1 Introduction

The software package ZygosityPredictor allows to predict how many copies of a gene are affected by mutations, in particular small variants (single nucleotide variants, SNVs, and small insertions and deletions, Indels). In addition to the basic calculations of the affected copy number of a variant, ZygosityPredictor can phase multiple variants in a gene and ultimately make a prediction if and how many wild-type copies of the gene are left. This information proves to be of particular use in the context of translational medicine. For example, in cancer genomes, ZygosityPredictor can address whether unmutated copies of tumor-suppressor genes are present. ZygosityPredictor was developed to handle somatic and germline small variants. In addition to the small variant context, it can assess larger deletions, which may cause losses of exons or whole genes.

2 Important

To use ZygosityPredictor, the input data has to meet some requirements. The most important one is that all input variants, SNVs, Indels and sCNAs must originate from a fully clonal tumor. If indications are present that a sample consists of several sub clones (e.g. in tumor samples), the results must be seen with caution as the implementation is founded on the assumption of clonality. The second important requirement applies mainly to the second part of the tool and will thereofre be explained in section “Predict Zygosity”

3 Installation

The following code can be used to install ZygosityPredictor. The installation needs to be done once.

if (!require("BiocManager", quietly = TRUE))


4 load example data

To demonstrate the use of ZygosityPredictor, NGS data from the Seq-C2 project were used [1]. In the following chunk, all required datalayer of the WGS_LL_T_1 sample are loaded. The variants are loaded as GRanges objects, one for somatic copy number alterations (GR_SCNA), one for germline- and one for somatic small variants (GR_GERM_SMALL_VARS and GR_SOM_SMALL_VARS). The input formats will be discussed in more detail in section 4.

## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##     filter, lag
## The following objects are masked from 'package:base':
##     intersect, setdiff, setequal, union
## Loading required package: stats4
## Loading required package: BiocGenerics
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:dplyr':
##     combine, intersect, setdiff, union
## The following objects are masked from 'package:stats':
##     IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##     anyDuplicated, aperm, append,, basename, cbind,
##     colnames, dirname,, duplicated, eval, evalq, Filter, Find,
##     get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
##     match, mget, order, paste, pmax,, pmin,,
##     Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
##     tapply, union, unique, unsplit, which.max, which.min
## Loading required package: S4Vectors
## Attaching package: 'S4Vectors'
## The following objects are masked from 'package:dplyr':
##     first, rename
## The following object is masked from 'package:utils':
##     findMatches
## The following objects are masked from 'package:base':
##     expand.grid, I, unname
## Loading required package: IRanges
## Attaching package: 'IRanges'
## The following objects are masked from 'package:dplyr':
##     collapse, desc, slice
## Loading required package: GenomeInfoDb
# file to sequence alignment 
FILE_BAM <- system.file("extdata", "ZP_example.bam", 
                        package = "ZygosityPredictor")
VCF <- system.file("extdata", "ZP_example_chr7.vcf.gz", 
                        package = "ZygosityPredictor")
# meta information of the sample
PURITY <- 0.98
PLOIDY <- 1.57
SEX <- "female"
# variants
# used gene model

5 Calculation of affected copies of a variant

Two functions are provided to calculate how many copies are affected by single small variants, based on two formulas, one for germline variants and one for somatic variants.

5.1 Germline variants

To calculate the affected copies for a germline variant by using aff_germ_copies(), the following inputs are required:

  • af: numeric; between 0 and 1; calculated allele frequency of the variant in the tumor sample
  • tcn: numeric; total copy number at the position of the variant
  • purity: numeric; between 0 and 1; purity or tumor cell content of the tumor sample
  • c_normal: numeric; expected copy number at the position of the variant in normal tissue, 1 for gonosomes in male samples, and 2 for male autosomes and all chromosomes in female samples. (The function can also assess the c_normal parameter by itself, but then the following two inputs must be provided: chr and sex)
  • chr: (only if c_normal is not provided) character; can be either a single number or in the “chr1” format; chromosome of the variant
  • sex: (only if c_normal is not provided) character; either “male” or “female” / “m” or “f”; sex of the sample
  • af_normal: (default 0.5) numeric; allele-frequency of germline variant in normal tissue. 0.5 represents heterozygous variants in diploid genome, 1 would be homozygous. Could be relevant if germline CNVs are present at the position. Then also the c_normal parameter would have to be adjusted.

the output is a numeric value that indicates the affected copies.

## as an example we take the first variant of our prepared input data and 
## extract the required information from different input data layer
## the allele frequency and the chromosome can be taken from the GRanges object

AF = elementMetadata(GR_GERM_SMALL_VARS[1])[["af"]]
CHR = seqnames(GR_GERM_SMALL_VARS[1]) %>%

## the total copy number (tcn) can be extracted from the CNV object by selecting
## the CNV from the position of the variant

TCN = elementMetadata(
  subsetByOverlaps(GR_SCNA, GR_GERM_SMALL_VARS[1])

## purity and sex can be taken from the global variables of the sample
## with this function call the affected copies are calculated for the variant
## [1] 1.50733

5.2 Somatic variants

To calculate how many copies are affected by a somatic variant by aff_som_copies(), the same inputs are required, but a different formula is evaluated:

## the function for somatic variants works the same way as the germline function

AF = elementMetadata(GR_SOM_SMALL_VARS[1])[["af"]]
CHR = seqnames(GR_SOM_SMALL_VARS[1]) %>%
TCN = elementMetadata(
  subsetByOverlaps(GR_SCNA, GR_SOM_SMALL_VARS[1])

## [1] 1.471406

5.3 Calculate affected copies of a set of variants

In order to apply the previously mentioned functions to a whole set of variants and calculate the affected copies, the following code can be used.

## as an example we calculate the affected copies for the somatic variants:
  ## cnv information for every variant is required.. merge with cnv object
  IRanges::mergeByOverlaps(GR_SCNA) %>% 
  as_tibble() %>%
  ## select relevant columns
  select(chr=1, pos=2, gene, af, tcn) %>%
  mutate_at(.vars=c("tcn", "af"), .funs=as.numeric) %>%
  rowwise() %>%
    aff_copies = aff_som_copies(chr, af, tcn, PURITY, SEX),
    wt_copies = tcn-aff_copies
## # A tibble: 10 × 7
## # Rowwise: 
##    chr         pos gene        af   tcn aff_copies wt_copies
##    <fct>     <int> <chr>    <dbl> <dbl>      <dbl>     <dbl>
##  1 chr6   29100982 OR2J1   0.358   4.06     1.47       2.59 
##  2 chr6   29101487 OR2J1   0.328   4.06     1.35       2.72 
##  3 chr6   29101522 OR2J1   0.328   4.06     1.35       2.72 
##  4 chr7  107767595 SLC26A3 0.0263  2.49     0.0665     2.42 
##  5 chr7  107774037 SLC26A3 0.0476  2.49     0.120      2.37 
##  6 chr16  48216115 ABCC11  0.352   3.07     1.10       1.97 
##  7 chr16  48231866 ABCC11  0.347   3.07     1.08       1.99 
##  8 chrX   88753422 CPXCR1  0.5     1.12     0.579      0.539
##  9 chrX   88753806 CPXCR1  0.491   1.12     0.569      0.549
## 10 chr17  41771694 JUP     0.857   1.06     0.947      0.117

There s also the function predict_per_variant that basically does the same with slightly adjusted inputs.

## Warning in FUN(X[[i]], ...): column gt_cna contains annotations of balanced
## segments. They are removed from imbalance phasing if enabled
## Warning in FUN(X[[i]], ...): column gt_cna contains annotations of balanced
## segments. They are removed from imbalance phasing if enabled
## Warning in FUN(X[[i]], ...): column gt_cna contains annotations of balanced
## segments. They are removed from imbalance phasing if enabled
## Warning in check_gr_small_vars(germSmallVars, "germline", ZP_env): Input
## germSmallVars empty/does not contain variants. Assuming there are no germline
## small variants
## Warning in check_opt_incdel(includeIncompleteDel, ploidy): Large scale
## deletions cannot be included without ploidyPlease provide input ploidy. Provide
## ploidy=2to assume diploid case
## Warning in check_opt_incdel(includeHomoDel, ploidy): Large scale deletions
## cannot be included without ploidyPlease provide input ploidy. Provide
## ploidy=2to assume diploid case
## $evaluation_per_variant
## # A tibble: 10 × 19
##    gene    mut_id    pos ref   alt       af   tcn cna_type all_imb gt_cna seg_id
##    <chr>   <chr>   <int> <chr> <chr>  <dbl> <dbl> <chr>    <lgl>   <chr>   <int>
##  1 OR2J1   m1     2.91e7 C     A     0.358   4.06 HZ       FALSE   <NA>        5
##  2 OR2J1   m2     2.91e7 T     C     0.328   4.06 HZ       FALSE   <NA>        5
##  3 OR2J1   m3     2.91e7 C     T     0.328   4.06 HZ       FALSE   <NA>        5
##  4 SLC26A3 m1     1.08e8 C     T     0.0263  2.49 LOH      FALSE   2:sub       7
##  5 SLC26A3 m2     1.08e8 G     C     0.0476  2.49 LOH      FALSE   2:sub       7
##  6 ABCC11  m1     4.82e7 G     T     0.352   3.07 HZ       TRUE    1:2         1
##  7 ABCC11  m2     4.82e7 C     T     0.347   3.07 HZ       TRUE    1:2         1
##  8 CPXCR1  m1     8.88e7 A     C     0.5     1.12 HZ       FALSE   <NA>        8
##  9 CPXCR1  m2     8.88e7 G     A     0.491   1.12 HZ       FALSE   <NA>        8
## 10 JUP     m1     4.18e7 GTGT… G     0.857   1.06 LOH      FALSE   1:0         2
## # ℹ 8 more variables: tcn_assumed <lgl>, origin <chr>, chr <fct>, class <chr>,
## #   aff_cp <dbl>, wt_cp <dbl>, vn_status <dbl>, pre_info <chr>
## $combined_uncovered

6 Predict Zygosity

In this section, we will use the WGS_LL_T_1 dataset from the Seq-C2 project as an example to investigate whether mutations in the following genes result in total absence of wildtype copies. The genes which were selected as an example for the analysis are shown below. The example data set was reduced to these genes.

6.1 Format of input data

Some inputs are optional, while others are compulsory. The latter are labeled with “**”. Of note, ZygosityPredictor is applied downstream of variant calling, therefore the variant calls, including information on identified somatic copy number aberrations (sCNAs), have to be provided. The inputs can be divided into five classes:

  • File paths:
    • bamDna** : character; path to indexed alignment (.bam format)
    • bamRna: character; path to rna-sequencing data (.bam format).
    • vcf: character, or character vector containing several vcf file paths; path to variant call file (.vcf.gz format) or .vcf. for extended SNP phasing if variants on the same gene are too far away from each other for direct phasing
  • Sample meta information:
    • purity** : numeric; between 0 and 1; indicates purity or tumor cell content of the sample
    • ploidy: numeric; ground ploidy of the sample
    • sex** : character; “male” or “female” / “m” or “f”; sex of the patient the sample was taken from
  • Variants
    • somCna** : GRanges object; containing all genomic segments (sCNA) with annotated total copy number (default metadata column name “tcn”, custom name can be provided by COLNAME_TCN) and information about LOH (default column name “cna_type”, custom name can be provided by COLNAME_CNA_TYPE). The cna_type column should contain the string “LOH” if loss-of heterozygosity is present at the segment. If large deletions should be included to the analysis the total copy number has to be decreased accordingly. If the total copy number is smaller than 0.5, the tool will assume a homozygous deletion. An incomplete deletion is assumed if at least one copy is lost compared to the ploidy of the sample (works only if the ploidy is provided as an input)
    • somSmallVars: GRanges object; containing all somatic small variants. Required metadata columns are: reference base (“REF”/”ref”), alternative base (“ALT”/”alt”), allele frequency in the tumor sample (raw allele frequency, i.e. as measured in the tumor sample; not the corrected allele frequency in the supposedly pure tumor) (“AF”/”af”), gene (“gene”/”GENE”, according to the used gene model (GENCODE39 in the example data) and the annotation provided below). If no relevant somatic small variants are present, can also be NULL or not provided.
    • germSmallVars: GRanges object; Analogous to GR_SOM_SMALL_VARS. If no relevant germline small variants are present, can also be NULL or not provided.
  • Used Gene model
    • geneModel** : GRanges object; containing the gene model for the used reference genome. Required metadata columns are: “gene”. Artificially restricting the gene model can be used to tell the tool which genes to analyze. In the case of this vignette, the object contains only the genes we selected.
  • Haploblocks
    • haploBlocks: GRanges object; containing predefined haplock region, i.e. genomic regions in which SNPs could be phased to one specific haplotype. By providing this haploblock together with the SNPs and its genotype annotation (1|0 or 0|1) via input vcf, haploblock phasing will be enabled.
  • Options
    • logDir: character; If provided, detailed output will be stored in it
    • includeIncompleteDel: logical, default=TRUE; Should incomplete deletions (monoallelic deletions in a diploid sample) be included in the evaluation? Since these often span large parts of the chromosomes and will lead to many affected genes, it can be advisable to include or exclude them, depending on the research question.
    • includeHomoDel: logical, default=TRUE; Should homozygous deletions be included in the evaluation?
    • AllelicImbalancePhasing: logical, default=FALSE; If TRUE, performs (if read-level phasing fails) allelic imbalance phasing. Results must be seen with caution as for somatic variants incorrect constellations might be determined.
    • showReadDetail: logical, default=FALSE; If this option is TRUE, another table is added to the output that contains more detailed information about the classification of the read pairs. More detailed information is provided in section 4.3.4.
    • assumeSomCnaGaps: logical, default=FALSE; Only required if the somCna object lacks copy number information for genomic segments on which small variants are detected. By default, variants in such regions will be excluded from the analysis as required information about the copy number is missing. These variants will be attached to the final output list in a separate tibble. To include them, this flag must be set TRUE and the ground ploidy must be given as an input. This ground ploidy will then be taken as tcn in the missing regions.
    • byTcn: logical, default=TRUE; optional if includeHomoDel or includeIncompleteDel is TRUE. If FALSE the tool will not use tcn as a criterion to assign large deletions. It will use the cna_type column and check for indicating strings like HOMDEL/HomoDel/DEL. Some commonly used strings are covered. It is recommended to leave this flag TRUE.
    • printLog: logical, default=TRUE; If TRUE, the tool will print detailed information how the assessment is done for each gene.
    • verbose: logical, default=FALSE; prints debugging information
    • colnameTcn: character; indicating the name of the metadata column containing the tcn information in the somCna object. If not provided the tool tries to detect the column according to default names.
    • colnameCnaType: character; The same as for colnameTcn, but for cna-type information.
    • distCutOff: numeric, default=5000; if input vcf is provided and SNP phasing is performed, this will limt the distance at which the SNP phasing should not be tried anymore. As the probability of finding overlapping reads at such a long distance is very low and the runtime will increase exponentially.
    • snpQualityCutOff
    • refGen: character, default=“hg38”; Required if vcf files is provided. Either “hg38” or “hg19”. Relevant for VariantAnnotation vcf loading

6.2 Predict zygosity for a set of genes in a sample

The prediction of zygosity for a set of genes in a sample can be assessed by the predict_zygosity() function.

Important note: The runtime of the analysis depends strongly on the number of genes to be assessed and on the number of input variants. It is therefore recommended to reduce the number of genes to the necessary ones. Also, depending on the research question to be addressed, the variants should be filtered to the most relevant ones, not only because of runtime considerations, but also to sharpen the final result. A large number of mutations in a gene, some of which are of little biological relevance or even SNPs, will inevitably reduce the validity of the results.

fp <- predict_zygosity(
  purity = PURITY, 
  ploidy = PLOIDY,
  sex = SEX,
  somCna = GR_SCNA, 
  somSmallVars = GR_SOM_SMALL_VARS, 
  germSmallVars = GR_GERM_SMALL_VARS, 
  geneModel = GR_GENE_MODEL,
  bamDna = FILE_BAM,
  haploBlocks = GR_HAPLOBLOCKS
## Warning in check_gr_gene_model(geneModel, ZP_env):
## Warning in FUN(X[[i]], ...): column gt_cna contains annotations of balanced
## segments. They are removed from imbalance phasing if enabled
## Warning in FUN(X[[i]], ...): column gt_cna contains annotations of balanced
## segments. They are removed from imbalance phasing if enabled
## Warning in FUN(X[[i]], ...): column gt_cna contains annotations of balanced
## segments. They are removed from imbalance phasing if enabled
## no RNA file provided: Analysis will be done without RNA reads

6.3 Interpretation of results

Of note, the results displayed here were chosen to explain and exemplify the functionality of the tool; biological and medical impact of the specific variants has not been a selection criterion. The result which is returned by the function consists of a list of tibbles:

  • Evaluation per variant
  • Evaluation per gene
  • Main Phasing info
  • detailed read-level phasing (RLP) info
  • detailed alleleic imbalance phasing (AIP) info, if enabled
  • Read pair info (only if showReadDetail=TRUE and logDir is provided)
  • Variants not covered by somCna (only if present and no sCNA gap assumption was done)

One way of accessing the results is a simple extraction of the tibbles from the list. In addition, two accessor function are implemented: ZP_ov() shows an overview of the full resultand and gene_ov() shows more detailed information about a selected gene.

## Zygosity Prediction of 12 input variants
## 0 are uncovered by sCNA input and were not evaluated
## 7 genes analyzed:
## |status              |eval_by        |  n|
## |:-------------------|:--------------|--:|
## |all_copies_affected |aff_cp         |  2|
## |wt_copies_left      |aff_cp         |  2|
## |wt_copies_left      |direct-phasing |  1|
## |wt_copies_left      |insufficient   |  2|
## |undefined           |NA             |  0|
## phased genes:
## CPXCR1, OR2J1

The function provides an overview about the evaluations done by ZygosityPredictor. One can see that in our case 12 input small variants were used to predict gene status. Of those, two got the status all_copies_affected.

6.3.1 Evaluation per variant

The first result of the function is the evaluation per variant. In this step all information required for subsequent steps is annotated and the affected copies per variant are calculated. For every variant, the function checks whether it already affects all copies of the gene. The format of the output is a tibble; the number of rows corresponds to the total number of input variants. The tool annotates a few self-explanatory columns such as the origin of the respective variant (germline/somatic) or the class (snv/ins/del). It also appends information from the sCNA results: the total copy number at the position of the variant and the information if a loss of heterozygosity is present (cna_type). Also, an ID is assigned to every small variant. Then, the genes are numbered consecutively in order to unambiguously assign variants to genes in the following analysis. The most important results of this step are the calculation of the affected and wildtype copies, as well as, depending on the data, an initial check of whether a variant already affects all copies.

Of note, there can be situations in which left wildtype copies are below 0.5,
but still this information is not sufficient to predict “all_copies_affected” without doubt. Depending on the origin of the variant, further criteria must be met (e.g., LOH). The procedure for this first check is shown in the pre_info column.

6.3.2 Evaluation per gene + phasing info

By using gene_ov(), more detailed information can be viewed about how the tool came to a gene status. What the accessor function does is basically filtering the output tibbles to the gene of relevance. Detailed explanation about all columns can be found below the next chunk.

gene_ov(fp, OR2J1)
## Top level: Gene status
## |gene  | n_mut|status         | conf|eval_by |    wt_cp|warning |wt_cp_range |info                                     |phasing        | eval_time_s|
## |:-----|-----:|:--------------|----:|:-------|--------:|:-------|:-----------|:----------------------------------------|:--------------|-----------:|
## |OR2J1 |     3|wt_copies_left |    1|aff_cp  | 2.592225|none    |1.24 - 2.72 |most relevant phasing combination: m1-m2 |direct-phasing |       3.287|
## Sub level: Evaluation per variant
## |gene  |origin  |class |chr  |      pos|ref |alt |   af|  tcn|tcn_assumed |cna_type |all_imb |gt_cna | seg_id| aff_cp| wt_cp|mut_id |
## |:-----|:-------|:-----|:----|--------:|:---|:---|----:|----:|:-----------|:--------|:-------|:------|------:|------:|-----:|:------|
## |OR2J1 |somatic |snv   |chr6 | 29100982|C   |A   | 0.36| 4.06|FALSE       |HZ       |FALSE   |NA     |      5|   1.47|  2.59|m1     |
## |OR2J1 |somatic |snv   |chr6 | 29101487|T   |C   | 0.33| 4.06|FALSE       |HZ       |FALSE   |NA     |      5|   1.35|  2.72|m2     |
## |OR2J1 |somatic |snv   |chr6 | 29101522|C   |T   | 0.33| 4.06|FALSE       |HZ       |FALSE   |NA     |      5|   1.35|  2.72|m3     |
## Sub level: Main phasing combinations
## |comb  | nconst|const |phasing        |via | conf|unplausible |subclonal | wt_cp| min_poss_wt_cp| max_poss_wt_cp| score|gene  |
## |:-----|------:|:-----|:--------------|:---|----:|:-----------|:---------|-----:|--------------:|--------------:|-----:|:-----|
## |m2-m3 |      1|same  |direct-phasing |8   | 1.00|0           |0         |  2.72|           1.37|           2.72|     1|OR2J1 |
## |m1-m2 |      1|same  |direct-phasing |4   | 0.86|0           |0         |  2.59|           1.25|           2.59|     1|OR2J1 |
## |m1-m3 |      1|same  |direct-phasing |7   | 0.86|0           |0         |  2.59|           1.24|           2.59|     1|OR2J1 |
## Sub level: All read-level-phasing combinations, including SNPs
## Showing 3 of 3 phasing attempts
## |  both| mut1| mut2| dev_var| skipped|const | nconst| p_same| p_diff| none_raw| DNA_rds| RNA_rds|subclonal |unplausible | dist|class_comb |comb  | ncomb|gene  | conf|
## |-----:|----:|----:|-------:|-------:|:-----|------:|------:|------:|--------:|-------:|-------:|:---------|:-----------|----:|:----------|:-----|-----:|:-----|----:|
## |  2.00|    0|    0|       0|       0|same  |      1|      1|   0.14|        0|       2|       0|FALSE     |FALSE       |  505|snv-snv    |m1-m2 |     4|OR2J1 |   NA|
## |  2.00|    0|    0|       0|       0|same  |      1|      1|   0.14|        0|       2|       0|FALSE     |FALSE       |  540|snv-snv    |m1-m3 |     7|OR2J1 |   NA|
## | 39.99|    0|    0|       0|       0|same  |      1|      1|   0.00|       22|      62|       0|FALSE     |FALSE       |   35|snv-snv    |m2-m3 |     8|OR2J1 |   NA|

The first prompted tibble originates from the eval_per_gene tibble of the output. The tibble shown below originates from eval_per_variant and shows all input small variants of the gene. The next tibble originates from main_phasing_info and shows the main phasing combinations. Finally, the last tibble comes from detailed_RLP_info and contains every phasing combination including the ones with and between SNPs.

What can be seen is that the final gene status of OR2J1 is wt_copies_left. The tool predicted around 2.6 remaining wt copies with maximum confidence level of 1. Below it can be seen that 3 small variants inside the gene were in the input leading to 3 main phasing combinations. All of them were phased via direct read level phasing. As all main combinations could be solved, the left wt copies could be accurately defined and the gene status assigned. In this particular case, the gene status could have been solved even without phasing all main combinations by the case we refer to as insufficient. As the final gene status is defnied via the so called integrated_affected_copies, which are defnied for diff constellation via: aff_cp(m1)+aff_cp(m2) and for the same constellation: max(aff_cp(m1), aff_cp(m2)), we can pre-calculate the maximum affected copies in case of a diff constellation. If the difference of the total copy number and the integrated affected copies leaves more than 0.5 wt copies, the status wt_cp_left can be concluded. As can be seen in the main phasing combinations in column: min_poss_wt_cp, none of the main phasing combinations could lead to less than 0.5 wt copies even if the variants were found on different reads.

The outputs contain columns providing more detailed information about gene status definition which will be explained in the following. All read-level phasing combinations

(either visible via gene_ov() or accessable via fp$detailed_RLP_info):

  • both: number of reads classified as both (both variants present), adjusted with basecall and maping quality
  • mut1: number of reads classified as mut1 (only the first variant of combination pßresent), adjusted with basecall and maping quality
  • mut2: number of reads classified as mut2 (only the second variant of combination pßresent), adjusted with basecall and maping quality
  • dev_var: number of reads having another variant at one of the positions of the expected variants
  • skipped: number of reads not mapping to the position of the variant (only expected with RNA reads)
  • nstatus: variant constellation in numeric representation (2 = all copies affected, 1 = wt copies left, 0 = undefined)
  • status: variant constellation in character representation
  • nstatus: numeric representation of constellation
  • xsq_same/xsq_diff: X-squared values of chi-squared tests. _same is the one against expected same result
  • p_same/p_diff: p-value of chi-squared tests
  • v_same/v_diff: Cramers V value of chi-squared tests (5)
  • none: number of reads classified as none (none of both variants found)
  • DNA_rds/RNA_rds: number of DNA/RNA reads used
  • dist: Distance between variants
  • class_comb: mutational classes corresponding to character combination identifier
  • comb: combination identifier in character representation (mX-mY).
  • ncomb: combination identifier in numeric representation. Derived from position in phasing matrix
  • subclonal: TRUE if reads with classification both and either mut1 or mut2 were found. There are two explanantions why soimething like this could happen in a cancer sample: First that the tumor is not fully clonal and a subclone is present carrying only the variant that happened earlier during tumor development. The second variant is only carried by a subclone that emerged later. The second explanation could be that the reads are actually from the same cells but a duplication of one allel happend between their occurence. We could imagine a scenario were the genotype of a gene is 1:2 and both variants are on the duplicated allele. If one of them happend before the duplication, we would expect reads carrying only the earlier variant but also reads carrying both of them.
  • unplausible TRUE if reads with classification both, mut1 and mut2 are found for the same variant combination. Compared to the “subclonal” case this is more difficult to explain and might also be an artifact. The only way this could happen in a cancer sample is that the same mutational event happens independently on both alleles and or in different subclones. This case is generally very unlikely and is therefore annotated as unplasuible. Such a setting is also reflected in the p-valkue and thereofre the confidence, as similary with both expected results is found. Main phasing combinations (either visible via gene_ov() or accessable via fp$phasing_info):

  • phasing: Info which phasing approach was used (direct, indirect, haploblock, imbalance, flagged)
  • via: combinations which were used to solve this combination. For direct phasing the number is the numeric representation of the combi9nation identifier. For indirect phasing it will look like this: 4-7-8 which measn thta the combinations 4, 7 and 8 were used to solve.
  • conf: aggregated confidence for the combination
  • wt_cp: exact left wt copies. If phasing of combination was not succesful, wt copies can not be calculated acurately which leads to NA
  • min_poss_wt_cp: Minimal possible wt copies if variants are on different copies according to affected copynumber of the two variants in the combination. If greater than 0.5, the constellation is unable to contributre to a status of all copies affrected for the gene.
  • max_poss_wt_cp: maximum possible wt copies if the two variants are on the same copy
  • score: contribution to gene status. (2 is a contribution of all copies affected by the combination, 1 has more than 0.5 wt copies left)
  • gene: Gene that w2as tried to solve with the combination

7 References


  1. Fang LT, Zhu B, Zhao Y, Chen W, Yang Z, Kerrigan L, Langenbach K, de Mars M, Lu C, Idler K, et al. Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing. Nature Biotechnology. 2021;39(9):1151-1160 / PMID:34504347
  2. Lawrence M, Huber W, Pages H, Aboyoun P, Carlson M, et al. (2013) Software for Computing and Annotating Genomic Ranges. PLoS Comput Biol 9(8): e1003118. doi:10.1371/journal.pcbi.1003118*
  3. Wickham H, François R, Henry L, Müller K (2022). dplyr: A Grammar of Data Manipulation. R package version 1.0.10,
  4. Wickham H (2022). stringr: Simple, Consistent Wrappers for Common String Operations.,
  5. Cramér, Harald. 1946. Mathematical Methods of Statistics. Princeton: Princeton University Press, page 282 (Chapter 21. The two-dimensional case)
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