This package analyses genes suspected of having a recessive mode of inheritance. We test for enrichment of compound heterozygous variants in genes and similarity of suspected syndromes between probands. A combined test is calculated by incorporating results from testing for similarity of Human Phenotype Ontology (HPO) terms among probands (see https://github.com/jeremymcrae/hpo_similarity).
We identified probands with rare compound heterozygous variants in genes (where at least one of the compound het pair has a loss-of-function consequence), or rare homozygous loss-of-function variants. These variants were identified with https://github.com/jeremymcrae/clinical-filter. Probands who had diagnoses were excluded from further analysis, as any additional recessive variants cannot contribute to their disorder. The numbers of probands in each of the functional categories were tallied for each gene. Only one proband per family per gene was permitted, so that the probands contributing to the tallies were independent evidence.
The numbers of probands were tallied in genes for two functional categories: - LoF/Lof: homozygous loss-of-function (LoF) and compound hets where both variants were LoF. - LoF/Func: compound hets where only one variant is LoF.
The baseline rate of rare LoF/LoF and rare LoF/Func variation was determined from two sources: - ExAC version 0.3 datasets. - DDD unaffected parents (for each gene, the parents of the probands with variants in that gene were excluded).
The frequency of rare (allele frequency < 0.01) loss-of-function and rare functional variants in each gene was calculated (gene coordinates were obtained from gencode v19). The frequencies of the variants in each gene for these classes were summed, to get an overall rate of rare functional and rare LoF variation for each gene.
The baseline rate of biallelic LoF events in a gene is the rate of rare LoF variation squared, whereas the baseline rate of LoF/Func is the rate of Lof variation multiplied by the sum of the rate of LoF variation and functional variation (LoF * (LoF + Func)).
The probability of getting more than or equal to the number of the observed inherited events in each class was estimated using a binomial model where n=number of families with undiagnosed probands and k=baseline rate of variation.
The package can be installed or updated using R 3.1.0 or greater with:
library(devtools) # if necessary install with install.packages("devtools") devtools::install_github("jeremymcrae/recessiveStats") # Alternatively, clone the repository, run R 3.1 from within the top level of # the repository and use the devtools::build() to build the package for other R # versions.
# load the package library(recessiveStats) cohort_n = 1000 hgnc = "DNAH14" chrom = "1" counts = list() counts$biallelic_lof = 5 counts$biallelic_func = 10 counts$lof_func = 6 # define the variants in the control population, including allele counts variants = read.table(header = TRUE, text = " chrom pos AC AN CQ 1 1000 1 1000 missense_variant 1 1000 5 1000 stop_gained 1 1000 8 1000 stop_lost 1 1000 20 1000 synonymous_variant") analyse_inherited_enrichment(counts, variants, cohort_n) # For ease of use, you can also load variant counts from ExAC with: variants = get_exac_variants_for_gene(hgnc, chrom) # The ExAC variants object should be a list of tables, one for each of the ExAC # populations, e.g. 'AFR', 'EAS', 'NFE'. You'll need to pick the population that # matches your cohort. For this example we'll use the 'NFE'. analyse_inherited_enrichment(counts, variants[['NFE']], cohort_n) # alternatively, provide your path to the ExAC VCF e.g. variants = get_exac_variants_for_gene(hgnc, chrom, fileName='PATH_TO_VCF')
Rather than naming a gene, you can give a chromosome range (but define the gene symbol as NULL, otherwise all of the variants that don't match the gene symbol are removed):
start=225083964 end=225586996 variants = get_exac_variants_for_gene(hgnc=NULL, chrom='1', start=start, end=end) analyse_inherited_enrichment(counts, variants[['NFE']], cohort_n) # define your own MAF threshold for variant inclusion analyse_inherited_enrichment(counts, variants[['NFE']], threshold=0.005, cohort_n)
You can also take the autozygosity into account. Calculate the proportion of
probands who have an autozygous region overlapping the gene
bcftools roh is
recommended). Then you can include the rate as:
RATE=0.005 analyse_inherited_enrichment(counts, variants[['NFE']], cohort_n, autozygosity=RATE)
Also, if your probands are of multiple ethnicities, you can account for differences in allele frequencies between ethnicities by specifying the number of probands that would be classified as belonging to each ExAC population. For example:
cohort_n = list("AFR"=100, "EAS"=50, "NFE"=800, "SAS"=50) analyse_inherited_enrichment(counts, variants, cohort_n)
The populations available in ExAC are:
code | description ----- | -------------------- AFR | African/African American AMR | American EAS | East Asian FIN | Finnish NFE | Non-Finnish European OTH | Other SAS | South Asian
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