Provides an integrated suite of tools for handling single
    nucleotide polymorphism (SNP) genotype data in large-scale genetic
    studies. Supports importing and merging genotype files, performing
    quality control on SNP markers and samples, and preparing data for
    downstream analyses using popular software such as 'FImpute' and
    'PLINK'. Offers S4 classes and methods to efficiently encapsulate SNP
    data, along with utilities for generating genotype summary statistics
    and visualization. Additional functionalities include anticlustering
    approaches for batch effect control, automated script generation for
    external software, and streamlined workflows for large datasets
    commonly encountered in animal and plant breeding programs. Designed
    to facilitate reproducible and scalable SNP data analyses in
    quantitative and statistical genetics.

Linkage disequilibrium visualizations of up to several hundreds of single nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene names. Two types of plots are available for small numbers of SNPs (<40) and for large numbers (tested up to 500). Both can be extended by combining other ggplots, e.g. association studies results, and functions enable to directly visualize the effect of SNP selection methods, as minor allele frequency filtering and TagSNP selection, with a second correlation heatmap. The SNPs correlations are computed on Genotype Data objects from the 'GWASTools' package using the 'SNPRelate' package, and the plots are customizable 'ggplot2' and 'gtable' objects and are annotated using the 'biomaRt' package. Usage is detailed in the vignette with example data and results from up to 500 SNPs of 1,200 scans are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>.

A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example to export to a PLINK set.

Tools for performing variable selection in three-way data using N-PLS 
    in combination with L1 penalization, Selectivity Ratio and VIP scores. 
    The N-PLS model (Rasmus Bro, 1996 <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) 
    is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries 
    to maximize the covariance between X and Y data arrays. The package also adds
    variable selection through L1 penalization, Selectivity Ratio and VIP scores.