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

Gene-Environment Interactions01:20

Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

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...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
Human Genetics01:28

Human Genetics

Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
Genome-wide Association Studies-GWAS01:11

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...
Heritability01:06

Heritability

Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic" a trait is,...

You might also read

Related Articles

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

Sort by
Same author

[Study on the status of media exposure to tuberculosis prevention information and its impact on health beliefs and preventive behaviors among lower-grade university students in Guangzhou City].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2026
Same author

[The application of thrombopoietin receptor agonists in the treatment of non-muscle myosin heavy chain 9-related disease].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2026
Same author

[Retrospective analysis of 55 cases of spring thunderstorm asthma in Chongqing City].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2025
Same author

Checking the Cox Proportional Hazards Model with Interval-Censored Data.

Journal of the American Statistical Association·2025
Same author

Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State.

Journal of the American Statistical Association·2025
Same author

[A case report of Alport syndrome a denovo mosaic variation in the COL4A5 gene].

Zhonghua er ke za zhi = Chinese journal of pediatrics·2025
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

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

Related Experiment Video

Updated: Jun 14, 2026

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

A general framework for studying genetic effects and gene-environment interactions with missing data.

Y J Hu1, D Y Lin, D Zeng

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7420, USA.

Biostatistics (Oxford, England)
|March 30, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical framework to handle missing genetic data in association studies. The methods accurately analyze genetic effects and gene-environment interactions across various study designs.

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Related Experiment Videos

Last Updated: Jun 14, 2026

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Missing genetic data is a common challenge in association studies, impacting genotype and haplotype analysis.
  • Existing methods may not adequately address correlated genetic and environmental variables or unspecified environmental distributions.

Purpose of the Study:

  • To develop a general likelihood-based framework for robust inference on genetic effects and gene-environment interactions with missing genetic data.
  • To accommodate correlated genetic and environmental variables and various study designs and phenotypes.
  • To provide computationally efficient algorithms for practical application.

Main Methods:

  • A likelihood-based statistical framework was developed for genetic association studies with missing data.
  • The framework accommodates cross-sectional, case-control, and cohort designs for diverse phenotypes.
  • Expectation-Maximization (EM) algorithms were implemented for parameter estimation.

Main Results:

  • Maximum likelihood estimators were proven to be consistent, asymptotically normal, and efficient.
  • The proposed inferential and numerical methods demonstrated strong performance in simulations.
  • The framework was successfully illustrated using a genome-wide association study for lung cancer.

Conclusions:

  • The developed framework provides a robust and efficient approach for genetic association studies with missing data.
  • The methods are applicable to various study designs and phenotypes, enhancing genetic analysis.
  • This work offers practical tools for analyzing complex genetic and environmental interactions.