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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

Polygenic Traits

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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Solution of multiple trait animal models with missing data on some traits.

V Ducrocq¹, B Besbes

¹Station de Génétique Quantitative et Appliquée, Institut National de la Recherche Agronomique, Jouy-en-Josas Cedex, France.

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie

|March 15, 2011

Summary

Transformations like canonical, triangular, and combined methods can optimize animal model evaluations. The extended canonical transformation offers the fastest convergence, especially for reduced models with multiple traits and missing data.

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Published on: September 17, 2019

Area of Science:

Animal breeding and genetics
Quantitative genetics
Statistical modeling

Background:

Mixed model equations in animal breeding can be computationally intensive.
Handling missing data and multiple traits simultaneously presents challenges in genetic evaluations.

Purpose of the Study:

To compare the efficiency of different transformations for solving multiple trait animal models.
To evaluate methods for handling missing records within these models.

Main Methods:

Applied canonical, triangular, and combined transformations to mixed model equations.
Utilized an Expectation-Maximization (EM) based approach to handle missing data.
Compared convergence speed and computational time on real datasets with simulated missing patterns.

Main Results:

The combined transformation consistently outperformed the triangular transformation.
The extended canonical transformation demonstrated the fastest convergence, reducing CPU time significantly.
Efficiency gains were most pronounced in reduced animal models and with a higher number of traits.

Conclusions:

The extended canonical transformation is a highly efficient method for multiple trait animal model evaluations, particularly with missing data.
This approach offers substantial computational advantages, leading to faster genetic evaluations.