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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.
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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.
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Hardy-Weinberg Principle

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One-Way ANOVA: Unequal Sample Sizes

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...

One-Way ANOVA: Equal Sample Sizes

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Related Experiment Video

Updated: Jul 8, 2026

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

Required sample size and nonreplicability thresholds for heterogeneous genetic associations.

Ramal Moonesinghe¹, Muin J Khoury, Tiebin Liu

¹National Office of Public Health Genomics, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA.

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

|January 5, 2008

Summary

Replicating gene-disease associations is challenging due to heterogeneity. This study provides methods to estimate sample sizes needed for replication, highlighting that high between-study heterogeneity can make replication impossible.

Related Experiment Videos

Last Updated: Jul 8, 2026

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
Biostatistics
Epidemiology

Background:

Many proposed gene-disease associations lack consistent replication across diverse populations.
Non-replication can stem from false positives or genuine genetic effect heterogeneity.

Purpose of the Study:

To develop methods for estimating sample size required for replicating gene-disease associations.
To assess the impact of between-study heterogeneity on replication power and sample size requirements.

Main Methods:

Proposed methods for sample size estimation in the presence of between-study heterogeneity.
Meta-analysis used to summarize data across multiple studies.
Empirical analysis of 91 gene-disease associations to evaluate observed heterogeneity.

Main Results:

Identified thresholds of between-study heterogeneity (tau(0)(2)) that preclude successful replication.
Observed heterogeneity in many gene-disease associations frequently approaches or exceeds these non-replication thresholds.
Increasing heterogeneity significantly inflates required sample sizes, especially near critical thresholds.

Conclusions:

Some true gene-disease associations may be practically impossible to replicate consistently due to substantial between-study heterogeneity.
Minimizing between-study heterogeneity is crucial for successful replication of genetic associations.
The findings have implications for the design and interpretation of genetic association studies.