marker_h2: Compute a marker-based estimate of heritability, given phenotypic observations at individual plant or plot level.

Description

Given a genetic relatedness matrix and phenotypic observations at individual plant or plot level, this function computes REML-estimates of the genetic and residual variance and their standard errors, using the AI-algorithm (Gilmour et al. 1995). Based on this, heritability estimates and confidence intervals are given (the estimator h_r^2 in Kruijer et al.).

Usage

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marker_h2(data.vector, geno.vector, covariates = NULL, K, alpha = 0.05,
          eps = 1e-06, max.iter = 100, fix.h2 = FALSE, h2 = 0.5)

Arguments

data.vector

A vector of phenotypic observations. Needs to be of type numeric. May contain missing values.

geno.vector

A vector of genotype labels, either a factor or character. This vector should correspond to data.vector, and hence needs to be of the same length.

covariates

A data-frame or matrix with optional covariates, the rows corresponding to the phenotypic observations in data.vector and geno.vector. May contain missing values. Factors are not allowed, and need to be encoded by columns of type numeric or integer. The data-frame or matrix should not contain an intercept, which is included by default.

K

A genetic relatedness or kinship matrix, typically marker-based. Must have row- and column-names corresponding to the levels of geno.vector

alpha

Confidence level, for the 1-alpha confidence intervals.

eps

Numerical precision, used as convergence criterion in the AI-algorithm.

max.iter

Maximal number of iterations in the AI-algorithm.

fix.h2

Compute the log-likelihood and inverse AI-matrix for a fixed heritability value. Default is FALSE.

h2

When fix.h2 is TRUE, the value of the heritability. Must be of type numeric, between 0 and 1.

Details

Value

A list with the following components:

Author(s)

Willem Kruijer.

References

See Also

For marker-based estimation of heritability using genotypic means, see marker_h2_means.

Examples

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data(LD)
data(K_atwell)
# Heritability estimation for all observations:
#out <- marker_h2(data.vector=LD$LD,geno.vector=LD$genotype,
#                 covariates=LD[,4:8],K=K_atwell)
# Heritability estimation for a randomly chosen subset of 20 accessions:
set.seed(123)
sub.set <- which(LD$genotype %in% sample(levels(LD$genotype),20))
out <- marker_h2(data.vector=LD$LD[sub.set],geno.vector=LD$genotype[sub.set],
                 covariates=LD[sub.set,4:8],K=K_atwell)

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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