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 using supported trait
    identifiers, 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. The existing
    efo_id argument is retained for backward compatibility. The package prepares
    computational artifacts for downstream workflows; it does not perform biological
    causality testing, clinical interpretation, therapeutic design, or wet-lab validation.
    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>.

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).