Calculate standard heritability from model object

Compute standard heritability using the classic ratio method of genotypic and phenotypic variance. See Falconer & Mackay (1996)

Usage

h2_Standard(model,
            target,
            options = NULL,
            marginal = TRUE,
            stratification = NULL,
            vc = NULL,
            ...)
H2_Standard(model,
            target,
            options = NULL,
            marginal = TRUE,
            stratification = NULL,
            vc = NULL,
            ...)

Arguments

model: Model object of class lmerMod/merMod or asreml
target: The name of the random effect for which heritability is to be calculated.
options: NULL by default, for internal checking of model object before calculations
marginal: Logical; if TRUE, construct marginal (strata-averaged) mappings so that each genotype receives a single averaged effect per term. If FALSE, mappings will only consider the main genotype effect and ignore the iteracting terms.
stratification: A one-row data frame defining the stratum in which genotype effects should be evaluated. The columns must correspond to model terms that interact with target.
vc: A list of precomputed variance components. Should be in the same structure as the output of var_comp()
...: Additional arguments that specify heritability calculation when interactions with genotype effects are modelled

Value

Numeric value

Details

The equation used to calculate standard heritability (broad-sense) is: $$H^2_{Standard} = \frac{\sigma^2_g}{\sigma^2_g + \frac{1}{n_g}\sum_{n_g}^{i=1} \sigma^2_p / n_{gi}}$$ where:

$n_g$ is the number of genotypes
$n_{gi}$ is the number of replicate for a given genotype i
$\sigma_g$ is the variance attributed to genotype differences
$\sigma_p$ is the variance attributed to phenotypic differences

The equation used to calculate standard heritability (narrow-sense) is: $$h^2_{Standard} = \frac{\operatorname{Var}(g_i - g_j)}{\operatorname{Var}(y_i.. - y_j..)}$$ where:

$g_i$ is the random effect of the $i^{th}$ genotype
$y_i..$ is the sample average of the $i^{th}$ genotype

References

Falconer, D. S., & Mackay, T. F. C. (1996). Introduction to quantitative genetics (4th ed.). Longman.

Examples

# lme4 model
lettuce_subset <- lettuce_phenotypes |> subset(loc == "L2")
lettuce_lme4 <- lme4::lmer(y ~ rep + (1 | gen), data = lettuce_subset)
H2_Standard(lettuce_lme4, target = "gen")
#> [1] 0.8294971

# asreml model (Requires license)
if (FALSE) { # \dontrun{
lettuce_asreml <- asreml::asreml(fixed = y ~ rep,
                                 random = ~ gen,
                                 data = lettuce_subset,
                                 trace = FALSE
                                 )

H2_Standard(lettuce_asreml, target = "gen")
} # }