Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Data Validation

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:

Punnett Squares

Punnett Squares

Punnett Squares

Punnett Squares

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Feeding Laboratory Mice: Comparing a Standard Versus a Purified Diet - Marked Effects on Digestive Physiology.

Journal of animal physiology and animal nutrition·2026

Same author

Review: Improving residual feed intake modelling in the context of nutritional- and genetic studies for dairy cattle.

Animal : an international journal of animal bioscience·2024

Same author

Developing flexible models for genetic evaluations in smallholder crossbred dairy farms.

Journal of dairy science·2023

Same author

Validation of single-step genomic BLUP random regression test-day models and SNP effects analysis on milk yield in French Saanen goats.

Journal of dairy science·2023

Same author

Genetic relationships between weight loss in early lactation and daily milk production throughout lactation in Holstein cows.

Journal of dairy science·2023

Same author

Detailed analysis of mortality rates in the female progeny of 1,001 Holstein bulls allows the discovery of new dominant genetic defects.

Journal of dairy science·2022

Same journal

Invited review: Manufacturing Whey Protein Colloidal Particles via Liquid Antisolvent Precipitation Method: Particle Formation Mechanism and Ingredient Functionality Aspects.

Journal of dairy science·2026

Same journal

Colostrum programs early t lymphocyte-mediated immunity in neonatal dairy calves: effects of deprivation and preservation method on passive transfer and antigen-specific responses.

Journal of dairy science·2026

Same journal

Functional Characterization and Application of Autochthonous Lactic Acid Bacteria from Chinese Kefir for Improved Fermented Milk Quality.

Journal of dairy science·2026

Same journal

Distinct contributions of the Agr and LuxS quorum-sensing systems to stress tolerance, biofilm formation, and persistence of Staphylococcus aureus in dairy-processing environments.

Journal of dairy science·2026

Same journal

Integrating automated body condition scores and lactation data via optimization algorithms for maximized milk revenue and minimized cost of delayed conception in dairy cows.

Journal of dairy science·2026

Same journal

Assessing genotype by feed interactions for milk production traits in dairy cattle.

Journal of dairy science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 3, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

A general method to validate breeding value prediction software.

H Leclerc¹, M Wensch-Dorendorf, J Wensch

¹Institut National de la Recherche Agronomique, UR337, Station de Génétique Quantitative et Appliquée, F-78352 Jouyen-Josas, France. helene.leclerc@jouy.inra.fr

Journal of Dairy Science

|July 25, 2008

Summary

This summary is machine-generated.

A new method validates genetic evaluation software by replacing real data with simulated data to ensure accurate breeding value predictions. This approach enhances the reliability of national genetic evaluations for livestock.

More Related Videos

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Related Experiment Videos

Last Updated: Jul 3, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Area of Science:

Animal Breeding and Genetics
Quantitative Genetics
Statistical Genetics

Background:

National genetic evaluations rely on data quality, analytical models, and software accuracy.
Validating breeding value prediction software is crucial for reliable genetic improvement programs.

Purpose of the Study:

To develop and implement a general strategy for validating national breeding value prediction software.
To ensure the accuracy and reliability of genetic evaluation software through a novel validation approach.

Main Methods:

A validation strategy was developed by replacing real performance data with simulated data.
Simulated data were generated with known fixed and random effects and residuals to match BLUP estimates.
The method was applied to a multiple-trait model and a random regression test-day model, including Legendre polynomials and heterogeneous variances.

Main Results:

The proposed method successfully validated the genetic evaluation software by comparing BLUP estimates with simulated true effects.
The approach demonstrated high efficiency and usefulness in assessing the correctness of the software.
Implementation for complex models, like random regression test-day models, proved feasible.

Conclusions:

The developed simulation-based strategy is an effective tool for validating national genetic evaluation software.
This method enhances confidence in the accuracy of breeding values derived from national genetic evaluations.
The approach is adaptable to various linear models, offering broad applicability in animal breeding.