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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.2K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

944
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...
944
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.8K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.8K
Genetic Drift03:33

Genetic Drift

42.8K
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.
42.8K
Genomics02:02

Genomics

39.5K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
39.5K
Polygenic Traits01:18

Polygenic Traits

68.7K
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...
68.7K

You might also read

Related Articles

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

Sort by
Same author

Genomes to fields 2024 maize genotype by environment prediction competition.

BMC research notes·2026
Same author

Efficient Generation of <i>SOCS2</i> Knock-Out Sheep by Electroporation of CRISPR-Cas9 Ribonucleoprotein Complex with Dual-sgRNAs.

The CRISPR journal·2025
Same author

Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates.

Genetics·2024
Same author

Megavariate methods capture complex genotype-by-environment interactions.

Genetics·2024
Same author

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.

bioRxiv : the preprint server for biology·2024
Same author

GIS-based G × E modeling of maize hybrids through enviromic markers engineering.

The New phytologist·2024
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jan 5, 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

11.6K

bWGR: Bayesian Whole-Genome Regression.

Alencar Xavier1,2, William M Muir2, Katy M Rainey2

  • 1Corteva Agrisciences, 8305 NW 62nd Ave, Johnston IA.

Bioinformatics (Oxford, England)
|October 25, 2019
PubMed
Summary
This summary is machine-generated.

The bWGR R package provides efficient Bayesian and likelihood whole-genome regression methods for predicting complex traits. It supports various genetic architectures and model types, enhancing genomic analysis.

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.2K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.4K

Related Experiment Videos

Last Updated: Jan 5, 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

11.6K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.2K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.4K

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Whole-genome regression methods are crucial for genome-wide prediction, cross-validation, and association studies.
  • Predicting complex traits with diverse genetic architectures requires flexible modeling approaches.

Purpose of the Study:

  • Introduce bWGR, an R package for fitting and cross-validating Bayesian and likelihood whole-genome regression models.
  • Provide a user-friendly and computationally efficient tool for genomic data analysis.

Main Methods:

  • Implement a suite of Bayesian alphabet methods using Gibbs sampling and Expectation-Maximization.
  • Enable fitting of multivariate and complex hierarchical models within the whole-genome regression framework.
  • Develop an R package available on the CRAN repository for easy installation and use.

Main Results:

  • The bWGR package facilitates efficient fitting and cross-validation of various whole-genome regression methods.
  • It supports diverse priors for modeling complex traits with different genetic architectures.
  • The package is capable of handling multivariate and hierarchical models efficiently.

Conclusions:

  • bWGR offers a comprehensive and efficient solution for whole-genome regression analysis.
  • The package enhances the prediction of complex traits by accommodating various genetic architectures and model complexities.
  • Its user-friendly nature and computational efficiency make it a valuable tool for researchers in genomics and statistical genetics.