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

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...

Epistasis Analysis

Epistasis Analysis

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...

You might also read

Related Articles

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

Sort by

Same author

Gab1 but not Grb2 mediates tumor progression in Met overexpressing colorectal cancer cells.

Carcinogenesis·2008

Same author

Long-term donor-specific tolerance in rat cardiac allografts by intrabone marrow injection of donor bone marrow cells.

Transplantation·2008

Same author

Lsr2 of Mycobacterium tuberculosis is a DNA-bridging protein.

Nucleic acids research·2008

Same author

Amphetamine selectively enhances avoidance responding to a less salient stimulus in rats.

Journal of neural transmission (Vienna, Austria : 1996)·2008

Same author

Retrospective analysis of anterior correction and fusion for adolescent idiopathic thoracolumbar/lumbar scoliosis: the relationship between preserving mobile segments and trunk balance.

International orthopaedics·2008

Same author

Intrarenal antigens activate CD4+ cells via co-stimulatory signals from dendritic cells.

Journal of the American Society of Nephrology : JASN·2008

Same journal

Geographic distribution of sex chromosome polymorphism in Anastrepha fraterculus sp. 1 from Argentina.

BMC genetics·2020

Same journal

Development and characterization of a pupal-colour based genetic sexing strain of Anastrepha fraterculus sp. 1 (Diptera: Tephritidae).

BMC genetics·2020

Same journal

Improvement on the genetic engineering of an invasive agricultural pest insect, the cherry vinegar fly, Drosophila suzukii.

BMC genetics·2020

Same journal

Precise single base substitution in the shibire gene by CRISPR/Cas9-mediated homology directed repair in Bactrocera tryoni.

BMC genetics·2020

Same journal

Climate stress resistance in male Queensland fruit fly varies among populations of diverse geographic origins and changes during domestication.

BMC genetics·2020

Same journal

Genetic structure and symbiotic profile of worldwide natural populations of the Mediterranean fruit fly, Ceratitis capitata.

BMC genetics·2020

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 9, 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

Mapping haplotype-haplotype interactions with adaptive LASSO.

Ming Li¹, Roberto Romero, Wenjiang J Fu

¹Department of Epidemiology, Michigan State University, East Lansing, Michigan 48824, USA.

|August 31, 2010

Summary

This summary is machine-generated.

This study introduces a novel penalized logistic regression method for modeling complex genetic interactions between haplotypes. The approach efficiently identifies genetic variants contributing to disease susceptibility, as shown in simulations and a small for gestational age neonates dataset.

More Related Videos

Associated Chromosome Trap for Identifying Long-range DNA Interactions

Associated Chromosome Trap for Identifying Long-range DNA Interactions

Published on: April 23, 2011

Related Experiment Videos

Last Updated: Jun 9, 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

Associated Chromosome Trap for Identifying Long-range DNA Interactions

Associated Chromosome Trap for Identifying Long-range DNA Interactions

Published on: April 23, 2011

Area of Science:

Genetics
Biostatistics
Computational Biology

Background:

Complex diseases arise from intricate interactions between genetic and environmental factors.
Genetic variant interactions, particularly haplotype-haplotype interactions, are crucial for disease susceptibility.
Existing statistical methods struggle with modeling higher-order epistatic complexity in genetic association studies.

Purpose of the Study:

To develop a new statistical strategy for modeling haplotype-haplotype interactions.
To address the challenges posed by higher-order epistatic complexity in genetic association studies.
To provide an efficient tool for identifying genetic contributions to complex diseases.

Main Methods:

Utilized a penalized logistic regression framework with an adaptive L1-penalty.
Employed a novel parameter estimation method combining the modified Gauss-Seidel method within the EM algorithm.
Allowed for simultaneous estimation of effects and variable selection in genetic models.

Main Results:

Simulation studies demonstrated a low false positive rate and reasonable power for detecting haplotype interactions.
The method successfully identified significant haplotype interactions in a dataset of small for gestational age (SGA) neonates.
Significant interactions between maternal and offspring genomes were detected.

Conclusions:

The developed approach offers an efficient tool for modeling and testing complex haplotype interactions.
The method is valuable for understanding the genetic architecture of complex diseases.
R codes for the method are publicly available for download.