sim_trait {simtrait}

Simulate a complex trait from genotypes

Description

Simulate a complex trait given a SNP genotype matrix and model parameters, which are minimally: the number of causal loci, the heritability, and either the true ancestral allele frequencies used to generate the genotypes or the mean kinship of all individuals. An optional minimum marginal allele frequency for the causal loci can be set. The output traits have by default a zero mean and unit variance (for outbred individuals), but those parameters can be modified. The code selects random loci to be causal, constructs coefficients for these loci (scaled appropriately) and random Normal independent non-genetic effects and random group effects if specified. There are two models for constructing causal coefficients: random coefficients (RC; default) and fixed effect sizes (FES; i.e., coefficients roughly inversely proportional to allele frequency; use fes = TRUE). Suppose there are m loci and n individuals.

Usage

sim_trait(
  X,
  m_causal,
  herit,
  p_anc = NULL,
  kinship = NULL,
  mu = 0,
  sigma_sq = 1,
  labs = NULL,
  labs_sigma_sq = NULL,
  maf_cut = NA,
  loci_on_cols = FALSE,
  m_chunk_max = 1000,
  fes = FALSE
)

Arguments

X

The m-by-n genotype matrix (if loci_on_cols = FALSE, transposed otherwise), or a BEDMatrix object. This is a numeric matrix consisting of reference allele counts (in c(0, 1, 2, NA) for a diploid organism).

m_causal

The desired number of causal loci.

herit

The desired heritability (proportion of trait variance due to genetics).

p_anc

The length-m vector of true ancestral allele frequencies. Optional but recommended for simulations. Either this or kinship must be specified.

kinship

The mean kinship value of the individuals in the data. The n-by-n kinship matrix of the individuals in the data is also accepted. Optional but recommended for real data. Either this or p_anc must be specified.

mu

The desired parametric mean value of the trait (scalar, default 0).

sigma_sq

The desired parametric variance factor of the trait (scalar, default 1). Corresponds to the variance of an outbred individual.

labs

Optional labels assigning individuals to groups, to simulate group effects. If vector, length must be number of individuals. If matrix, individuals must be along rows, and levels along columns (for multiple levels of group effects). The levels are not required to be nested (as the name may falsely imply). Values can be numeric or strings, simply assigning the same values to individuals in the same group. If this is non-NULL, then labs_sigma_sq must also be given!

labs_sigma_sq

Optional vector of group effect variance proportions, one value for each level given in labs (a scalar if labs is a vector, otherwise its length should be the number of columns of labs). Ignored unless labs is also given. As these are variance proportions, each value must be non-negative and sum(labs_sigma_sq) + herit <= 1 is required so residual variance is non-negative.

maf_cut

The optional minimum allele frequency threshold (default NA, no threshold). This prevents rare alleles from being causal in the simulation. Threshold is applied to the sample allele frequencies and not their true parametric values (p_anc), even if these are available.

loci_on_cols

If TRUE, X has loci on columns and individuals on rows; if FALSE (the default), loci are on rows and individuals on columns. If X is a BEDMatrix object, loci are always on the columns (loci_on_cols is ignored).

m_chunk_max

BEDMatrix-specific, sets the maximum number of loci to process at the time. If memory usage is excessive, set to a lower value than default (expected only for extremely large numbers of individuals).

fes

If TRUE, causal coefficients are inversely proportional to the square root of p_anc * ( 1 - p_anc ) (estimated when p_anc is unavailable), which ensures fixed effect sizes (FES) per causal locus. Signs (+/-) are drawn randomly with equal probability. If FALSE (the default), random coefficients (RC) are drawn from a standard Normal distribution. In both cases coefficients are rescaled to result in the desired heritability.

Details

To center and scale the trait and locus coefficients vector correctly to the desired parameters (mean, variance, heritability), the parametric ancestral allele frequencies (p_anc) must be known. This is necessary since in the heritability model the genotypes are random variables (with means given by p_anc and a covariance structure given by p_anc and the kinship matrix), so these genotype distribution parameters are required. If p_anc are known (true for simulated genotypes), then the trait will have the specified mean and covariance matrix in agreement with cov_trait(). To simulate traits using real genotypes, where p_anc is unknown, a compromise that works well in practice is possible if the mean kinship is known (see package vignette). We recommend estimating the mean kinship using the popkin package!

Value

A named list containing:

• trait: length-n vector of the simulated trait

• causal_indexes: length-m_causal vector of causal locus indexes

• causal_coeffs: length-m_causal vector of coefficients at the causal loci

• group_effects: length-n vector of simulated group effects, or 0 (scalar) if not simulated

However, if herit = 0 then causal_indexes and causal_coeffs will have zero length regardless of m_causal.

See Also

cov_trait(), sim_trait_mvn()

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)
# made up mean kinship
kinship <- 0.2
# desired heritability
herit <- 0.8

# 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$trait
# randomly-picked causal locus indexes
obj$causal_indexes
# regression coefficients 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)