Overview

The gwask package includes functions for performing post-GWAS analysis of k-mer GWAS.

Installation

Dependencies

Some Bioconductor packages will need to be installed for this package to work:

• Biostrings
• GenomeInfoDb
• GenomicRanges
• IRanges
• MatrixGenerics
• Rsamtools
• S4Vectors
• VariantAnnotation

GAPIT3 also needs to be installed.

WARNING: The GAPIT3 package has recently changed its name to GAPIT, breaking compatibility with gwask. We have not tested the newly named package yet. Most users of the package will not need GAPIT functionality; in this case, simply remove the corresponding line from the DESCRIPTION file before installing. If you do need GAPIT compatibility, the recommended route at the moment is to install a version of GAPIT where it was still called GAPIT3.

Other required packages should be pulled in automatically from CRAN.

Installing the package

Package sources can be downloaded from GitHub by running git clone https://github.com/malemay/gwask. Then, running the following command in R should install the package from source:

install.packages("gwask", repos = NULL, type = "source")

Documentation

There is no vignette availble at the moment for gwask, however all functions included in the package are individually documented. A complete list of the available functions can be found here:

• adjust_gaps: Adjust plotting position according to alignment gaps
• cluster_haplotypes: Cluster the most similar haplotypes together
• cluster_ld: Greedy clustering of an LD matrix
• extract_signals: Extract signal ranges from GWAS results
• fill_gaps: Fill the deleted positions with dashes
• format_gapit_gwas: Format GAPIT GWAS results for generating Manhattan plots
• format_haplotypes: Format haplotypes for plotting using k-mer overlap information
• format_kmer_gwas: Format k-mer GWAS results for downstream analyses
• gapit_vcf: Launch a MLM GWAS analysis on a VCF file read with VariantAnnotation::readVcf using GAPIT
• get_haplotypes: Extract and filter the haplotypes from a set of sequences
• gg_color_hue: Select colors for a discrete scale as in ggplot2
• gg_hue: Generating a vector of colors comparable to those used by ggplot2
• grid.colorscale: Plot the color scale used in a haplotype plot
• grid.haplotypes: Plot a set of haplotypes using grid functions
• grid.phenotable: Plot a contingency table of observed phenotypes and haplotypes
• is_valid_ld: Check the validity of an LD matrix
• kmer_ld: Compute the pairwise LD between k-mers based on their presence/absence
• ld_plot: Plot an LD matrix using grid functions
• ld_sort: Re-arrange the samples in an LD matrix according to user-specified criteria
• link_phenotypes: Link haplotypes to their observed phenotypes
• mafft_align: Align a set of sequences using mafft
• manhattanGrob: A function that returns a graphical object (grob) representing a manhattan plot
• map_color: Map a set of numeric values onto a color palette
• match_kmers: Match a set of k-mers to positions on sequences
• nucdiff: Find positions that differ between two sequences
• pvalueGrob: A grob representing p-values of a GWAS analysis at a locus to plot using grid functions
• pvalue_tx_grob: Arrange a transcript grob and several p-value grobs in the same plot
• read_fasta: Read a fasta file into a named character vector
• read_kmer_pvalues: Read the (sorted) k-mer p-values from the output of a k-mer GWAS analysis
• subsample_kmers: Subsample from a set of significant k-mers prior to computing LD
• transcriptGrob: Generate a grob representing a transcript using grid functions
• transcriptsGrob: Generate a grob of all the possible transcripts for genes in a genomic region using grid
• vcf_to_gapit: Convert VCF records read with VariantAnnotation::readVCF into a format usable by GAPIT

Citation

If you use this software, plase cite our publication:

Lemay, M.-A., de Ronne, M., Bélanger, R., & Belzile, F. (2023). k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean. The Plant Genome, 16, e20374. doi:10.1002/tpg2.20374

References

Voichek, Y. and Weigel, D., 2020. Identifying genetic variants underlying phenotypic variation in plants without complete genomes. Nature Genetics, 52(5), pp.534-540. https://doi.org/10.1038/s41588-020-0612-7