simtrait: simtrait: simulate complex traits from genotypes

In OchoaLab/simtrait: Simulate Complex Traits from Genotypes

simtrait: simulate complex traits from genotypes

Description

This package enables simulation of complex (polygenic and continuous) traits from a simulated or real genotype matrix. The focus is on constructing the mean and covariance structure of the data to yield the desired heritability. The main function is sim_trait(), which returns the simulated trait and the vector of causal loci (randomly selected) and their coefficients. The causal coefficients are constructed under two models: random coefficients (RC) and fixed effect sizes (FES). The function cov_trait() computes the expected covariance matrix of the trait given the model parameters (namely the desired heritability and the true kinship matrix). Infinitesimal traits (without causal loci) can also be simulated using sim_trait_mvn().

Details

Package also provides some functions for evaluating genetic association approaches. pval_srmsd() and pval_infl() quantify null p-value accuracy, while pval_aucpr() quantifies predictive power.

The recommended inputs are simulated genotypes with known ancestral allele frequencies. The bnpsd package simulates genotypes for admixed individuals, resulting in a complex population structure.

For real data it is necessary to estimate the kinship matrix. popkin::popkin()' provides high-accuracy kinship estimates.

Author(s)

Maintainer: Alejandro Ochoa [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/OchoaLab/simtrait

• Report bugs at https://github.com/OchoaLab/simtrait/issues

Examples

# construct a dummy genotype matrix
X <- matrix(
    data = c(
        0, 1, 2,
        1, 2, 1,
        0, 0, 1
    ),
    nrow = 3,
    byrow = TRUE
)
# made up ancestral allele frequency vector for example
p_anc <- c(0.5, 0.6, 0.2)
# desired heritability
herit <- 0.8
# create a dummy kinship matrix for example
# make sure it is positive definite!
kinship <- matrix(
    data = c(
        0.6, 0.1, 0.0,
        0.1, 0.5, 0.0,
        0.0, 0.0, 0.5
    ),
    nrow = 3
)

# create simulated trait and associated data
# default is *random coefficients* (RC) model
obj <- sim_trait(X = X, m_causal = 2, herit = herit, p_anc = p_anc)
# trait vector
obj$trait
# randomly-picked causal locus indeces
obj$causal_indexes
# regression coefficient vector
obj$causal_coeffs

# *fixed effect sizes* (FES) model
obj <- sim_trait(X = X, m_causal = 2, herit = herit, p_anc = p_anc, fes = TRUE)

# either model, can apply to real data by replacing `p_anc` with `kinship`
obj <- sim_trait(X = X, m_causal = 2, herit = herit, kinship = kinship)

# covariance of simulated traits
V <- cov_trait(kinship = kinship, herit = herit)

# draw simulated traits (matrix of replicates) from infinitesimal model
traits <- sim_trait_mvn( rep = 10, kinship = kinship, herit = herit )
traits

# Metrics for genetic association approaches

# simulate truly null p-values, which should be uniform
pvals <- runif(10)
# for toy example, take these p-value to be truly causal
causal_indexes <- c(1, 5, 7)

# calculate desired measures
# this one quantifies p-value uniformity
pval_srmsd( pvals, causal_indexes )
# related, calculates inflation factors
pval_infl( pvals )
# this one quantifies predictive power
pval_aucpr( pvals, causal_indexes )

OchoaLab/simtrait documentation built on April 19, 2024, 7:36 p.m.