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

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Genome-wide Association Studies-GWAS

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...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Vector Algebra: Method of Components

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

Human Genetics

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...

You might also read

Related Articles

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

Sort by

Same author

High quality genome assemblies of African cattle breeds using PacBio HiFi sequencing.

Scientific data·2026

Same author

Ecotypes, <i>Wolbachia</i>, and urbanization shape <i>Culex pipiens</i> population structure in a West Nile virus hotspot.

bioRxiv : the preprint server for biology·2026

Same author

Neural posterior estimation for population genetics.

Genetics·2026

Same author

Coalescence and translation: A language model for population genetics.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Parameterizing the genetic architecture under stabilizing selection.

bioRxiv : the preprint server for biology·2026

Same author

Tracing the evolutionary histories of ultra-rare variants using variational dating of large ancestral recombination graphs.

bioRxiv : the preprint server for biology·2026

Same journal

Adaptive Dynamics of Quantitative Traits in a Steadily Changing Environment.

Genetics·2026

Same journal

Functional Landscape of Zebrafish Gonadotropins and Receptors: A Comprehensive Genetic Analysis.

Genetics·2026

Same journal

Synergistic actions of Nup43 and Myosin VI drive actin cone assembly during Drosophila spermiogenesis.

Genetics·2026

Same journal

Identification of two Cryptococcus neoformans heme transporters involved in Fhb1-mediated nitrosative stress protection in a fission yeast model.

Genetics·2026

Same journal

Analysis of a hypomorphic mei-P26 mutation reveals coordination between developmental programming of germ cells and meiotic chromosome dynamics.

Genetics·2026

Same journal

Neural and Genetic Mechanisms Regulating Copulation Latency in Male Drosophila melanogaster.

Genetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 30, 2026

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

Genetic prediction with ARG-powered linear algebra.

Hanbin Lee¹, Nathaniel S Pope², Jerome Kelleher³

¹Department of Statistics, University of Michigan, Ann Arbor, MI 48109, United States.

|March 29, 2026

Summary

This summary is machine-generated.

Ancestral recombination graphs (ARGs) enable efficient quantitative genetic analysis of complex traits. New algorithms using ARGs achieve near-linear runtime scaling for variance component estimation and genetic value prediction in large biobank datasets.

Keywords:

ancestral recombination graph genetic prediction genomic prediction linear mixed model polygenic score

More Related Videos

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

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

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

Published on: June 21, 2018

Related Experiment Videos

Last Updated: Mar 30, 2026

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

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

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

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

Published on: June 21, 2018

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Ancestral recombination graphs (ARGs) model genetic relatedness, drift, recombination, and mutation.
Efficient ARG storage is crucial for processing large genomic datasets and fitting linear mixed models.

Purpose of the Study:

To develop efficient algorithms for variance component estimation and genetic value prediction using ARGs.
To apply these methods to biobank-scale phenotype and genome datasets.

Main Methods:

A generative model for complex traits with additive effects on an ARG was described.
Algorithms leveraging the succinct tree sequence representation of ARGs and randomized linear algebra were developed.
Restricted maximum likelihood (REML) was used for variance component estimation.

Main Results:

Algorithms demonstrated nearly linear runtime scaling with sample size.
REML outperformed the Haseman-Elston method for variance component estimation.
Inferred ARGs yielded variance component estimates and genetic predictions comparable to true ARGs in simulations.

Conclusions:

The developed algorithms provide efficient solutions for quantitative genetic analysis on biobank-scale data using ARGs.
Variance component estimates can be interpreted as mutational and additive genetic variance.
The Python package tslmm implements these algorithms, utilizing the tskit library.