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Description

Local Haplotype Clustering and Visualization.

A local haplotyping visualization toolbox to capture major patterns of co-inheritance between clusters of linked variants, whilst connecting findings to phenotypic and demographic traits across individuals. 'crosshap' enables users to explore and understand genomic variation across a trait-associated region. For an example of successful local haplotype analysis, see Marsh et al. (2022) <doi:10.1007/s00122-022-04045-8>.

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crosshap

What does it do?

crosshap is an LD-based local haplotype analysis and visualization tool.

Given a genomic variant data for a region of interest, crosshap performs LD-based local haplotyping. Tightly linked variants are clustered into Marker Groups (MGs), and individuals are grouped into local haplotypes by shared allelic combinations of MGs. Following this, crosshap provides a range of visualization options to examine relevant characteristics of the linked Marker Groups and local haplotypes.

Why would I use it?

crosshap was originally designed to explore local haplotype patterns that may underlie phenotypic variability in quantitative trait locus (QTL) regions. It is ideally suited to complement and follow-up GWAS results (takes same inputs). crosshap equips users with the tools to explain why a region reported a GWAS hit, what variants are causal candidates, what populations are they present/absent in, and what the features are of those populations.

Alternatively, crosshap can simply be a tool to identify patterns of linkage among local variants, and to classify individuals based on shared haplotypes.

Note: crosshap is designed for in-depth, user-driven analysis of inheritance patterns in specific regions of interest, not genome-wide scans.

Installation

crosshap is available on CRAN:

install.packages("crosshap")

For the latest features, you can install the development version of crosshap from GitHub with:

# install.packages("devtools")
devtools::install_github("JacobIMarsh/crosshap")

Usage

Documentation

In short, a typical crosshap analysis workflow involves the following steps. For a detailed explanation and walk through, see our Getting started vignette.

  1. Read in raw inputs
read_vcf(region.vcf)
read_LD(plink.ld)
read_metadata(metadata.txt)
read_pheno(pheno.txt)
  1. Run local haplotyping at a range of epsilon values
HapObject <- run_haplotyping(vcf, LD, metadata, pheno, epsilon, MGmin)
  1. Build clustering tree to optimize epsilon value
clustree_viz(HapObject)
  1. Visualize local haplotypes and Marker Groups
crosshap_viz(HapObject, epsilon)

From here you can examine haplotype and Marker Group features from the visualization, and export relevant information from the haplotype object.

HapObject$Haplotypes_MGmin30_E0.6$Indfile
HapObject$Haplotypes_MGmin30_E0.6$Hapfile
HapObject$Haplotypes_MGmin30_E0.6$Varfile

Contact

For technical queries feel free to contact me: [email protected] . Please contact Prof. David Edwards for all other queries: [email protected] .

Metadata

Version

1.4.0

License

Unknown

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