Title: Allelic Series Test
Version: 0.1.1.5
Description: 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>.
License: BSD_3_clause + file LICENSE
URL: https://github.com/insitro/AllelicSeries
Encoding: UTF-8
Imports: CompQuadForm, glue, methods, Rcpp, RNOmni, SKAT
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.3.2
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), withr
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2025-04-02 02:13:31 UTC; zmccaw
Author: Zachary McCaw ORCID iD [aut, cre], Christoph Klein ORCID iD [ctb], Thomas Soare ORCID iD [ctb], Jianhui Gao ORCID iD [ctb], insitro [cph]
Maintainer: Zachary McCaw <zmccaw@alumni.harvard.edu>
Repository: CRAN
Date/Publication: 2025-04-02 21:50:13 UTC

AllelicSeries: Allelic Series Test

Description

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.

Author(s)

Maintainer: Zachary McCaw zmccaw@alumni.harvard.edu (ORCID)

Other contributors:

See Also

Useful links:

Allelic Series Burden Test

Description

Burden test with allelic series weights.

Usage

ASBT(
  anno,
  geno,
  pheno,
  apply_int = TRUE,
  covar = NULL,
  indicator = FALSE,
  is_pheno_binary = FALSE,
  method = "none",
  min_mac = 0,
  return_beta = FALSE,
  score_test = FALSE,
  weights = c(1, 2, 3)
)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

geno

(n x snps) genotype matrix.

pheno

(n x 1) phenotype vector.

apply_int

Apply rank-based inverse normal transform to the phenotype? Default: TRUE. Ignored if phenotype is binary.

covar

(n x p) covariate matrix. Defaults to an (n x 1) intercept.

indicator

Convert raw counts to indicators?

is_pheno_binary

Is the phenotype binary? Default: FALSE.

method

Method for aggregating across categories: ("none", "max", "sum"). Default: "none".

min_mac

Minimum minor allele count for inclusion. Default: 0.

return_beta

Return the estimated effect size? Default: FALSE.

score_test

Run a score test? If FALSE, performs a Wald test.

weights

(L x 1) vector of annotation category weights. Note that the number of annotation categories L is inferred from the length of weights.

Value

If return_beta = TRUE, a list of including the effect size data.frame "betas" and the p-value "pval". If return_beta = FALSE, a numeric p-value.

Examples

# Generate data.
data <- DGP(n = 1e3, snps = 1e2)

# Run the Allelic Series Burden Test.
# Note: the output is a scalar p-value.
results <- ASBT(
  anno = data$anno,
  geno = data$geno,
  pheno = data$pheno,
  covar = data$covar
)

Allelic Series Burden Test from Summary Statistics

Description

Allelic series burden test from summary statistics.

Usage

ASBTSS(
  anno,
  beta,
  se,
  check = TRUE,
  eps = 1,
  lambda = 1,
  ld = NULL,
  method = "none",
  return_beta = FALSE,
  weights = c(1, 2, 3)
)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

beta

(snps x 1) vector of effect sizes for the coding genetic variants within a gene.

se

(snps x 1) vector of standard errors for the effect sizes.

check

Run input checks? Default: TRUE.

eps

Epsilon added to the diagonal of the LD matrix if not positive definite. Note, smaller values increase the chances of a false positive.

lambda

Optional genomic inflation factor. Defaults to 1, which results in no rescaling.

ld

(snps x snps) matrix of correlations among the genetic variants. Although ideally provided, an identity matrix is assumed if not.

method

Method for aggregating across categories: ("none", "sum"). Default: "none".

return_beta

Return the estimated effect size? Default: FALSE.

weights

(L x 1) vector of annotation category weights. Note that the number of annotation categories L is inferred from the length of weights.

Value

If return_beta = TRUE, a list of including the effect size data.frame "betas" and the p-value "pval". If return_beta = FALSE, a numeric p-value.

Notes

Examples

# Generate data.
data <- DGP(n = 1e3)
sumstats <- CalcSumstats(data = data)

# Run allelic series burden test from sumstats.
results <- ASBTSS(
  anno = sumstats$sumstats$anno,
  beta = sumstats$sumstats$beta, 
  se = sumstats$sumstats$se,
  ld = sumstats$ld
)
show(results)

Allelic Series SKAT Test

Description

Sequence kernel association test (SKAT) with allelic series weights.

Usage

ASKAT(
  anno,
  geno,
  pheno,
  apply_int = TRUE,
  covar = NULL,
  is_pheno_binary = FALSE,
  min_mac = 0,
  return_null_model = FALSE,
  weights = c(1, 2, 3)
)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

geno

(n x snps) genotype matrix.

pheno

(n x 1) phenotype vector.

apply_int

Apply rank-based inverse normal transform to the phenotype? Default: TRUE. Ignored if phenotype is binary.

covar

(n x p) covariate matrix. Defaults to an (n x 1) intercept.

is_pheno_binary

Is the phenotype binary? Default: FALSE.

min_mac

Minimum minor allele count for inclusion. Default: 0.

return_null_model

Return the null model in addition to the p-value? Useful if running additional SKAT tests. Default: FALSE.

weights

(L x 1) vector of annotation category weights. Note that the number of annotation categories L is inferred from the length of weights.

Value

If return_null_model, a list containing the p-value and the SKAT null model. Otherwise, a numeric p-value.

Examples

# Generate data.
data <- DGP(n = 1e3, snps = 1e2)

# Run the Allelic Series SKAT Test.
# Note: the output is a scalar p-value.
results <- ASKAT(
  anno = data$anno,
  geno = data$geno,
  pheno = data$pheno,
  covar = data$covar
)

Allelic Series SKAT-O from Summary Statistics

Description

Allelic series sequence kernel association test from summary statistics.

Usage

ASKATSS(
  anno,
  beta,
  se,
  check = TRUE,
  eps = 1,
  lambda = 1,
  ld = NULL,
  maf = NULL,
  weights = c(1, 2, 3)
)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

beta

(snps x 1) vector of effect sizes for the coding genetic variants within a gene.

se

(snps x 1) vector of standard errors for the effect sizes.

check

Run input checks? Default: TRUE.

eps

Epsilon added to the diagonal of the LD matrix if not positive definite. Note, smaller values increase the chances of a false positive.

lambda

Optional genomic inflation factor. Defaults to 1, which results in no rescaling.

ld

(snps x snps) matrix of correlations among the genetic variants. Although ideally provided, an identity matrix is assumed if not.

maf

(snps x 1) vector of minor allele frequencies. Although ideally provided, defaults to the zero vector.

weights

(L x 1) vector of annotation category weights. Note that the number of annotation categories L is inferred from the length of weights.

Value

Numeric p-value of the allelic series SKAT-O test.

Notes

Examples

# Generate data.
data <- DGP(n = 1e3)
sumstats <- CalcSumstats(data = data)

# Run allelic series SKAT test from sumstats.
# Note: the SKAT test requires MAF.
results <- ASKATSS(
  anno = sumstats$sumstats$anno,
  beta = sumstats$sumstats$beta, 
  maf = sumstats$sumstats$maf,
  se = sumstats$sumstats$se,
  ld = sumstats$ld
)
show(results)

Aggregator

Description

Aggregates genotypes within annotation categories.

Usage

Aggregator(
  anno,
  geno,
  drop_empty = TRUE,
  indicator = FALSE,
  method = "none",
  min_mac = 0,
  weights = c(1, 2, 3)
)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

geno

(n x snps) genotype matrix.

drop_empty

Drop empty columns? Default: TRUE.

indicator

Convert raw counts to indicators? Default: FALSE.

method

Method for aggregating across categories: ("none", "max", "sum"). Default: "none".

min_mac

Minimum minor allele count for inclusion. Default: 0.

weights

(L x 1) vector of annotation category weights. Note that the number of annotation categories L is inferred from the length of weights.

Value

(n x L) Numeric matrix without weighting, (n x 1) numeric matrix with weighting.

Notes

Baseline Counts Test from Sumstats

Description

Baseline Counts Test from Sumstats

Usage

BaselineSS(anno, beta, ld, se, n_anno = 3L, return_beta = FALSE)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

beta

(snps x 1) vector of effect sizes for the coding genetic variants within a gene.

ld

(snps x snps) matrix of correlations among the genetic variants.

se

(snps x 1) vector of standard errors for the effect sizes.

n_anno

Number of annotation categories L.

return_beta

Return estimated effect sizes and standard errors? Default: FALSE.

Value

If return_beta, a list containing the category effect sizes, standard errors, and the p-value. Otherwise, the numeric p-value only.

COding-variant Allelic Series Test

Description

Main allelic series test. Performs both Burden and SKAT type tests, then combines the results to calculate an omnibus p-value.

Usage

COAST(
  anno,
  geno,
  pheno,
  add_intercept = TRUE,
  apply_int = TRUE,
  covar = NULL,
  include_orig_skato_all = FALSE,
  include_orig_skato_ptv = FALSE,
  is_pheno_binary = FALSE,
  min_mac = 0,
  ptv_anno = 3,
  pval_weights = NULL,
  return_omni_only = FALSE,
  score_test = FALSE,
  weights = c(1, 2, 3)
)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

geno

(n x snps) genotype matrix.

pheno

(n x 1) phenotype vector.

add_intercept

Add an intercept if not present in the supplied covariate matrix covar? Default: TRUE.

apply_int

Apply rank-based inverse normal transform to the phenotype? Default: TRUE. Ignored if phenotype is binary.

covar

(n x p) covariate matrix. Defaults to an (n x 1) intercept.

include_orig_skato_all

Include the original version of SKAT-O applied to all variants in the omnibus test? Default: FALSE.

include_orig_skato_ptv

Include the original version of SKAT-O applied to PTV variants only in the omnibus test? Default: FALSE.

is_pheno_binary

Is the phenotype binary? Default: FALSE.

min_mac

Minimum minor allele count for inclusion. Default: 0.

ptv_anno

Annotation of the PTV category, only required if include_orig_skato_ptv is set to TRUE.

pval_weights

Optional vector of relative weights for combining the component tests to perform the omnibus test. By default, 50% of weight is given to the 6 burden tests, and 50% to the 1 SKAT test. If specified, the weight vector should have length 7, and the length should be increased if either include_orig_skato_all or include_orig_skato_ptv is active.

return_omni_only

Return only the omnibus p-value? Default: FALSE.

score_test

Use a score test for burden analysis? If FALSE, uses a Wald test.

weights

(L x 1) vector of annotation category weights. Note that the number of annotation categories L is inferred from the length of weights.

Value

An object of class COAST with slots for effect sizes, variant counts, and p-values.

Examples

# Generate data.
data <- DGP(n = 1e3, snps = 1e2)

# Run the COding-variant Allelic Series Test.
results <- COAST(
  anno = data$anno,
  geno = data$geno,
  pheno = data$pheno,
  covar = data$covar
)
show(results)

Allelic Series Output Class

Description

Allelic Series Output Class

Slots

Betas

Effect sizes and standard errors.

Counts

Allele, variant, and carrier counts.

Pvals

Result p-values.

COding-variant Allelic Series Test from Summary Statistics

Description

Main function for performing the allelic series test from summary statistics. Performs both Burden and SKAT type tests, then combines the results to calculate an omnibus p-value. Note that not all tests included in COAST are available when working with summary statistics.

Usage

COASTSS(
  anno,
  beta,
  se,
  check = TRUE,
  eps = 1,
  lambda = c(1, 1, 1),
  ld = NULL,
  maf = NULL,
  pval_weights = c(0.25, 0.25, 0.5),
  weights = c(1, 2, 3)
)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

beta

(snps x 1) vector of effect sizes for the coding genetic variants within a gene.

se

(snps x 1) vector of standard errors for the effect sizes.

check

Run input checks? Default: TRUE.

eps

Epsilon added to the diagonal of the LD matrix if not positive definite. Note, epsilon should increase as the sample size decreases.

lambda

Optional (3 x 1) vector of inflation factors, one for each component test. Defaults to a 1s vector, which results in no rescaling.

ld

(snps x snps) matrix of correlations among the genetic variants. Although ideally provided, an identity matrix is assumed if not.

maf

(snps x 1) vector of minor allele frequencies. Although ideally provided, defaults to the zero vector.

pval_weights

(3 x 1) vector of relative weights for combining the component tests to perform the omnibus test. The default of c(0.25, 0.25, 0.50) gives the SKAT test equal weight to the two burden tests.

weights

(L x 1) vector of annotation category weights. Note that the number of annotation categories L is inferred from the length of weights.

Value

Numeric p-value.

Examples

# Generate data.
data <- DGP(n = 1e3)
sumstats <- CalcSumstats(data = data)

# Run the Coding-variant Allelic Series Test from summary statistics.
results <- COASTSS(
  anno = sumstats$sumstats$anno,
  beta = sumstats$sumstats$beta, 
  se = sumstats$sumstats$se,
  ld = sumstats$ld,
  maf = sumstats$sumstats$maf,
)
show(results)

Calculate Regression Parameters

Description

Calculate phenotypic regression coefficients and the residual variation based on proportion of variation explained (PVE) by each factor.

Usage

CalcRegParam(pve_age = 0.1, pve_pcs = 0.2, pve_sex = 0.1)

Arguments

pve_age

PVE by age.

pve_pcs

PVE by PCs (collectively).

pve_sex

PVE by sex.

Value

List containing the (5 x 1) regression coefficient vector "coef" and the residual standard deviation "sd".

Calculate Summary Statistics

Description

Generate summary statistics from individual-level data. Provide either a list of data as generated by DGP, or all of anno, geno, and pheno.

Usage

CalcSumstats(
  anno = NULL,
  covar = NULL,
  data = NULL,
  geno = NULL,
  pheno = NULL,
  add_intercept = TRUE,
  is_binary = FALSE
)

Arguments

anno

(snps x 1) annotation vector.

covar

(subjects x covars) covariate matrix.

data

List of data containing the annotation vector anno, the covariate data covar, the genotype matrix geno, and the phenotype vector pheno, as returned by DGP. Overrides the other arguments if provided.

geno

(subjects x snps) genotype matrix.

pheno

(subjects x 1) phenotype vector.

add_intercept

Add an intercept if not present in the supplied covariate matrix covar? Default: TRUE.

is_binary

Is the phenotype binary? Default: FALSE.

Value

List containing the following items:

Examples

data <- DGP()
sumstats <- CalcSumstats(data = data)

Check Inputs

Description

Check Inputs

Usage

CheckInputs(anno, covar, geno, is_pheno_binary, pheno, weights)

Arguments

anno

(snps x 1) annotation vector.

covar

(n x p) covariate matrix.

geno

(n x snps) genotype matrix.

is_pheno_binary

Is the phenotype binary?

pheno

(n x 1) phenotype vector.

weights

(L x 1) annotation category weights.

Value

None.

Input Checks for Summary Statistics

Description

Input Checks for Summary Statistics

Usage

CheckInputsSS(anno, beta, se, lambda, ld, weights, is_skat = FALSE, maf = NULL)

Arguments

anno

(snps x 1) annotation vector with values in c(0, 1, 2).

beta

(snps x 1) vector of effect sizes for the coding genetic variants within a gene.

se

(snps x 1) vector of standard errors for the effect sizes.

lambda

Genomic inflation factor.

ld

(snps x snps) matrix of correlations among the genetic variants. Although ideally provided, an identity matrix is assumed if not.

weights

(L x 1) annotation category weights.

is_skat

Logical, is the check for the SKAT test?

maf

(snps x 1) vector of minor allele frequencies. Although ideally provided, defaults to the zero vector.

Value

Logical indicating whether the matrix was positive definite.

Collapse Variants

Description

Collapse variants with minor allele counts below the min_mac threshold into an aggregated variant, separately within each variant category. Note that the ordering of the variants will change, and that collapsing does not guarantee that the resulting aggregate variant will itself have a MAC greater than or equal to min_mac.

Usage

CollapseGeno(anno, geno, min_mac = 11)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

geno

(n x snps) genotype matrix.

min_mac

Minimum minor allele count (MAC) for retention as an individual variant. Variants with a MAC strictly less than the minimum MAC will be collapsed into an aggregated variant, separately within each annotation category.

Value

List containing the collapsed genotypes geno and corresponding annotations anno, plus a data.frame vars specifying which variants were collapsed within each annotation category.

Comparator Test

Description

Runs burden, SKAT, and SKAT-O, using default settings.

Usage

Comparator(covar, geno, pheno, apply_int = TRUE, is_pheno_binary = FALSE)

Arguments

covar

(n x p) covariate matrix.

geno

(n x snps) genotype matrix.

pheno

(n x 1) phenotype vector.

apply_int

Apply rank-based inverse normal transform to the phenotype? Default: TRUE. Ignored if phenotype is binary.

is_pheno_binary

Is the phenotype binary? Default: FALSE.

Value

Numeric vector of p-values.

Examples

# Generate data.
data <- DGP(n = 1e3, snps = 1e2)

# Run the comparators.
results <- Comparator(
  geno = data$geno,
  pheno = data$pheno,
  covar = data$covar
)

Contains Intercept?

Description

Check if a design matrix contains an intercept.

Usage

ContainsInt(x)

Arguments

x

Design matrix.

Value

Logical.

Correlation C++

Description

Correlation C++

Usage

CorCpp(x)

Arguments

x

Numeric matrix.

Value

Numeric matrix of correlation among the columns.

Notes

Verified this function is faster that R's built-in correlation function for large genotype matrices.

Count Variants and Carriers

Description

Count Variants and Carriers

Usage

Counts(anno, geno, n_anno, min_mac = 0L)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

geno

(n x snps) genotype matrix.

n_anno

Number of annotation categories L.

min_mac

Minimum minor allele count for inclusion. Default: 0.

Value

Data.frame of allele, variant, and carrier counts.

Data Generating Process

Description

Generate a data set consisting of:

Usage

DGP(
  anno = NULL,
  beta = c(1, 2, 3),
  binary = FALSE,
  geno = NULL,
  include_residual = TRUE,
  indicator = FALSE,
  maf_range = c(0.001, 0.005),
  method = "none",
  n = 100,
  prop_anno = c(0.5, 0.4, 0.1),
  prop_causal = 1,
  random_signs = FALSE,
  random_var = 0,
  snps = 100,
  weights = c(1, 1, 1)
)

Arguments

anno

Annotation vector, if providing genotypes. Should match the number of columns in geno.

beta

If method = "none", a (L x 1) coefficient with effect sizes for each annotation category. By default, there are L = 3 annotation categories corresponding to BMVs, DMVs, and PTVs. If method != "none", a scalar effect size for the allelic series burden score.

binary

Generate binary phenotype? Default: FALSE.

geno

Genotype matrix, if providing genotypes.

include_residual

Include residual? If FALSE, returns the expected value. Intended for testing.

indicator

Convert raw counts to indicators? Default: FALSE.

maf_range

Range of minor allele frequencies: c(MIN, MAX).

method

Genotype aggregation method. Default: "none".

n

Sample size.

prop_anno

Proportions of annotations in each category. Length should equal the number of annotation categories. Default of c(0.5, 0.4, 0.1) is based on the approximate empirical frequencies of BMVs, DMVs, and PTVs.

prop_causal

Proportion of variants which are causal. Default: 1.0.

random_signs

Randomize signs? FALSE for burden-type genetic architecture, TRUE for SKAT-type.

random_var

Frailty variance in the case of random signs. Default: 0.

snps

Number of SNP in the gene. Default: 100.

weights

Annotation category weights. Length should match prop_anno.

Value

List containing: genotypes, annotations, covariates, phenotypes.

Examples

# Generate data.
data <- DGP(n = 100)

# View components.
table(data$anno)
head(data$covar)
head(data$geno[, 1:5])
hist(data$pheno)

# Generate data with L != 3 categories.
data <- DGP(
  beta = c(1, 2, 3, 4),
  prop_anno = c(0.25, 0.25, 0.25, 0.25),
  weights = c(1, 1, 1, 1)
)

Data.frame or Null Class

Description

Data.frame or Null Class

Filter Noncausal Variants

Description

Remove a random fraction of variants, which are designated non-causal.

Usage

FilterGenos(anno, geno, prop_causal = 1)

Arguments

anno

(snps x 1) annotation vector.

geno

(n x snps) genotype matrix.

prop_causal

Proportion of variants which are causal.

Value

List containing the (n x snps) genotype matrix "geno" and the (snps x 1) annotation vector "anno".

Generate Genotype Annotations

Description

Returns a vector of length = the number of columns (SNPs) in the genotype matrix. Each SNP is categorized into one of L categories, where L is determined by the length of prop_anno.

Usage

GenAnno(snps, prop_anno = c(0.5, 0.4, 0.1))

Arguments

snps

Number of SNPs in the gene.

prop_anno

Proportions of annotations in each category. Length should equal the number of annotation categories. Default of c(0.5, 0.4, 0.1) is based on the approximate empirical frequencies of BMVs, DMVs, and PTVs.

Value

(snps x 1) integer vector.

Generate Covariates

Description

Generate an (n x 6) covariate matrix with columns representing an intercept, age, sex, and 3 genetic PCs. Because these simulations address rare variant analysis, correlation between genotypes and the genetic PCs (based on common variants) is unnecessary.

Usage

GenCovar(n)

Arguments

n

Sample size.

Value

(n x 6) numeric matrix.

Generate Genotypes

Description

Generates genotypes in linkage equilibrium with accompanying annotations.

Usage

GenGeno(n, snps, maf_range = c(0.001, 0.005), prop_anno = c(0.5, 0.4, 0.1))

Arguments

n

Sample size.

snps

Number of SNP in the gene.

maf_range

Range of minor allele frequencies: c(MIN, MAX).

prop_anno

Proportions of annotations in each category. Length should equal the number of annotation categories. Default of c(0.5, 0.4, 0.1) is based on the approximate empirical frequencies of BMVs, DMVs, and PTVs.

Value

List containing the (n x snps) genotype matrix "geno" and the (snps x 1) annotation vector "anno".

Generate Genotype Matrix

Description

Generate genotypes for n subject at snps variants in linkage equilibrium. Genotypes are generated such that the MAC is always >= 1.

Usage

GenGenoMat(n, snps, maf_range = c(0.001, 0.005))

Arguments

n

Sample size.

snps

Number of SNP in the gene.

maf_range

Range of minor allele frequencies: c(MIN, MAX).

Value

(n x snps) numeric matrix.

Generate Phenotypes

Description

Simulate a phenotype based on annotations, covariates, and genotypes.

Usage

GenPheno(
  anno,
  beta,
  covar,
  geno,
  reg_param,
  binary = FALSE,
  include_residual = TRUE,
  indicator = FALSE,
  method = "none",
  prop_causal = 1,
  random_signs = FALSE,
  random_var = 0,
  weights = c(1, 1, 1)
)

Arguments

anno

(snps x 1) annotation vector.

beta

If method = "none", a (L x 1) coefficient with effect sizes for each annotation category. By default, there are L = 3 annotation categories corresponding to BMVs, DMVs, and PTVs. If method != "none", a scalar effect size for the allelic series burden score.

covar

Covariate matrix.

geno

(n x snps) genotype matrix.

reg_param

Regression parameters.

binary

Generate binary phenotype? Default: FALSE.

include_residual

Include residual? If FALSE, returns the expected value. Intended for testing.

indicator

Convert raw counts to indicators? Default: FALSE.

method

Genotype aggregation method. Default: "none".

prop_causal

Proportion of variants which are causal.

random_signs

Randomize signs? FALSE for burden-type genetic architecture, TRUE for SKAT-type.

random_var

Frailty variance in the case of random signs. Default: 0.

weights

Annotation category weights used for aggregation if method != "none".

Value

(n x 1) numeric vector.

Phenotype generation

Genomic Control

Description

Genomic Control

Usage

GenomicControl(lambda, pval, df = 1)

Arguments

lambda

Genomic inflation factor.

pval

Numeric p-value.

df

Degrees of freedom. Should not require modification in most cases.

Value

Corrected p-value.

Ordinary Least Squares

Description

Fits the standard OLS model.

Usage

OLS(y, X)

Arguments

y

(n x 1) Numeric vector.

X

(n x p) Numeric matrix.

Value

List containing the following:

Calculate Residual Variance

Description

Calculate Residual Variance

Usage

ResidVar(y, X)

Arguments

y

(n x 1) Numeric phenotype vector.

X

(n x q) Numeric covariate matrix.

Value

Scalar residual variance.

Calculate Score Statistic

Description

Calculate Score Statistic

Usage

Score(y, G, X, v)

Arguments

y

(n x 1) Numeric phenotype vector.

G

(n x p) Numeric genotype matrix.

X

(n x q) Numeric covariate matrix.

v

Scalar residual variance.

Value

Scalar score statistic.

Allelic Sum Test from Sumstats

Description

Allelic Sum Test from Sumstats

Usage

SumCountSS(anno, beta, ld, se, weights, return_beta = FALSE)

Arguments

anno

(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L.

beta

(snps x 1) vector of effect sizes for the coding genetic variants within a gene.

ld

(snps x snps) matrix of correlations among the genetic variants.

se

(snps x 1) vector of standard errors for the effect sizes.

weights

(L x 1) vector of annotation category weights. Note that the number of annotation categories L is inferred from the length of weights.

return_beta

Return estimated effect sizes and standard errors? Default: FALSE.

Value

If return_beta, a list containing the category effect sizes, standard errors, and the p-value. Otherwise, the numeric p-value only.

Check if Positive Definite

Description

Check if Positive Definite

Usage

isPD(x, force_symmetry = FALSE, tau = 1e-08)

Arguments

x

Numeric matrix.

force_symmetry

Force the matrix to be symmetric?

tau

Threshold the minimum eigenvalue must exceed for the matrix to be considered positive definite.

Value

Logical indicating whether the matrix is PD.

Print Method for COAST Object.

Description

Print method for objects of class COAST.

Usage

## S3 method for class 'COAST'
print(x, ...)

Arguments

x

An object of class COAST.

...

Unused.

Show Method for COAST Object

Description

Show Method for COAST Object

Usage

## S4 method for signature 'COAST'
show(object)

Arguments

object

An object of class COAST.