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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
359
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

You might also read

Related Articles

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

Sort by
Same author

Statistics and AI - A Fireside Conversation.

Harvard data science review·2026
Same author

Cardiovascular-Kidney-Metabolic Syndrome: Conceptualising an Approach to Health Economic Modelling.

Diabetes, obesity & metabolism·2026
Same author

Artificial Intelligence in Image-Based Cardiovascular Disease Analysis.

Annual review of biomedical data science·2026
Same author

Multi-organ imaging and genetics show the impact of sleep patterns on the human brain and body.

Communications medicine·2026
Same author

Scalable subclonal reconstruction of cancer cells in DNA sequencing data using a penalized likelihood model.

bioRxiv : the preprint server for biology·2026
Same author

Connectome-based spatial statistics enabling large-scale population analyses of human connectome across cohorts.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Apr 23, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K

Functional-mixed effects models for candidate genetic mapping in imaging genetic studies.

Ja-An Lin1, Hongtu Zhu, Ahn Mihye

  • 1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.

Genetic Epidemiology
|October 2, 2014
PubMed
Summary

This study introduces a novel functional-mixed effects modeling (FMEM) framework for analyzing genetic influences on brain imaging data. FMEM improves the identification of genetic associations with brain structure and function compared to traditional methods.

Keywords:
adaptive smoothingcandidate genetic mappingfunctional-mixed effects modelsjumping surface modellikelihood ratio statisticvariance components

More Related Videos

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

14.4K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

22.2K

Related Experiment Videos

Last Updated: Apr 23, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K
Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

14.4K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

22.2K

Area of Science:

  • Neuroimaging genetics
  • Statistical modeling
  • Biostatistics

Background:

  • Analyzing high-dimensional imaging data alongside genetic markers presents significant statistical challenges.
  • Existing methods often struggle to efficiently integrate complex genetic effects and spatial correlations within imaging data.
  • Candidate gene approaches in imaging genetics require robust frameworks for accurate association mapping.

Purpose of the Study:

  • To develop a functional-mixed effects modeling (FMEM) framework for the joint analysis of high-dimensional imaging data and genetic markers.
  • To enhance the efficiency and accuracy of candidate gene studies in neuroimaging.
  • To identify specific brain regions influenced by candidate genes (CR1, CD2AP, PICALM).

Main Methods:

  • Developed a novel FMEM framework incorporating a mixed effects model with genetic random effects and a jumping surface model for variance components.
  • Introduced a two-stage adaptive smoothing procedure for estimating piecewise smooth functions and preserving spatial edges.
  • Utilized weighted likelihood ratio tests for assessing genetic marker effects across voxels.

Main Results:

  • FMEM demonstrated superior sensitivity and specificity in identifying regions of interest compared to voxel-wise approaches in simulation studies.
  • The framework effectively models nonlinear genetic effects and accommodates spatial smoothness and genetic marker correlations.
  • FMEM successfully identified brain regions associated with candidate genes CR1, CD2AP, and PICALM.

Conclusions:

  • The proposed FMEM framework offers a powerful and efficient tool for imaging genetic studies, improving the detection of genetic associations.
  • FMEM provides valuable insights into the complex interplay between genetic factors and brain structure/function.
  • This approach facilitates more precise candidate gene mapping in neuroimaging research.