Centre for Applied Bioinformatics, University of Western Australia, Perth WA, 6009, Australia.
School of Biological Sciences, University of Western Australia, Perth WA, 6009, Australia.
Bioinformatics. 2023 Aug 1;39(8). doi: 10.1093/bioinformatics/btad518.
Genome-wide association studies (GWAS) excels at harnessing dense genomic variant datasets to identify candidate regions responsible for producing a given phenotype. However, GWAS and traditional fine-mapping methods do not provide insight into the complex local landscape of linkage that contains and has been shaped by the causal variant(s). Here, we present crosshap, an R package that performs robust density-based clustering of variants based on their linkage profiles to capture haplotype structures in a local genomic region of interest. Following this, crosshap is equipped with visualization tools for choosing optimal clustering parameters (ɛ) before producing an intuitive figure that provides an overview of the complex relationships between linked variants, haplotype combinations, phenotype, and metadata traits.
The crosshap package is freely available under the MIT license and can be downloaded directly from CRAN with R >4.0.0. The development version is available on GitHub alongside issue support (https://github.com/jacobimarsh/crosshap). Tutorial vignettes and documentation are available (https://jacobimarsh.github.io/crosshap/).
全基因组关联研究 (GWAS) 擅长利用密集的基因组变异数据集来识别负责产生特定表型的候选区域。然而，GWAS 和传统的精细映射方法无法深入了解包含因果变异 (s) 并受其影响的复杂局部连锁景观。在这里，我们介绍了 crosshap，这是一个 R 包，它可以根据变体的连锁谱对变体进行稳健的基于密度的聚类，以捕获感兴趣的局部基因组区域中的单倍型结构。之后，crosshap 配备了可视化工具，可以在生成直观的图形之前选择最佳聚类参数 (ɛ)，该图形提供了链接变体、单倍型组合、表型和元数据特征之间复杂关系的概述。
crosshap 包根据麻省理工学院的许可证免费提供，可直接从 CRAN 下载，需要 R > 4.0.0。开发版本可在 GitHub 上获得，同时还提供问题支持 (https://github.com/jacobimarsh/crosshap)。教程简介和文档也可用 (https://jacobimarsh.github.io/crosshap/)。
