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相关概念视频

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.6K
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...
13.6K
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.9K
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.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.9K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

277
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.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
277
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

179
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...
179
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

683
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.
The test works...
683

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相关实验视频

Updated: Jul 26, 2025

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

3.7K

在全基因组关联研究中加权多重测试程序.

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
概括

本研究介绍了wBHa,这是全基因组关联研究 (GWAS) 的新型加权多重测试程序. wBHa提高了罕见遗传变异的检测,同时保持了高的整体功率,优于现有的方法.

科学领域:

  • 遗传学 是一个遗传学.
  • 统计遗传学 统计遗传学
  • 生物信息学是一种生物信息学.

背景情况:

  • 控制错误发现率 (FDR) 的多种测试程序在全基因组关联研究 (GWAS) 中至关重要.
  • 有权重的多重测试程序可以通过结合共变量信息来提高功率.
  • 现有的方法可能无法将罕见变异的检测与整体功率的最佳平衡.

研究的目的:

  • 在GWAS的背景下评估最近的加权多重测试程序.
  • 引入和评估一个新的程序,wBHa,旨在优先检测罕见变种.
  • 用模拟来比较wBHa与其他加权程序的性能.

主要方法:

  • 进行了一项模拟研究,以评估各种加权多重测试程序的性能.
  • 新的wBHa程序是为了优先考虑具有低小等位基因频率的遗传变异而开发的.
  • 绩效是根据检测关联的能力和识别罕见变异的能力来评估的.

主要成果:

  • 与其他加权多重测试程序相比,wBHa程序表现良好.
  • 在所有模拟设置中,wBHa在检测罕见变异方面始终优于其他方法.
  • 该程序保持了良好的总体检测能力,同时优先考虑罕见变异.
关键词:
错误发现率 错误发现率基因组广泛的关联研究研究.权重的MTP是权重的MTP.

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Infinium Assay for Large-scale SNP Genotyping Applications
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相关实验视频

Last Updated: Jul 26, 2025

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

  • wBHa是GWAS的高效加权多重测试程序.
  • 新方法有效地平衡了罕见变异的检测和整体统计能力.
  • wBHa为遗传关联研究提供了一种改进的方法,特别是用于识别低频变异.