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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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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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相关实验视频

Updated: Jul 4, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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球体进化多目标 (SEMO) 算法用于识别疾病多位置SNP相互作用.

Fuxiang Ren1, Shiyin Li1, Zihao Wen2,3

  • 1College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.

Genes
|January 26, 2024
PubMed
概括
此摘要是机器生成的。

一个新的算法,SEMO,有效地检测复杂的单核酸多态 (SNP) 相互作用的疾病易感性. 它准确地识别了与乳腺癌相关的SNP组合,改进了现有的方法.

关键词:
生物遗传标志物生物遗传标志物疾病模型 疾病模型多个位置的SNP相互作用.多目标优化多目标优化单核酸多形态的单核酸多形态球体进化算法 球体进化算法

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

Last Updated: Jul 4, 2025

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科学领域:

  • 遗传学和生物信息学 遗传学和生物信息学
  • 计算生物学 计算生物学
  • 统计遗传学 统计遗传学

背景情况:

  • 单核酸多态 (SNP) 是了解复杂疾病易感性和病原性的重要生物遗传标记.
  • 鉴定高维SNP相互作用是由于组合式搜索效率低下而具有计算挑战性的.

研究的目的:

  • 引入球体进化多目标 (SEMO) 算法,以有效检测多位置SNP相互作用.
  • 评估SEMO的表现与识别SNP协会的最先进算法对比.

主要方法:

  • SEMO算法使用球形搜索因子和历史记忆反来实现平衡的搜索和获取.
  • 结合K2-Score和LR-Score的多目标健身功能在进化代期间评估SNP关联.

主要成果:

  • SEMO表现出卓越的性能,比模拟数据上的六种比较算法更快,更准确地检测SNP相互作用.
  • 对WTCCC乳腺癌数据集的应用确定了与乳腺癌相关的显著的两点和三点SNP相互作用.

结论:

  • 该 SEMO 算法有效地识别复杂的 SNP 相互作用,以高的速度和准确性.
  • SEMO检测新型SNP组合的能力为乳腺癌研究和遗传标记物发现提供了新的方法.