Related Experiment Video
Updated: Jan 5, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
Genetic evaluation for latent variables derived from factor analysis in broilers
J T Paiva1, H R Oliveira2, M Nascimento3
1Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, Brazil.
Abstract:
1. The aim of this study was to investigate the associations between several carcass, performance and meat quality traits in broilers through factor analysis and use the latent variables (i.e. factors) as pseudo-phenotypes in genetic evaluations.2. Factors were extracted using the principal components method and varimax rotation algorithm. Genetic parameters were estimated via Bayesian inference under a multiple-trait animal model.3. All factors taken together explained 71% of the original variance of the data. The first factor, denominated as 'weight', was associated with carcass and body weight traits; and the second factor, defined as 'tenderness', represented traits related to water-holding capacity and shear force. The third factor, 'colour', was associated with traits related to meat colour, whereas the fourth, referenced as 'viscera', was related to heart, liver and abdominal fat.4. The four biological factors presented moderate to high heritability (ranging from 0.35 to 0.75), which may confer genetic gains in this population.5. In conclusion, it seems possible to reduce the number of traits in the genetic evaluation of broilers using latent variables derived from factor analysis.
Related Concept Videos
Factorial Design
Heritability
Multiple Allele Traits
Background and Environment Affect Phenotype
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
Two-Way ANOVA
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

