Abstract
Spontaneously arising (de novo) genetic variants are important in human disease, yet every individual carries many such variants, with a median of 1 de novo variant affecting the protein-coding portion of the genome. A recently described mutational model (Samocha et al., 2014) provides a powerful framework for the robust statistical evaluation of such coding variants, enabling the interpretation of de novo variation in human disease. Here we describe a new open-source software package, denovolyzeR, that implements this model and provides tools for the analysis of de novo coding sequence variants.
Keywords: de novo variant, exome sequencing
INTRODUCTION
Spontaneously arising (de novo) genetic variants are important in human disease. Every individual carries approximately 100 such variants that are not present in their parents’ DNA, but rather have arisen via mutational events in the parental germ cell (egg or sperm) or early embryo, with a median of 1 de novo variant affecting the protein-coding portion of genome, referred to as the exome (Conrad et al., 2011; Lynch, 2010).
Exome sequencing and analysis of de novo variants has successfully identified genes underlying rare and genetically homogeneous Mendelian diseases. In Kabuki syndrome, for example, non-synonymous de novo variants were identified in KMT2D (MLL2) in 9 out of 10 unrelated individuals (Ng et al., 2010). An accumulation of this magnitude would be extremely improbable in the absence of a causal role in the disease given both the rarity and independence of de novo variants.
By contrast, it is more challenging to dissect the role of de novo variants in conditions with high levels of locus heterogeneity, including heritable complex traits and some Mendelian conditions, where de novo variants may be spread across many genes, and may make a smaller overall contribution to pathogenesis. Here it may be possible to assess the global contribution of de novo coding variants to disease by comparing their frequency in cases and controls, given sufficiently large sample sizes. However, at the level of individual genes, the interpretation of de novo variants is complicated by the background mutation rate, which varies greatly between genes. Additionally, as more individuals are sequenced, it is inevitable that multiple de novo variants will be observed in some genes by chance.
A statistical framework has recently been developed to address these challenges, with respect to de novo single nucleotide variants (SNVs) in coding sequence (Samocha et al., 2014). Briefly, the mutability of each gene is individually determined based on local sequence context, and the probability that a de novo event will arise in a single copy of the gene in one generation is calculated. The consequence of each possible de novo SNV is computed, and de novo probabilities are tabulated for each variant class (e.g. synonymous, missense, etc). In order to more fully assess loss-of-function (lof) variation, the probability of a frameshifting insertion or deletion is also estimated for all genes (proportional to the length of the gene and the ratio of nonsense to frameshifting indels genome-wide under the assumption that the two classes have similar selective pressure against them). For a given study population, de novo variants can be evaluated by comparing the observed numbers of variants with the number expected based on this model and the population size, using a Poisson framework. This permits robust significance estimates for the pileup of de novo variation in individual genes and gene sets, and increases the power of genome-wide analyses.
In this unit, we describe the application of this statistical framework to analyze de novo variants using denovolyzeR, an open-source software package written for the R statistical software environment (R Core Team, 2015). We present protocols for four analyses: to assess (i) whether there is a genome-wide excess of de novo variation for different functional classes of variant, (ii) whether there is a genome-wide excess of genes with multiple de novo variants, (iii) whether individual genes carry an excess of de novo variants, and (iv) whether a pre-specified set of genes collectively shows an enrichment of de novo variants.
BASIC PROTOCOL 1: ASSESSING THE GENOME-WIDE BURDEN OF DE NOVO VARIANTS
This protocol will assess whether there is a genome-wide excess of de novo variation for different functional classes of variant.
Materials
A computer running the R software environment, available for UNIX platforms, Windows and MacOS from http://www.r-project.org.
- The denovolyzeR package. The latest stable release can be installed directly from the Comprehensive R Archive Network (CRAN) from within R
install.packages(“denovolyzeR”)
Other download and installation options, including for the latest development version, are described at http://denovolyzer.org
- dplyr and reshape packages. These dependencies may be installed automatically when denovolyzeR is installed (depending on your installation route). Otherwise they can be installed by running:
install.packages(“dplyr”,”reshape”)
A table of de novo variants. The minimum input comprises two columns of data: gene names, and variant classes (functional consequence of each variant). Example data is included in the denovolyzeR package, and will be used in this protocol. The dataset comprises a data.frame of de novo variants identified in 1078 individuals with autism (Samocha et al., 2014), named autismDeNovos. It is assumed that readers are able to import their own data into the R environment, using the read.table function or equivalent (in R, ?read.table will provide help).
-
1)In R, load the denovolyzeR package.
library(denovolyzeR)
-
2)Prepare input data. View demonstration data provided with the denovolyzeR package. Alternatively, users may import their own data in an equivalent format.
dim(autismDeNovos); head(autismDeNovos) ## [1] 1040 2 ## gene class ## 1 BCORL1 mis ## 2 SPANXD mis ## 3 GLRA2 mis ## 4 RPS6KA3 non ## 5 TSR2 mis ## 6 GNL3L syn
Variant classes must be labeled using the following terms:
“syn” (synonymous), “mis” (missense), “non” (nonsense), “splice” (canonical splice site) or “frameshift”. Alternatively, “lof” may be used collectively for loss-of-function classes (“non + splice + frameshift”). Whichever input format is chosen, summary statistics can be produced for “lof”, “prot” (protein-altering = mis + lof), and “all”. “prot” and “all” are not valid input classes. In-frame insertions/deletions are currently not evaluated within the statistical framework.
A variety of gene identifiers may be used. Valid identifiers recognized by the software include: hgncID, hgncSymbol, enstID, ensgID, geneName. The default option (“geneName”) specifies gene symbols by default, more specifically, these correspond to the “external_gene_name” provided by the Ensembl genome browser (Cunningham et al.). ensgID and enstID refer to Ensembl gene and transcript identifiers. hgncID and hgncSymbol refer to HUGO Gene Nomenclature Committee ID numbers and symbols. Within the R environment, the BiomaRt package (Durinck et al., 2009) from Bioconductor provides tools to convert gene identifiers.
-
3)
Compare the observed burden of de novo variation to expectation.
The denovolyzeByClass function will perform the required analysis. The function has three required arguments:
genes: a vector of gene identifiers, for genes that contain de novo variants
classes: a vector of variant consequences (corresponding to the gene list)
- nsamples: the total number of samples analyzed (including samples without de novo variants). For the example data, 1078 individuals were sequenced.
denovolyzeByClass(genes=autismDeNovos$gene, classes=autismDeNovos$class, nsamples=1078) ## class observed expected enrichment pValue ## 1 syn 254 302.3 0.840 0.998000 ## 2 mis 655 679.0 0.965 0.826000 ## 3 lof 131 94.3 1.390 0.000199 ## 4 prot 786 773.2 1.020 0.328000 ## 5 all 1040 1075.5 0.967 0.864000
For each variant class, this function returns the observed number of variants, the expected number of variants, enrichment (= observed/expected), and the p value (obtained from a Poisson test).
The output can be customized using the “includeClasses” argument, either to display only a subset of variant classes of interest
denovolyzeByClass(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078,
includeClasses=c(“mis”,”lof”))
## class observed expected enrichment pValue
## 1 mis 655 679.0 0.965 0.826000
## 2 lof 131 94.3 1.390 0.000199
or to display increased granularity. By default, nonsense, frameshift & splice variants are analyzed in combination as “lof”, but may be analyzed separately.
denovolyzeByClass(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078,
includeClasses=c(“frameshift”,”non”,”splice”,”lof”))
## class observed expected enrichment pValue
## 1 non 52 34.8 1.500 0.003740
## 2 splice 15 16.1 0.934 0.638000
## 3 frameshift 64 43.4 1.470 0.002070
## 4 lof 131 94.3 1.390 0.000199
Further information on function options, and help generally, is available using the help function.
help(denovolyzeByClass)
BASIC PROTOCOL 2: ASSESSING THE NUMBER OF GENES WITH MULTIPLE DE NOVO VARIANTS
The occurrence of multiple de novo events in a single gene, in a cohort of individuals with a common phenotype, may implicate that gene in the pathogenesis of the condition under study. Before evaluating single genes, it is instructive to assess the total number of genes harboring multiple de novo variants. Here, the number of genes containing multiple de novo variants is compared with an empirical distribution derived by permutation.
Materials
As for protocol 1
-
1)Ensure the denovolyzeR library and data for analysis are loaded.
library(denovolyzeR)
-
2)
The denovolyzeMultiHits function will perform the required analysis. The same three arguments are required as for BASIC PROTOCOL 1: genes (vector of genes containing de novo variants), classes (a vector of variant consequences) and nsamples (number of samples). In addition, nperms determines the number of permutations run (defaults to 100).
The function addresses the questions “given nVars variants in a set of genes, how many genes are expected to contain more than one variant? Do we observe more than this?”
denovolyzeMultiHits(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078,
nperms=100)
## obs expMean expMax pValue nVars
## syn 3 3.6 11 0.62 254
## mis 31 20.3 31 0.04 655
## lof 5 1.2 5 0.01 131
## prot 43 29.0 43 0.01 786
## all 66 47.4 64 0.00 1040
For each variant class, the function returns the observed number of genes containing multiple de novo variants in the user data provided (“obs”), the average number of genes containing multiple hits across nperms permutations (“expMean”), the maximum number of genes containing multiple hits in any permutation (“expMax”), and an empirical p value (“pValue”). In this case some of the p values are returned as 0, indicating < 1/nperms (in this case <0.01). We can obtain a better estimate by increasing the number of permutations:
denovolyzeMultiHits(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078,
nperms=5000,
includeClasses=“prot”)
## obs expMean expMax pValue nVars
## prot 43 28.1 46 0.0026 786
Note that the exact numbers may change slightly between runs of denovolyzeMultiHits due to stochastic changes in the permutations. These stochastic fluctuations are likely to be small for large numbers of permutations. Finally, the function reports the total number of de novo variants of each class, which is the number used as input to the permutation (“nVars”).
-
3)
This function can be run in two modes. The expected number of genes containing >1 hit is obtained by permutation: given nVars de novo variants, how many genes contain >1 variant? There are two options for selecting nVars. By default, this number is derived from the input data - in other words, the total number of lof variants that are permuted across the defined gene list is the total number of lof variants in the input data. An alternative approach uses the expected number of lof variants in the gene list, as determined by the model.
In the example above autismDeNovos contains 131 lof variants, so by default this is the number used in the permutation:sum(autismDeNovos$class %in% c(“frameshift”,”non”,”splice”)) ## [1] 131 denovolyzeMultiHits(genes=autismDeNovos$gene, classes=autismDeNovos$class, nsamples=1078, includeClasses=“lof”) ## obs expMean expMax pValue nVars ## lof 5 0.9 5 0.01 131
The expected number of de novo variants is controlled by the nVars argument, whose default value is “actual”. This is a conservative approach, addressing the question: “given the number of variants in our dataset, do we see more genes with >1 variant than expected?” An alternative approach simply asks whether there are more genes with >1 variant than our de novo model predicts. This is accessed by setting nVars=“expected”.
denovolyzeMultiHits(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078,
includeClasses=“lof”,
nVars=“expected”)
## obs expMean expMax pValue nVars
## lof 5 0.5 3 0 94.26139
BASIC PROTOCOL 3: ASSESSING THE FREQUENCY OF DE NOVO VARIANTS IN INDIVIDUAL GENES
In the previous protocol, we assessed whether there were more genes containing multiple de novo variants than expected by chance. In the example data, we noted five genes with multiple loss-of-function hits. In this next protocol, we will determine whether any individual genes carry an excess of de novo variants, using the denovolyzeByGene function.
Materials
As for protocol 1
- Ensure the denovolyzeR library and data for analysis are loaded.
library(denovolyzeR)
- Call the denovolyzeByGene function. The same three arguments are required as for the previous protocols: genes (vector of names of genes containing de novo variants), classes (a vector of variant consequences) and nsamples (number of samples). This function will return one row per gene, ordered according the significance of any enrichment in de novo variants. Given the size of the data, we will only view the first few lines here, using the head function.
head( denovolyzeByGene(genes=autismDeNovos$gene, classes=autismDeNovos$class, nsamples=1078) ) ## lof.obs lof.exp lof.pValue prot.obs prot.exp prot.pValue ## DYRK1A 3 0 2.69e-08 3 0.1 2.77e-05 ## SCN2A 3 0 1.83e-06 5 0.1 3.70e-07 ## CHD8 3 0 7.19e-07 4 0.2 2.44e-05 ## RFX8 0 0 1.00e+00 2 0.0 2.34e-05 ## SUV420H1 1 0 6.37e-03 3 0.1 3.17e-05 ## POGZ 2 0 1.23e-04 2 0.1 5.07e-03
denovolyzeR will output one line for every gene that contains at least one variant in the input data. In order to view only genes with multiple hits, we can use the subset function to select genes with more than one observed protein-altering variant:
library(dplyr)
denovolyzeByGene(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078) %>%
subset(prot.obs>1)
## lof.obs lof.exp lof.pValue prot.obs prot.exp prot.pValue
## DYRK1A 3 0.0 2.69e-08 3 0.1 2.77e-05
## SCN2A 3 0.0 1.83e-06 5 0.1 3.70e-07
## CHD8 3 0.0 7.19e-07 4 0.2 2.44e-05
## RFX8 0 0.0 1.00e+00 2 0.0 2.34e-05
## SUV420H1 1 0.0 6.37e-03 3 0.1 3.17e-05
## POGZ 2 0.0 1.23e-04 2 0.1 5.07e-03
## ARID1B 2 0.0 1.64e-04 2 0.2 1.13e-02
## PLEKHA8 0 0.0 1.00e+00 2 0.0 4.47e-04
## TUBA1A 0 0.0 1.00e+00 2 0.0 5.37e-04
## SLCO1C1 0 0.0 1.00e+00 2 0.0 7.41e-04
## NTNG1 0 0.0 1.00e+00 2 0.0 8.20e-04
## TSNARE1 0 0.0 1.00e+00 2 0.0 1.20e-03
## MEGF11 0 0.0 1.00e+00 2 0.1 1.88e-03
## SRBD1 0 0.0 1.00e+00 2 0.1 1.92e-03
## TBR1 1 0.0 5.25e-03 2 0.1 1.93e-03
## KRBA1 0 0.0 1.00e+00 2 0.1 2.02e-03
## KIRREL3 0 0.0 1.00e+00 2 0.1 2.15e-03
## NR3C2 1 0.0 8.62e-03 2 0.1 2.18e-03
## ABCA13 0 0.0 1.00e+00 3 0.3 2.71e-03
## UBE3C 0 0.0 1.00e+00 2 0.1 3.28e-03
## SETD5 0 0.0 1.00e+00 2 0.1 3.91e-03
## AGAP2 0 0.0 1.00e+00 2 0.1 4.21e-03
## ZNF423 0 0.0 1.00e+00 2 0.1 5.71e-03
## GSE1 0 0.0 1.00e+00 2 0.1 5.74e-03
## ZNF638 1 0.0 1.49e-02 2 0.1 6.61e-03
## ADCY5 0 0.0 1.00e+00 2 0.1 6.71e-03
## SCN1A 0 0.0 1.00e+00 2 0.1 9.09e-03
## LAMB2 0 0.0 1.00e+00 2 0.2 1.15e-02
## MYO7B 0 0.0 1.00e+00 2 0.2 1.16e-02
## PLXNB1 1 0.0 1.37e-02 2 0.2 1.20e-02
## ZFYVE26 1 0.0 2.01e-02 2 0.2 1.33e-02
## SBF1 0 0.0 1.00e+00 2 0.2 1.34e-02
## BRCA2 0 0.0 1.00e+00 2 0.2 1.48e-02
## TRIO 0 0.0 1.00e+00 2 0.2 2.32e-02
## ALMS1 0 0.0 1.00e+00 2 0.2 2.39e-02
## RELN 1 0.0 3.91e-02 2 0.2 2.63e-02
## ANK2 1 0.0 3.27e-02 2 0.3 2.85e-02
## KMT2C 1 0.1 5.09e-02 2 0.3 4.33e-02
## FAT1 0 0.0 1.00e+00 2 0.3 4.44e-02
## GPR98 0 0.0 1.00e+00 2 0.4 5.21e-02
## AHNAK2 0 0.0 1.00e+00 2 0.4 6.25e-02
## MUC5B 0 0.0 1.00e+00 2 0.4 7.34e-02
## SYNE1 0 0.1 1.00e+00 2 0.6 1.31e-01
In this example we have used the pipe notation “%>%” to pass the output of denovolyzeR to the subset function. The pipe is available as part of the dplyr package, which is required for denovoloyzeR installation.
The p-values returned are not corrected for multiple testing. These default options apply two tests (“lof” and “prot”) across 19618 genes, so a Bonferroni corrected p-value threshold at α = 0.05 would be 1.3×10−06 (0.05/2 * 19618).
By default this function compares the number of lof variants against expectation for each gene, and then the total number of protein-altering variants (lof + missense). It can also be configured to return other classes if relevant, using the includeClasses argument.
head(
denovolyzeByGene(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078,
includeClasses=“syn”)
)
## syn.obs syn.exp syn.pValue
## PBLD 2 0 3.04e-05
## ADNP2 2 0 4.96e-04
## SPRR2D 1 0 1.70e-03
## C1ORF146 1 0 2.78e-03
## PTMS 1 0 2.78e-03
## RBM20 1 0 3.49e-03
BASIC PROTOCOL 4: ASSESSING A PRE-SPECIFIED GENE SET
This protocol assesses whether a pre-specified set of genes collectively shows an enrichment of de novo variants. Note that any of the previous analyses can be restricted to a pre-specified gene set in the same way, using the includeGenes argument. This may be appropriate if a smaller panel of genes have been sequenced (rather than whole exome sequencing), or to explore biologically relevant gene sets, e.g. defined by gene ontology, or expression profile.
Materials
As for protocol 1
- Ensure the denovolyzeR library and data for analysis are loaded.
library(denovolyzeR)
- Define a gene set. This should be a vector of genes, which may be entered by hand, or read from file using read.table or equivalent. In this example, we use an example gene set included with the denovolyzeR package, a list of 837 genes that interact with the fragile X mental retardation protein (FMRP), taken from (Darnell et al., 2011).
nrow(fmrpGenes);head(fmrpGenes) ## [1] 837 ## ensgID enstID hgncID hgncSymbol geneName ## 1 ENSG00000142599 ENST00000337907 9965 RERE RERE ## 2 ENSG00000149527 ENST00000449969 29037 PLCH2 PLCH2 ## 3 ENSG00000078369 ENST00000378609 4396 GNB1 GNB1 ## 4 ENSG00000157933 ENST00000378536 10896 SKI SKI ## 5 ENSG00000171735 ENST00000303635 18806 CAMTA1 CAMTA1 ## 6 ENSG00000188157 ENST00000379370 329 AGRN AGRN
- Evaluate the frequency of de novo events in our pre-specified genelist, using the denovolyzeByClass function. Specify the genelist using the includeGenes argument, which defaults to “all”, but accepts a vector of genes.
denovolyzeByClass(genes=autismDeNovos$gene, classes=autismDeNovos$class, nsamples=1078, includeGenes=fmrpGenes$geneName) ## class observed expected enrichment pValue ## 1 syn 28 33.6 0.835 8.53e-01 ## 2 mis 83 74.4 1.110 1.74e-01 ## 3 lof 32 9.1 3.500 3.18e-09 ## 4 prot 115 83.6 1.380 6.47e-04 ## 5 all 143 117.1 1.220 1.13e-02
In this example we see a highly significant enrichment of de novo lof variants in genes that interact with FMRP in our cohort of autism cases. Care should be taken to ensure that the same gene identifiers are used throughout the analysis. For example, if the list of genes containing de novo variants includes KMT2D (previously known as MLL2) but the gene set uses the old symbol MLL2, these will not be matched. The function will give a warning if gene identifiers are used that are not found in the internal mutation probability tables.
For many genes, the Ensembl gene name and HGNC symbol will be identical, but in some instances they differ (e.g. where there is no HGNC identifier, and Ensembl uses a symbol from another source). Note that we receive a warning if we pass a list of genes described using Ensembl gene symbols (the demonstration data), but tell the software to match to HGNC symbols.
denovolyzeByClass(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078,
geneId=”hgncSymbol”)
## Warning in parseInput(genes, classes, nsamples, groupBy,
includeGenes,
## includeClasses, : 3 gene identifiers in input list do not match the
## probability table, and are excluded from analysis.
## class observed expected enrichment pValue
## 1 syn 254 302.3 0.840 0.998000
## 2 mis 652 679.0 0.960 0.854000
## 3 lof 131 94.3 1.390 0.000199
## 4 prot 783 773.2 1.010 0.368000
## 5 all 1037 1075.5 0.964 0.883000
Similarly, we will get a warning if “includeGenes” contains non-matching identifiers
denovolyzeByClass(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078,
includeGenes=fmrpGenes$enstID)
## Warning in parseInput(genes, classes, nsamples, groupBy,
includeGenes,
## includeClasses, : 837 gene identifiers in “includeGene” are not in
the
## probability table, and are excluded from analysis.
## [1] class observed expected enrichment pValue
## <0 rows> (or 0-length row.names)
SUPPORT PROTOCOL 1: GETTING HELP
Help on any of the functions described is available using the standard R help functions, e.g. help(denovolyze) or ?denovolyze. Additional details are also available in the package vignette, accessed using browseVignettes(“denovolyzeR”).
SUPPORT PROTOCOL 2: VIEWING THE MUTATIONAL PROBABILITY TABLES
Users may want to view or export the probability tables that underpin these analyses. These are best accessed using the viewProbabilityTable function.
probabilityTable <- viewProbabilityTable() nrow(probabilityTable); head(probabilityTable) ## [1] 19618 ## hgncID hgncSymbol enstID ensgID geneName syn ## 1 5 A1BG ENST00000263100 ENSG00000121410 A1BG 8.997970e-06 ## 2 7 A2M ENST00000318602 ENSG00000175899 A2M 1.543159e-05 ## 3 16 SERPINA3 ENST00000467132 ENSG00000196136 SERPINA3 5.694582e-06 ## 4 17 AADAC ENST00000232892 ENSG00000114771 AADAC 4.252483e-06 ## 5 18 AAMP ENST00000248450 ENSG00000127837 AAMP 6.496774e-06 ## 6 19 AANAT ENST00000250615 ENSG00000129673 AANAT 3.530488e-06 ## mis non splice frameshift lof ## 1 1.738961e-05 5.763794e-07 2.639868e-07 6.532817e-07 1.493648e-06 ## 2 3.545894e-05 1.960148e-06 1.477263e-06 4.616751e-07 3.899086e-06 ## 3 1.176919e-05 4.433874e-07 1.387157e-07 5.276555e-07 1.109759e-06 ## 4 1.018458e-05 5.312742e-07 1.748982e-07 1.051152e-06 1.757324e-06 ## 5 1.313861e-05 5.042914e-07 4.247556e-07 3.344019e-06 4.273066e-06 ## 6 7.729807e-06 1.707018e-07 9.988864e-08 4.132204e-07 6.838108e-07 ## prot all ## 1 1.888326e-05 2.788123e-05 ## 2 3.935803e-05 5.478962e-05 ## 3 1.287895e-05 1.857353e-05 ## 4 1.194191e-05 1.619439e-05 ## 5 1.741167e-05 2.390845e-05 ## 6 8.413618e-06 1.194411e-05
This may be useful, for example, to verify that the input gene list contains the correct identifiers
#Count the number of input gene names length(autismDeNovos$gene) ## [1] 1040 #Count how many are in the “geneName” column of the probability table: sum(autismDeNovos$gene %in% probabilityTable$geneName) ## [1] 1040 #Count how many are in the “hgncSymbol” column of the probability table: sum(autismDeNovos$gene %in% probabilityTable$hgncSymbol) ## [1] 1037 #Count how many are in the “enstID” column of the probability table: sum(autismDeNovos$gene %in% probabilityTable$enstID) ## [1] 0
SUPPORT PROTOCOL 3: USING AN ALTERNATIVE MUTATIONAL PROBABILITY TABLE
denovolyzeR relies on a pre-computed tabulation of the probability of de novo variation arising in each gene, as described in the Introduction and Background Information. The default probability table was generated by calculating the probability of de novo events for every base of the canonical Gencode transcripts, as defined in Gencode v19. It is beyond the scope of this protocol to describe methods to compute these tables, but denovolyzeR does allow for the import of alternative tables, if required. For example, the original paper describing this analytical framework (Samocha et al., 2014) calculated mutational probabilities based on RefSeq transcript definitions, whereas denovolyzeR now uses Gencode definitions. Tables may also be computed to include other functional consequences (e.g. damaging missense variants, as determined by in silico SNV consequence prediction algorithms).
Materials
An alternative probability table. Examples are available to download from http://denovolyzer.org/
-
Locate and load the chosen probability table. For this example, we will use “probTable_Samocha2014.rda” downloaded from the above link, which is now located in our Downloads folder:
#pathToProbabilityTable=”~/Downloads” #replace this with the path on your local system load(file.path(pathToProbabilityTable,”probTable_Samocha2014.rda”)) head(probTable_Samocha2014) ## refseqID geneID class value ## 1 NM_017582 UBE2Q1 syn 5.428059e-06 ## 2 NM_014372 RNF11 syn 2.306612e-06 ## 3 NM_014455 RNF115 syn 3.153658e-06 ## 4 NM_001357 DHX9 syn 1.205980e-05 ## 5 NM_001101376 FAM183A syn 1.572484e-06 ## 6 NM_001042549 NSL1 syn 2.643121e-06
This table has two sets of gene identifiers: “refseqID””, and gene symbols (“geneID”).
-
Run chosen analysis, specifying the chosen probability table using the “probTable” argument, and the appropriate gene identifier.
denovolyzeByClass(genes=autismDeNovos$gene, classes=autismDeNovos$class, nsamples=1078, probTable=probTable_Samocha2014, geneId=”geneID”) ## Warning in parseInput(genes, classes, nsamples, groupBy, includeGenes, ## includeClasses, : 43 gene identifiers in input list do not match the ## probability table, and are excluded from analysis. ## class observed expected enrichment pValue ## 1 syn 247 295.8 0.835 0.998000 ## 2 mis 625 668.6 0.935 0.957000 ## 3 lof 125 92.4 1.350 0.000721 ## 4 prot 750 761.0 0.985 0.660000 ## 5 all 997 1056.9 0.943 0.969000In this instance there is a warning that not all of the input identifiers are recognized. This is because there is not a one-to-one mapping between the identifiers associated with RefSeq and Gencode transcripts.
COMMENTARY
Background Information
The mutational model is described in full detail in (Samocha et al., 2014). Briefly, it is based on a determination of the probability of each base in the coding sequence of the human genome mutating to each of the other possible bases. The predicted impact of these changes is aggregated across the gene to establish the probability of specific types of mutation (synonymous, missense, etc).
Previous work established that the mutability of a base is sufficiently modeled by accounting for the local sequence context of one nucleotide on either side of the base of interest (Krawczak et al., 1998; Kryukov et al., 2007). We analyzed human variation and trinucleotide context using intergenic single nucleotide polymorphisms (SNPs) from the 1000 Genomes project to create a mutation rate table, which provides the relative probabilities of each possible trinucleotide (XY1Z) to trinucleotide (XY2Z) change.
We then consider each base in the coding sequence and use the mutation rate table to determine its probability of mutating to the other bases. We predict the impact of the mutation on the protein product and aggregate the probabilities by mutation type across each gene. These per-gene probabilities are then adjusted according to the completeness of sequencing coverage for each gene, and a regional divergence score, reflecting divergence between humans and macaques, that captures small regional differences in genome mutability that are not fully captured by local trinucleotide context.
Given that the number of de novo variants per trio follows a Poisson distribution (Neale et al., 2012), we use the Poisson distribution to evaluate the excesses of de novo events. As an example, to determine if a particular gene has more de novo loss-of-function variants than expected, we multiply that gene’s probability of a loss-of-function mutation by the number of trios and by 2 (for the number of chromosomes) in the study. This gives the expected number of de novo loss-of-function variants (denoted as lambda, λ) with which the observed number is compared. Specifically, the ppois command in R is used. The three de novo loss-of-function variants seen in DYRK1A are used in the example below. With denovolyze, we get the following result:
denovolyzeByGene(genes=autismDeNovos$gene,
classes=autismDeNovos$class,
nsamples=1078,
includeGenes=”DYRK1A”)
## gene class obs exp pValue
## 1 DYRK1A lof 3 0.0 2.69e-08
## 2 DYRK1A prot 3 0.1 2.77e-05
We can reproduce this with the ppois function. Note that by default, ppois(q,lambda) will return the probability of observing ≤ q events for a given λ. We are interested in computing p(obs ≥ q). ppois(q,lambda,lower.tail=FALSE) gives us p(obs > q), and therefore we must use ppois(q-1,lambda,lower.tail=FALSE) to obtain p(obs ≥ q).
n_lof_dyrk1a <- 3
probabilityTable[probabilityTable$geneName==”DYRK1A”,”lof”]
## [1] 2.528297e-06
prob_lof_dyrk1a <- 2.528297e-06
n_trios <- 1078
ppois(q=n_lof_dyrk1a-1, #observed - 1
lambda=prob_lof_dyrk1a*n_trios*2, #expected
lower.tail=FALSE)
## [1] 2.688463e-08
Control subjects
Since this analytic framework compares de novo events to a model-derived expectation, there is no direct comparison of cases with controls. As described above, direct case-control comparison is not statistically powerful in this context. It is nonetheless recommended to include a control arm in analyses, for example by repeating the analyses described above on a cohort of controls which have been subjected to the same sequencing and de novo identification pipeline. While cases and controls are not directly compared, it is valuable to confirm that the burden of variants in the control population does not deviate substantially from model-derived expectations, in order to validate the model for the specific sequencing approaches used in each study.
Large variants
This analytical framework assesses single nucleotide variants and small (single basepair) insertions and deletions only. Larger insertions, deletions and other structural variants are not assessed.
Critical Parameters
The number of samples (nsamples) should be the total number of samples in the study, not just those that carry de novo variants.
The analysis described in BASIC PROTOCOL 2 is highly sensitive to the choice of argument passed to nVars. Full details are provided in step 3 of that protocol.
Troubleshooting
This methodology is dependent on accurate upstream identification of de novo variants. Mendelian violations (putative variants in sequence data that do not follow normal Mendelian inheritance patterns) comprise technical artefacts and sequencing errors as well as true de novo variants. Indeed, a set of variants specifically selected as Mendelian violations will inevitably be enriched for such errors. A detailed description of strategies for accurate de novo variant detection is beyond the scope of this protocol, but stringent quality control should be applied. Variant confirmation with two independent technologies (e.g. next-generation sequencing and Sanger sequencing) remains the gold standard.
It is also recommended to apply the same variant detection and analysis pipeline to a cohort of control trios as outlined above.
Time Considerations
These analyses are not especially computationally intensive, and will run on a desktop or laptop computer in seconds. The denovolyzeMultiHits function uses permutation, and computation time increases linearly with the number of permutations. Elapsed times (in seconds) to run the three principal functions on de novo variants from 1078 samples, using default settings, on a MacBook Air (1.7GHz i7, 8Gb RAM) are as follows:
system.time(denovolyzeByClass(genes=autismDeNovos$gene,classes=autismDe Novos$class,nsamples=1078))[“elapsed”] ## elapsed ## 0.136 system.time(denovolyzeMultiHits(genes=autismDeNovos$gene,classes=autism DeNovos$class,nsamples=1078,nperms=1000))[“elapsed”] ## elapsed ## 5.397 system.time(denovolyzeByGene(genes=autismDeNovos$gene,classes=autismDeN ovos$class,nsamples=1078))[“elapsed”] ## elapsed ## 0.159
Acknowledgments
J.S.W. is supported by the NIHR Biomedical Research Unit in Cardiovascular Disease at Royal Brompton & Harefield NHS Foundation Trust and Imperial College London, Academy of Medical Sciences, Wellcome Trust, British Heart Foundation Arthritis Research U.K., Fondation Leducq, and an HICF award from Wellcome Trust and Department of Health. J.G.H. is supported by the John S. LaDue Fellowship at Harvard Medical School. M.J.D. is supported by grants R01MH089208 (NIH) and 1U01MH100229-01 (NIH/NIMH).
Footnotes
INTERNET RESOURCES
The home page of the R project. The R software environment can be downloaded from this site.
http://cran.r-project.org/web/packages/denovolyzeR/
The Comprehensive R Archive Network (CRAN) is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. The latest stable version of denovolyzeR should always be available on CRAN, and archived there in perpetuity.
The home page for the denovolyzeR project, with further supporting material
http://github.com/jamesware/denovolyzeR
GitHub is a web-based Git repository hosting service. The latest development version of denovolyzeR is hosted here prior to release on CRAN.
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