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

Genetic Variation

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,...

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

What is Population Genetics?

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

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...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by

Same author

A French national observatory of people with HIV initiating lenacapavir-based treatment after regulatory approval.

Antimicrobial agents and chemotherapy·2026

Same author

keju: powerful and accurate inference in Massively Parallel Reporter Assays.

bioRxiv : the preprint server for biology·2026

Same author

Re-description, diet, and trophic niche overlap of three syntopic anuran species (Amphibia, Anura) in the Kim Bang Proposed Species and Habitat Conservation Area, Vietnam.

ZooKeys·2026

Same author

Language models transmit behavioural traits through hidden signals in data.

Nature·2026

Same author

CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation.

Proceedings of machine learning research·2026

Same author

A new skink of the genus <i>Scincella</i> Mittleman, 1950 (Squamata, Scincidae) from Dak Lak Province, Vietnam.

ZooKeys·2026

Same journal

Genetic survey of biomarkers at early and mid-pregnancy identifies pregnancy-specialized immune regulation.

PLoS genetics·2026

Same journal

Argonaute proteins orchestrate Meiotic Sex Chromosome Inactivation and timing of the spermatogenic transcriptional program.

PLoS genetics·2026

Same journal

Genome wide association study meta-analysis of neuropathologic lesions of Alzheimer's disease and related dementias in a multi-site autopsy cohort.

PLoS genetics·2026

Same journal

Microtubule stiffening by the doublecortin-domain protein ZYG-8 contributes to mitotic spindle orientation during zygote division in Caenorhabditis elegans.

PLoS genetics·2026

Same journal

Multiple instance fine-mapping: Predicting causal regulatory variants with a deep sequence model.

PLoS genetics·2026

Same journal

Nuclear ubiquitin-conjugating enzyme TrUbc4 and F-box protein TrFwd1-mediated modification of Cre1 in Trichoderma reesei establishes a regulatory mechanism for carbon catabolite repression.

PLoS genetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 20, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Scalable probabilistic PCA for large-scale genetic variation data.

Aman Agrawal¹, Alec M Chiu², Minh Le³

¹Department of Computer Science, Indian Institute of Technology, Delhi, India.

|May 30, 2020

Summary

This summary is machine-generated.

ProPCA is a scalable method for computing principal components (PCs) in large genetic datasets. It efficiently analyzes population structure, aiding in genome-wide association studies (GWAS) and identifying signals of recent selection.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Dec 20, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Principal component analysis (PCA) is crucial for understanding population structure in genome-wide association studies (GWAS).
Scalable computational methods are needed for analyzing large genetic variation datasets.
Population stratification can confound GWAS results.

Purpose of the Study:

To present ProPCA, a highly scalable method for computing principal components (PCs) from large genetic variation data.
To demonstrate the efficiency and utility of ProPCA in large-scale genetic studies.
To identify novel signals of recent natural selection using ProPCA-inferred population structure.

Main Methods:

ProPCA utilizes a probabilistic generative model for efficient PC computation.
The method was applied to genotype data from the UK Biobank (488,363 individuals, 146,671 SNPs).
Computation of the top five PCs was performed in approximately thirty minutes.

Main Results:

ProPCA successfully computed the top five PCs on a large UK Biobank dataset.
The analysis was completed in a computationally efficient timeframe.
Leveraging ProPCA-identified population structure revealed novel genome-wide signals of recent selection, including mutations in RPGRIP1L and TLR4.

Conclusions:

ProPCA offers a scalable and efficient solution for PC computation in large genetic datasets.
The method facilitates the analysis of population structure for GWAS and selection studies.
ProPCA aids in discovering biologically relevant genetic signals within large biobanks.