Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.2K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.2K
Longitudinal Research02:20

Longitudinal Research

12.8K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.8K
Longitudinal Studies01:26

Longitudinal Studies

297
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
297
Residual Plots01:07

Residual Plots

5.2K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
5.2K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

361
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
361

You might also read

Related Articles

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

Sort by
Same author

Guiding eQTL mapping and genomic prediction of gene expression in three pig breeds with tissue-specific epigenetic annotations from early development.

Genomics·2025
Same author

Detection of genomic regions affecting thermotolerance traits in growing pigs during acute and chronic heat stress.

Genetics, selection, evolution : GSE·2025
Same author

Feeding Behaviour in Group-Housed Growing-Finishing Pigs and Its Relationship with Growth and Feed Efficiency.

Veterinary sciences·2025
Same author

Is there an advantage of using genomic information to estimate gametic variances and improve recurrent selection in animal populations?

Genetics, selection, evolution : GSE·2025
Same author

Integrating computer vision algorithms and RFID system for identification and tracking of group-housed animals: an example with pigs.

Journal of animal science·2024
Same author

Divergent selection for feed efficiency in pigs altered the duodenum transcriptomic response to feed intake and its DNA methylation profiles.

Physiological genomics·2024

Related Experiment Video

Updated: Nov 1, 2025

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
06:21

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

6.0K

New residual feed intake criterion for longitudinal data.

Ingrid David1, Van-Hung Huynh Tran2, Hélène Gilbert2

  • 1GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France. ingrid.david@inrae.fr.

Genetics, Selection, Evolution : GSE
|June 26, 2021
PubMed
Summary

The multi-structured antedependence (SAD) model offers genetically independent residual feed intake (RFI) measurements over time, outperforming the phenotypic regression model for analyzing longitudinal feed efficiency data in pigs.

More Related Videos

Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid
09:09

Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid

Published on: August 8, 2017

7.6K
An Efficient Single—Person Technique for Milk Sampling from Laboratory Mice
04:56

An Efficient Single—Person Technique for Milk Sampling from Laboratory Mice

Published on: March 28, 2025

1.0K

Related Experiment Videos

Last Updated: Nov 1, 2025

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
06:21

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

6.0K
Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid
09:09

Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid

Published on: August 8, 2017

7.6K
An Efficient Single—Person Technique for Milk Sampling from Laboratory Mice
04:56

An Efficient Single—Person Technique for Milk Sampling from Laboratory Mice

Published on: March 28, 2025

1.0K

Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Livestock Production

Background:

  • Residual Feed Intake (RFI) is a key metric for feed efficiency, typically calculated via multiple regression.
  • Traditional methods using phenotypic regression may result in RFI not being genetically independent of production traits.
  • Analyzing longitudinal data with repeated measurements presents challenges for standard regression approaches.

Purpose of the Study:

  • To introduce and evaluate a structured antedependence (SAD) approach for analyzing longitudinal RFI data.
  • To compare the multi-SAD model with a phenotypic regression model for genetic analysis of feed efficiency.
  • To assess the genetic independence of RFI from production traits using different modeling strategies.

Main Methods:

  • A multi-structured antedependence (SAD) regression model was developed to handle longitudinal data and genetic dependencies.
  • The multi-SAD model was compared against a traditional phenotypic regression model.
  • The models were applied to feed intake and production trait data from 2435 French Large White pigs over 10 weeks.

Main Results:

  • The multi-SAD model yielded stable heritability estimates for RFI over time (0.14-0.16), unlike the U-shaped profile seen with the phenotypic model (0.19-0.28).
  • Genetic correlations between RFI at different time points were similar in pattern but higher with the phenotypic model.
  • Breeding values estimated using the multi-SAD model showed moderate to high correlations (0.66-0.83) with those from the phenotypic model.

Conclusions:

  • The multi-SAD model is superior for the genetic analysis of longitudinal RFI, ensuring genetic independence from production traits.
  • This model effectively handles missing production records at certain time points.
  • The multi-SAD approach provides a robust method for improving selection for feed efficiency in livestock populations.