ht.mean). So, a variant with an allele frequency close to 50% gives us evidence of the non-existence of a duplication CNV, so the CNV could be discarded. • On the opposite side, if the variant occurs in the same region of a certain duplication CNV, the allele frequency is expected to be close to 33.3% (expected.dup.ht.mean1) when the variant is not in the same allele than the duplication CNV, and 66.6% (expected.dup.ht.mean2) when the variant is in the same allele than the duplication CNV call. So, a variant with an allele frequency close to 33.3% or 66.6% gives us evidence of the existence of duplication CNV. Although we can expect that most of the variants are close to the expected means (33.3%, 50%, and 66.6%), many of them can be far from any expected mean. The scoring model implemented in the filterCNVs() function measures the evidence - for discarding a certain CNV - using a scoring model. The scoring model is based on the fuzzy logic, where elements can have any value between 1 (True) and 0 (False). Following this idea, each variant will be scored with a value between 0 and 1 depending on how close is the allele frequency to the nearest expected mean. • For those variants with an allele frequency close to the expected mean when no duplication CNV occurs (defaults 50%), the score will be positive in the interval [0, 1]. • For those variants with an allele frequency close to the expected mean when a duplication CNV occurs (defaults 33.3%, 66.6%), the score will be negative in the interval [-1, 0]. The total score is computed among all the variants matching the CNV. If the score is greater than the dup.threshold.score, the CNV can be discarded. A common way of applying the fuzzy logic is using the sigmoid function. CNVfilteR uses the sigmoid function implemented in the pracma package, which is defined as \begin{aligned} y = 1/(1 + e^{-c1(x−c2)}) \end{aligned} The scoring model is built on 6 sigmoids defined on 6 different intervals. The c1 parameter is 2 by default (sigmoid.c1), and the c2 parameter is defined for the 6 sigmoids (sigmoid.c2.vector). • First sigmoid: interval [20, expected.dup.ht.mean1], c2=28 • Second sigmoid: interval [expected.dup.ht.mean1, sigmoid.int1], c2=38.3 • Third sigmoid: interval [sigmoid.int1, expected.ht.mean], c2=44.7 • Fourth sigmoid: interval [expected.ht.mean, sigmoid.int2], c2=55.3 • Fifth sigmoid: interval [sigmoid.int2, expected.dup.ht.mean2], c=61.3 • Sixth sigmoid: interval [expected.dup.ht.mean2, 80], c2=71.3 Where sigmoid.int1 is the mean between expected.dup.ht.mean1 and expected.ht.mean, and sigmoid.int2 is the mean between expected.dup.ht.mean2 and expected.ht.mean. The scoring model can be plotted using the plotScoringModel() function. p <- resultsfilterParameters
plotScoringModel(expected.ht.mean = p$expected.ht.mean, expected.dup.ht.mean1 = p$expected.dup.ht.mean1,
expected.dup.ht.mean2 = p$expected.dup.ht.mean2, sigmoid.c1 = p$sigmoid.c1,
sigmoid.c2.vector = p$sigmoid.c2.vector) And the scoring model can be modified when calling the filterCNVs() function. Let’s see how the model changes when we modify the parameter sigmoid.c1 (1 instead of 2): plotScoringModel(expected.ht.mean = p$expected.ht.mean,
expected.dup.ht.mean1 = p$expected.dup.ht.mean1, expected.dup.ht.mean2 = p$expected.dup.ht.mean2,
sigmoid.c1 = 1,
sigmoid.c2.vector = p\$sigmoid.c2.vector)`