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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.
GWAS does not require the identification of the target gene involved in...
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Multiple Comparison Tests01:13

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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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Wald-Wolfowitz Runs Test II01:17

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

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The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
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Wald-Wolfowitz Runs Test I01:17

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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
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Updated: Jul 26, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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Weighted multiple testing procedures in genome-wide association studies.

Ludivine Obry1, Cyril Dalmasso1

  • 1Université Paris-Saclay, CNRS, Univ Evry, Laboratoire de Mathématiques et Modélisation d'Evry, Evry-Courcouronnes, France.

Peerj
|June 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces wBHa, a novel weighted multiple testing procedure for genome-wide association studies (GWAS). wBHa enhances the detection of rare genetic variants while maintaining high overall power, outperforming existing methods.

Keywords:
False discovery rateGenome wide association studiesWeighted MTP

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Multiple testing procedures controlling the false discovery rate (FDR) are crucial in genome-wide association studies (GWAS).
  • Weighted multiple testing procedures can improve power by incorporating covariate information.
  • Existing methods may not optimally balance the detection of rare variants with overall power.

Purpose of the Study:

  • To evaluate recent weighted multiple testing procedures in the context of GWAS.
  • To introduce and assess a new procedure, wBHa, designed to prioritize rare variant detection.
  • To compare the performance of wBHa against other weighted procedures using simulations.

Main Methods:

  • A simulation study was conducted to assess the performance of various weighted multiple testing procedures.
  • The new wBHa procedure was developed to prioritize genetic variants with low minor allele frequencies.
  • Performance was evaluated based on power to detect associations and the ability to identify rare variants.

Main Results:

  • The wBHa procedure demonstrated good performance compared to other weighted multiple testing procedures.
  • wBHa consistently outperformed other methods in detecting rare variants across all simulated settings.
  • The procedure maintained good overall detection power while prioritizing rare variants.

Conclusions:

  • wBHa is an efficient weighted multiple testing procedure for GWAS.
  • The new method effectively balances the detection of rare variants with overall statistical power.
  • wBHa offers an improved approach for genetic association studies, particularly for identifying low-frequency variants.