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

Epistasis Analysis01:09

Epistasis Analysis

4.9K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
4.9K
Multiple Allele Traits01:49

Multiple Allele Traits

32.5K
The Concept of Multiple Allelism
32.5K
Polygenic Traits01:18

Polygenic Traits

58.3K
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...
58.3K
Polygenic Traits01:18

Polygenic Traits

7.1K
7.1K
X-linked Traits01:19

X-linked Traits

45.5K
In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
45.5K
X-linked Traits01:19

X-linked Traits

6.3K
6.3K

You might also read

Related Articles

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

Sort by
Same author

Dynamics of genetic and somatic trade-offs in ageing and mortality.

Nature·2026
Same author

SUPPRESSOR OF LAZY QUADRUPLE 1 acts at ER-plasma membrane contact sites to control a gravitropism pathway in the <i>Arabidopsis</i> stem.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

iWAX: interpretable Wav2vec-AASIST-XGBoost framework for voice spoofing detection.

Scientific reports·2025
Same author

Subjective and Objective Assessments of Olfactory Function in Patients Taking Anti-obesity Medications.

Clinical and experimental otorhinolaryngology·2025
Same author

Genetic Modulation of Lifespan: Dynamic Effects, Sex Differences, and Body Weight Trade-offs.

bioRxiv : the preprint server for biology·2025
Same author

Factors Affecting Visible Contamination of Positive Airway Pressure Devices in Patients With Obstructive Sleep Apnea.

Clinical and experimental otorhinolaryngology·2025

Related Experiment Video

Updated: Apr 28, 2026

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

4.6K

A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes.

Il-Youp Kwak1, Candace R Moore2, Edgar P Spalding2

  • 1Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.

Genetics
|June 17, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces fast, powerful methods for quantitative trait loci (QTL) mapping of multiple, function-valued phenotypes. The funqtl R package enables deeper understanding of complex biological processes by analyzing QTL effects over time.

Keywords:
QTLfunction-valued traitgrowth curvesmodel selection

More Related Videos

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

20.2K
Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
10:08

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Published on: August 12, 2019

13.6K

Related Experiment Videos

Last Updated: Apr 28, 2026

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

4.6K
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

20.2K
Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
10:08

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Published on: August 12, 2019

13.6K

Area of Science:

  • Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Traditional quantitative trait loci (QTL) mapping methods primarily analyze single phenotypes.
  • Technological advancements enable high-throughput phenotyping, including complex function-valued traits like growth curves.
  • Existing methods for function-valued QTL mapping are often computationally intensive and limited to single-QTL models.

Purpose of the Study:

  • To develop computationally efficient and high-powered statistical methods for mapping QTL associated with function-valued phenotypes.
  • To enable the identification of multiple QTL and the detailed characterization of their time-dependent effects.
  • To provide a user-friendly implementation for the analysis of complex trait genetics.

Main Methods:

  • Development of two novel, penalized likelihood-based statistical approaches for function-valued QTL mapping.
  • Implementation of these methods within the R package 'funqtl'.
  • Focus on maintaining high statistical power and precision while reducing computational load.

Main Results:

  • The proposed methods demonstrate high power and precision in identifying QTL for function-valued traits.
  • The approaches are computationally efficient, allowing for faster analysis compared to existing methods.
  • The methods facilitate the visualization and interpretation of QTL effects across the function-valued phenotype.

Conclusions:

  • The developed methods offer a significant advancement in the statistical genetics of complex traits.
  • The 'funqtl' package provides a valuable tool for researchers studying the genetic basis of dynamic biological processes.
  • These methods pave the way for more sophisticated multi-QTL analyses of function-valued phenotypes.