Package: GENESIS 2.43.0

Stephanie M. Gogarten

GENESIS: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness

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.

Authors:Matthew P. Conomos [aut], Stephanie M. Gogarten [aut, cre], Lisa Brown [aut], Han Chen [aut], Thomas Lumley [aut], Kenneth Rice [aut], Tamar Sofer [aut], Adrienne Stilp [aut], Timothy Thornton [aut], Chaoyu Yu [aut]

GENESIS_2.43.0.tar.gz
GENESIS_2.43.0.zip(r-4.7-x86_64)GENESIS_2.43.0.zip(r-4.7-arm64)GENESIS_2.43.0.zip(r-4.6-x86_64)GENESIS_2.43.0.zip(r-4.6-arm64)GENESIS_2.43.0.zip(r-4.5-x86_64)
GENESIS_2.43.0.tgz(r-4.6-x86_64)GENESIS_2.43.0.tgz(r-4.6-arm64)GENESIS_2.43.0.tgz(r-4.5-x86_64)GENESIS_2.43.0.tgz(r-4.5-arm64)
GENESIS_2.43.0.tar.gz(r-4.7-arm64)GENESIS_2.43.0.tar.gz(r-4.7-x86_64)GENESIS_2.43.0.tar.gz(r-4.6-arm64)GENESIS_2.43.0.tar.gz(r-4.6-x86_64)
GENESIS_2.43.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
GENESIS/json (API)

# Install 'GENESIS' in R:
install.packages('GENESIS', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/uw-gac/genesis/issues

Datasets:
  • HapMap_ASW_MXL_KINGmat - Matrix of Pairwise Kinship Coefficient Estimates for the combined HapMap ASW and MXL Sample found with the KING-robust estimator from the KING software.
  • sample_annotation_1KG - Annotation for 1000 genomes Phase 3 samples

On BioConductor:GENESIS-2.43.0(bioc 3.24)GENESIS-2.42.0(bioc 3.23)

snpgeneticvariabilitygeneticsstatisticalmethoddimensionreductionprincipalcomponentgenomewideassociationqualitycontrolbiocviews

8.77 score 44 stars 1 packages 982 downloads 243 mentions 30 exports 107 dependencies

Last updated from:df969fc093. Checks:1 WARNING, 13 NOTE, 2 OK. Indexed: yes.

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Exports:admixMapassocTestAggregateassocTestSinglecalcISAFBetacalcScorecomputeVSIFcomputeVSIFNullModelcorrectK0correctK2correctKineffectAllelefitNullModelfitNullModelFastScoreisNullModelFastScoreisNullModelSmalljointScoreTestkin2gdskingToMatrixmakeSparseMatrixmat2gdsnullModelFastScorenullModelInvNormnullModelSmallpcairpcairPartitionpcrelatepcrelateSampBlockpcrelateToMatrixsamplesGdsOrdervarCompCI

Dependencies:backportsBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobbootbroomcachemclicliprcodetoolscpp11crayondata.tableDBIdigestDNAcopydplyrfastmapforcatsforeachformatRformula.toolsfutile.loggerfutile.optionsgdsfmtgenericsGenomicRangesglmnetglueGWASExactHWGWASToolshavenhmsigraphIRangesiteratorsjomolambda.rlatticelifecyclelme4lmtestlogistfmagrittrMASSMatrixMatrixModelsmemoisemgcvmiceminqamitmlnlmenloptrnnetnumDerivoperator.toolsordinalpanpillarpkgconfigplyrprettyunitsprogresspurrrquantregquantsmoothR6rbibutilsRcppRcppEigenRdpackreadrreformulasreshape2RhpcBLASctlrlangrpartRSQLiteS4VectorssandwichSeqArraySeqinfoSeqVarToolsshapesnowSNPRelateSparseMstringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithrXVectorzoo

Analyzing Sequence Data using the GENESIS Package
Overview | Convert VCF to GDS | Create a SeqVarData object | Population structure and relatedness | KING | PC-AiR | PC-Relate | Association tests | Null model | Single variant tests | Aggregate tests | References

Last update: 2025-06-28
Started: 2018-04-26

Genetic Association Testing using the GENESIS Package
Overview | Data | Preparing Scan Annotation Data | Reading in Genotype Data | R Matrix | GDS files | PLINK files | HapMap Data | Reading in the GRM from PC-Relate | Mixed Model Association Testing | Fit the Null Model | Quantitative Phenotypes | Multiple Fixed Effect Covariates | Multiple Random Effects | Heterogeneous Residual Variances | Binary Phentoypes | Run SNP-Phenotype Association Tests | Output | The Null Model | The Association Tests | Heritability Estimation | References

Last update: 2021-08-09
Started: 2016-02-04

Population Structure and Relatedness Inference using the GENESIS Package
Overview | Principal Components Analysis in Related Samples (PC-AiR) | Relatedness Estimation Adjusted for Principal Components (PC-Relate) | Data | Reading in Genotype Data | R Matrix | GDS files | PLINK files | HapMap Data | LD pruning | Pairwise Measures of Ancestry Divergence | Running PC-AiR | Reference Population Samples | Partition a Sample without Running PCA | Output from PC-AiR | Plotting PC-AiR PCs | Running PC-Relate | Output from PC-Relate | References

Last update: 2021-08-09
Started: 2015-03-20

Readme and manuals

Help Manual

Help pageTopics
GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatednessGENESIS-package GENESIS
admixMapadmixMap
Aggregate Association TestingassocTestAggregate assocTestAggregate,GenotypeIterator-method assocTestAggregate,SeqVarIterator-method assocTestAggregate-methods
Genotype Association Testing with Mixed ModelsassocTestSingle assocTestSingle,GenotypeIterator-method assocTestSingle,SeqVarIterator-method assocTestSingle-methods
Computes variant-specific inflation factorscomputeVSIF computeVSIFNullModel
Return the effect allele for association testingeffectAllele effectAllele,GenotypeData-method effectAllele,SeqVarGDSClass-method effectAllele-methods
Fit a Model Under the Null HypothesiscalcScore fitNullModel fitNullModel,AnnotatedDataFrame-method fitNullModel,data.frame-method fitNullModel,GenotypeData-method fitNullModel,ScanAnnotationDataFrame-method fitNullModel,SeqVarData-method fitNullModel-methods fitNullModelFastScore fitNullModelFastScore,SeqVarData-method fitNullModelFastScore-methods isNullModelFastScore isNullModelSmall nullModelFastScore nullModelInvNorm nullModelSmall
Defunct functions in package 'GENESIS'admixMapMM assocTestMM assocTestSeq assocTestSeqWindow fitNullMM fitNullReg GENESIS-defunct king2mat pcrelate,GenotypeData-method pcrelate,SeqVarData-method pcrelateMakeGRM pcrelateReadInbreed pcrelateReadKinship
Matrix of Pairwise Kinship Coefficient Estimates for the combined HapMap ASW and MXL Sample found with the KING-robust estimator from the KING software.HapMap_ASW_MXL_KINGmat
Perform a joint score testjointScoreTest
Store kinship matrix in GDSkin2gds mat2gds
Convert KING text output to an R MatrixkingToMatrix kingToMatrix,character-method kingToMatrix,snpgdsIBDClass-method
Make a sparse matrix from a dense matrix or a table of pairwise valuesmakeSparseMatrix makeSparseMatrix,data.frame-method makeSparseMatrix,data.table-method makeSparseMatrix,Matrix-method makeSparseMatrix,matrix-method makeSparseMatrix-methods
PC-AiR: Principal Components Analysis in Related Samplespcair pcair,gds.class-method pcair,GdsGenotypeReader-method pcair,GenotypeData-method pcair,MatrixGenotypeReader-method pcair,SeqVarGDSClass-method pcair,SNPGDSFileClass-method pcair-methods
Partition a sample into an ancestry representative 'unrelated subset' and a 'related subset'pcairPartition
PC-Relate: Model-Free Estimation of Recent Genetic RelatednesscalcISAFBeta correctK0 correctK2 correctKin pcrelate pcrelate,GenotypeIterator-method pcrelate,SeqVarIterator-method pcrelateSampBlock samplesGdsOrder
Creates a Genetic Relationship Matrix (GRM) of Pairwise Kinship Coefficient Estimates from PC-Relate OutputpcrelateToMatrix pcrelateToMatrix,pcrelate-method
PC-AiR: Plotting PCsplot.pcair
PC-AiR: Principal Components Analysis in Related Samplesprint.pcair print.summary.pcair summary.pcair
Annotation for 1000 genomes Phase 3 samplessample_annotation_1KG
Variance Component Confidence IntervalsvarCompCI