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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.
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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.
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crosshap:R包用于局部单质型可视化,用于特征关联分析.

Jacob I Marsh1,2, Jakob Petereit1,2, Brady A Johnston3

  • 1Centre for Applied Bioinformatics, University of Western Australia, Perth WA, 6009, Australia.

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概括

crosshap,一个R包,通过基于密度的聚类来识别复杂的单元型结构和变异关系. 该工具增强了对基因组区域及其与表型的联系的理解,超出了传统的GWAS方法.

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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 统计遗传学 统计遗传学

背景情况:

  • 全基因组关联研究 (GWAS) 识别了表型的候选区域,但缺乏对局部链接不平衡的洞察力.
  • 精细映射方法难以完全解决由因果变异形成的复杂的单元型结构.

研究的目的:

  • 引入crosshap,一个R包,用于基于密度的变体集群.
  • 为了捕捉和可视化局部基因组区域内的复杂的单元型结构.
  • 为了解变体,单元型,表型和元数据之间的关系提供工具.

主要方法:

  • 使用基于链接配置文件的变体基于密度的聚类.
  • 使用可视化工具来选择最佳集群参数 (epsilon).
  • 产生直观的图形,说明变体-哈普类型-表型关系.

主要成果:

  • 在当地的基因组区域中成功捕获复杂的单元型结构.
  • 提供参数选择和关系概述的可视化.
  • 提供了一种超越传统GWAS的细化绘图的新方法.

结论:

  • crosshap通过揭示详细的单元型结构来增强复杂的基因组区域的分析.
  • 该套件有助于更深入地了解变异对表型的贡献.
  • crosshap 是统计遗传学和生物信息学研究人员的一个有价值的工具.