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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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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.
To conduct the sign test, we first calculate the differences in...
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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Multiple Comparison Tests01:13

Multiple Comparison Tests

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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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Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Adaptive Set-Based Methods for Association Testing.

Yu-Chen Su1, William James Gauderman1, Kiros Berhane1

  • 1Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America.

Genetic Epidemiology
|December 29, 2015
PubMed
Summary
This summary is machine-generated.

Set-based methods enhance genome-wide association studies (GWAS) by analyzing single nucleotide polymorphisms (SNPs) in sets. The standard adaptive rank truncated product (ARTP) test demonstrated the highest power in simulations for detecting smaller genetic effects.

Keywords:
adaptive methodsgene set enrichmentglobal testspathwaysset-tests

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Traditional genome-wide association studies (GWAS) with one single nucleotide polymorphism (SNP)-at-a-time methods are limited in detecting small genetic effect sizes.
  • Set-based methods offer increased power by jointly analyzing multiple SNPs within biologically relevant sets (e.g., genes, pathways).
  • Self-contained set-based methods are generally more powerful than competitive approaches, but optimal methods remain debated, especially adaptive strategies.

Purpose of the Study:

  • To comprehensively compare the performance of various adaptive self-contained set-based methods for GWAS.
  • To evaluate the power of the standard adaptive rank truncated product (ARTP) test against its variations and other adaptive methods.
  • To identify the most effective set-based approach for detecting genetic variants with smaller effect sizes in GWAS.

Main Methods:

  • Conducted a comprehensive simulation study to assess the performance of multiple adaptive set-based methods.
  • Included the standard ARTP test, novel ARTP variations, and other adaptive approaches.
  • Utilized permutation testing to ensure a level playing field and assess statistical significance for all methods.

Main Results:

  • The standard adaptive rank truncated product (ARTP) test exhibited the highest statistical power across the simulated scenarios.
  • The global model of random effects (GMRE) and a least absolute shrinkage and selection operator (LASSO)-based test closely followed ARTP in power.
  • The study confirmed the utility of adaptive set-based methods for detecting smaller genetic effects missed by traditional GWAS.

Conclusions:

  • The standard ARTP test is a highly powerful and recommended method for set-based association analysis in GWAS.
  • Adaptive set-based approaches, particularly ARTP, offer significant advantages over single-SNP methods for identifying complex genetic architectures.
  • Further research into adaptive set-based methods can refine the detection of genetic variants influencing disease risk.