MyNixOS website logo
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>.

R-CMD-check Codecov

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

Platforms (77)

    Darwin
    FreeBSD
    Genode
    GHCJS
    Linux
    MMIXware
    NetBSD
    none
    OpenBSD
    Redox
    Solaris
    WASI
    Windows
Show all
  • aarch64-darwin
  • aarch64-freebsd
  • aarch64-genode
  • aarch64-linux
  • aarch64-netbsd
  • aarch64-none
  • aarch64-windows
  • aarch64_be-none
  • arm-none
  • armv5tel-linux
  • armv6l-linux
  • armv6l-netbsd
  • armv6l-none
  • armv7a-darwin
  • armv7a-linux
  • armv7a-netbsd
  • armv7l-linux
  • armv7l-netbsd
  • avr-none
  • i686-cygwin
  • i686-darwin
  • i686-freebsd
  • i686-genode
  • i686-linux
  • i686-netbsd
  • i686-none
  • i686-openbsd
  • i686-windows
  • javascript-ghcjs
  • loongarch64-linux
  • m68k-linux
  • m68k-netbsd
  • m68k-none
  • microblaze-linux
  • microblaze-none
  • microblazeel-linux
  • microblazeel-none
  • mips-linux
  • mips-none
  • mips64-linux
  • mips64-none
  • mips64el-linux
  • mipsel-linux
  • mipsel-netbsd
  • mmix-mmixware
  • msp430-none
  • or1k-none
  • powerpc-netbsd
  • powerpc-none
  • powerpc64-linux
  • powerpc64le-linux
  • powerpcle-none
  • riscv32-linux
  • riscv32-netbsd
  • riscv32-none
  • riscv64-linux
  • riscv64-netbsd
  • riscv64-none
  • rx-none
  • s390-linux
  • s390-none
  • s390x-linux
  • s390x-none
  • vc4-none
  • wasm32-wasi
  • wasm64-wasi
  • x86_64-cygwin
  • x86_64-darwin
  • x86_64-freebsd
  • x86_64-genode
  • x86_64-linux
  • x86_64-netbsd
  • x86_64-none
  • x86_64-openbsd
  • x86_64-redox
  • x86_64-solaris
  • x86_64-windows