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

Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Polygenic Traits01:18

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 Traits01:18

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...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
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...

You might also read

Related Articles

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

Sort by
Same author

A large-scale multi-ancestry mitochondrial variant association analysis for cardiometabolic traits.

Nature communications·2026
Same author

Proteomics-Based Soluble Urokinase Plasminogen Activator Receptor Levels Are Associated With Adverse Cardiovascular Outcomes in the General Population: Insights From the UK Biobank.

Journal of the American Heart Association·2026
Same author

Genome-wide association study of cardiovascular disease in people with HIV from the Million Veteran Program (MVP).

AIDS (London, England)·2026
Same author

Exome-wide association study of blood lipids in 1,158,017 individuals from diverse populations.

Nature genetics·2026
Same author

Subtypes of Type 2 Diabetes and Prediabetes: Mortality and Excess Life Lost in South Asians.

Diabetes care·2026
Same author

X-Chromosome-Wide Association Study Identifies Novel Genetic Signals for Heart Failure and Subtypes.

medRxiv : the preprint server for health sciences·2026
Same journal

Shaping mammalian genomes: The regulation and co-option of transposable elements in early development and gametogenesis.

Advances in genetics·2026
Same journal

Transposable elements in aging: From biomarkers to effectors.

Advances in genetics·2026
Same journal

Transposable elements in human cancer: Regulation, activation, and genomic consequences.

Advances in genetics·2026
Same journal

Retrotransposons as both "architects" and "saboteurs" in the nervous system.

Advances in genetics·2026
Same journal

Transposable element analysis in OMICS data.

Advances in genetics·2026
Same journal

The implication of lncRNAs in the regulation of inflammation.

Advances in genetics·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Multigenic modeling of complex disease by random forests.

Yan V Sun1

  • 1Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.

Advances in Genetics
|October 30, 2010
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) identify gene variants for complex traits, but much heritability remains unexplained. Machine learning methods like Random Forests (RF) offer a promising approach to uncover complex gene interactions and environmental factors.

Related Experiment Videos

Last Updated: Jun 7, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Area of Science:

  • Human Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Complex human traits are influenced by numerous genes and environmental factors, leading to significant unexplained variation in risk phenotypes.
  • Traditional genome-wide association studies (GWAS) using univariate regression have identified many genetic loci but explain only a small fraction of trait heritability.
  • The intricate interactions among genes, and between genes and the environment, are hypothesized to contribute to the 'missing heritability'.

Purpose of the Study:

  • To review and discuss the application of machine learning, specifically Random Forests (RF), for analyzing genome-wide association study (GWAS) data.
  • To highlight the potential of RF in capturing complex interaction effects and addressing genetic heterogeneity in complex traits.
  • To explore the predictive and variable selection capabilities of RF within the context of genetic association studies.

Main Methods:

  • Review of Random Forests (RF) methodology and its theoretical underpinnings for handling high-dimensional genetic data.
  • Discussion of RF's application in genome-wide association studies (GWAS) for identifying complex interactions among single-nucleotide polymorphisms (SNPs) and environmental factors.
  • Exploration of computational efficiency and modeling advantages of RF over traditional regression-based approaches for GWAS.

Main Results:

  • Random Forests (RF) demonstrate a powerful capacity to model complex interaction effects within GWAS data, surpassing limitations of traditional univariate methods.
  • RF offers a computationally efficient framework for exploring genetic heterogeneity and uncovering novel associations in complex traits.
  • The variable selection features of RF are valuable for identifying key genetic contributors and interaction patterns in large-scale genetic datasets.

Conclusions:

  • Machine learning methods, particularly Random Forests (RF), present a viable and computationally efficient alternative for analyzing complex genetic interactions in GWAS.
  • Addressing the 'missing heritability' likely requires methods capable of modeling gene-gene and gene-environment interactions, where RF shows significant promise.
  • Further advancements and refinements of RF algorithms are warranted to maximize their success and utility in future GWAS.