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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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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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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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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A fast and powerful tree-based association test for detecting complex joint effects in case-control studies.

Han Zhang1, William Wheeler1, Zhaoming Wang2

  • 1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA, Information Management Services, Inc., Silver Spring, Maryland 20904, USA, and Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, Maryland 20877, USA.

Bioinformatics (Oxford, England)
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Summary

A new tree-based association test (TREAT) offers a powerful alternative to logistic regression for analyzing complex genetic associations. TREAT identified a novel link between the CDKN2B gene and esophageal squamous cell carcinoma (ESCC).

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Traditional logistic regression models struggle with non-additive genetic effects in case-control studies.
  • Tree-structure models can capture complex, non-additive interactions but are computationally intensive for hypothesis testing.
  • Existing methods are suboptimal when joint effects deviate from additive assumptions.

Purpose of the Study:

  • To develop a computationally efficient algorithm for tree-structure models.
  • To introduce a robust tree-based association test (TREAT) for hypothesis testing.
  • To identify novel genetic associations with diseases, particularly non-additive effects.

Main Methods:

  • Developed a fast algorithm for building tree-structure models.
  • Proposed the TREe-based Association Test (TREAT) with adaptive model selection.
  • Applied TREAT as a multilocus association test to a large dataset (>20,000 genes/regions).
  • Conducted simulation studies to compare TREAT's power against existing tests.

Main Results:

  • Identified a significant novel association between the gene CDKN2B and esophageal squamous cell carcinoma (ESCC).
  • Demonstrated TREAT's power advantage over commonly used association tests via simulations.
  • Successfully applied the tree-structure model for hypothesis testing in a large-scale genetic study.

Conclusions:

  • TREAT provides a powerful and computationally feasible approach for detecting complex genetic associations.
  • The method is effective in identifying novel disease-gene relationships, such as CDKN2B and ESCC.
  • TREAT enhances the ability to analyze non-additive genetic effects in large-scale association studies.