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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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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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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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Pleiotropy01:33

Pleiotropy

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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,...
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Position-effect Variegation02:32

Position-effect Variegation

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In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
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相关实验视频

Updated: May 20, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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SC-VAR:使用单细胞表观基因组数据解释多基因疾病风险的计算工具.

Gefei Zhao1,2, Binbin Lai1,2,3,4

  • 1Institute of Medical Technology, Peking University Health Science Center, 38 Xueyuan Rd, Hai Dian Qu, Beijing 100191, China.

Briefings in bioinformatics
|March 24, 2025
PubMed
概括

全基因组关联研究 (GWAS) 经常难以解释非编码变体. SC-VAR集成单细胞表观遗传学数据,以改善与疾病相关的基因和细胞类型的识别.

关键词:
疾病相关性得分得分 疾病相关性得分全基因组关联研究 (GWAS)没有编码的变体注释.多基因疾病的风险是多基因疾病的风险.单细胞表观基因组学

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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相关实验视频

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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 系统生物学 系统生物学

背景情况:

  • 从全基因组关联研究 (GWAS) 中解释非编码变体是一个重大挑战.
  • 传统工具无法充分捕捉 cis 调节元件 (CREs) 的空间和细胞类型特异性.
  • 现有的方法缺乏整合单细胞表观基因组信息以进行全面的变异注释.

研究的目的:

  • 介绍一下SC-VAR,这是一种新的计算工具,用于提高对GWAS疾病相关风险的解释.
  • 利用单细胞表观遗传学数据来预测编码和非编码变体的功能结果.
  • 识别与疾病风险相关的敏感细胞类型,CREs和向基因.

主要方法:

  • 开发了SC-VAR计算工具的开发.
  • 单细胞表观基因组数据与GWAS数据的整合.
  • 功能性结果的预测,包括风险基因,途径和细胞类型.

主要成果:

  • 在预测验证的与疾病相关的基因和途径方面,SC-VAR的性能优于最先进的方法.
  • 该工具成功地识别了易患疾病的细胞类型及其相关的CREs和基因.
  • SC-VAR捕捉了人类组织和发育阶段的疾病风险.

结论:

  • 通过结合单细胞表观基因组数据,SC-VAR显著提高了GWAS发现的解释.
  • 该工具通过识别特定的细胞类型和涉及的调控元素,提供了对疾病机制的更全面的理解.
  • SC-VAR有潜力推进各种组织和生命阶段复杂疾病的研究.