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相关概念视频

Polygenic Traits01:18

Polygenic Traits

65.6K
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...
65.6K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.3K
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...
13.3K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

14.9K
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,...
14.9K
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Pleiotropy01:33

Pleiotropy

40.3K
Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
40.3K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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相关实验视频

Updated: Jun 17, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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快速和可扩展的集体学习方法,用于多元基因风险预测.

Tony Chen1, Haoyu Zhang2, Rahul Mazumder3

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215.

Proceedings of the National Academy of Sciences of the United States of America
|August 7, 2024
PubMed
概括
此摘要是机器生成的。

一种使用总结级数据 (ALL-Sum) 的新方法,聚合L0Learn,显著改善了多基因风险评分 (PRS) 的计算. 它为个性化医学应用提供了更高的准确性,更快的计算速度和更低的内存使用量.

关键词:
L0学习学习.组合学习组合学习受到惩罚的回归回归.多基因风险得分的多基因风险得分.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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

Last Updated: Jun 17, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Infinium Assay for Large-scale SNP Genotyping Applications
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Infinium Assay for Large-scale SNP Genotyping Applications

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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:

  • 多基因风险评分 (PRS) 对风险分层和个性化医学至关重要.
  • 现有的PRS方法在计算效率,准确性和多样化的遗传架构方面扎.

研究的目的:

  • 引入使用总结级数据 (ALL-Sum) 的聚合L0Learn,这是计算PRS的高效和可扩展的方法.
  • 在速度,准确性和适应性方面解决当前PRS方法的局限性.

主要方法:

  • 开发了ALL-Sum,一种使用L0L2惩罚回归对全基因组关联研究 (GWAS) 总结统计数据的集合学习方法.
  • 采用跨调节参数的集体学习来建模各种遗传架构.
  • 用大规模模拟和现实世界的数据对11个复杂的特征进行了验证.

主要成果:

  • 在模拟中,ALL-Sum的精度超过了现有方法的10%,速度是速度的20倍,内存效率是三倍.
  • 现实世界数据分析显示,ALL-Sum实现了25%更高的PRS准确性,15倍更快的计算速度和50%更少的内存使用量.
  • 在多样化的遗传架构和稳定性中表现出强度,具有不同链接不平衡数据的稳定性.

结论:

  • ALL-Sum为PRS计算提供了一个快速,可扩展和准确的解决方案,推进了个性化医疗.
  • 该方法在各种遗传架构和数据源中具有稳定性.
  • ALL-Sum可以作为一个R包用于在遗传研究中可访问的使用.