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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Related Experiment Video

Updated: Feb 21, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Single Marker Association Analysis for Unrelated Samples.

Gang Zheng, Ao Yuan1, Qizhai Li2

  • 1Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, 4000 Reservoir Rd. NW, Washington, DC, 20057, USA. ay312@georgetown.edu.

Methods in Molecular Biology (Clifton, N.J.)
|October 6, 2017
PubMed
Summary
This summary is machine-generated.

This study presents methods for single marker association analysis for binary and quantitative traits. It details statistical tests and R package applications for genetic association studies.

Keywords:
ANOVAAdditiveAssociationBinary traitCase–control designDominantGenetic modelGenotype relative risksMAX3Mode of inheritancePenetranceQuantitative traitRassocRecessiveRobustness

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Area of Science:

  • Genetics
  • Biostatistics

Background:

  • Single marker association analysis is crucial for identifying genetic variants linked to traits.
  • Binary and quantitative traits require distinct analytical approaches.

Purpose of the Study:

  • To outline methods for single marker association analysis for both binary and quantitative traits.
  • To provide practical guidance on applying these methods using R.

Main Methods:

  • For binary traits: Pearson's chi-squared test, trend test, and robust tests on case-control data.
  • For quantitative traits: Linear regression models and analysis of variance.
  • Utilized the R package "Rassoc" and existing R functions for illustration.

Main Results:

  • Demonstrated application of various statistical tests for genetic association.
  • Provided a practical framework for conducting single-marker analyses in R.

Conclusions:

  • The study offers comprehensive guidelines for single-marker association analysis.
  • Facilitates the application of statistical genetics methods in research using R.