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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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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,...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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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

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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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使用自然遗传变异预测基因调节相互作用

Maura John1, Dominik Grimm1, Arthur Korte2

  • 1Technical University of Munich & Weihenstephan-Triesdorf University of Applied Sciences, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.

Methods in molecular biology (Clifton, N.J.)
|September 8, 2023
PubMed
概括

全基因组关联研究 (GWAS) 揭示了基因型-表型联系. 本章详细介绍了用于推断基因调节网络及其变异的先进GWAS方法.

关键词:
在GWAS中,GWAS就是GWAS.基因监管网络是基因监管网络.在TWAS中,TWAS就是TWAS.

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科学领域:

  • 遗传学 遗传学 是一个
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 全基因组关联研究 (GWAS) 对于理解遗传变异和可观察的特征之间的关系至关重要.
  • 传统的GWAS专注于单变联,但先进的方法可以揭示复杂的遗传结构.

研究的目的:

  • 介绍进行全基因组关联研究 (GWAS) 的最新方法和工具.
  • 将GWAS扩展到复杂的模型,以推断基因调节网络及其动态.

主要方法:

  • 使用基于变的显著性值来进行可靠的GWAS分析.
  • 应用单变量GWAS分析作为更复杂建模的基础.
  • 开发和描述从GWAS数据推断基因调节网络的方法.

主要成果:

  • 证明了GWAS超越简单协会的能力.
  • 为推断基因调节相互作用和网络结构提供了一个框架.
  • 展示了基因调节网络如何变化和分析.

结论:

  • 先进的GWAS方法提供了对遗传架构和基因调节的有力见解.
  • 描述的方法可以推断和分析复杂的基因调节网络.
  • 这项工作增强了GWAS对系统生物学方法的实用性.