genpar: Genetic parameters for selection
In EstimateBreed: Estimation of Environmental Variables and Genetic Parameters

genpar

R Documentation

Genetic parameters for selection

Description

Function for determining selection parameters, based on an experiment carried out on the rice crop. Intended for isolated evaluation of the performance of lines within a given population.

Usage

genpar(.data, GEN, REP, vars, K = 0.05, check = FALSE, verbose = FALSE)

Arguments

`.data`	The name of the object containing data.
`GEN`	The column with the selected genotypes within the population.
`REP`	The column with the repetitions (if any).
`vars`	The column with the variable of interest.
`K`	Selection pressure (Default 0.05).
`check`	Logical argument. Checks the model's assumptions statistical if the value is equal to TRUE.
`verbose`	Logical argument. Runs the code silently if FALSE.

Value

A list containing the following components:

`Environmental variance (sigmaE)`	The environmental variance (sigmaE) represents the variability in phenotypic traits attributable to environmental factors. This variance is important for understanding how environmental conditions influence the observed phenotype.
`Genotypic variance (sigmaG)`	The genotypic variance (sigmaG) reflects the variability in phenotypic traits attributable to genetic differences between individuals. It is crucial for assessing the genetic potential of a population for a specific trait.
`Phenotypic variance (sigmaP)`	The phenotypic variance (sigmaP) is the total observed variability in the phenotype, which is the sum of environmental and genotypic variances. This measure helps understand the overall range of variation observed in a given dataset.
`Environmental coefficient of variance (ECV)`	The environmental coefficient of variance (ECV) is the ratio of environmental variance to the mean of the phenotypic value, expressed as a percentage. It gives an idea of the magnitude of environmental variation relative to the mean value.
`Genotypic coefficient of variance (GCV)`	The genotypic coefficient of variance (GCV) is the ratio of genotypic variance to the mean of the phenotypic value, also expressed as a percentage. It is used to estimate how much genetic variability can be exploited for improving desirable traits.
`Phenotypic coefficient of variance (PCV)`	The phenotypic coefficient of variance (PCV) is the ratio of phenotypic variance to the mean of the phenotypic value, expressed as a percentage. It provides insight into the overall impact of both genetic and environmental factors on the observed variation.
`Heritability (h2b)`	Heritability (h2b) is the proportion of phenotypic variance attributable to genotypic variance. It indicates the potential for selecting specific traits within a population.
`Genetic advance (GA)`	Genetic advance (GA) represents the amount of genetic progress that can be achieved in one generation by selecting the best individuals for specific traits.
`Genetic advance as percentage of the mean (GAM)`	Genetic advance as a percentage of the mean (GAM) is a measure of how much genetic progress represents relative to the population's mean. This value helps assess the effectiveness of selection strategies.

Author(s)

Willyan Junior Adorian Bandeira

Ivan Ricardo Carvalho

Murilo Vieira Loro

Leonardo Cesar Pradebon

Jose Antonio Gonzalez da Silva

References

Yadav, S. P. S., Bhandari, S., Ghimire, N. P., Mehata, D. K., Majhi, S. K., Bhattarai, S., Shrestha, S., Yadav, B., Chaudhary, P., & Bhujel, S. (2024). Genetic variability, character association, path coefficient, and diversity analysis of rice (Oryza sativa L.) genotypes based on agro-morphological traits. International Journal of Agronomy, 2024, Article ID 9946332. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1155/2024/9946332")}

Examples

library(EstimateBreed)
data("genot2")

#Geting parameters without cheking model assumptions
parameters <- genpar(genot2,Gen,Rep,var =c("VAR1", "VAR2"))
parameters$anova
parameters$gp

#Checking model assumptions
parameters <- genpar(genot2,Gen,Rep,var =c("VAR1", "VAR2"),check=TRUE)
parameters$anova
parameters$gp

EstimateBreed documentation built on April 4, 2025, 5:37 a.m.