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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

33
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...
33
Heritability01:06

Heritability

195
Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
195
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

439
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
439
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

115
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
115
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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...
3.0K
Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K

You might also read

Related Articles

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

Sort by
Same author

Genomic prediction of wild-derived powdery mildew resistance for strawberry (Fragaria × ananassa) pre-breeding.

Heredity·2026
Same author

Single-Step Genomic BLUP With Unknown Parent Groups and Metafounders in Norwegian Red Evaluations.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2025
Same author

Single-step genomic BLUP with genetic groups and automatic adjustment for allele coding.

Genetics, selection, evolution : GSE·2022
Same author

Single-step genomic evaluation of Russian dairy cattle using internal and external information.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2021
Same author

Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping.

Frontiers in genetics·2021
Same author

Extending long-range phasing and haplotype library imputation algorithms to large and heterogeneous datasets.

Genetics, selection, evolution : GSE·2020

Related Experiment Video

Updated: Jun 16, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

665

A computationally feasible multi-trait single-step genomic prediction model with trait-specific marker weights.

Ismo Strandén1, Janez Jenko2

  • 1Natural Resources Institute Finland (Luke), Jokioinen, Finland. ismo.stranden@luke.fi.

Genetics, Selection, Evolution : GSE
|August 16, 2024
PubMed
Summary

Assigning trait-specific marker weights in genomic prediction models improves accuracy. A new multi-trait single-step single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) model handles large datasets effectively, enhancing genomic evaluation accuracy.

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.1K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.6K

Related Experiment Videos

Last Updated: Jun 16, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

665
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.1K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.6K

Area of Science:

  • Animal breeding and genetics
  • Quantitative genetics
  • Genomic evaluation

Background:

  • Genomic evaluation models can improve prediction accuracy by accounting for varying marker influences.
  • Standard multi-trait models become computationally intensive with trait-specific marker weights.

Purpose of the Study:

  • To develop and implement a computationally feasible multi-trait single-step SNPBLUP model for large genomic datasets.
  • To enable the use of precomputed trait-specific marker weights within the single-step framework.

Main Methods:

  • Developed a modified multi-trait single-step SNPBLUP model incorporating precomputed trait-specific marker weights.
  • Tested the model using simulated data and marker weights derived from BayesA.
  • Evaluated computational performance (memory, time) and prediction accuracy compared to the standard model.

Main Results:

  • The modified model showed slightly increased memory and per-iteration computing time.
  • Model convergence was slower, leading to longer total computation time.
  • Despite computational trade-offs, prediction accuracy was improved by using marker weights.

Conclusions:

  • The marker-weighted single-step SNPBLUP model effectively accommodates trait-specific marker weights.
  • This approach enhances prediction accuracy in genomic evaluations.
  • The model is suitable for large genomic data evaluations utilizing precomputed marker weights.