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.
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.
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.
更多相关视频
08:27Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
10:57Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay
Published on: August 14, 2018
相关概念视频
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Interpreting R Charts
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
Multiple Allele Traits
Comparing Copy Number Variations and SNPs
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%...
Introduction to R
Statistical Software for Data Analysis and Clinical Trials
