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

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Pedigree Analysis

Pedigree Analysis

Behavioral Genetics and Its Designs

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...

X-linked Traits

X-linked Traits

In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.

Probability Laws

Probability Laws

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

You might also read

Related Articles

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

Sort by

Same author

Smoothly Time-Varying Continuous Time Markov Chains in Phylogenetics.

ArXiv·2026

Same author

One Health Genomic Surveillance at Human-Animal Interfaces in Rural Ghana Reveals Underreported Viruses of Zoonotic and Economic Concern.

Viruses·2026

Same author

Reconciling fast Hepatitis B evolutionary rates with ancient co-divergence.

bioRxiv : the preprint server for biology·2026

Same author

Genomic surveillance of a deeply sampled local population reveals age-specific drivers of RSV transmission.

medRxiv : the preprint server for health sciences·2026

Same author

HIV-1 virome profiling using HIV-PULSE to guide therapeutic and curative interventions.

EBioMedicine·2026

Same author

Global approaches to infectious disease surveillance and modeling.

Nature medicine·2026

Search research articles

Related Experiment Video

Updated: Jul 17, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Accelerating Bayesian inference of dependency between mixed-type biological traits.

Zhenyu Zhang¹, Akihiko Nishimura², Nídia S Trovão³

¹Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America.

Plos Computational Biology

|August 28, 2023

Summary

This summary is machine-generated.

We developed a faster computational method for analyzing biological trait evolution, improving upon existing techniques for large datasets. This advance enables deeper insights into complex evolutionary relationships and trait dependencies.

More Related Videos

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

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

Published on: September 17, 2019

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Related Experiment Videos

Last Updated: Jul 17, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

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

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

Published on: September 17, 2019

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Area of Science:

Evolutionary biology
Computational biology
Genomics

Background:

Inferring dependencies between biological traits considering evolutionary history is crucial but computationally challenging for large datasets.
Current phylogenetic multivariate probit models struggle with scalability and accurately characterizing conditional dependencies as specimen counts increase.
The state-of-the-art bouncy particle sampler (BPS) faces computational bottlenecks with high-dimensional latent variable integration.

Purpose of the Study:

To develop a novel, computationally efficient inference pipeline for phylogenetic probit models.
To overcome the limitations of existing methods in handling large numbers of specimens and complex trait dependencies.
To enable the analysis of larger and more complex biological datasets, including mixed-type traits and evolutionary relationships.

Main Methods:

Proposed a new inference pipeline combining Zigzag Hamiltonian Monte Carlo (Zigzag-HMC) with linear-time gradient evaluations.
Developed a joint sampling scheme for highly correlated latent variables and covariance matrix elements.
Extended the phylogenetic probit model to incorporate categorical traits for broader applicability.

Main Results:

Achieved a 5-fold speedup compared to the bouncy particle sampler (BPS) in analyzing HIV-1 evolution from 535 viruses.
Successfully inferred partial correlations between viral mutations and virulence, and studied influenza H1N1 glycosylation evolution in ~900 viruses.
Demonstrated the model's utility in studying Aquilegia flower and pollinator co-evolution with categorical traits.

Conclusions:

The novel inference pipeline significantly outperforms BPS, making large-scale phylogenetic trait dependency analysis feasible.
This computational advancement opens new avenues for studying complex evolutionary processes across various biological systems.
The extended model and efficient methods facilitate deeper understanding of trait evolution and co-evolutionary dynamics.