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Calculate heritability from mixed model object

h2.Rd

A case-specific wrapper for calculating heritability.

  • The upper case prefix H2_ refers to the wrapper or subfunctions e.g. H2_Delta() for calculating broad sense heritability

Usage

H2(model, target, method = c("Cullis", "Oakey", "Delta", "Piepho", "Standard"), options)

Arguments

model

Model object of class lmerMod/merMod or asreml

target

The name of the random effect for which heritability is to be calculated.

method

Character vector of name of method to calculate heritability. See details.

options

NULL by default, for internal checking of model object before calculations

Value

A named numeric vector, length matching number of methods supplied

Details

The following methods are currently implemented for broad-sense heritability H2(method = "XX"):

  • "Cullis": $$H^2_{Cullis} = 1 - \frac{PEV^{BLUP}_{\overline\Delta ij}}{2\sigma^2_g}$$

  • "Oakey": $$H^2_{Oakey} = \frac{\sum_{i = n_z+1}^{n_g} \lambda_i}{\sum_{n_g}^{\lambda_i\neq 0}}$$

  • "Delta": $$H^2_{\Delta ..} = 1 - \frac{PEV^{BLUP}_{\overline\Delta ..}}{2\sigma^2_g}$$

  • "Piepho": $$H^2_{Piepho} = \frac{\sigma^2_g}{\sigma^2_g + \overline{PEV_{BLUE_g}} / 2}$$

  • "Standard": $$H^2_{Standard} = \frac{\sigma^2_g}{\sigma^2_g + \frac{1}{n_g}\sum_{n_g}^{i=1} \sigma^2_p / n_{gi}}$$

For further details of a specific method - take a look at help file for each subfunctions ?H2_Cullis

References

  • Cullis, B. R., Smith, A. B., & Coombes, N. E. (2006). On the design of early generation variety trials with correlated data. Journal of Agricultural, Biological, and Environmental Statistics, 11(4), 381–393. https://doi.org/10.1198/108571106X154443

  • Oakey, H., Verbyla, A., Pitchford, W., Cullis, B., & Kuchel, H. (2006). Joint modeling of additive and non-additive genetic line effects in single field trials. Theoretical and Applied Genetics, 113(5), 809–819. https://doi.org/10.1007/s00122-006-0333-z

  • Schmidt, P., Hartung, J., Rath, J., & Piepho, H.-P. (2019). Estimating Broad-Sense Heritability with Unbalanced Data from Agricultural Cultivar Trials. Crop Science, 59(2), 525–536. https://doi.org/10.2135/cropsci2018.06.0376

  • Piepho, H.-P., & Möhring, J. (2007). Computing Heritability and Selection Response From Unbalanced Plant Breeding Trials. Genetics, 177(3), 1881–1888. https://doi.org/10.1534/genetics.107.074229

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

See also

H2_Cullis(), H2_Oakey(), H2_Delta(), H2_Piepho(), H2_Standard()

Examples

# lme4 model
lettuce_subset <- lettuce_phenotypes |> subset(loc == "L2")
lettuce_lme4 <- lme4::lmer(y ~ rep + (1 | gen), data = lettuce_subset)
H2(lettuce_lme4, target = "gen", method = c("Standard", "Delta"))
#>  Standard     Delta 
#> 0.8294971 0.8294971 

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

H2(lettuce_asreml, target = "gen", method = c("Standard", "Delta"))
} # }