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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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Multiple Allele Traits01:49

Multiple Allele Traits

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The Concept of Multiple Allelism
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Genetic Variation01:25

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
387
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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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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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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相关实验视频

Updated: Sep 11, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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MR-EILLS:一种基于不变的门德尔随机化方法,集成多个异构的GWAS总结数据集.

Lei Hou1,2, Hao Chen3,4, Xiao-Hua Zhou5,6,7

  • 1Healthcare Big Data Research Institute, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, P. R. China.

Nature communications
|August 18, 2025
PubMed
概括

我们开发了门德尔的随机化方法MR-EILLS,以解决不同种群中遗传数据的异质性. 这种方法准确地推断出因果关系,即使使用无效的仪器,对现有方法进行了改进.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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相关实验视频

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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:

  • 遗传学 是一个遗传学.
  • 统计遗传学 统计遗传学
  • 流行病学 流行病学

背景情况:

  • 遗传结构的多样性导致了全基因组协会研究 (GWAS) 总结数据集的异质性.
  • 整合异质GWAS数据使得因果暴露结果效应的推断变得复杂.

研究的目的:

  • 引入使用环境不变线性最小方程 (MR-EILLS) 的门德尔随机化,以在异质人群中进行强大的因果推理.
  • 开发一种能够处理单变量和多变量场景的方法,包括无效的仪器变量.

主要方法:

  • MR-EILLS利用环境不变的线性最小平方来检测跨种群不变的因果关系.
  • 该方法适应了违反工具变量假设的情况,例如可交换性和排除限制.
  • 它为单个或多个暴露提供了不偏见的因果效应估计,无论仪器是否有效.

主要成果:

  • 与传统的孟德尔随机化和元分析方法相比,MR-EILLS显示出更高的估计准确性,稳定的I型错误率和更高的统计能力.
  • 应用在5个祖先的11个血细胞特征和20个疾病结局上,确定了预期和新的因果关系.
  • 结果显示了生物学上可解释的因果关系和通过观察性研究支持的额外关联.

结论:

  • MR-EILLS是一种强大而准确的门德尔随机化工具,用于分析异构的GWAS数据.
  • 该方法提高了多种群遗传研究中因果推断的可靠性.
  • 这种方法有助于在不同祖先之间发现复杂的特征和疾病之间的遗传因果关系.