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DESCRIPTION
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Package: GENESIS
Type: Package
Title: GENetic EStimation and Inference in Structured samples
(GENESIS): Statistical methods for analyzing genetic data from
samples with population structure and/or relatedness
Version: 2.37.1
Date: 2025-01-31
Author: Matthew P. Conomos, Stephanie M. Gogarten,
Lisa Brown, Han Chen, Thomas Lumley, Kenneth Rice, Tamar Sofer, Adrienne Stilp, Timothy Thornton, Chaoyu Yu
Maintainer: Stephanie M. Gogarten <sdmorris@uw.edu>
Description: The GENESIS package provides methodology for estimating,
inferring, and accounting for population and pedigree structure
in genetic analyses. The current implementation provides
functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate
(Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components
Analysis on genome-wide SNP data for the detection of population
structure in a sample that may contain known or cryptic relatedness.
Unlike standard PCA, PC-AiR accounts for relatedness in the sample
to provide accurate ancestry inference that is not confounded by
family structure. PC-Relate uses ancestry representative principal
components to adjust for population structure/ancestry and accurately
estimate measures of recent genetic relatedness such as kinship
coefficients, IBD sharing probabilities, and inbreeding coefficients.
Additionally, functions are provided to perform efficient variance
component estimation and mixed model association testing for both
quantitative and binary phenotypes.
License: GPL-3
URL: https://github.com/UW-GAC/GENESIS
Depends:
Imports: Biobase, BiocGenerics, BiocParallel, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate,
data.table, graphics, grDevices, igraph, Matrix, methods, reshape2, stats, utils
Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey,
testthat, BiocStyle, knitr, rmarkdown,
GWASdata, dplyr, ggplot2, GGally, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene
VignetteBuilder: knitr
biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod,
DimensionReduction, PrincipalComponent, GenomeWideAssociation,
QualityControl, BiocViews
NeedsCompilation: no