Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Polygenic Traits

Polygenic Traits

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...

Polygenic Traits

Polygenic Traits

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...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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...

You might also read

Related Articles

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

Sort by

Same author

Early childhood gut microbiomes show strong geographic differences among subjects at high risk for type 1 diabetes.

Diabetes care·2014

Same author

Pyrokinin β-neuropeptide affects necrophoretic behavior in fire ants (S. invicta), and expression of β-NP in a mycoinsecticide increases its virulence.

PloS one·2012

Same author

Objective Bayes model selection in probit models.

Statistics in medicine·2011

Same author

Generalized shrinkage F-like statistics for testing an interaction term in gene expression analysis in the presence of heteroscedasticity.

BMC bioinformatics·2011

Same author

Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes.

PloS one·2011

Same author

Testing for the existence of clusters.

SORT (Barcelona)·2011

Search research articles

Related Experiment Video

Updated: Jul 4, 2026

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

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

Published on: July 27, 2021

Nonparametric functional mapping of quantitative trait loci.

Jie Yang¹, Rongling Wu, George Casella

¹Genetics Institute, University of Florida, Gainesville, Florida 32611, USA. jyang81@.ufl.edu

|June 10, 2008

Summary

This study introduces nonparametric functional mapping to detect quantitative trait loci (QTL) for dynamic traits when functional forms are unknown. The method enhances QTL detection power and parameter precision using B-splines and a likelihood ratio test.

More Related Videos

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Related Experiment Videos

Last Updated: Jul 4, 2026

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

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

Published on: July 27, 2021

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

Genetics
Biostatistics
Bioinformatics

Background:

Functional mapping aids in identifying quantitative trait loci (QTL) for dynamic traits by integrating biological process mathematics into mixture models.
Current functional mapping methods may be suboptimal when the underlying functional form of trait dynamics is not clearly defined.

Purpose of the Study:

To propose a novel nonparametric functional mapping approach for QTL detection in dynamic traits.
To estimate underlying phenotypic trajectories without assuming a specific functional form.

Main Methods:

Utilizes nonparametric function estimation, specifically B-splines, to model phenotypic trajectories.
Develops a nonparametric test for QTL detection, framed as a likelihood ratio test within a mixed model framework.
Evaluates performance using both dense and general genetic maps.

Main Results:

The nonparametric approach effectively estimates functional forms of phenotypic trajectories.
Demonstrates increased power for QTL detection and precision in parameter estimation compared to traditional methods in simulation studies.
Successfully applied to real-world examples.

Conclusions:

Nonparametric functional mapping offers a robust alternative for QTL detection when functional forms are unknown.
This method enhances the power and precision of genetic analysis for complex, dynamic traits.
Provides a flexible framework applicable to various genetic map densities.