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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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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
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Comparing Copy Number Variations and SNPs02:26

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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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Truncation in Survival Analysis01:09

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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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相关实验视频

Updated: Jul 6, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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用GWAS总结统计数据从培训数据中获取的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.

Nature communications
|January 3, 2024
PubMed
概括

PRStuning仅使用训练数据总结统计数据调整多基因风险评分 (PRS) 参数,增强疾病风险预测的隐私和准确性. 这种方法可以提高PRS的性能,而不需要外部个体级数据.

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科学领域:

  • 遗传学 遗传学 是一个
  • 生物统计学 生物统计学
  • 计算生物学 计算生物学

背景情况:

  • 多基因风险评分 (PRS) 方法通过聚合来自全基因组关联研究 (GWAS) 的单核酸多态 (SNP) 效应来预测疾病风险.
  • 当前的PRS参数调整通常需要外部个体级GWAS数据,这引发了隐私和安全方面的担忧.
  • 排除调整数据可能会损害预测的准确性.

研究的目的:

  • 引入PRStuning,一种用于调整PRS参数的新方法.
  • 为了使PRS参数优化只使用培训数据总结统计.
  • 解决隐私问题,提高PRS开发中的预测准确性.

主要方法:

  • PRStuning利用来自培训数据的GWAS总结统计数据来预测和选择最佳的PRS参数.
  • 采用经验贝叶斯方法来调整预测的性能,考虑到测试数据的潜在高估.
  • 该方法结合了疾病遗传架构以实现性能收缩.

主要成果:

  • 在各种PRS方法中,PRStuning展示了准确的参数调整.
  • 该方法有效地预测了不同参数设置的PRS性能.
  • 模拟和现实数据证实了PRStuning的可靠性和准确性.

结论:

  • PRStuning为PRS参数调节提供了一个保护隐私和准确的替代方案.
  • 该方法增强了GWAS总结统计数据在遗传风险预测方面的实用性.
  • PRStuning提高了对常见疾病的多基因风险评分的性能和适用性.