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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...
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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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Multiple Allele Traits

The Concept of Multiple Allelism

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Updated: Jun 5, 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

Accounting for multiple comparisons in a genome-wide association study (GWAS).

Randall C Johnson1, George W Nelson, Jennifer L Troyer

  • 1Basic Research Program, SAIC-Frederick, Inc. NCI-Frederick, Frederick, MD, USA.

BMC Genomics
|December 24, 2010
PubMed
Summary

Adjusting for multiple comparisons in genetic studies is crucial. New methods like SLIDE and simpleℳ offer improved power and interpretability for genome-wide association studies (GWAS) compared to traditional Bonferroni corrections.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Genomic Association Studies

Background:

  • Genome-wide association studies (GWAS) require careful consideration of multiple comparisons for statistical significance.
  • Traditional Bonferroni adjustments are often too conservative due to linkage disequilibrium (LD) between genetic markers.
  • Existing methods to account for genetic data dependency include permutation testing, principal components analysis (PCA), and LD block analysis.

Purpose of the Study:

  • To evaluate seven implementations of commonly used methods for correcting multiple comparisons in GWAS.
  • To compare the performance of these methods against traditional Bonferroni adjustments using real genetic data.

Main Methods:

  • Utilized data from 1514 European American participants genotyped for 700,078 SNPs in a GWAS for AIDS.
  • Implemented and compared three classes of corrections: permutation testing alternatives, PCA, and LD block analysis.
  • Assessed seven specific implementations including Haploview algorithms, PRESTO, SLIDE, and simpleℳ.

Main Results:

  • Bonferroni correction using LD blocks (via Haploview) was insufficiently conservative (α = 0.15-0.20).
  • Methods PRESTO, SLIDE, and simpleℳ showed moderate power increases over traditional Bonferroni for European American data (α = 0.05 thresholds: 1 × 10(-7) to 7 × 10(-8)).

Conclusions:

  • Correcting for LD blocks using the tested methods resulted in an anti-conservative Bonferroni adjustment.
  • SLIDE and simpleℳ are valuable for statistical tests not supported by standard permutation packages.
  • SLIDE provides easily interpretable genome-wide corrected p-values for GWAS consumers.