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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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Polygenic Traits01:18

Polygenic Traits

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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相关实验视频

Updated: Jul 24, 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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用GWAS从培训数据中的总结统计数据调整多基因风险评分方法的调整参数.

Wei Jiang1, Ling Chen2, Matthew J Girgenti3

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Research square
|July 3, 2023
PubMed
概括
此摘要是机器生成的。

PRStuning提供了一种新的方法来优化多基因风险评分 (PRS) 模型,仅使用全基因组关联研究 (GWAS) 总结统计数据. 这种方法提高了基因风险预测的准确性,而不需要个人级别的数据.

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相关实验视频

Last Updated: Jul 24, 2025

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

  • 遗传学 遗传学是一种遗传学.
  • 生物统计学 生物统计学
  • 计算生物学 计算生物学

背景情况:

  • 预测常见疾病的遗传风险对于预防和治疗至关重要.
  • 多基因风险评分 (PRS) 方法结合了来自全基因组关联研究 (GWAS) 的单核酸多态 (SNP) 效应.
  • 当前的PRS方法通常需要外部个体级GWAS数据来进行超参数调整,这给隐私和安全带来了挑战,并可能降低预测准确度.

研究的目的:

  • 引入PRStuning,一种用于PRS方法的自动超参数调整的新方法.
  • 从培训数据中仅使用GWAS总结统计数据来实现PRS模型优化,解决隐私问题.
  • 通过选择最佳的超参数而不会影响数据安全,提高PRS模型的预测准确度.

主要方法:

  • PRStuning仅使用GWAS总结统计数据来预测各种超参数值的PRS方法的性能.
  • 它采用经验贝叶斯方法,通过缩小基于估计疾病遗传架构的预测性能来缓解过拟合.
  • 该方法选择了为PRS模型构建提供最佳预测性能的超参数.

主要成果:

  • 在不同的PRS方法和参数设置中,PRStuning准确地预测PRS性能.
  • 该方法有效地识别了PRS模型中表现最好的超参数.
  • 模拟和真实数据应用证实了PRStuning在优化PRS方面的有效性和准确性.

结论:

  • PRStuning为PRS超参数优化提供了一个保护隐私和高效的解决方案.
  • 该方法提高了基因风险预测的准确性和可靠性.
  • 促进对常见疾病开发更有效的PRS模型.