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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.2K
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...
13.2K
Sampling Plans01:23

Sampling Plans

169
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
169
Bootstrapping01:24

Bootstrapping

592
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
592
Stratified Sampling Method01:16

Stratified Sampling Method

11.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.9K
Random Sampling Method01:09

Random Sampling Method

11.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

33
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...
33
  1. Home
  3. Research Domains
  5. Health Sciences
  7. Epidemiology
  9. Epidemiological Methods
  11. Hap-sample2: Data-based Resampling For Association Studies With Admixture

HAP-SAMPLE2: Data-based Resampling for Association Studies with Admixture

George Sun1, Bryan W Ting1, Fred A Wright1,2

  • 1Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27607, United States.

Bioinformatics (Oxford, England)
|June 13, 2025

Related Experiment Videos

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.6K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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

Published on: December 7, 2021

2.1K

View abstract on PubMed

Summary
This summary is machine-generated.

HAP-SAMPLE2 enhances genotype-phenotype data simulation by incorporating population admixture and rare variant analysis. This tool is valuable for large-scale genetic studies, offering advanced simulation capabilities.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • HAP-SAMPLE2 builds upon the original HAP-SAMPLE tool.
  • It introduces advanced features for simulating genotype-phenotype data.
  • The enhanced tool addresses population admixture and rare variant analysis.

Purpose of the Study:

  • To extend the capabilities of genotype-phenotype data simulation.
  • To provide a tool for analyzing population admixture and rare variants.
  • To support large-scale genetic projects.

Main Methods:

  • Simulation of genotype-phenotype data with user-defined parameters (disease prevalence, allele effect sizes).
  • Incorporation of population admixture modeling.
  • Implementation of rare variant analysis, including burden testing with specified weighting schemes.

Related Experiment Videos

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.6K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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

Published on: December 7, 2021

2.1K

Main Results:

  • HAP-SAMPLE2 enables efficient simulation of complex datasets.
  • The tool facilitates the creation of admixed populations and preservation of substructures.
  • It supports novel variation introduction via artificial recombination.

Conclusions:

  • HAP-SAMPLE2 is suitable for large-scale projects like the 1000 Genomes Project.
  • The software offers robust capabilities for population admixture and rare variant analysis.
  • It provides an efficient method for simulating complex genetic datasets for research.