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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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On multi-marker tests for association in case-control studies.

Margaret A Taub1, Holger R Schwender2, Samuel G Younkin1

  • 1Department of Biostatistics, Johns Hopkins University Baltimore, MD, USA.

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|January 1, 2014
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Summary
This summary is machine-generated.

Multi-marker tests leverage genetic linkage disequilibrium (LD) to boost association study power. The Conneely and Boehnke (2007) method offers a practical and powerful approach for detecting causal variants.

Keywords:
genome-wide association studieslinkage disequilibriummulti-marker testsmultiplicity adjustmentsingle nucleotide polymorphisms

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Genomic Association Studies

Background:

  • Genome-wide association studies (GWAS) identify DNA loci linked to traits using single-marker tests.
  • Current methods often lack power for small effect sizes or low-frequency variants due to Bonferroni correction.
  • Linkage disequilibrium (LD) between markers offers potential for increased statistical power.

Purpose of the Study:

  • To establish a theoretical power benchmark for multi-marker association tests in case-control studies.
  • To develop and evaluate methods that incorporate LD for enhanced detection of causal variants.
  • To assess the impact of prior biological knowledge on association study power.

Main Methods:

  • Developed a theoretical framework for maximum achievable power in multi-marker tests.
  • Utilized genotype correlations within LD blocks to model score test statistics.
  • Derived marker weights to maximize the non-centrality parameter for optimal power.
  • Assessed power loss for various multi-marker approaches.

Main Results:

  • The Conneely and Boehnke (2007) method, using maximum test statistics within LD blocks, demonstrated practical power across settings.
  • Multi-marker tests incorporating LD significantly increase power compared to single-marker tests.
  • Prior biological knowledge requires high specificity and strength to approach maximum achievable power.

Conclusions:

  • Multi-marker association testing effectively utilizes LD to enhance the power of GWAS.
  • The Conneely and Boehnke (2007) method provides a robust strategy for detecting genetic associations.
  • Leveraging LD is crucial for identifying variants with small effects or low frequencies.