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
Introduction to R01:11

Introduction to R

4.0K
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
4.0K
What is Population Genetics?01:25

What is Population Genetics?

64.2K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
64.2K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

931
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...
931

You might also read

Related Articles

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

Sort by
Same author

Disentangling the effect of brain insulin resistance on brain health (BIR-BrainHealth): Rationale and study protocol.

Diabetic medicine : a journal of the British Diabetic Association·2026
Same author

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

Cell·2026
Same author

Host genetics and diet jointly shape the microbiome of Drosophila melanogaster but do not predict lifespan or age-related traits.

Biogerontology·2026
Same author

Functional consequences of calmodulin variants identified among schizophrenia patients and controls.

Translational psychiatry·2025
Same author

mLeveraging genetic correlations to prioritize drug groups for repurposing in type 2 diabetes.

The pharmacogenomics journal·2025
Same author

The burden of endometriosis on quality of life in Danish women: an analysis of the Danish Blood Donor Study.

BMC medicine·2025

Related Experiment Video

Updated: Jan 1, 2026

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
11:37

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

Published on: June 22, 2017

16.7K

qgg: an R package for large-scale quantitative genetic analyses.

Palle Duun Rohde1, Izel Fourie Sørensen1, Peter Sørensen1

  • 1Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark.

Bioinformatics (Oxford, England)
|December 29, 2019
PubMed
Summary

The R package qgg facilitates large-scale genetic analyses for quantitative traits and diseases. It offers tools for genetic parameter estimation, association analyses, and phenotype prediction from genomic data.

More Related Videos

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.7K
A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.9K

Related Experiment Videos

Last Updated: Jan 1, 2026

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
11:37

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

Published on: June 22, 2017

16.7K
Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.7K
A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.9K

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Quantitative traits and diseases are influenced by complex genetic factors.
  • Large-scale genetic data analysis requires efficient computational tools.
  • Accurate estimation of genetic parameters and predictions are crucial in genetic studies.

Purpose of the Study:

  • To introduce the R package qgg as a comprehensive environment for large-scale genetic analyses.
  • To provide infrastructure for efficient processing of large genetic datasets.
  • To offer functions for genetic parameter estimation, association studies, and genomic predictions.

Main Methods:

  • Development of the R package qgg.
  • Implementation of functions for genetic data processing.
  • Integration of methods for estimating genetic parameters.
  • Inclusion of functions for single and multiple marker association analyses.
  • Development of genomic-based prediction models for phenotypes.

Main Results:

  • The qgg package provides an efficient infrastructure for large-scale genetic data.
  • Functions are available for estimating genetic parameters.
  • The package supports single and multiple marker association analyses.
  • Genomic-based predictions of phenotypes can be performed using qgg.

Conclusions:

  • The qgg R package offers a valuable resource for researchers conducting large-scale genetic analyses.
  • It streamlines the process of analyzing genetic data for quantitative traits and diseases.
  • qgg enables robust genetic parameter estimation, association studies, and predictive modeling.