Package: GENESIS 2.37.0
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:
GENESIS_2.37.0.tar.gz
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GENESIS.pdf |GENESIS.html✨
GENESIS/json (API)
NEWS
| # 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
- 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-2.35.0(bioc 3.20)GENESIS-2.34.0(bioc 3.19)
snpgeneticvariabilitygeneticsstatisticalmethoddimensionreductionprincipalcomponentgenomewideassociationqualitycontrolbiocviews
Last updated 23 days agofrom:dd2a91b06f. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 30 2024 |
| R-4.5-win-x86_64 | NOTE | Oct 30 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 30 2024 |
| R-4.4-win-x86_64 | NOTE | Oct 30 2024 |
| R-4.4-mac-x86_64 | NOTE | Oct 31 2024 |
| R-4.4-mac-aarch64 | NOTE | Oct 31 2024 |
| R-4.3-win-x86_64 | NOTE | Oct 30 2024 |
| R-4.3-mac-x86_64 | NOTE | Oct 31 2024 |
| R-4.3-mac-aarch64 | NOTE | Oct 31 2024 |
Exports:admixMapassocTestAggregateassocTestSinglecalcISAFBetacalcScorecomputeVSIFcomputeVSIFNullModelcorrectK0correctK2correctKineffectAllelefitNullModelfitNullModelFastScoreisNullModelFastScoreisNullModelSmalljointScoreTestkin2gdskingToMatrixmakeSparseMatrixmat2gdsnullModelFastScorenullModelInvNormnullModelSmallpcairpcairPartitionpcrelatepcrelateSampBlockpcrelateToMatrixsamplesGdsOrdervarCompCI
Dependencies:askpassbackportsBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobbootbroomcachemclicliprcodetoolscpp11crayoncurldata.tableDBIDNAcopydplyrfansifastmapforcatsforeachformatRformula.toolsfutile.loggerfutile.optionsgdsfmtgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesglmnetglueGWASExactHWGWASToolshavenhmshttrigraphIRangesiteratorsjomojsonlitelambda.rlatticelifecyclelme4lmtestlogistfmagrittrMASSMatrixMatrixModelsmemoisemgcvmicemimeminqamitmlnlmenloptrnnetnumDerivopenssloperator.toolsordinalpanpillarpkgconfigplogrplyrprettyunitsprogresspurrrquantregquantsmoothR6RcppRcppEigenreadrreshape2rlangrpartRSQLiteS4VectorssandwichSeqArraySeqVarToolsshapesnowSNPRelateSparseMstringistringrsurvivalsystibbletidyrtidyselecttzdbucminfUCSC.utilsutf8vctrsvroomwithrXVectorzlibbioczoo
Analyzing Sequence Data using the GENESIS Package
Rendered fromassoc_test_seq.Rmdusingknitr::rmarkdownon Oct 30 2024.Last update: 2021-08-09
Started: 2018-04-26
Genetic Association Testing using the GENESIS Package
Rendered fromassoc_test.Rmdusingknitr::rmarkdownon Oct 30 2024.Last update: 2021-08-09
Started: 2016-02-04
Population Structure and Relatedness Inference using the GENESIS Package
Rendered frompcair.Rmdusingknitr::rmarkdownon Oct 30 2024.Last update: 2021-08-09
Started: 2015-03-20
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness | GENESIS-package GENESIS |
| admixMap | admixMap |
| Aggregate Association Testing | assocTestAggregate assocTestAggregate,GenotypeIterator-method assocTestAggregate,SeqVarIterator-method assocTestAggregate-methods |
| Genotype Association Testing with Mixed Models | assocTestSingle assocTestSingle,GenotypeIterator-method assocTestSingle,SeqVarIterator-method assocTestSingle-methods |
| Computes variant-specific inflation factors | computeVSIF computeVSIFNullModel |
| Return the effect allele for association testing | effectAllele effectAllele,GenotypeData-method effectAllele,SeqVarGDSClass-method effectAllele-methods |
| Fit a Model Under the Null Hypothesis | calcScore 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 test | jointScoreTest |
| Store kinship matrix in GDS | kin2gds mat2gds |
| Convert KING text output to an R Matrix | kingToMatrix kingToMatrix,character-method kingToMatrix,snpgdsIBDClass-method |
| Make a sparse matrix from a dense matrix or a table of pairwise values | makeSparseMatrix makeSparseMatrix,data.frame-method makeSparseMatrix,data.table-method makeSparseMatrix,Matrix-method makeSparseMatrix,matrix-method makeSparseMatrix-methods |
| PC-AiR: Principal Components Analysis in Related Samples | pcair 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 Relatedness | calcISAFBeta 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 Output | pcrelateToMatrix pcrelateToMatrix,pcrelate-method |
| PC-AiR: Plotting PCs | plot.pcair |
| PC-AiR: Principal Components Analysis in Related Samples | print.pcair print.summary.pcair summary.pcair |
| Annotation for 1000 genomes Phase 3 samples | sample_annotation_1KG |
| Variance Component Confidence Intervals | varCompCI |
