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
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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...

You might also read

Related Articles

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

Sort by
Same author

Harnessing interfacial click polymerization using pyridinium-yne films as photochromic, radical generation and sensing platforms.

Nature communications·2026
Same author

Expression of PIEZO1 in lung adenocarcinoma correlates with PD-L1 expression, cell migration, and poor prognosis: an exploratory study.

Discover oncology·2026
Same author

Functional Capacity and Gut Microbiota Shifts in Heart Failure Patients Following Cardiac Rehabilitation.

Journal of clinical medicine·2026
Same author

Establishment and Characterization of a Long-Term Ovarian Cell Line (SBO) from Asian Seabass (<i>Lates calcarifer</i>) Expressing Germline Stem Cell Markers.

International journal of molecular sciences·2026
Same author

GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification.

IEEE transactions on medical imaging·2026
Same author

Recent progress in small-molecule targeted therapies for melanoma treatment.

Bioorganic & medicinal chemistry·2026
Same journal

FIGLA Novel Variant c.385-9G>A Affects RNA Splicing in a Minigene Assay.

Annals of human genetics·2026
Same journal

Epigenetic Shifts in MTNR1A, MTNR1B and Fn14 and Their Links to Preeclampsia Risk.

Annals of human genetics·2026
Same journal

Hip Bone Marrow Adiposity as a Risk Factor for Alzheimer's Disease: Insights From Mendelian Randomization Analysis.

Annals of human genetics·2026
Same journal

A Novel Biallelic REL Frameshift Variant p.(Tyr9Ilefs*2) Causing Immunodeficiency-92 With Profound c-Rel Deficiency.

Annals of human genetics·2026
Same journal

Identification of PSMA4 as a Therapeutic Target for Atherosclerosis: A Comprehensive Multiomics Mendelian Randomization Analysis.

Annals of human genetics·2026
Same journal

Genetic Insights Into Hypertension and Breast Cancer Risk in African Women: A Mendelian Randomization and Colocalization Analyses.

Annals of human genetics·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Variable selection method for quantitative trait analysis based on parallel genetic algorithm.

Siuli Mukhopadhyay1, Varghese George, Hongyan Xu

  • 1Department of Mathematics, Indian Institute of Technology Bombay, Powai, Mumbai, India.

Annals of Human Genetics
|October 6, 2009
PubMed
Summary
This summary is machine-generated.

Parallel genetic algorithm (PGA) effectively identifies key genetic and environmental factors for complex human diseases. This variable selection tool accurately pinpoints significant markers associated with quantitative traits in association studies.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Related Experiment Videos

Last Updated: Jun 19, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Quantitative trait analyses require identifying crucial genetic and environmental factors.
  • Complex human diseases often result from intricate interactions between multiple genetic and environmental influences.
  • Accurate variable selection is essential for understanding disease etiology and developing targeted interventions.

Purpose of the Study:

  • To introduce and evaluate a Parallel Genetic Algorithm (PGA) for identifying significant genetic and environmental factors in human disease association studies.
  • To demonstrate the utility of PGA in fine-mapping quantitative trait loci (QTLs) using haplotype analysis.
  • To present PGA as an accessible and effective tool for variable selection in complex trait research.

Main Methods:

  • Utilized a Parallel Genetic Algorithm (PGA) for variable selection.
  • Incorporated analysis of multiple genomic markers and environmental factors.
  • Applied haplotype analysis for fine-mapping and marker selection.
  • Validated the method using both simulated datasets and real-world genetic association data.

Main Results:

  • PGA successfully identified relevant genetic and environmental variables in association studies.
  • The algorithm demonstrated proficiency in fine-mapping and selecting markers associated with quantitative traits.
  • Both simulated and real data analyses confirmed the accuracy and efficacy of the PGA approach.
  • PGA proved to be a user-friendly tool for complex variable selection.

Conclusions:

  • Parallel Genetic Algorithm (PGA) is a powerful and accurate method for selecting important genetic and environmental factors in complex disease research.
  • PGA facilitates fine-mapping and marker selection, enhancing the understanding of quantitative trait associations.
  • The ease of use and demonstrated effectiveness make PGA a valuable tool for genetic association studies.