To run an allelic series test, there are 4 key inputs:
A numeric annotation vector, of the same length as the number of variants, coded as 0 for benign missense variants (BMVs), 1 for deleterious missense variants (DMVs), and 2 for protein truncating variants (PTVs).
A covariates matrix, with as many rows as subjects and including columns such as age and sex. If omitted, defaults to an intercept only.
A genotype matrix, with subjects as rows and variants as columns. The number of columns should correspond to the length of the annotation vector.
A numeric phenotype vector, either continuous or binary.
The example data used below were generated using the DGP
function provided with the package. The data set includes 100 subjects,
300 variants, and a continuous phenotype. The true effect sizes follow
an allelic series, with magnitudes proportional to
c(1, 2, 3) for BMVs, DMVs, and PTVs respectively.
set.seed(101)
n <- 100
data <- AllelicSeries::DGP(
n = n,
snps = 300,
beta = c(1, 2, 3) / sqrt(n),
)
# Annotations.
anno <- data$anno
head(anno)## [1] 2 0 0 0 0 1## int age sex pc1 pc2 pc3
## [1,] 1 0.06340401 1 1.54356127 -2.3816890 0.3378446
## [2,] 1 -0.26084149 1 -0.34178454 -1.4227627 -0.2141389
## [3,] 1 0.10148711 1 1.25528541 0.4608104 -0.6585620
## [4,] 1 0.48607024 1 -0.87800487 0.4316516 0.3552285
## [5,] 1 -0.52207993 1 -0.03553949 -0.1124594 -0.4243847
## [6,] 1 -1.41018843 0 0.37024952 1.3373260 -0.4423871## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 0 0 0 0
## [2,] 0 0 0 0 0
## [3,] 0 0 0 0 0
## [4,] 0 0 0 0 0
## [5,] 0 0 0 0 0
## [6,] 0 0 0 0 0## [1] 3.1059934 1.1354490 0.4306503 0.7834604 1.0421679 -0.4964317The example data generated by the preceding are available under
vignettes/vignette_data.
The COding-variant Allelic Series Test (COAST) is run using the
COAST function. By default, the output of
COAST includes a data.frame of counts showing the number of
alleles, variants, and carriers in each class that contributed to the
test, and a data.frame of p-values, with the omni test
denoting the final, overall p-value. Inspection of the component
p-values may be useful for determining which model(s) detected evidence
of an association. In the present case, the baseline count model
provided the greatest power.
results <- AllelicSeries::COAST(
anno = anno,
geno = geno,
pheno = pheno,
covar = covar
)
show(results)## Counts:
## anno alleles variants carriers
## 1 0 287 163 96
## 2 1 162 109 82
## 3 2 61 28 45
##
##
## P-values:
## test type pval
## 1 baseline burden 3.11e-26
## 2 ind burden 1.32e-09
## 3 max_count burden 3.08e-10
## 4 max_ind burden 5.37e-09
## 5 sum_count burden 1.66e-20
## 6 sum_ind burden 2.55e-11
## 7 allelic_skat skat 2.66e-07
## 8 omni omni 3.74e-25The counts data.frame is accessed via:
## anno alleles variants carriers
## 1 0 287 163 96
## 2 1 162 109 82
## 3 2 61 28 45and the p-values data.frame via:
## test type pval
## 1 baseline burden 3.112702e-26
## 2 ind burden 1.322084e-09
## 3 max_count burden 3.076876e-10
## 4 max_ind burden 5.374363e-09
## 5 sum_count burden 1.661854e-20
## 6 sum_ind burden 2.554417e-11
## 7 allelic_skat skat 2.658137e-07
## 8 omni omni 3.735235e-25apply_int = TRUE applies the rank-based inverse normal
transformation from the RNOmni package.
This transformation is expected to improve power for phenotypes that
have a skewed or kurtotic (e.g. long-tailed) distribution. It is applied
by default in the case of continuous phenotype, and is ignored in the
case of a binary phenotype.include_orig_skato_all = TRUE includes standard SKAT-O
applied to all variants as a component of the omnibus test, while
include_orig_skato_ptv = TRUE includes standard SKAT-O
applied to PTVs only. Including standard SKAT-O as a component of the
omnibus test can improve power to detect associations between the
phenotype and genes that may not be allelic series.AllelicSeries::COAST(
anno = anno,
geno = geno,
pheno = pheno,
covar = covar,
include_orig_skato_all = TRUE,
include_orig_skato_ptv = TRUE,
)is_pheno_binary = TRUE is required to indicate that the
supplied phenotype is binary, and should be analyzed using a logistic
regression model.AllelicSeries::COAST(
anno = anno,
geno = geno,
pheno = 1 * (pheno > 0),
covar = covar,
is_pheno_binary = TRUE
)min_mac is used to filter the variant set to those
containing a minimum minor allele count (MAC). The following example
filters to only those variants with a MAC of at least 2:return_omni_only = TRUE is used to return
p_omni only when the component p-values are not of
interest:AllelicSeries::COAST(
anno = anno,
geno = geno,
pheno = pheno,
covar = covar,
return_omni_only = TRUE
)score_test = TRUE specifies the use of a score-type
allelic series burden test. The default of
score_test = FALSE specifies a Wald-type allelic series
burden test.weights specifies the relative phenotypic effects of
BMVs, DMVs, and PTVs. An increasing pattern such as the default setting
of weights = c(1, 2, 3) targets allelic series. Setting
weights = c(1, 1, 1) would target a genetic architecture
where all variants have equivalent expected magnitudes.