# denovolyzeR intro In denovolyzeR: Statistical Analyses of De Novo Genetic Variants

## Statistics for de novo variant analysis

#### Introduction

This package provides functions to analyse de novo genetic variants using the statistical framework described in Samocha et al (2014) Nature Genetics 10.1038/ng.3050. This vignette demonstrates the usage of the denovolyzeR package to recapitulate the analyses described in this paper:

1. What is the overall burden of de novo variation in the study cohort: are there more de novos than expected? As well as looking at the total burden of de novos, results are returned for different classes of variant, such as loss-of-function (lof), missense (mis) and synonymous (syn).
2. Do de novo variants cluster in specific genes: are there more genes containing multiple de novos than expected?
3. Are there any individual genes that contain more de novos than expected?

If using the package, please cite Ware et al (2015) Curr Protoc Hum Genet. 10.1002/0471142905.hg0725s87.

## Installation

# Install the package if you haven't already.
# OPTION 1 - install the latest release from CRAN:
install.packages("denovolyzeR")

# OPTION 2 - install the latest development version from GitHub.  Either download and install, or use devtools:
if(!"devtools" %in% installed.packages()){
install.packages("devtools")
}
devtools::install_github("jamesware/denovolyzeR")


## Example analysis

We start with a table of de novo variants. An example dataset is provided:

library(denovolyzeR)
# have a look at the example data:
dim(autismDeNovos)


#### Overall de novo burden

First we want to know whether there are more de novos than expected, using the denovolyzeByClass() function. These variants were obtained by sequencing 1,078 cases, so we use nsamples=1078.

denovolyzeByClass(genes=autismDeNovos$gene, classes=autismDeNovos$class,
nsamples=1078)


The total number of de novos is almost exactly as our model predicts. However, we see a statistically significant excess of LOF variants in this population.

#### Genes containing multiple de novos

Next, we look to see if the total number of genes that contain more than one de novo is greater than expected, using the denovolyzeMultiHits() function.

denovolyzeMultiHits(genes=autismDeNovos$gene, classes=autismDeNovos$class,
nsamples=1078)


obs = the number of genes in our dataset with >1 de novo variant
expMean = the expected number of genes containing >1 de novo: an average obtained by permutation
expMax = the maximum number of genes containing >1 de novo in nperms permutations (default nperms=100)
pValue = an empirical p value
nVars = the total number of de novo variants in each class
Note that the number of observed genes with >1 protein-altering variant does not equal the number of genes with >1 lof + number of genes with >1 missense, as genes containing 1 lof + 1 missense will only be counted as "multihits" in the combined analysis.

Here it looks like there may be an excess of genes with >1 lof variant, >1 missense, and >1 protein-altering variant. We will want to increase the number if permutations here to get a handle on our level of significance.

denovolyzeMultiHits(genes=autismDeNovos$gene, classes=autismDeNovos$class,
nsamples=1078,
nperms=1000)


There is another important option here. The expected number of genes containing >1 hit is obtained by permutation: Given n de novo variants, how many genes contain >1 de novo? There are two options for selecting n: by default it is derived from your data: e.g. in the example above autismDeNovos contains r sum(autismDeNovos$class %in% c("frameshift","non","splice")) lof variants, so this is the number used in the permutation. This is controlled by the default parameter nVars="actual" sum(autismDeNovos$class %in% c("frameshift","non","splice"))


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,
nperms=1000,
nVars="expected")


#### Do any individual genes contain more de novos than expected

We see r library(dplyr); autismDeNovos %>% filter(class %in% c("frameshift","non","splice")) %>% group_by(gene) %>% summarize(n=n()) %>% filter(n>1) %>% nrow genes containing >1 de novo lof variant. This is more than expected, but are any of these genes individually significant? We can denovolyzeByGene() to find out.

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.

head(
denovolyzeByGene(genes=autismDeNovos$gene, classes=autismDeNovos$class,
nsamples=1078)
)


Several genes meet statistical significance after correcting for multiple testing. Default options apply two tests across r load("../R/sysdata.rda"); length(unique(pDNM$geneName)) genes, so a Bonferroni corrected p-value threshold at$\alpha$= 0.05 would be$r signif(0.05*0.5*(1/length(unique(pDNM$geneName))),2)$.

#### Geneset analysis

The analyses presented so far have been exome-wide. It may be appropriate to restrict analyses to a geneset of interest - for example, it may be relevant to examine the burden of de novo variation in a pathway of interest, or initial variant detection may have been restricted to a set of candidate genes (rather than whole exome sequencing). All of the above funtions can be targeted to a subset of genes using the includeGenes argument.

The package includes as an example a list of r nrow(fmrpGenes) genes that interact with the fragile X mental retardation protein (FMRP). Is this geneset enriched for de novos, and recurrent de novos, in our autism trios?

nrow(fmrpGenes); head(fmrpGenes)

denovolyzeByClass(genes=autismDeNovos$gene, classes=autismDeNovos$class,
nsamples=1078,
includeGenes=fmrpGenes$geneName) denovolyzeMultiHits(genes=autismDeNovos$gene,
classes=autismDeNovos$class, nsamples=1078, nperms=1000, includeGenes=fmrpGenes$geneName)


## Other functions

viewProbabilityTable provides access to the underlying de novo probability tables used to calculate expected de novo burdens throughout this package:

head(
viewProbabilityTable()
)


Most of the core functionality of this package is contained in the denovolyze function. The denovolyzeByClass and denovolyzeByGene functions used in this vignette are convenience functions that set defaults appropriate to the most common usages of this function. Full details of additional options and default behaviours are available using ?denovolyze.