Simplify your R data analysis and data visualization workflow by turning your data frame into an interactive 'Tableau'-like interface, leveraging the 'graphic-walker' JavaScript library and the 'htmlwidgets' package.

Generate Manhattan, Q-Q, and PCA plots from GWAS and PCA results using 'ggplot2'.

Provides a reproducible pipeline to conduct genome‑wide association studies
    (GWAS) and extract single‑nucleotide polymorphisms (SNPs) for a human trait or
    disease. Given aggregated GWAS dataset(s) and a user‑defined significance threshold,
    the package retrieves significant SNPs from the GWAS Catalog and the
    Experimental Factor Ontology (EFO), annotates their gene context, and can write a
    harmonised metadata table in comma-separated values (CSV) format, genomic intervals in the Browser Extensible Data
    (BED) format, and sequences in the FASTA (text-based sequence) format with user-defined flanking regions for clustered
    regularly interspaced short palindromic repeats (CRISPR) guide design.
    For details on the resources and methods see:
    Buniello et al. (2019) <doi:10.1093/nar/gky1120>;
    Sollis et al. (2023) <doi:10.1093/nar/gkac1010>;
    Jinek et al. (2012) <doi:10.1126/science.1225829>;
    Malone et al. (2010) <doi:10.1093/bioinformatics/btq099>;
    Experimental Factor Ontology (EFO) <https://www.ebi.ac.uk/efo>.

Identifying disease-associated significant SNPs using clustering approach. This package is implementation of method proposed in Xu et al (2019) <DOI:10.1038/s41598-019-50229-6>.

Exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes
  as typically obtained in a Genome Wide Association Study (GWAS).

Extract and reform data from GWAS (genome-wide association study) results, and then make a single integrated forest plot containing multiple windows of which each shows the result of individual SNPs (or other items of interest).

Performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <DOI:10.1093/bioinformatics/bty472>).