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相关概念视频

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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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.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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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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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
176
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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在大规模的全基因组关联分析中测试贝叶斯等级假设.

Anirban Samaddar1, Tapabrata Maiti1, Gustavo de Los Campos1,2,3

  • 1Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.

Genetics
|November 19, 2024
PubMed
概括

我们开发了一种新的贝叶斯层次假设测试 (BHHT) 方法,以改善高维基因组数据中的变量选择,特别是具有对线特征的变量选择. 对于复杂的特征,BHHT提供了更高的功率和更好的错误控制,从而在大型生物库数据集中发现了更多的发现.

关键词:
贝叶斯的等级假设测试测试贝叶斯的等级假设测试.贝叶斯的变量选择选择是贝叶斯的.在GWAS中,GWAS就是GWAS.英国生物银行数据一致直线性 (collinearity) 是一个直线性.错误发现率 错误发现率链接不平衡 关系不平衡多解析度推断推断的结论.在之前的尖峰和板块之前.

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

  • 基因组学就是基因组学.
  • 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:

  • 高维基因组数据分析通常涉及变量选择和假设测试.
  • 基因组数据中的对线性,例如单核酸多态 (SNP) 之间的链接不平衡,挑战了现有的方法.
  • 变量选择中的功率减少可能会阻碍与复杂特征相关的变体的识别.

研究的目的:

  • 引入一个新的贝叶斯层次假设测试 (BHHT) 程序.
  • 为了应对在高度直线性基因组特征存在时的变量选择和推断的挑战.
  • 为分析超高维基因组数据提供一种强大而准确的方法.

主要方法:

  • 开发了一种称为贝叶斯层次假设测试 (BHHT) 的多解析度测试程序.
  • 使用模拟来评估功率和虚假发现率 (FDR) 性能与最先进的方法相比.
  • 针对八种复杂特征的超高维基基因型 (~1500万个SNP) 的英国生物库大规模数据 (n~300,000) 应用了BHHT.

主要成果:

  • 与模拟中现有的方法相比,BHHT在模拟中证明了具有竞争力或优越功率的FDR性能.
  • 对英国生物银行数据的应用显示,对复杂特征的发现明显多于传统的以SNP为中心的方法.
  • 该方法有效地扩展到生物库大小的数据集,拥有数百万个SNP.

结论:

  • BHHT是一种强大且可扩展的方法,用于在超高维基因组数据中进行变量选择和假设测试.
  • 该程序提供了改进的精细映射分辨率和错误控制,特别是在存在对线性时.
  • 有开源软件可用,促进了BHHT在大规模遗传研究中的应用.