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

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

Population Growth

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.

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.

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by

Same author

Quantitative trait analysis of polymorphisms in two bilirubin metabolism enzymes to physiologic bilirubin levels in Chinese newborns.

The Journal of pediatrics·2014

Same author

Evaluating the Y chromosomal timescale in human demographic and lineage dating.

Investigative genetics·2014

Same author

Potentially functional polymorphisms in the ERCC2 gene and risk of esophageal squamous cell carcinoma in Chinese populations.

Scientific reports·2014

Same author

Variation and signatures of selection on the human face.

Journal of human evolution·2014

Same author

Association of the HLA-DRB1 with scleroderma in Chinese population.

PloS one·2014

Same author

Anti-nociceptive and anti-inflammatory activities of the extracts of Stauntonia chinensis.

Pakistan journal of pharmaceutical sciences·2014

Search research articles

Related Experiment Video

Updated: May 25, 2026

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

Manifold learning for human population structure studies.

Hoicheong Siu¹, Li Jin, Momiao Xiong

¹MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China.

|January 25, 2012

Summary

This summary is machine-generated.

Locally linear embedding (LLE) effectively reduces high-dimensional population genetics data, revealing population structure. Rare variants are more powerful than common variants for identifying population substructure using this method.

More Related Videos

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

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

Published on: September 20, 2024

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: May 25, 2026

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

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

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

Published on: September 20, 2024

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Population Genetics
Genomics
Bioinformatics

Background:

Next-generation sequencing generates vast population genetics data.
The intrinsic dimensionality of genomic data is significantly lower than its observed dimension.
Understanding population structure is crucial for genetics and association studies.

Purpose of the Study:

To apply Locally Linear Embedding (LLE) for population structure and historical inference.
To investigate LLE properties and its relationship with Principal Component Analysis (PCA).
To develop methods for identifying informative genomic regions for population structure analysis.

Main Methods:

Locally Linear Embedding (LLE) for dimensionality reduction.
Comparison of LLE with Principal Component Analysis (PCA).
Development of a novel statistic integrating genomic information content and LASSO algorithm for region identification.

Main Results:

LLE successfully projected high-dimensional genomic data into a lower-dimensional space.
Rare variants showed higher correlation with population structure (e.g., 89.2% in CEU) than common variants (e.g., 25.1% in CEU).
Methodologies were applied to 1000 Genomes Project and HapMap datasets, validating LLE's utility.

Conclusions:

Locally Linear Embedding (LLE) is a powerful tool for population structure analysis.
Rare variants are more informative for identifying population substructure compared to common variants.
Next-generation sequencing data combined with LLE offers rich resources for population genetics research.