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

Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Genetic Drift03:33

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
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

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...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...

You might also read

Related Articles

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

Sort by
Same author

Joint association of Insulin resistance and frailty index with incident ASCVD and MACE in individuals with cardiovascular-kidney-metabolic syndrome stages 0-3: a multi-cohort study.

Cardiovascular diabetology·2026
Same author

Joint Frailty Mixture Cure Model for Recurrent Event Data With Dependent Censoring: An MCEM Approach.

Statistics in medicine·2026
Same author

Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome.

Cell·2026
Same author

Machine learning-based risk classification of depressive symptoms among patients with hearing loss: evidence from the Health and Retirement Study (HRS).

Comprehensive psychoneuroendocrinology·2026
Same author

Arachidonic Acid Metabolism in PMN-MDSCs Suppresses Antitumor Capacity of T cells in KRAS-Mutant Cholangiocarcinoma.

Cancer discovery·2026
Same author

Haplotype-resolved methylation profiling across three generations reveals principles of human epigenetic inheritance.

Journal of genetics and genomics = Yi chuan xue bao·2026

Related Experiment Video

Updated: May 15, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A novel generalized ridge regression method for quantitative genetics.

Xia Shen1, Moudud Alam, Freddy Fikse

  • 1Division of Computational Genetics, Department of Clinical Sciences, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden. xia.shen@slu.se

Genetics
|January 22, 2013
PubMed
Summary

A new generalized ridge regression (RR) algorithm efficiently handles large genomic datasets for genome-wide association studies and genomic selection. This method significantly speeds up computation, enabling robust QTL mapping and improved prediction accuracy in genomic evaluations.

More Related Videos

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

Related Experiment Videos

Last Updated: May 15, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

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

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Increasing molecular marker density necessitates advanced statistical models for genome-wide association studies (GWAS) and genomic selection (GS).
  • Traditional models struggle with datasets where the number of parameters (SNPs) vastly exceeds the number of observations (individuals).

Purpose of the Study:

  • To develop and present a computationally efficient generalized ridge regression (RR) algorithm for high-dimensional genomic data.
  • To implement and evaluate a heteroscedastic effects model (HEM) for improved QTL mapping and genomic prediction.

Main Methods:

  • Developed a generalized ridge regression (RR) algorithm where computational cost depends on observations, not parameters.
  • Implemented the algorithm in the R package `bigRR`, utilizing the `hglm` package.
  • Developed and tested a heteroscedastic effects model (HEM) for enhanced robustness and accuracy.

Main Results:

  • The RR algorithm demonstrated high computational efficiency, processing 216,130 SNPs for 84 individuals in under 10 seconds.
  • Permutation tests became feasible, allowing for reliable genome-wide significance thresholds.
  • HEM outperformed ordinary RR in quantitative trait loci (QTL) mapping due to SNP-specific shrinkage, and provided better genomic evaluation predictions.

Conclusions:

  • The proposed computationally efficient RR algorithm and HEM are valuable tools for analyzing large-scale genomic data.
  • These methods enhance the feasibility and accuracy of GWAS, QTL mapping, and genomic selection, particularly with high-density marker data.