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相关概念视频

Epistasis Analysis01:09

Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Genome-wide Association Studies-GWAS01:11

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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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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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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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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: Jun 22, 2025

Genetic Screen for Identification of Multicopy Suppressors in Schizosaccharomyces pombe
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一种基于显式编码的多任务和搜索的高阶SNP表观相互作用的新型检测方法

Shouheng Tuo1,2,3, Jiewei Jiang4

  • 1School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China. tuo_sh@126.com.

Interdisciplinary sciences, computational life sciences
|July 2, 2024
PubMed
概括

一个新的算法,MTHS-EE-DHEI,有效地识别了复杂疾病遗传学的高阶SNP表观相互作用 (HEI). 它在检测遗传易感性因素方面优于现有的方法.

关键词:
显式编码是一种明确的编码.高级SNP上皮质相互作用 (HEI)多任务协调搜索 (MTHS) 是一种多任务协调搜索.单核酸多态性 (SNP) 是一种单核酸多态性.

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相关实验视频

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科学领域:

  • 遗传学 遗传学 是一个
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 复杂的疾病具有由单核酸多态 (SNP) 影响的遗传基础.
  • 在SNP之间检测高阶表观相互作用 (HEI) 是具有挑战性的,因为它具有巨大的搜索空间和计算复杂性.
  • 高等教育机构虽然对个体的影响很小,但对疾病易感性可能有显著的联合影响.

研究的目的:

  • 开发和评估一种新的算法,MTHS-EE-DHEI,以有效检测高阶SNP表观性相互作用 (HEI).
  • 改进对导致复杂疾病易感性的遗传因素的识别.
  • 为分析复杂的遗传相互作用提供计算效率高的方法.

主要方法:

  • 提出了一个基于显式编码的多任务和搜索算法 (MTHS-EE-DHEI).
  • 采用了三阶段的方法:与轻量级评估函数的和搜索,G测试过和机器学习验证 (MDR,RF).
  • 在模拟和真实数据集 (AMD,RA,BC) 上评估性能.

主要成果:

  • 与四个最先进的算法相比,MTHS-EE-DHEI显示出更高的检测能力和计算效率.
  • 成功识别了有意义的SNP组合,有助于复杂疾病的易感性.
  • 在各种数据集上验证了有效性,包括与年龄相关的黄斑变性,类风湿性关节炎和乳腺癌.

结论:

  • MTHS-EE-DHEI是一种有效和高效的工具,用于发现高阶SNP表皮性相互作用 (HEI).
  • 该算法为复杂疾病的遗传结构提供了宝贵的见解.
  • 这些发现有助于进一步了解复杂疾病中的遗传易感性.