H2cal: Broad-sense heritability in plant breeding

In inti: Tools and Statistical Procedures in Plant Science

Broad-sense heritability in plant breeding

Description

Heritability in plant breeding on a genotype difference basis

Usage

H2cal(
  data,
  trait,
  gen.name,
  rep.n,
  env.n = 1,
  year.n = 1,
  env.name = NULL,
  year.name = NULL,
  fixed.model,
  random.model,
  summary = FALSE,
  emmeans = FALSE,
  weights = NULL,
  plot_diag = FALSE,
  outliers.rm = FALSE,
  trial = NULL
)

Arguments

data	Experimental design data frame with the factors and traits.
trait	Name of the trait.
gen.name	Name of the genotypes.
rep.n	Number of replications in the experiment.
env.n	Number of environments (default = 1). See details.
year.n	Number of years (default = 1). See details.
env.name	Name of the environments (default = NULL). See details.
year.name	Name of the years (default = NULL). See details.
fixed.model	The fixed effects in the model (BLUEs). See examples.
random.model	The random effects in the model (BLUPs). See examples.
summary	Print summary from random model (default = FALSE).
emmeans	Use emmeans for calculate the BLUEs (default = FALSE).
weights	an optional vector of ‘prior weights’ to be used in the fitting process (default = NULL).
plot_diag	Show diagnostic plots for fixed and random effects (default = FALSE). Options: "base", "ggplot". .
outliers.rm	Remove outliers (default = FALSE). See references.
trial	Column with the name of the trial in the results (default = NULL).

Details

The function allows to made the calculation for individual or multi-environmental trials (MET) using fixed and random model.

1. The variance components based in the random model and the population summary information based in the fixed model (BLUEs).

2. Heritability under three approaches: Standard (ANOVA), Cullis (BLUPs) and Piepho (BLUEs).

3. Best Linear Unbiased Estimators (BLUEs), fixed effect.

4. Best Linear Unbiased Predictors (BLUPs), random effect.

5. Table with the outliers removed for each model.

For individual experiments is necessary provide the trait, gen.name, rep.n.

For MET experiments you should env.n and env.name and/or year.n and year.name according your experiment.

The BLUEs calculation based in the pairwise comparison could be time consuming with the increase of the number of the genotypes. You can specify emmeans = FALSE and the calculate of the BLUEs will be faster.

If emmeans = FALSE you should change 1 by 0 in the fixed model for exclude the intersect in the analysis and get all the genotypes BLUEs.

For more information review the references.

Value

list

Author(s)

Maria Belen Kistner

Flavio Lozano Isla

References

Bernal Vasquez, Angela Maria, et al. “Outlier Detection Methods for Generalized Lattices: A Case Study on the Transition from ANOVA to REML.” Theoretical and Applied Genetics, vol. 129, no. 4, Apr. 2016.

Buntaran, H., Piepho, H., Schmidt, P., Ryden, J., Halling, M., and Forkman, J. (2020). Cross validation of stagewise mixed model analysis of Swedish variety trials with winter wheat and spring barley. Crop Science, 60(5).

Schmidt, P., J. Hartung, J. Bennewitz, and H.P. Piepho. 2019. Heritability in Plant Breeding on a Genotype Difference Basis. Genetics 212(4).

Schmidt, P., J. Hartung, J. Rath, and H.P. Piepho. 2019. Estimating Broad Sense Heritability with Unbalanced Data from Agricultural Cultivar Trials. Crop Science 59(2).

Tanaka, E., and Hui, F. K. C. (2019). Symbolic Formulae for Linear Mixed Models. In H. Nguyen (Ed.), Statistics and Data Science. Springer.

Zystro, J., Colley, M., and Dawson, J. (2018). Alternative Experimental Designs for Plant Breeding. In Plant Breeding Reviews. John Wiley and Sons, Ltd.

Examples

library(inti)

dt <- potato

hr <- H2cal(data = dt
            , trait = "stemdw"
            , gen.name = "geno"
            , rep.n = 5
            , fixed.model = "0 + (1|bloque) + geno"
            , random.model = "1 + (1|bloque) + (1|geno)"
            , emmeans = TRUE
            , plot_diag = FALSE
            , outliers.rm = TRUE
            )

 hr$tabsmr
 hr$blues
 hr$blups
 hr$outliers
 

inti documentation built on Oct. 27, 2023, 9:06 a.m.