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.
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$tabsmr
#> trait rep geno env year mean std min max V.g V.e
#> 1 stemdw 5 15 1 1 12.59867 4.749994 2.818 22.302 19.96002 9.410932
#> V.p repeatability H2.s H2.p H2.c
#> 1 21.84221 0.913828 0.913828 0.9502395 0.9533473
hr$blues
#> # A tibble: 15 × 6
#> geno stemdw SE df lower.CL upper.CL
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 G01 15.7 1.03 120. 13.7 17.8
#> 2 G02 10.1 1.03 120. 8.08 12.2
#> 3 G03 9.70 1.03 120. 7.65 11.7
#> 4 G04 15.2 1.03 120. 13.1 17.2
#> 5 G05 12.9 1.09 123. 10.7 15.0
#> 6 G06 22.3 1.03 120. 20.3 24.3
#> 7 G07 2.82 1.03 120. 0.778 4.86
#> 8 G08 10.4 1.03 120. 8.38 12.5
#> 9 G09 15.7 1.03 120. 13.6 17.7
#> 10 G10 9.24 1.03 120. 7.20 11.3
#> 11 G11 6.42 1.03 120. 4.38 8.47
#> 12 G12 16.1 1.03 120. 14.1 18.2
#> 13 G13 14.6 1.03 120. 12.6 16.7
#> 14 G14 16.3 1.03 120. 14.3 18.3
#> 15 G15 11.5 1.03 120. 9.43 13.5
hr$blups
#> # A tibble: 15 × 2
#> geno stemdw
#> <chr> <dbl>
#> 1 G01 15.6
#> 2 G02 10.2
#> 3 G03 9.82
#> 4 G04 15.1
#> 5 G05 12.8
#> 6 G06 20.6
#> 7 G07 3.25
#> 8 G08 10.5
#> 9 G09 15.5
#> 10 G10 9.39
#> 11 G11 6.70
#> 12 G12 15.9
#> 13 G13 14.5
#> 14 G14 16.1
#> 15 G15 11.5
hr$outliers
#> $fixed
#> bloque geno stemdw resi res_MAD rawp.BHStud index adjp bholm out_flag
#> 68 IV G05 80.65 60.36709 18.84505 0 68 0 0 OUTLIER
#>
#> $random
#> bloque geno stemdw resi res_MAD rawp.BHStud index adjp
#> 68 IV G05 80.65 61.39925 18.886676 0.0000000000 68 0.0000000000
#> 100 IV G06 33.52 12.02340 3.698449 0.0002169207 100 0.0002169207
#> bholm out_flag
#> 68 0.00000000 OUTLIER
#> 100 0.03232119 OUTLIER
#>