Summarization and quantitative trait analysis of CNV ranges
18 July 2019
Abstract
The CNVRanger package implements a comprehensive tool suite for the analysis of copy number variation (CNV). This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes.Package
CNVRanger 1.0.3
Contents
1 Introduction
Copy number variation (CNV) is a frequently observed deviation from the diploid state due to duplication or deletion of genomic regions. CNVs can be experimentally detected based on comparative genomic hybridization, and computationally inferred from SNP-arrays or next-generation sequencing data. These technologies for CNV detection have in common that they report, for each sample under study, genomic regions that are duplicated or deleted with respect to a reference. Such regions are denoted as CNV calls in the following and will be considered the starting point for analysis with the CNVRanger package.
The following figure provides an overview of the analysis capabilities of CNVRanger.
(A) The CNVRanger package
imports CNV calls from a simple file format into
R, and stores them in dedicated Bioconductor data structures, and
(B) implements three frequently used approaches for summarizing CNV calls
across a population:
(i) the CNVRuler procedure
that trims region margins based on regional density
Kim et al., 2012,
(ii) the reciprocal overlap procedure that requires sufficient mutual overlap
between calls
Conrad et al., 2010, and
(iii) the GISTIC procedure
that identifies recurrent CNV regions
Beroukhim et al., 2007.
(C) CNVRanger builds on regioneR
for overlap analysis of CNVs with functional genomic regions,
(D) implements RNA-seq expression Quantitative Trait Loci (eQTL) analysis
for CNVs by interfacing with edgeR, and
(E) interfaces with
PLINK for traditional
genome-wide association studies (GWAS) between CNVs and quantitative phenotypes.
The key parts of the functionality implemented in CNVRanger were developed, described, and applied in several previous studies:
Genome-wide detection of CNVs and their association with meat tenderness in Nelore cattle da Silva et al., 2016
Widespread modulation of gene expression by copy number variation in skeletal muscle Geistlinger et al., 2018
CNVs are associated with genomic architecture in a songbird da Silva et al., 2018
2 Applicability and Scope
As described in the above publications, CNVRanger has been
developed and extensively tested for SNP-based CNV calls as obtained with
PennCNV.
We also tested CNVRanger for sequencing-based CNV calls as obtained with
CNVnator (a read-depth approach) or
LUMPY (an approach that combines
evidence from split-reads and discordant read-pairs).
In general, CNVRanger can be applied to CNV calls associated with integer copy number states, where we assume the states to be encoded as:
0: homozygous deletion (2-copy loss)1: heterozygous deletion (1-copy loss)2: normal diploid state3: 1-copy gain4: amplification (>= 2-copy gain)
Note that for CNV calling software that uses a different encoding or that does not provide integer copy number states, it is often possible to (at least approximately) transform the output to a format that is compatible with the input format of CNVRanger. See Section 4.1 Input data format for details.
CNVRanger is designed to work with CNV calls from one tool at a time. See EnsembleCNV and FusorSV for combining CNV calls from multiple SNP-based callers or multiple sequencing-based callers, respectively.
CNVRanger assumes CNV calls provided as input to be already filtered by quality, using the software that was used for CNV calling, or specific tools for that purpose. CNVRanger provides downstream summarization and association analysis for CNV calls, it does not implement functions for CNV calling or quality control. CNVRanger is applicable for diploid species only.
3 Key functions
| Analysis step | Function |
|---|---|
| (A) Input | GenomicRanges::makeGRangesListFromDataFrame |
| (B) Summarization | populationRanges |
| (C) Overlap analysis | regioneR::overlapPermTest |
| (D) Expression analysis | cnvEQTL |
| (E) Phenotype analysis | cnvGWAS |
Note: we use the package::function notation for functions from other packages.
For functions from this package and base R functions, we use the function name
without preceding package name.
4 Reading and accessing CNV data
The CNVRanger package uses Bioconductor core data structures implemented in the GenomicRanges and RaggedExperiment packages to efficiently represent, access, and manipulate CNV data.
We start by loading the package.
library(CNVRanger)4.1 Input data format
CNVRanger reads CNV calls from a simple file format, providing at least chromosome, start position, end position, sample ID, and integer copy number for each call.
For demonstration, we consider CNV calls as obtained with PennCNV from SNP-chip data in a Brazilian cattle breed (da Silva et al., 2016).
Here, we use a data subset and only consider CNV calls on chromosome 1 and 2,
for which there are roughly 3000 CNV calls as obtained for 711 samples.
We use read.csv to read comma-separated values, but we could use a different
function if the data were provided with a different delimiter
(for example, read.delim for tab-separated values).
data.dir <- system.file("extdata", package="CNVRanger")
call.file <- file.path(data.dir, "Silva16_PONE_CNV_calls.csv")
calls <- read.csv(call.file, as.is=TRUE)
nrow(calls)## [1] 3000head(calls)## chr start end NE_id state
## 1 chr1 16947 45013 NE001423 3
## 2 chr1 36337 67130 NE001426 3
## 3 chr1 16947 36337 NE001428 3
## 4 chr1 36337 105963 NE001519 3
## 5 chr1 36337 83412 NE001534 3
## 6 chr1 36337 83412 NE001648 3length(unique(calls[,"NE_id"]))## [1] 711We observe that this example dataset satisfies the basic five-column input format required by CNVRanger.
The last column contains the integer copy number state for each call, encoded as
0: homozygous deletion (2-copy loss)1: heterozygous deletion (1-copy loss)2: normal diploid state3: 1-copy gain4: amplification (>= 2-copy gain)
For CNV detection software that uses a different encoding, it is necessary to convert to the above encoding. For example, GISTIC uses
-2: homozygous deletion (2-copy loss)-1: heterozygous deletion (1-copy loss)0: normal diploid state1: 1-copy gain2: amplification (>= 2-copy gain)
which can be converted by simply adding 2.
In Section 7.2 Application to TCGA data we also describe how to transform segmented log2 copy ratios to integer copy number states.
The basic five-column input format can be augmented with additional columns, providing additional characteristics and metadata for each CNV call. In Section 8 CNV-phenotype association analysis, we demonstrate how to make use of such an extended input format.
4.2 Representation as a GRangesList
Once read into an R data.frame, we group the calls by sample ID and convert
them to a GRangesList.
Each element of the list corresponds to a sample, and contains the genomic
coordinates of the CNV calls for this sample (along with the copy number state
in the state metadata column).
grl <- GenomicRanges::makeGRangesListFromDataFrame(calls,
split.field="NE_id", keep.extra.columns=TRUE)
grl## GRangesList object of length 711:
## $NE001357
## GRanges object with 5 ranges and 1 metadata column:
## seqnames ranges strand | state
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr1 4569526-4577608 * | 3
## [2] chr1 15984544-15996851 * | 1
## [3] chr1 38306432-38330161 * | 3
## [4] chr1 93730576-93819471 * | 0
## [5] chr2 40529044-40540747 * | 3
##
## $NE001358
## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | state
## [1] chr1 105042452-105233446 * | 1
##
## $NE001359
## GRanges object with 4 ranges and 1 metadata column:
## seqnames ranges strand | state
## [1] chr1 4569526-4577608 * | 3
## [2] chr1 31686841-31695808 * | 0
## [3] chr1 93730576-93819471 * | 0
## [4] chr2 2527718-2535261 * | 0
##
## ...
## <708 more elements>
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsThe advantage of representing the CNV calls as a GRangesList is that it allows
to leverage the comprehensive set of operations on genomic regions implemented
in the GenomicRanges packages - for instance, sorting of the calls
according to their genomic coordinates.
grl <- GenomicRanges::sort(grl)
grl## GRangesList object of length 711:
## $NE001357
## GRanges object with 5 ranges and 1 metadata column:
## seqnames ranges strand | state
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr1 4569526-4577608 * | 3
## [2] chr1 15984544-15996851 * | 1
## [3] chr1 38306432-38330161 * | 3
## [4] chr1 93730576-93819471 * | 0
## [5] chr2 40529044-40540747 * | 3
##
## $NE001358
## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | state
## [1] chr1 105042452-105233446 * | 1
##
## $NE001359
## GRanges object with 4 ranges and 1 metadata column:
## seqnames ranges strand | state
## [1] chr1 4569526-4577608 * | 3
## [2] chr1 31686841-31695808 * | 0
## [3] chr1 93730576-93819471 * | 0
## [4] chr2 2527718-2535261 * | 0
##
## ...
## <708 more elements>
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths4.3 Representation as a RaggedExperiment
An alternative matrix-like representation of the CNV calls can be obtained with the RaggedExperiment data class. It resembles in many aspects the SummarizedExperiment data class for storing gene expression data as e.g. obtained with RNA-seq.
ra <- RaggedExperiment::RaggedExperiment(grl)
ra## class: RaggedExperiment
## dim: 3000 711
## assays(1): state
## rownames: NULL
## colnames(711): NE001357 NE001358 ... NE003967 NE003968
## colData names(0):As apparent from the dim slot of the object, it stores the CNV calls in the
rows and the samples in the columns.
Note that the CN state is now represented as an assay matrix which can be
easily accessed and subsetted.
RaggedExperiment::assay(ra[1:5,1:5])## NE001357 NE001358 NE001359 NE001360 NE001361
## chr1:4569526-4577608 3 NA NA NA NA
## chr1:15984544-15996851 1 NA NA NA NA
## chr1:38306432-38330161 3 NA NA NA NA
## chr1:93730576-93819471 0 NA NA NA NA
## chr2:40529044-40540747 3 NA NA NA NAAs with SummarizedExperiment objects, additional information for
the samples are annotated in the colData slot.
For example, we annotate the steer weight and its feed conversion ratio (FCR)
using simulated data.
Feed conversion ratio is the ratio of dry matter intake to live-weight gain.
A typical range of feed conversion ratios is 4.5-7.5 with a lower number being
more desirable as it would indicate that a steer required less feed per pound of
gain.
weight <- rnorm(ncol(ra), mean=1100, sd=100)
fcr <- rnorm(ncol(ra), mean=6, sd=1.5)
RaggedExperiment::colData(ra)$weight <- round(weight, digits=2)
RaggedExperiment::colData(ra)$fcr <- round(fcr, digits=2)
RaggedExperiment::colData(ra)## DataFrame with 711 rows and 2 columns
## weight fcr
## <numeric> <numeric>
## NE001357 1124.26 5.12
## NE001358 896.96 4.76
## NE001359 972.21 4.98
## NE001360 1207.7 8.03
## NE001361 1089.15 5.22
## ... ... ...
## NE003962 1076.94 5.87
## NE003963 993.55 7.67
## NE003966 1075.07 5.88
## NE003967 983.24 4.62
## NE003968 1180.64 5.795 Summarizing individual CNV calls across a population
In CNV analysis, it is often of interest to summarize individual calls across
the population, (i.e. to define CNV regions), for subsequent association
analysis with expression and phenotype data.
In the simplest case, this just merges overlapping individual calls into
summarized regions.
However, this typically inflates CNV region size, and more appropriate approaches
have been developed for this purpose.
As mentioned in the Introduction, we emphasize the need for quality control of CNV calls and appropriate accounting for sources of technical bias before applying these summarization functions (or in general downstream analysis with CNVRanger).
For instance, protocols for read-depth CNV calling typically exclude calls
overlapping defined repetitive and low-complexity regions including the UCSC
list of segmental duplications
Trost et al., 2018,
Zhou et al., 2018.
We also note that
CNVnator, a very popular read-depth
CNV caller, implements the \(q0\)-filter to explicitely flag and, if desired,
exclude calls that are likely to stem from such regions.
If systematically over-represented in the input CNV calls, summarization procedures
such as GISTIC will identify these regions as recurrent independent of whether
there are biological or technical reasons for that.
5.1 Trimming low-density areas
Here, we use the approach from CNVRuler
to summarize CNV calls to CNV regions (see
Figure 1
in
Kim et al., 2012
for an illustration of the approach).
This trims low-density areas as defined by the density argument,
which is set here to <10% of the number of calls within a summarized region.
cnvrs <- populationRanges(grl, density=0.1)
cnvrs## GRanges object with 303 ranges and 2 metadata columns:
## seqnames ranges strand | freq type
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr1 16947-111645 * | 103 gain
## [2] chr1 1419261-1630187 * | 18 gain
## [3] chr1 1896112-2004603 * | 218 gain
## [4] chr1 4139727-4203274 * | 1 gain
## [5] chr1 4554832-4577608 * | 23 gain
## ... ... ... ... . ... ...
## [299] chr2 136310067-136322489 * | 2 loss
## [300] chr2 136375337-136386940 * | 1 loss
## [301] chr2 136455546-136466040 * | 1 loss
## [302] chr2 136749793-136802493 * | 39 both
## [303] chr2 139194749-139665914 * | 58 both
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsNote that CNV frequency (number of samples overlapping each region) and CNV type
(gain, loss, or both) have also been annotated in the columns freq and type,
respectively.
5.2 Reciprocal overlap
We also provide an implementation of the Reciprocal Overlap (RO) procedure that
requires calls to sufficiently overlap with one another as e.g. used by
Conrad et al., 2010.
This merges calls with an RO above a threshold as given by the ro.thresh argument.
For example, an RO of 0.51 between two genomic regions A and B requires that
B overlaps at least 51% of A, and that A also overlaps at least 51% of B.
ro.cnvrs <- populationRanges(grl[1:100], mode="RO", ro.thresh=0.51)
ro.cnvrs## GRanges object with 85 ranges and 2 metadata columns:
## seqnames ranges strand | freq type
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr1 16947-45013 * | 6 gain
## [2] chr1 36337-67130 * | 6 gain
## [3] chr1 36337-105963 * | 6 gain
## [4] chr1 1419261-1506862 * | 3 gain
## [5] chr1 1539361-1625471 * | 3 gain
## ... ... ... ... . ... ...
## [81] chr2 136215094-136232653 * | 2 loss
## [82] chr2 136749793-136776410 * | 1 gain
## [83] chr2 138738929-139004086 * | 1 loss
## [84] chr2 139194749-139274355 * | 1 gain
## [85] chr2 139324752-139665914 * | 3 loss
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths5.3 Identifying recurrent regions
In particular in cancer, it is important to distinguish driver from passenger mutations, i.e. to distinguish meaningful events from random background aberrations. The GISTIC method identifies those regions of the genome that are aberrant more often than would be expected by chance, with greater weight given to high amplitude events (high-level copy-number gains or homozygous deletions) that are less likely to represent random aberrations (Beroukhim et al., 2007).
By setting est.recur=TRUE, we deploy a GISTIC-like significance estimation
cnvrs <- populationRanges(grl, density=0.1, est.recur=TRUE)
cnvrs## GRanges object with 303 ranges and 3 metadata columns:
## seqnames ranges strand | freq type
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr1 16947-111645 * | 103 gain
## [2] chr1 1419261-1630187 * | 18 gain
## [3] chr1 1896112-2004603 * | 218 gain
## [4] chr1 4139727-4203274 * | 1 gain
## [5] chr1 4554832-4577608 * | 23 gain
## ... ... ... ... . ... ...
## [299] chr2 136310067-136322489 * | 2 loss
## [300] chr2 136375337-136386940 * | 1 loss
## [301] chr2 136455546-136466040 * | 1 loss
## [302] chr2 136749793-136802493 * | 39 both
## [303] chr2 139194749-139665914 * | 58 both
## pvalue
## <numeric>
## [1] 0.00980392156862742
## [2] 0.107843137254902
## [3] 0
## [4] 0.558823529411765
## [5] 0.0882352941176471
## ... ...
## [299] 0.236111111111111
## [300] 0.421296296296296
## [301] 0.421296296296296
## [302] 0.0588235294117647
## [303] 0.0392156862745098
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsand filter for recurrent CNVs that exceed a significance threshold of 0.05.
subset(cnvrs, pvalue < 0.05)## GRanges object with 17 ranges and 3 metadata columns:
## seqnames ranges strand | freq type
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr1 16947-111645 * | 103 gain
## [2] chr1 1896112-2004603 * | 218 gain
## [3] chr1 15984544-15996851 * | 116 loss
## [4] chr1 31686841-31695808 * | 274 loss
## [5] chr1 69205418-69219486 * | 46 loss
## ... ... ... ... . ... ...
## [13] chr2 97882695-97896935 * | 80 loss
## [14] chr2 124330343-124398570 * | 39 loss
## [15] chr2 135096060-135271140 * | 84 gain
## [16] chr2 135290754-135553033 * | 83 gain
## [17] chr2 139194749-139665914 * | 58 both
## pvalue
## <numeric>
## [1] 0.00980392156862742
## [2] 0
## [3] 0.0185185185185185
## [4] 0.00462962962962965
## [5] 0.0416666666666666
## ... ...
## [13] 0.0231481481481481
## [14] 0.0462962962962963
## [15] 0.0196078431372549
## [16] 0.0294117647058824
## [17] 0.0392156862745098
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsWe can illustrate the landscape of recurrent CNV regions using the function
plotRecurrentRegions.
We therefore provide the summarized CNV regions, a valid
UCSC genome assembly, and a
chromosome of interest.
plotRecurrentRegions(cnvrs, genome="bosTau6", chr="chr1")
The function plots (from top to bottom):
(i) an ideogram of the chromosome (note that staining bands are not available for bosTau6),
(ii) a genome axis indicating the chromosomal position, (iii) a bar plot showing
for each CNV region the number of samples with a CNV call in that region, and
(iv) an annotation track that indicates whether this is a recurrent region
according to a significance threshold (an argument to the function, default: 0.05).
6 Overlap analysis of CNVs with functional genomic regions
Once individual CNV calls have been summarized across the population, it is
typically of interest whether the resulting CNV regions overlap with functional
genomic regions such as genes, promoters, or enhancers.
As a certain amount of overlap can be expected just by chance, an assessment of
statistical significance is needed to decide whether the observed overlap is
greater (enrichment) or less (depletion) than expected by chance.
The regioneR package implements a general framework for testing overlaps of genomic regions based on permutation sampling. This allows to repeatedly sample random regions from the genome, matching size and chromosomal distribution of the region set under study (here: the CNV regions). By recomputing the overlap with the functional features in each permutation, statistical significance of the observed overlap can be assessed.
We demonstrate in the following how this strategy can be used to assess the overlap between the detected CNV regions and protein-coding regions in the cattle genome. We expect to find a depletion as protein-coding regions are highly conserved and rarely subject to long-range structural variation such as CNV. Hence, is the overlap between CNVs and protein-coding genes less than expected by chance?
To obtain the location of protein-coding genes, we query available Bos taurus annotation from Ensembl
library(AnnotationHub)
ah <- AnnotationHub::AnnotationHub()## snapshotDate(): 2019-05-02ahDb <- AnnotationHub::query(ah, pattern = c("Bos taurus", "EnsDb"))
ahDb## AnnotationHub with 11 records
## # snapshotDate(): 2019-05-02
## # $dataprovider: Ensembl
## # $species: Bos taurus
## # $rdataclass: EnsDb
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## # tags, rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH53189"]]'
##
## title
## AH53189 | Ensembl 87 EnsDb for Bos Taurus
## AH53693 | Ensembl 88 EnsDb for Bos Taurus
## AH56658 | Ensembl 89 EnsDb for Bos Taurus
## AH57731 | Ensembl 90 EnsDb for Bos Taurus
## AH60745 | Ensembl 91 EnsDb for Bos Taurus
## ... ...
## AH64416 | Ensembl 93 EnsDb for Bos Taurus
## AH64886 | Ensembl 94 EnsDb for Bos taurus
## AH67912 | Ensembl 95 EnsDb for Bos taurus
## AH69141 | Ensembl 96 EnsDb for Bos taurus
## AH73832 | Ensembl 97 EnsDb for Bos taurusand retrieve gene coordinates in the UMD3.1 assembly (Ensembl 92).
ahEdb <- ahDb[["AH60948"]]## downloading 0 resources## loading from cache
## 'AH60948 : 67694'## require("ensembldb")bt.genes <- ensembldb::genes(ahEdb)
GenomeInfoDb::seqlevelsStyle(bt.genes) <- "UCSC"
bt.genes## GRanges object with 24616 ranges and 8 metadata columns:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## ENSBTAG00000046619 chr1 19774-19899 - |
## ENSBTAG00000006858 chr1 34627-35558 + |
## ENSBTAG00000039257 chr1 69695-71121 - |
## ENSBTAG00000035349 chr1 83323-84281 - |
## ENSBTAG00000001753 chr1 124849-179713 - |
## ... ... ... ... .
## ENSBTAG00000025951 chrX 148526584-148535857 + |
## ENSBTAG00000029592 chrX 148538917-148539037 - |
## ENSBTAG00000016989 chrX 148576705-148582356 - |
## ENSBTAG00000025952 chrX 148774930-148780901 - |
## ENSBTAG00000047839 chrX 148804071-148805135 + |
## gene_id gene_name gene_biotype
## <character> <character> <character>
## ENSBTAG00000046619 ENSBTAG00000046619 RF00001 rRNA
## ENSBTAG00000006858 ENSBTAG00000006858 pseudogene
## ENSBTAG00000039257 ENSBTAG00000039257 protein_coding
## ENSBTAG00000035349 ENSBTAG00000035349 pseudogene
## ENSBTAG00000001753 ENSBTAG00000001753 protein_coding
## ... ... ... ...
## ENSBTAG00000025951 ENSBTAG00000025951 protein_coding
## ENSBTAG00000029592 ENSBTAG00000029592 RF00001 rRNA
## ENSBTAG00000016989 ENSBTAG00000016989 protein_coding
## ENSBTAG00000025952 ENSBTAG00000025952 protein_coding
## ENSBTAG00000047839 ENSBTAG00000047839 P2RY8 protein_coding
## seq_coord_system
## <character>
## ENSBTAG00000046619 chromosome
## ENSBTAG00000006858 chromosome
## ENSBTAG00000039257 chromosome
## ENSBTAG00000035349 chromosome
## ENSBTAG00000001753 chromosome
## ... ...
## ENSBTAG00000025951 chromosome
## ENSBTAG00000029592 chromosome
## ENSBTAG00000016989 chromosome
## ENSBTAG00000025952 chromosome
## ENSBTAG00000047839 chromosome
## description
## <character>
## ENSBTAG00000046619 NULL
## ENSBTAG00000006858 NULL
## ENSBTAG00000039257 NULL
## ENSBTAG00000035349 NULL
## ENSBTAG00000001753 NULL
## ... ...
## ENSBTAG00000025951 NULL
## ENSBTAG00000029592 NULL
## ENSBTAG00000016989 NULL
## ENSBTAG00000025952 NULL
## ENSBTAG00000047839 P2Y receptor family member 8 [Source:VGNC Symbol;Acc:VGNC:32531]
## gene_id_version symbol entrezid
## <character> <character> <list>
## ENSBTAG00000046619 ENSBTAG00000046619.1 RF00001 NA
## ENSBTAG00000006858 ENSBTAG00000006858.5 NA
## ENSBTAG00000039257 ENSBTAG00000039257.2 NA
## ENSBTAG00000035349 ENSBTAG00000035349.3 NA
## ENSBTAG00000001753 ENSBTAG00000001753.4 507243
## ... ... ... ...
## ENSBTAG00000025951 ENSBTAG00000025951.4 NA
## ENSBTAG00000029592 ENSBTAG00000029592.1 RF00001 NA
## ENSBTAG00000016989 ENSBTAG00000016989.5 NA
## ENSBTAG00000025952 ENSBTAG00000025952.3 785083
## ENSBTAG00000047839 ENSBTAG00000047839.1 P2RY8 100299937
## -------
## seqinfo: 48 sequences from UMD3.1 genomeTo speed up the example, we restrict analysis to chromosomes 1 and 2.
sel.genes <- subset(bt.genes, seqnames %in% paste0("chr", 1:2))
sel.genes <- subset(sel.genes, gene_biotype == "protein_coding")
sel.cnvrs <- subset(cnvrs, seqnames %in% paste0("chr", 1:2))Now, we are applying an overlap permutation test with 100 permutations
(ntimes=100), while maintaining chromosomal distribution of the CNV
region set (per.chromosome=TRUE).
Furthermore, we use the option count.once=TRUE to count an overlapping CNV
region only once, even if it overlaps with 2 or more genes.
We also allow random regions to be sampled from the entire genome (mask=NA),
although in certain scenarios masking certain regions such as telomeres and
centromeres is advisable.
Also note that we use 100 permutations for demonstration only.
To draw robust conclusions a minimum of 1000 permutations should be carried out.
library(regioneR)
library(BSgenome.Btaurus.UCSC.bosTau6.masked)
res <- regioneR::overlapPermTest(A=sel.cnvrs, B=sel.genes, ntimes=100,
genome="bosTau6", mask=NA, per.chromosome=TRUE, count.once=TRUE)
res## $numOverlaps
## P-value: 0.0198019801980198
## Z-score: -1.9667
## Number of iterations: 100
## Alternative: less
## Evaluation of the original region set: 97
## Evaluation function: numOverlaps
## Randomization function: randomizeRegions
##
## attr(,"class")
## [1] "permTestResultsList"summary(res[[1]]$permuted)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 92.0 106.8 111.5 111.9 118.0 131.0The resulting permutation p-value indicates a significant depletion. Out of the 303 CNV regions, 97 overlap with at least one gene. In contrast, when repeatedly drawing random regions matching the CNV regions in size and chromosomal distribution, the mean number of overlapping regions across permutations was 111.9 \(\pm\) 7.6.
This finding is consistent with our observations across the whole genome (da Silva et al., 2016) and findings from the 1000 Genomes Poject (Sudmant et al., 2015).
plot(res)
Note: the function regioneR::permTest allows to incorporate user-defined
functions for randomizing regions and evaluating additional measures of overlap
such as total genomic size in bp.
7 CNV-expression association analysis
Studies of expression quantitative trait loci (eQTLs) aim at the discovery of
genetic variants that explain variation in gene expression levels
(Nica and Dermitzakis, 2013).
Mainly applied in the context of SNPs, the concept also naturally extends to the
analysis of CNVs.
The CNVRanger package implements association testing between CNV regions and RNA-seq read counts using edgeR, which applies generalized linear models based on the negative-binomial distribution while incorporating normalization factors for different library sizes.
In the case of only one CN state deviating from 2n for a CNV region under investigation, this reduces to the classical 2-group comparison. For more than two states (e.g. 0n, 1n, 2n), edgeR’s ANOVA-like test is applied to test all deviating groups for significant expression differences relative to 2n.
7.1 Application to individual CNV and RNA-seq assays
We demonstrate the functionality by loading RNA-seq read count data from skeletal muscle samples for 183 Nelore cattle steers, which we analyzed for CNV-expression effects as previously described (Geistlinger et al., 2018).
rseq.file <- file.path(data.dir, "counts_cleaned.txt")
rcounts <- read.delim(rseq.file, row.names=1, stringsAsFactors=FALSE)
rcounts <- as.matrix(rcounts)
dim(rcounts)## [1] 939 183rcounts[1:5, 1:5]## NE001407 NE001408 NE001424 NE001439 NE001448
## ENSBTAG00000000088 64 65 233 135 134
## ENSBTAG00000000160 20 28 50 13 18
## ENSBTAG00000000176 279 373 679 223 417
## ENSBTAG00000000201 252 271 544 155 334
## ENSBTAG00000000210 268 379 486 172 443For demonstration, we restrict analysis to the 939 genes on chromosome 1 and 2, and store the RNA-seq expression data in a SummarizedExperiment.
library(SummarizedExperiment)
rranges <- GenomicRanges::granges(sel.genes)[rownames(rcounts)]
rse <- SummarizedExperiment(assays=list(rcounts=rcounts), rowRanges=rranges)
rse## class: RangedSummarizedExperiment
## dim: 939 183
## metadata(0):
## assays(1): rcounts
## rownames(939): ENSBTAG00000000088 ENSBTAG00000000160 ...
## ENSBTAG00000048210 ENSBTAG00000048228
## rowData names(0):
## colnames(183): NE001407 NE001408 ... NE003840 NE003843
## colData names(0):Assuming distinct modes of action, effects observed in the CNV-expression analysis are typically divided into (i) local effects (cis), where expression changes coincide with CNVs in the respective genes, and (ii) distal effects (trans), where CNVs supposedly affect trans-acting regulators such as transcription factors.
However, due to power considerations and to avoid detection of spurious effects, stringent filtering of (i) not sufficiently expressed genes, and (ii) CNV regions with insufficient sample size in groups deviating from 2n, should be carried out when testing for distal effects. Local effects have a clear spatial indication and the number of genes locating in or close to a CNV region of interest is typically small; testing for differential expression between CN states is thus generally better powered for local effects and less stringent filter criteria can be applied.
In the following, we carry out CNV-expression association analysis by providing
the CNV regions to test (cnvrs), the individual CNV calls (grl) to determine
per-sample CN state in each CNV region, the RNA-seq read counts (rse),
and the size of the genomic window around each CNV region (window).
The window argument thereby determines which genes are considered for testing
for each CNV region and is set here to 1 Mbp.
Further, use the filter.by.expr and min.samples arguments to exclude from
the analysis (i) genes with very low read count across samples, and (ii) CNV
regions with fewer than min.samples samples in a group deviating from 2n.
res <- cnvEQTL(cnvrs, grl, rse, window="1Mbp", verbose=TRUE)## Restricting analysis to 179 intersecting samples## Preprocessing RNA-seq data ...## Summarizing per-sample CN state in each CNV region## Excluding 286 cnvrs not satisfying min.samples threshold## Analyzing 12 regions with >=1 gene in the given window## 1 of 12## 2 of 12## 3 of 12## 4 of 12## 5 of 12## 6 of 12## 7 of 12## 8 of 12## 9 of 12## 10 of 12## 11 of 12## 12 of 12res## GRangesList object of length 12:
## $chr1:16947-111645
## GRanges object with 5 ranges and 5 metadata columns:
## seqnames ranges strand | logFC.CN0 logFC.CN1
## <Rle> <IRanges> <Rle> | <numeric> <numeric>
## ENSBTAG00000018278 chr1 922635-929992 + | <NA> <NA>
## ENSBTAG00000021997 chr1 944294-1188287 - | <NA> <NA>
## ENSBTAG00000020035 chr1 351708-362907 + | <NA> <NA>
## ENSBTAG00000011528 chr1 463572-478996 - | <NA> <NA>
## ENSBTAG00000012594 chr1 669920-733729 - | <NA> <NA>
## logFC.CN3 PValue
## <numeric> <numeric>
## ENSBTAG00000018278 -0.194859708204693 0.0073151463593773
## ENSBTAG00000021997 0.0812496004002579 0.179616021014383
## ENSBTAG00000020035 -0.0745028563584966 0.740325113668282
## ENSBTAG00000011528 -0.0118392582964246 0.915565410373996
## ENSBTAG00000012594 0.00661188778069866 0.943057146391022
## AdjPValue
## <numeric>
## ENSBTAG00000018278 0.400665393142863
## ENSBTAG00000021997 0.84156465759887
## ENSBTAG00000020035 0.997718543468309
## ENSBTAG00000011528 0.997718543468309
## ENSBTAG00000012594 0.997718543468309
##
## ...
## <11 more elements>
## -------
## seqinfo: 48 sequences from UMD3.1 genomeThe resulting GRangesList contains an entry for each CNV region tested, storing
the genes tested in the genomic window around the CNV
region, and (i) log2 fold change with respect to the 2n group,
(ii) edgeR’s DE p-value, and
(iii) the (per default) Benjamini-Hochberg adjusted p-value.
7.2 Application to TCGA data stored in a MultiAssayExperiment
In the previous section, we individually prepared the CNV and RNA-seq data for CNV-expression association analysis. In the following, we demonstrate how to perform an integrated preparation of the two assays when stored in a MultiAssayExperiment. We therefore consider glioblastoma GBM data from The Cancer Genome Atlas TCGA, which can conveniently be accessed with the curatedTCGAData package.
library(curatedTCGAData)
gbm <- curatedTCGAData::curatedTCGAData("GBM",
assays=c("GISTIC_Peaks", "CNVSNP", "RNASeq2GeneNorm"),
dry.run=FALSE)
gbm## A MultiAssayExperiment object of 3 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 3:
## [1] GBM_CNVSNP-20160128: RaggedExperiment with 146852 rows and 1104 columns
## [2] GBM_RNASeq2GeneNorm-20160128: SummarizedExperiment with 20501 rows and 166 columns
## [3] GBM_GISTIC_Peaks-20160128: RangedSummarizedExperiment with 68 rows and 577 columns
## Features:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample availability DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matricesThe returned MultiAssayExperiment contains three assays:
- the SNP-based CNV calls stored in a
RaggedExperiment(GBM_CNVSNP), - the recurrent CNV regions summarized across the population using the
GISTIC method
(
GBM_GISTIC_Peaks), and - the normalized RNA-seq gene expression values in a
SummarizedExperiment(GBM_RNASeq2GeneNorm).
To annotate the genomic coordinates of the genes measured in the RNA-seq assay,
we use the function symbolsToRanges from the TCGAutils package.
For demonstration, we restrict the analysis to chromosome 1 and 2.
library(TCGAutils)
gbm <- TCGAutils::symbolsToRanges(gbm, unmapped=FALSE)
for(i in 1:3)
{
rr <- rowRanges(gbm[[i]])
GenomeInfoDb::genome(rr) <- "hg19"
GenomeInfoDb::seqlevelsStyle(rr) <- "UCSC"
ind <- as.character(seqnames(rr)) %in% c("chr1", "chr2")
rowRanges(gbm[[i]]) <- rr
gbm[[i]] <- gbm[[i]][ind,]
}
gbm## A MultiAssayExperiment object of 3 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 3:
## [1] GBM_CNVSNP-20160128: RaggedExperiment with 17818 rows and 1104 columns
## [2] GBM_GISTIC_Peaks-20160128: RangedSummarizedExperiment with 12 rows and 577 columns
## [3] GBM_RNASeq2GeneNorm-20160128_ranged: RangedSummarizedExperiment with 2939 rows and 166 columns
## Features:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample availability DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matricesWe now restrict the analysis to intersecting patients of the three assays
using MultiAssayExperiment’s intersectColumns function, and select
Primary Solid Tumor samples using the splitAssays function from the
TCGAutils package.
gbm <- MultiAssayExperiment::intersectColumns(gbm)
TCGAutils::sampleTables(gbm)## $`GBM_CNVSNP-20160128`
##
## 01 02 10 11
## 154 13 146 1
##
## $`GBM_GISTIC_Peaks-20160128`
##
## 01
## 154
##
## $`GBM_RNASeq2GeneNorm-20160128_ranged`
##
## 01 02
## 147 13data(sampleTypes, package="TCGAutils")
sampleTypes## Code Definition Short.Letter.Code
## 1 01 Primary Solid Tumor TP
## 2 02 Recurrent Solid Tumor TR
## 3 03 Primary Blood Derived Cancer - Peripheral Blood TB
## 4 04 Recurrent Blood Derived Cancer - Bone Marrow TRBM
## 5 05 Additional - New Primary TAP
## 6 06 Metastatic TM
## 7 07 Additional Metastatic TAM
## 8 08 Human Tumor Original Cells THOC
## 9 09 Primary Blood Derived Cancer - Bone Marrow TBM
## 10 10 Blood Derived Normal NB
## 11 11 Solid Tissue Normal NT
## 12 12 Buccal Cell Normal NBC
## 13 13 EBV Immortalized Normal NEBV
## 14 14 Bone Marrow Normal NBM
## 15 15 sample type 15 15SH
## 16 16 sample type 16 16SH
## 17 20 Control Analyte CELLC
## 18 40 Recurrent Blood Derived Cancer - Peripheral Blood TRB
## 19 50 Cell Lines CELL
## 20 60 Primary Xenograft Tissue XP
## 21 61 Cell Line Derived Xenograft Tissue XCL
## 22 99 sample type 99 99SHgbm <- TCGAutils::splitAssays(gbm, sampleCodes="01")
gbm## A MultiAssayExperiment object of 3 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 3:
## [1] 01_GBM_CNVSNP-20160128: RaggedExperiment with 17818 rows and 154 columns
## [2] 01_GBM_GISTIC_Peaks-20160128: RangedSummarizedExperiment with 12 rows and 154 columns
## [3] 01_GBM_RNASeq2GeneNorm-20160128_ranged: RangedSummarizedExperiment with 2939 rows and 147 columns
## Features:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample availability DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matricesThe SNP-based CNV calls from TCGA are provided as segmented log2 copy number ratios.
ind <- grep("CNVSNP", names(gbm))
head( mcols(gbm[[ind]]) )## DataFrame with 6 rows and 2 columns
## Num_Probes Segment_Mean
## <numeric> <numeric>
## 1 166 0.1112
## 2 3 -1.2062
## 3 40303 0.1086
## 4 271 -0.3065
## 5 88288 0.1049
## 6 33125 0.351summary( mcols(gbm[[ind]])$Segment_Mean )## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.2199 -0.9779 -0.0035 -0.6395 0.0493 6.9689It is thus necessary to convert them to integer copy number states for further analysis with CNVRanger.
In a diploid genome, a single-copy gain in a perfectly pure, homogeneous sample has a copy ratio of 3/2. On log2 scale, this is log2(3/2) = 0.585, and a single-copy loss is log2(1/2) = -1.0.
We can roughly convert a log ratio lr to an integer copy number by
round( (2^lr) * 2)Note that this is not the ideal way to calculate absolute integer copy numbers. Especially in cancer, differences in tumor purity, tumor ploidy, and subclonality can substantially interfere with the assumption of a pure homogeneous sample. See ABSOLUTE and the PureCN package for accurately taking such tumor characteristics into account.
However, without additional information we transform the log ratios into integer copy number states using the rough approximation outlined above.
smean <- mcols(gbm[[ind]])$Segment_Mean
state <- round(2^smean * 2)
state[state > 4] <- 4
mcols(gbm[[ind]])$State <- state
mcols(gbm[[ind]]) <- mcols(gbm[[ind]])[,3:1]
table(mcols(gbm[[ind]])$State)##
## 0 1 2 3 4
## 2401 4084 9581 1005 747The data is now ready for CNV-expression association analysis, where we find
only four CNV regions with sufficient sample size for testing using the default
value of 10 for the minSamples argument.
res <- cnvEQTL(cnvrs="01_GBM_GISTIC_Peaks-20160128",
calls="01_GBM_CNVSNP-20160128",
rcounts="01_GBM_RNASeq2GeneNorm-20160128_ranged",
data=gbm, window="1Mbp", verbose=TRUE)## harmonizing input:
## removing 154 sampleMap rows not in names(experiments)## Preprocessing RNA-seq data ...## Summarizing per-sample CN state in each CNV region## Excluding 8 cnvrs not satisfying min.samples threshold## Analyzing 4 regions with >=1 gene in the given window## 1 of 4## 2 of 4## 3 of 4## 4 of 4res## GRangesList object of length 4:
## $chr1:3394251-6475685
## GRanges object with 29 ranges and 5 metadata columns:
## seqnames ranges strand | logFC.CN1 logFC.CN3
## <Rle> <IRanges> <Rle> | <numeric> <numeric>
## RPL22 chr1 6245080-6259679 - | -0.721168611202118 <NA>
## CAMTA1 chr1 6845384-7829766 + | -0.70045823909777 <NA>
## KLHL21 chr1 6650779-6662929 - | -0.789504789475756 <NA>
## DNAJC11 chr1 6694228-6761966 - | -0.636852847538795 <NA>
## PHF13 chr1 6673756-6684093 + | -0.615744216633161 <NA>
## ... ... ... ... . ... ...
## CHD5 chr1 6161847-6240194 - | -0.734544520067391 <NA>
## PRDM16 chr1 2985742-3355185 + | -0.328987990681955 <NA>
## HES5 chr1 2460184-2461684 - | 0.380841126461591 <NA>
## TNFRSF14 chr1 2487805-2495267 + | 0.150779753897248 <NA>
## TNFRSF25 chr1 6521214-6526255 - | -0.177540215512479 <NA>
## logFC.CN4 PValue AdjPValue
## <numeric> <numeric> <numeric>
## RPL22 <NA> 1.33805098550937e-08 5.27786777617587e-08
## CAMTA1 <NA> 3.11715100424714e-07 1.05389391095975e-06
## KLHL21 <NA> 5.85838436249863e-06 1.73310537390585e-05
## DNAJC11 <NA> 7.68837679200395e-06 2.18349900892912e-05
## PHF13 <NA> 1.07996745663116e-05 2.94914190080048e-05
## ... ... ... ...
## CHD5 <NA> 0.254308159934797 0.300931322589509
## PRDM16 <NA> 0.409873374590411 0.454703274936237
## HES5 <NA> 0.478298099039105 0.513197098742407
## TNFRSF14 <NA> 0.484284586137201 0.513197098742407
## TNFRSF25 <NA> 0.579484808513311 0.596281469629639
##
## ...
## <3 more elements>
## -------
## seqinfo: 25 sequences (1 circular) from hg19 genomeWe can illustrate differential expression of genes in the neighborhood of a CNV
region of interest using the function plotEQTL.
res[2]## GRangesList object of length 1:
## $chr1:7908902-8336254
## GRanges object with 10 ranges and 5 metadata columns:
## seqnames ranges strand | logFC.CN1 logFC.CN3
## <Rle> <IRanges> <Rle> | <numeric> <numeric>
## PARK7 chr1 8021714-8045342 + | -0.635594956567272 <NA>
## RERE chr1 8412464-8877699 - | -0.72885329855043 <NA>
## CAMTA1 chr1 6845384-7829766 + | -0.575417567198187 <NA>
## SLC45A1 chr1 8384390-8404227 + | -0.649539777403938 <NA>
## ENO1 chr1 8921059-8939151 - | -0.450427553717116 <NA>
## VAMP3 chr1 7831329-7841492 + | -0.394346822009913 <NA>
## PER3 chr1 7844380-7905237 + | -0.508551426712072 <NA>
## H6PD chr1 9294863-9331394 + | -0.37318198362022 <NA>
## ERRFI1 chr1 8071779-8086393 - | -0.400259777959591 <NA>
## SLC2A5 chr1 9097005-9148510 - | -0.275376250374944 <NA>
## logFC.CN4 PValue AdjPValue
## <numeric> <numeric> <numeric>
## PARK7 <NA> 1.75264034801237e-09 8.29583098059187e-09
## RERE <NA> 3.96626623882152e-09 1.76003064347705e-08
## CAMTA1 <NA> 1.08646381783371e-08 4.53758418036434e-08
## SLC45A1 <NA> 3.02035479900851e-06 9.32370394476541e-06
## ENO1 <NA> 0.000491954703861377 0.0010584479992169
## VAMP3 <NA> 0.000749608836038244 0.00152063506739187
## PER3 <NA> 0.0171184889246667 0.0289383979440794
## H6PD <NA> 0.0218808741033496 0.0361288851473911
## ERRFI1 <NA> 0.111427966137584 0.152142030687855
## SLC2A5 <NA> 0.240254261695246 0.289119535260381
##
## -------
## seqinfo: 25 sequences (1 circular) from hg19 genome(r <- GRanges(names(res)[2]))## GRanges object with 1 range and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 7908902-8336254 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsplotEQTL(cnvr=r, genes=res[[2]], genome="hg19", cn="CN1")
The plot shows consistent decreased expression (negative log2 fold change) of genes in the neighborhood of the CNV region, when comparing samples with a one copy loss (1\(n\)) in that region to the 2\(n\) reference group.
Note that a significant expression change is not only observed for genes locating within the CNV region (dosage effect, here: PARK7), but also genes locating in close proximity of the CNV region (neighborhood effect, here: CAMTA1 and RERE). This is consistent with previous observations in mouse Cahan et al., 2009 and our observations in cattle Geistlinger et al., 2018.
8 CNV-phenotype association analysis
Specifically developed for CNV calls inferred from SNP-chip data,
CNVRanger allows to carry out a probe-level genome-wide association
study (GWAS) with quantitative phenotypes.
CNV calls from other sources such as sequencing data are also supported by using
the start and end position of each call as the corresponding probes.
As previously described da Silva et al., 2016, we construct CNV segments from probes representing common CN polymorphisms (allele frequency >1%), and carry out a GWAS as implemented in PLINK using a standard linear regression of phenotype on allele dosage.
For CNV segments composed of multiple probes, the segment p-value is chosen from the probe p-values, using either the probe with minimum p-value or the probe with maximum CNV frequency.
For demonstration, we use CNV data of a wild population of songbirds (da Silva et al., 2018).
cnv.loc <- file.path(data.dir, "CNVOut.txt")
cnv.calls <- read.delim(cnv.loc, as.is=TRUE)
head(cnv.calls)## chr start end sample.id state num.snps start.probe end.probe
## 1 25 6463188 6475943 1068 3 12 AX-100224358 AX-100363929
## 2 1 98166149 98184039 1068 3 28 AX-100796878 AX-100422118
## 3 4 67895958 67938901 1068 3 8 AX-100222654 AX-100726215
## 4 8 30702029 30722351 1334 3 13 AX-100160546 AX-100828216
## 5 2 877347 942971 1334 3 26 AX-100292215 AX-100391883
## 6 1 98147555 98186543 546 3 48 AX-100939600 AX-100309013Here, we use the extensibility of the basic five-column input format described in Section 4.1. In addition to the required five columns (providing chromosome, start position, end position, sample ID, and integer copy number state), we provided three optional columns storing the number of probes supporting the call, and the Affymetrix ID of the first and last probe contained in the call.
As these columns are optional, it is not ultimately necessary to provide them in order to run a CNV GWAS. However, we recommend to provide this information when available as it allows for a more fine-grained probe-by-probe GWAS.
As described in Section 4.2, we store the CNV calls
in a GRangesList for further analysis.
cnv.calls <- GenomicRanges::makeGRangesListFromDataFrame(cnv.calls,
split.field="sample.id", keep.extra.columns=TRUE)
sort(cnv.calls)## GRangesList object of length 10:
## $112
## GRanges object with 2 ranges and 4 metadata columns:
## seqnames ranges strand | state num.snps start.probe
## <Rle> <IRanges> <Rle> | <integer> <integer> <character>
## [1] 1 100727703-100730748 * | 0 8 AX-100388724
## [2] 10 19062731-19096193 * | 3 9 AX-100271359
## end.probe
## <character>
## [1] AX-100765659
## [2] AX-100147230
##
## $175
## GRanges object with 2 ranges and 4 metadata columns:
## seqnames ranges strand | state num.snps start.probe
## [1] 8 4122253-4193189 * | 3 62 AX-100097083
## [2] 27 2299391-2308228 * | 3 6 AX-100610990
## end.probe
## [1] AX-100912769
## [2] AX-100178489
##
## $356
## GRanges object with 1 range and 4 metadata columns:
## seqnames ranges strand | state num.snps start.probe
## [1] 1 100728444-100730748 * | 0 6 AX-100700982
## end.probe
## [1] AX-100765659
##
## ...
## <7 more elements>
## -------
## seqinfo: 10 sequences from an unspecified genome; no seqlengthsIn the following, we use genomic estimated breeding values (GEBVs) for breeding time (BT) as the quantitative phenotype, and accordingly analyze for each CNV region whether change in copy number is associated with change in the genetic potential for breeding time.
8.1 Setting up a CNV-GWAS
For compatibility with PLINK’s fam file format, we read phenotype information from a tab-delimited file, containing exactly four columns: sample ID, family ID, sex, and the quantitative phenotype (here: breeding time, BT).
phen.loc <- file.path(data.dir, "Pheno.txt")
phen.df <- read.delim(phen.loc, as.is=TRUE)
head(phen.df)## sample.id fam sex BT
## 1 911 NA 2 -2.842842
## 2 622 NA 2 -2.884186
## 3 1195 622 2 -3.062731
## 4 112 NA 2 -3.161435
## 5 175 NA 2 -3.597768
## 6 2391 NA 2 3.623262Although family ID and sex are listed as columns, this info is not considered in
the current implementation and can be set to NA.
As described in Section 4.3, we
combine the CNV calls with the phenotype information in a RaggedExperiment for
coordinated representation and analysis.
re <- RaggedExperiment::RaggedExperiment(cnv.calls, colData=phen.df)
re## class: RaggedExperiment
## dim: 19 10
## assays(4): state num.snps start.probe end.probe
## rownames: NULL
## colnames(10): 112 175 ... 1334 2391
## colData names(4): sample.id fam sex BTIf probe information is available and has been annotated to the CNV calls, as we
did above, the probe IDs and corresponding genomic positions should be provided
in a separate file.
For compatibility with PLINK’s
map file format, this is
expected to be a tab-delimited file containing exactly three columns: probe ID,
chromosome, and the position in bp.
map.loc <- file.path(data.dir, "MapPenn.txt")
map.df <- read.delim(map.loc, as.is=TRUE)
head(map.df)## Name Chr Position
## 1 AX-100939600 1 98147555
## 2 AX-100088448 1 98148072
## 3 AX-100954037 1 98150537
## 4 AX-100836117 1 98151270
## 5 AX-100027637 1 98151959
## 6 AX-100215062 1 98151992Given a RaggedExperiment storing CNV calls together with phenotype information,
and optionally a map file for probe-level coordinates, the setupCnvGWAS function
sets up all files needed for the GWAS analysis.
The information required for analysis is stored in the resulting phen.info list:
phen.info <- setupCnvGWAS("example", cnv.out.loc=re, map.loc=map.loc)
phen.info## $samplesPhen
## [1] "911" "622" "1195" "112" "175" "2391" "1068" "546" "356" "1334"
##
## $phenotypes
## [1] "BT"
##
## $phenotypesdf
## BT
## 1 -2.842842
## 2 -2.884186
## 3 -3.062731
## 4 -3.161435
## 5 -3.597768
## 6 3.623262
## 7 3.216123
## 8 3.129881
## 9 3.106459
## 10 3.004740
##
## $phenotypesSam
## samplesPhen BT
## 1 911 -2.842842
## 2 622 -2.884186
## 3 1195 -3.062731
## 4 112 -3.161435
## 5 175 -3.597768
## 6 2391 3.623262
## 7 1068 3.216123
## 8 546 3.129881
## 9 356 3.106459
## 10 1334 3.004740
##
## $FamID
## samplesPhen V2
## 1 911 -9
## 2 622 -9
## 3 1195 622
## 4 112 -9
## 5 175 -9
## 6 2391 -9
## 7 1068 -9
## 8 546 -9
## 9 356 -9
## 10 1334 -9
##
## $SexIds
## samplesPhen V2
## 1 911 2
## 2 622 2
## 3 1195 2
## 4 112 2
## 5 175 2
## 6 2391 2
## 7 1068 2
## 8 546 2
## 9 356 2
## 10 1334 2
##
## $all.paths
## Inputs
## "C:\\Users\\biocbuild\\AppData\\Local\\CNVRanger\\CNVRanger/example/Inputs"
## PLINK
## "C:/Users/biocbuild/AppData/Local/CNVRanger/CNVRanger/example/PLINK/plink-1.07-dos"
## Results
## "C:\\Users\\biocbuild\\AppData\\Local\\CNVRanger\\CNVRanger/example/Results"The last item of the list displays the working directory:
all.paths <- phen.info$all.paths
all.paths## Inputs
## "C:\\Users\\biocbuild\\AppData\\Local\\CNVRanger\\CNVRanger/example/Inputs"
## PLINK
## "C:/Users/biocbuild/AppData/Local/CNVRanger/CNVRanger/example/PLINK/plink-1.07-dos"
## Results
## "C:\\Users\\biocbuild\\AppData\\Local\\CNVRanger\\CNVRanger/example/Results"For the GWAS, chromosome names are assumed to be integer (i.e. 1, 2, 3, ...).
Non-integer chromosome names can be encoded by providing a data.frame that
describes the mapping from character names to corresponding integers.
For the example data, chromosomes 1A, 4A, 25LG1, 25LG2, and LGE22 are correspondingly encoded via
# Define chr correspondence to numeric
chr.code.name <- data.frame(
V1 = c(16, 25, 29:31),
V2 = c("1A", "4A", "25LG1", "25LG2", "LGE22"))8.2 Running a CNV-GWAS
We can then run the actual CNV-GWAS, here without correction for multiple testing which is done for demonstration only. In real analyses, multiple testing correction is recommended to avoid inflation of false positive findings.
segs.pvalue.gr <- cnvGWAS(phen.info, chr.code.name=chr.code.name, method.m.test="none")
segs.pvalue.gr## GRanges object with 16 ranges and 6 metadata columns:
## seqnames ranges strand | SegName MinPvalue
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] 1 98171563-98184039 * | 2 0.0323
## [2] 8 4121283-4188293 * | 7 0.03494
## [3] 8 4193189 * | 8 0.1124
## [4] 1 98186123-98186543 * | 3 0.1202
## [5] 1 98147555-98171009 * | 1 0.1977
## ... ... ... ... . ... ...
## [12] 18 1278467-1295371 * | 13 0.3856
## [13] 11 18840662 * | 12 0.3926
## [14] 21 3326720-3329134 * | 14 0.3926
## [15] 11 18836038-18839377 * | 11 0.9688
## [16] 1 100728444-100730326 * | 4 0.9722
## NameProbe Frequency MinPvalueAdjusted Phenotype
## <character> <character> <numeric> <character>
## [1] AX-100075281 3 0.0323 BT
## [2] AX-100015058 3 0.03494 BT
## [3] AX-100912769 2 0.1124 BT
## [4] AX-100011247 2 0.1202 BT
## [5] AX-100171012 4 0.1977 BT
## ... ... ... ... ...
## [12] AX-100116173 1 0.3856 BT
## [13] AX-100673859 1 0.3926 BT
## [14] AX-100389358 1 0.3926 BT
## [15] AX-100337682 2 0.9688 BT
## [16] AX-100108194 2 0.9722 BT
## -------
## seqinfo: 10 sequences from an unspecified genome; no seqlengthsThe CNV-GWAS uses the concept of CNV segments to define more fine-grained CNV loci within CNV regions.
Figure 1: Definition of CNV segments
The figure shows construction of a CNV segment from 4 individual CNV calls in a CNV region based on pairwise copy number concordance between adjacent probes.
This procedure was originally proposed in our previous work in Nelore cattle (da Silva et al., 2016) and defines CNV segments based on CNV genotype similarity of subsequent SNP probes.
The default is min.sim=0.95, which will continuously add probe positions to a
given CNV segment until the pairwise genotype similarity drops below 95%.
An example of detailed up-down CNV genotype concordance that is used for the
construction of CNV segments is given in S12 Table in
da Silva et al., 2016.
As PLINK returns a p-value for each probe, only one of the p-values of the
probes contained in a CNV segment is chosen as the segment p-value.
This is similar to a common approach used in differential expression (DE) analysis of microarray gene expression data, where typically the most significant DE probe is chosen in case of multiple probes mapping to the same gene.
Here, the representative probe for the CNV segment can be chosen to be the probe
with lowest p-value (assign.probe="min.pvalue", default) or the one with highest
CNV frequency (assign.probe="high.freq").
Multiple testing correction based on the number of CNV segments tested is carried out using the FDR approach (default). Results can then be displayed as for regular GWAS via a Manhattan plot (which can optionally be exported to a pdf file).
## Define the chromosome order in the plot
order.chrs <- c(1:24, "25LG1", "25LG2", 27:28, "LGE22", "1A", "4A")
## Chromosome sizes
chr.size.file <- file.path(data.dir, "Parus_major_chr_sizes.txt")
chr.sizes <- scan(chr.size.file)
chr.size.order <- data.frame(chr=order.chrs, sizes=chr.sizes, stringsAsFactors=FALSE)
## Plot a pdf file with a manhatthan within the 'Results' workfolder
plotManhattan(all.paths, segs.pvalue.gr, chr.size.order, plot.pdf=FALSE)
8.3 Producing a GDS file in advance
The genomic data structure (GDS) file format supports efficient memory management
for genotype analysis.
To make use of this efficient data representation, CNV genotypes analyzed with
the cnvGWAS function
are stored in a CNV.gds file, which is automatically produced and placed in
the Inputs folder (i.e. all.paths[1]).
Therefore, running a GWAS implies that any GDS file produced by previous analysis
will be overwritten. Use produce.gds=FALSE to avoid overwriting in the GWAS run.
For convenience, a GDS file can be produced before the GWAS analysis with the
generateGDS function.
This additionally returns a GRanges object containing the genomic
position, name and, frequency of each probe used to construct the CNV segments
for the GWAS analysis.
Note that probes.cnv.gr object contains the integer chromosome names
(as the GDS file on disk).
Only the segs.pvalue.gr, which stores the GWAS results, has the character
chromosome names.
## Create a GDS file in disk and export the SNP probe positions
probes.cnv.gr <- generateGDS(phen.info, chr.code.name=chr.code.name)
probes.cnv.gr## GRanges object with 189 ranges and 3 metadata columns:
## seqnames ranges strand | Name freq snp.id
## <Rle> <IRanges> <Rle> | <character> <integer> <integer>
## [1] 1 98147555 * | AX-100939600 2 1
## [2] 1 98148072 * | AX-100088448 2 2
## [3] 1 98150537 * | AX-100954037 2 3
## [4] 1 98151270 * | AX-100836117 2 4
## [5] 1 98151959 * | AX-100027637 2 5
## ... ... ... ... . ... ... ...
## [185] 25 6471766 * | AX-100066308 1 185
## [186] 25 6473449 * | AX-100023435 1 186
## [187] 25 6474550 * | AX-100397956 1 187
## [188] 25 6475943 * | AX-100363929 1 188
## [189] 27 2308228 * | AX-100178489 1 189
## -------
## seqinfo: 15 sequences from an unspecified genome; no seqlengths## Run GWAS with existent GDS file
segs.pvalue.gr <- cnvGWAS(phen.info, chr.code.name=chr.code.name, produce.gds=FALSE)8.4 Using relative signal intensities
SNP-based CNV callers such as PennCNV and Birdsuit infer CNVs from SNP-chip intensities (log R ratios, LRRs) and allele frequencies (B allelel frequencies, BAFs). As an auxiliary analysis, it can be interesting to carry out the GWAS based on the LRR relative signal intensities itself (da Silva et al., 2018).
To perform the GWAS using LRR values, import the LRR/BAF values and set run.lrr=TRUE
in the cnvGWAS function:
# List files to import LRR/BAF
files <- list.files(data.dir, pattern = "\\.cnv.txt.adjusted$")
samples <- sub(".cnv.txt.adjusted$", "", files)
samples <- sub("^GT","", samples)
sample.files <- data.frame(file.names=files, sample.names=samples)
# All missing samples will have LRR = '0' and BAF = '0.5' in all SNPs listed in the GDS file
importLrrBaf(all.paths, data.dir, sample.files, verbose=FALSE)
# Read the GDS to check if the LRR/BAF nodes were added
cnv.gds <- file.path(all.paths[1], "CNV.gds")
(genofile <- SNPRelate::snpgdsOpen(cnv.gds, allow.fork=TRUE, readonly=FALSE))## File: C:\Users\biocbuild\AppData\Local\CNVRanger\CNVRanger\example\Inputs\CNV.gds (49.8K)
## + [ ] *
## |--+ sample.id { Str8 10 ZIP_ra(122.7%), 61B }
## |--+ snp.id { Str8 189 ZIP_ra(45.4%), 301B }
## |--+ snp.rs.id { Str8 189 ZIP_ra(31.9%), 791B }
## |--+ snp.position { Int32 189 ZIP_ra(86.2%), 659B }
## |--+ snp.chromosome { Str8 189 ZIP_ra(11.8%), 56B }
## |--+ genotype { Bit2 189x10, 473B } *
## |--+ CNVgenotype { Float64 189x10, 14.8K }
## |--+ phenotype [ data.frame ] *
## | |--+ samplesPhen { Str8 10, 44B }
## | \--+ BT { Float64 10, 80B }
## |--+ FamID { Str8 10, 31B }
## |--+ Sex { Str8 10, 20B }
## |--+ Chr.names [ data.frame ] *
## | |--+ V1 { Float64 5, 40B }
## | \--+ V2 { Int32,factor 5, 20B } *
## |--+ LRR { Float64 189x10, 14.8K }
## \--+ BAF { Float64 189x10, 14.8K }gdsfmt::closefn.gds(genofile)
# Run the CNV-GWAS with existent GDS
segs.pvalue.gr <- cnvGWAS(phen.info, chr.code.name=chr.code.name, produce.gds=FALSE, run.lrr=TRUE)9 Session info
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2012 R2 x64 (build 9600)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] ensembldb_2.8.0
## [2] AnnotationFilter_1.8.0
## [3] GenomicFeatures_1.36.4
## [4] AnnotationDbi_1.46.0
## [5] Gviz_1.28.0
## [6] TCGAutils_1.4.0
## [7] curatedTCGAData_1.6.0
## [8] MultiAssayExperiment_1.10.4
## [9] SummarizedExperiment_1.14.0
## [10] DelayedArray_0.10.0
## [11] BiocParallel_1.18.0
## [12] matrixStats_0.54.0
## [13] Biobase_2.44.0
## [14] BSgenome.Btaurus.UCSC.bosTau6.masked_1.3.99
## [15] BSgenome.Btaurus.UCSC.bosTau6_1.4.0
## [16] BSgenome_1.52.0
## [17] rtracklayer_1.44.1
## [18] Biostrings_2.52.0
## [19] XVector_0.24.0
## [20] regioneR_1.16.2
## [21] AnnotationHub_2.16.0
## [22] BiocFileCache_1.8.0
## [23] dbplyr_1.4.2
## [24] CNVRanger_1.0.3
## [25] RaggedExperiment_1.8.0
## [26] GenomicRanges_1.36.0
## [27] GenomeInfoDb_1.20.0
## [28] IRanges_2.18.1
## [29] S4Vectors_0.22.0
## [30] BiocGenerics_0.30.0
## [31] BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1
## [2] biovizBase_1.32.0
## [3] htmlTable_1.13.1
## [4] qqman_0.1.4
## [5] base64enc_0.1-3
## [6] dichromat_2.0-0
## [7] rstudioapi_0.10
## [8] bit64_0.9-7
## [9] interactiveDisplayBase_1.22.0
## [10] xml2_1.2.0
## [11] splines_3.6.0
## [12] knitr_1.23
## [13] zeallot_0.1.0
## [14] Formula_1.2-3
## [15] jsonlite_1.6
## [16] Rsamtools_2.0.0
## [17] cluster_2.1.0
## [18] shiny_1.3.2
## [19] BiocManager_1.30.4
## [20] readr_1.3.1
## [21] compiler_3.6.0
## [22] httr_1.4.0
## [23] backports_1.1.4
## [24] assertthat_0.2.1
## [25] Matrix_1.2-17
## [26] lazyeval_0.2.2
## [27] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [28] limma_3.40.2
## [29] later_0.8.0
## [30] acepack_1.4.1
## [31] htmltools_0.3.6
## [32] prettyunits_1.0.2
## [33] tools_3.6.0
## [34] gtable_0.3.0
## [35] glue_1.3.1
## [36] GenomeInfoDbData_1.2.1
## [37] dplyr_0.8.3
## [38] rappdirs_0.3.1
## [39] Rcpp_1.0.1
## [40] vctrs_0.2.0
## [41] ExperimentHub_1.10.0
## [42] xfun_0.8
## [43] stringr_1.4.0
## [44] rvest_0.3.4
## [45] mime_0.7
## [46] statmod_1.4.32
## [47] XML_3.98-1.20
## [48] SNPRelate_1.18.1
## [49] org.Hs.eg.db_3.8.2
## [50] edgeR_3.26.5
## [51] zlibbioc_1.30.0
## [52] scales_1.0.0
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## [54] ProtGenerics_1.16.0
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## [58] RColorBrewer_1.1-2
## [59] yaml_2.2.0
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