Package 'mvMAPIT'

Cauchy p combine method on mvmapit return

Description

This function takes in the p-values tibble that mvmapit returned. It then computes the combined p-values grouped by variant id.

Usage

cauchy_combined(pvalues, group_col = "id", p_col = "p")

Arguments

pvalues	Tibble with p-values from mvmapit function call. Grouping is based on the column named "id"
group_col	String that denotes column by which to group and combine p-values.
p_col	String that denotes p-value column.

Value

A Tibble with the combined p-values.

Examples

set.seed(837)
p <- 200
n <- 100
d <- 2
X <- matrix(
    runif(p * n),
    ncol = p
)
Y <- matrix(
    runif(d * n),
    ncol = d
)
mapit <- mvmapit(
    t(X),
    t(Y),
    test = "normal", cores = 1, logLevel = "INFO"
)
cauchy <- cauchy_combined(mapit$pvalues)

---

Fisher's combine method on mvmapit return

Description

This function takes in the p-values tibble that mvmapit returned. It then computes the combined p-values grouped by variant id.

Usage

fishers_combined(pvalues, group_col = "id", p_col = "p")

Arguments

pvalues	Tibble with p-values from mvmapit function call.
group_col	String that denotes column by which to group and combine p-values.
p_col	String that denotes p-value column.

Value

A Tibble with the combined p-values.

Examples

set.seed(837)
p <- 200
n <- 100
d <- 2
X <- matrix(
    runif(p * n),
    ncol = p
)
Y <- matrix(
    runif(d * n),
    ncol = d
)
mapit <- mvmapit(
    t(X),
    t(Y),
    test = "normal", cores = 1, logLevel = "INFO"
)
fisher <- fishers_combined(mapit$pvalues)

---

Harmonic mean p combine method on mvmapit return

Description

This function takes in the p-values tibble that mvmapit returned. It then computes the combined p-values grouped by variant id.

Usage

harmonic_combined(pvalues, group_col = "id", p_col = "p")

Arguments

pvalues	Tibble with p-values from mvmapit function call. Grouping is based on the column named "id"
group_col	String that denotes column by which to group and combine p-values.
p_col	String that denotes p-value column.

Value

A Tibble with the combined p-values.

Examples

set.seed(837)
p <- 200
n <- 100
d <- 2
X <- matrix(
    runif(p * n),
    ncol = p
)
Y <- matrix(
    runif(d * n),
    ncol = d
)
mapit <- mvmapit(
    t(X),
    t(Y),
    test = "normal", cores = 1, logLevel = "INFO"
)
harmonic <- harmonic_combined(mapit$pvalues)

---

Multivariate MArginal ePIstasis Test (mvMAPIT)

Description

This function runs a multivariate version of the MArginal ePIstasis Test (mvMAPIT) under the following model variations:

Usage

mvmapit(
  X,
  Y,
  Z = NULL,
  C = NULL,
  threshold = 0.05,
  accuracy = 1e-08,
  test = c("normal", "davies", "hybrid"),
  cores = 1,
  variantIndex = NULL,
  logLevel = "WARN",
  logFile = NULL
)

Arguments

X	is the p x n genotype matrix where p is the number of variants and n is the number of samples. Must be a matrix and not a data.frame.
Y	is the d x n matrix of d quantitative or continuous traits for n samples.
Z	is the matrix q x n matrix of covariates. Must be a matrix and not a data.frame.
C	is an n x n covariance matrix detailing environmental effects and population structure effects.
threshold	is a parameter detailing the value at which to recalibrate the Z test p values. If nothing is defined by the user, the default value will be 0.05 as recommended by the Crawford et al. (2017).
accuracy	is a parameter setting the davies function numerical approximation accuracy. This parameter is not needed for the normal test. Smaller p-values than the accuracy will be zero.
test	is a parameter defining what hypothesis test should be run. Takes on values 'normal', 'davies', and 'hybrid'. The 'hybrid' test runs first the 'normal' test and then the 'davies' test on the significant variants.
cores	is a parameter detailing the number of cores to parallelize over. It is important to note that this value only matters when the user has installed OPENMP on their operating system.
variantIndex	is a vector containing indices of variants to be included in the computation.
logLevel	is a string parameter defining the log level for the logging package.
logFile	is a string parameter defining the name of the log file for the logging output. Default is stdout.

Details

(1) Standard Model: y = m+g+e where m ~ MVN(0,omega^2K), g ~ MVN(0,sigma^2G), e ~ MVN(0,tau^2M). Recall from Crawford et al. (2017) that m is the combined additive effects from all other variants, represents the additive effect of the k-th variant under the polygenic background of all other variants; K is the genetic relatedness matrix computed using genotypes from all variants other than the k-th; g is the summation of all pairwise interaction effects between the k-th variant and all other variants; G represents a relatedness matrix computed based on pairwise interaction terms between the k-th variant and all other variants. Here, we also denote D = diag(x_k) to be an n × n diagonal matrix with the genotype vector x_k as its diagonal elements. It is important to note that both K and G change with every new marker k that is considered. Lastly; M is a variant specific projection matrix onto both the null space of the intercept and the corresponding genotypic vector x_k.

(2) Standard + Covariate Model: y = Wa+m+g+e where W is a matrix of covariates with effect sizes a.

(3) Standard + Common Environment Model: y = m+g+c+e i where c ~ MVN(0,eta^2C) controls for extra environmental effects and population structure with covariance matrix C.

(4) Standard + Covariate + Common Environment Model: y = Wa+m+g+c+e

This function will consider the following three hypothesis testing strategies which are featured in Crawford et al. (2017): (1) The Normal or Z test (2) Davies Method (3) Hybrid Method (Z test + Davies Method)

Value

A list of P values and PVEs

Examples

set.seed(837)
p <- 200
n <- 100
d <- 2
X <- matrix(
    runif(p * n),
    ncol = p
)
Y <- matrix(
    runif(d * n),
    ncol = d
)
mapit <- mvmapit(
    t(X),
    t(Y),
    test = "normal", cores = 1, logLevel = "INFO"
)

---

Multivariate MAPIT analysis and exhaustive search analysis.

Description

This data set contains the return object from the multivariate MAPIT method, the fisher combined p-values, and the result from an exhaustive search using regression on the SNPs that were significant in the mvMAPIT analysis.

Usage

mvmapit_data

Format

A nested list containing tibble data frames:

mvmapit

mvmapit return object; named list containing tibbles 'pvalues', 'pves', and 'duration'.

fisher

Tibble containing fisher combined p-values of the mvmapit data.

exhaustive_search

A dataframe containing the p-values of an exhaustive search together with the analysed interaction pair.

Source

data-raw/mvmapit_on_simulated_data.R

---

Multivariate MAPIT analysis of binding affinities in broadly neutralizing antibodies.

Description

This data set contains the return object from the multivariate MAPIT method, applied to two binding affinity traits for two broadly neutralizing antibodies. It also contains the regression coefficients on the same data as reported by Phillips et al. (2021).

Usage

phillips_data

Format

A named list containing tibble data frames:

fisher

Tibble containing among other columns the residue id, p-values, antibody species, trait of the Phillips data. Combined p-value with Fisher's method.

harmonic

Tibble containing among other columns the residue id, p-values, antibody species, trait of the Phillips data. Combined p-value with harmonic mean p method.

regression

Named list containing two tibbles containing regression coefficients as reported by Phillips et al.

Details

The antibody CR9114 was analyzed with influenza H1 and H3. The antibody CR6261 was analyzed with influenza H1 and H9.

In the data, the p-values are computed for the test whether a given residue position has a marginal epistatic effect on the binding affinities.

Phillips et al. (2021) Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies. eLife 10:e71393

Source

vignette/study-phillips-bnabs.Rmd

---

Simulate phenotye data

Description

This function simulates trait data from a genotype matrix.

Usage

simulate_traits(
  genotype_matrix,
  n_causal = 1000,
  n_trait_specific = 10,
  n_pleiotropic = 10,
  H2 = 0.6,
  d = 2,
  rho = 0.8,
  marginal_correlation = 0.3,
  epistatic_correlation = 0.3,
  group_ratio_trait = 1,
  group_ratio_pleiotropic = 1,
  maf_threshold = 0.01,
  seed = 67132,
  logLevel = "INFO",
  logFile = NULL
)

Arguments

genotype_matrix	Genotype matrix with samples as rows, and SNPs as columns.
n_causal	Number of SNPs that are causal.
n_trait_specific	Number of causal SNPs with single trait epistatic effects.
n_pleiotropic	Number of SNPs with epistatic effects on all traits.
H2	Broad-sense heritability. Can be vector.
d	Number of traits.
rho	Proportion of heritability explained by additivity.
marginal_correlation	Correlation between the additive effects of the trait.
epistatic_correlation	Correlation between the epistatic effects of the trait.
group_ratio_trait	Ratio of sizes of trait specific groups that interact, e.g. a ratio 1:3 would be value 3.
group_ratio_pleiotropic	Ratio of sizes of pleiotropic groups that interact, e.g. a ratio 1:3 would be value 3.
maf_threshold	is a float parameter defining the threshold for the minor allele frequency not included in causal SNPs.
seed	Random seed for simulation.
logLevel	is a string parameter defining the log level for the logging package.
logFile	is a string parameter defining the name of the log file for the logging output.

Details

This function takes a genotype matrix and simulates trait data under the following model: beta_i ~ MN(0, V_i, I), i in { additive, epistatic, residual}

The effect sizes follow a matrix normal distribution with no correlation between the samples but covariance between the effects for different traits

Value

A list object containing the trait data, the genotype data, as well as the causal SNPs and summary statistics.

Examples

p <- 200
f <- 10
g <- 4
n <- 100
d <- 3
X <- matrix(
    runif(p * n),
    ncol = p
)
data <- simulate_traits(
    X, n_causal = f, n_trait_specific = g, n_pleiotropic = g, d = d, maf_threshold = 0,
    logLevel = "ERROR"
)

---

Genotype and trait data with epistasis.

Description

A simulated dataset that has epistatic interactions.

Usage

simulated_data

Format

A named list with simulated data and simulation parameters:

parameters

Tibble containing simulation parameters for each trait.

trait

Matrix containing simulated data for 2 traits and 500 samples.

genotype

Matrix containing simulated genotype with 500 samples and 1000 variables.

additive

Tibble containing all variants with additive effects on the traits as well as the effect sizes.

epistatic

Tibble containing all variants with epistatic effects on the traits as well as the effect sizes.

interactions

Tibble containing all interactions, effect size, and trait they affect.

snps.filtered

SNPs that were used in the simulations.

Source

data-raw/simulate_epistasis.R

---