Allelic Series

Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic-series rare-variant association test for candidate-gene discovery" doi:10.1016/j.ajhg.2023.07.001.

Installation

install.packages("AllelicSeries")

library(AllelicSeries)

Example data

set.seed(101)
n <- 100
data <- DGP(
  n = n,
  snps = 300,
  beta = c(1, 2, 3) / sqrt(n),
)

The example data are a list with the following components:

anno: An snps by 1 annotation vector coded as 0 for benign missense variants (BMVs), 1 for deleterious missense variants (DMVs), and 2 for protein truncating variants (PTVs).
covar: An n by 6 covariate matrix including an intercept int, and covariates representing age, sex, and 3 genetic PCs (pc1 … pc3).
geno: An n by snps genotype matrix with additive coding and minor allele frequencies between 0.5% and 1.0%.
pheno: An n by 1 phenotype vector.

Note: Scaling beta by 1 / sqrt(n) makes the power invariant to the sample size n.

COding-variant Allelic Series Test

results <- COAST(
  anno = data$anno,
  covar = data$covar,
  geno = data$geno,
  pheno = data$pheno,
  weights = c(1, 2, 3)
)

The function COAST performs the allelic series test. The required inputs are the annotation vector, a covariate matrix, the per-variant genotype matrix, and the phenotype vector.

The function assumes 3 annotation categories, coded as: 0, 1, 2. The length of anno should match the number of columns of the genotype matrix geno.
If unspecified, covar will default to an intercept vector (i.e. a vector of 1s). If covar is provided, an intercept should be included manually, if desired.
weights encodes the relative importance of BMVs, DMVs, and PTVs. The example weights of c(1, 2, 3) target a genetic architecture where effect sizes increase with increasing deleteriousness: BMVs have an effect of 1, DMVs have an effect of 2, and PTVs have an effect of 3. Weights of c(1, 1, 1) target instead a genetic architecture where all variant types have equivalent expected magnitudes.

show(results)

##        p_count          p_ind    p_max_count      p_max_ind    p_sum_count 
##   7.707024e-29   6.745269e-06   4.299938e-13   6.228669e-07   6.756953e-18 
##      p_sum_ind p_allelic_skat         p_omni 
##   1.894500e-06   8.507977e-08   9.248429e-28

By default, the output of COAST is a vector of p-values, corresponding to the different components of the allelic series test and the overall omnibus test (p_omni). To return the omnibus p-value only, specify return_omni_only = TRUE when calling COAST.

Robust omnibus test

In the case that all variants have comparable magnitudes, standard SKAT-O test will have more power than an allelic series test with weights c(1, 2, 3). This is because the generative weighting scheme is in fact c(1, 1, 1). To perform a robust test that is powerful for detecting allelic series but as powerful as standard SKAT-O when all variants have similar magnitudes, we can incorporate standard SKAT-O in the allelic series omnibus test. To do this, specify include_orig_skato_all = TRUE when calling COAST. Another option is to incorporate standard SKAT-O incorporating PTVs only: include_orig_skato_ptv = TRUE.

results <- COAST(
  anno = data$anno,
  covar = data$covar,
  geno = data$geno,
  pheno = data$pheno,
  include_orig_skato_all = TRUE,
  include_orig_skato_ptv = TRUE,
  weights = c(1, 2, 3)
)
show(results)

##         p_count           p_ind     p_max_count       p_max_ind     p_sum_count 
##    7.707024e-29    6.745269e-06    4.299938e-13    6.228669e-07    6.756953e-18 
##       p_sum_ind  p_allelic_skat p_orig_skat_all p_orig_skat_ptv          p_omni 
##    1.894500e-06    8.507977e-08    1.250426e-05    2.237988e-13    9.248429e-28

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