metan (version 1.2.1)

gamem: Genotype analysis by mixed-effect models

Description

Analysis of genotypes in single experiments using mixed-effect models with estimation of genetic parameters.

Usage

gamem(.data, gen, rep, resp, block = NULL, prob = 0.05, verbose = TRUE)

Arguments

.data

The dataset containing the columns related to, Genotypes, replication/block and response variable(s).

gen

The name of the column that contains the levels of the genotypes, that will be treated as random effect.

rep

The name of the column that contains the levels of the replications (assumed to be fixed).

resp

The response variable(s). To analyze multiple variables in a single procedure a vector of variables may be used. For example resp = c(var1, var2, var3). Select helpers are also allowed.

block

Defaults to NULL. In this case, a randomized complete block design is considered. If block is informed, then an alpha-lattice design is employed considering block as random to make use of inter-block information, whereas the complete replicate effect is always taken as fixed, as no inter-replicate information was to be recovered (Mohring et al., 2015).

prob

The probability for estimating confidence interval for BLUP's prediction.

verbose

Logical argument. If verbose = FALSE the code are run silently.

Value

An object of class gamem, which is a list with the following items for each element (variable):

  • fixed: Test for fixed effects.

  • random: Variance components for random effects.

  • LRT: The Likelihood Ratio Test for the random effects.

  • blupGEN: The estimated BLUPS for genotypes

  • Details: A tibble with the following data: Ngen, the number of genotypes; OVmean, the grand mean; Min, the minimum observed (returning the genotype and replication/block); Max the maximum observed, MinGEN the winner genotype, MaxGEN, the loser genotype.

  • ESTIMATES: A tibble with the values for the genotypic variance, block-within-replicate variance (if an alpha-lattice design is used by informing the block in block), the residual variance and their respective contribution to the phenotypic variance; broad-sence heritability, heritability on the entry-mean basis, genotypic coefficient of variation residual coefficient of variation and ratio between genotypic and residual coefficient of variation.

  • residuals: The residuals of the model.

Details

gamem analyses data from a one-way genotype testing experiment. By default, a randomized complete block design is used according to the following model: $$Y_{ij} = m + g_i + r_j + e_{ij}$$ where \(Y_{ij}\) is the response variable of the ith genotype in the jth block; m is the grand mean (fixed); \(g_i\) is the effect of the ith genotype (assumed to be random); \(r_j\) is the effect of the jth replicate (assumed to be fixed); and \(e_{ij}\) is the random error.

When block is informed, then a resolvable alpha design is implemented, according to the following model:

$$Y_{ijk} = m + g_i + r_j + b_{jk} + e_{ijk}$$ where where \(y_{ijk}\) is the response variable of the ith genotype in the kth block of the jth replicate; m is the intercept, \(t_i\) is the effect for the ith genotype \(r_j\) is the effect of the jth replicate, \(b_{jk}\) is the effect of the kth incomplete block of the jth replicate, and \(e_{ijk}\) is the plot error effect corresponding to \(y_{ijk}\).

References

Mohring, J., E. Williams, and H.-P. Piepho. 2015. Inter-block information: to recover or not to recover it? TAG. Theor. Appl. Genet. 128:1541-54. doi:10.1007/s00122-015-2530-0

See Also

get_model_data waasb

Examples

Run this code
# NOT RUN {
library(metan)

# fitting the model considering an RCBD
# Genotype as random effects

rcbd <- gamem(data_g,
             gen = GEN,
             rep = REP,
             resp = c(PH, ED, EL, CL, CW, KW, NR, TKW, NKE))

# Likelihood ratio test for random effects
## Statistic
get_model_data(rcbd, "lrt")

## P-value
get_model_data(rcbd, "pval_lrt")

# Variance components
get_model_data(rcbd, "vcomp")

# Genetic parameters
get_model_data(rcbd, "genpar")

# BLUPs for genotypes
get_model_data(rcbd)

# fitting the model considering an alpha-lattice design
# Genotype and block-within-replicate as random effects
# Note that block effect was now informed.

alpha <- gamem(data_alpha,
               gen = GEN,
               rep = REP,
               block = BLOCK,
               resp = YIELD)
# Use the function  get_model_data() to easely extract the model values.
get_model_data(alpha, "genpar")
# }
# NOT RUN {
# }

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