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

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...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
What is Population Genetics?01:25

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.
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

You might also read

Related Articles

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

Sort by
Same author

Whole-Exome Sequencing Improves Risk Assessments of Adult Moyamoya Disease.

Journal of clinical neurology (Seoul, Korea)·2026
Same author

Genetic Variants Associated With Congenital Heart Disease: A Meta-Analysis of Ethnicity and Subtype-Specific Susceptibility.

Circulation. Genomic and precision medicine·2025
Same author

Associations of Fibroblast Growth Factor Receptor 2 Gene Variants With Anteroposterior and Vertical Phenotypes in Korean Patients With Skeletal Class III Malocclusion.

The Journal of craniofacial surgery·2025
Same author

Preliminary study of environmental risk and protective factors during pregnancy for cleft lip with or without palate in the Korean population.

Korean journal of orthodontics·2024
Same author

Target Gene-Based Association Study of High Mobility Group Box Protein 1 in Intracranial Aneurysms in Koreans.

Brain sciences·2024
Same author

Genetic associations and parent-of-origin effects of PVRL1 in non-syndromic cleft lip with or without cleft palate across multiple ethnic populations.

Epidemiology and health·2024

Related Experiment Video

Updated: May 17, 2026

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

Sample size and statistical power calculation in genetic association studies.

Eun Pyo Hong1, Ji Wan Park

  • 1Department of Medical Genetics, Hallym University College of Medicine, Chuncheon 200-702, Korea.

Genomics & Informatics
|October 30, 2012
PubMed
Summary

Sufficient sample size is crucial for genetic association studies to find disease-causing genes. Increasing markers analyzed requires larger sample sizes, with case-control studies needing fewer participants than case-parent studies for adequate statistical power.

Keywords:
case-control studiescase-parent studygenetic association studiessample sizestatistical power

More Related Videos

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Related Experiment Videos

Last Updated: May 17, 2026

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

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Genetics
  • Biostatistics
  • Human Complex Diseases

Background:

  • Genetic association studies are vital for identifying genes linked to complex human diseases.
  • Achieving adequate statistical power, especially in genome-wide association studies (GWAS), necessitates substantial sample sizes.
  • The relationship between sample size, number of markers, and statistical power is critical for study design.

Purpose of the Study:

  • To estimate statistical power in genetic association studies as the number of analyzed markers increases.
  • To compare the required sample sizes for case-control and case-parent study designs.
  • To provide useful estimates for determining sample size in population-based genetic association studies.

Main Methods:

  • Utilized the Genetic Power Calculator to compute effective sample size and statistical power.
  • Analyzed the impact of increasing marker numbers on required sample size.
  • Compared sample size requirements across different genetic models (dominant, etc.) and study designs.

Main Results:

  • Larger sample sizes are required when analyzing more markers; testing 1 million markers requires approximately 1,255 cases.
  • A dominant genetic model requires a smaller sample size for 80% power compared to other models.
  • Case-control studies with a 1:4 ratio and common diseases require fewer samples than case-parent studies.

Conclusions:

  • Sample size requirements increase with the number of single-nucleotide polymorphism (SNP) markers analyzed.
  • Factors like strong effect size, common SNPs, and high linkage disequilibrium (LD) reduce the necessary sample size.
  • The findings offer practical guidance for optimizing sample size in genetic association study designs.