Package: NAM 1.8.0

NAM: Nested Association Mapping

Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.

Authors:Alencar Xavier [aut, cre], William Muir [aut], Katy Rainey [aut], Shizhong Xu [aut]

NAM_1.8.0.tar.gz
NAM_1.8.0.zip(r-4.7-x86_64)NAM_1.8.0.zip(r-4.7-arm64)NAM_1.8.0.zip(r-4.6-x86_64)NAM_1.8.0.zip(r-4.6-arm64)NAM_1.8.0.zip(r-4.5-x86_64)
NAM_1.8.0.tgz(r-4.6-x86_64)NAM_1.8.0.tgz(r-4.6-arm64)NAM_1.8.0.tgz(r-4.5-x86_64)NAM_1.8.0.tgz(r-4.5-arm64)
NAM_1.8.0.tar.gz(r-4.7-arm64)NAM_1.8.0.tar.gz(r-4.7-x86_64)NAM_1.8.0.tar.gz(r-4.6-arm64)NAM_1.8.0.tar.gz(r-4.6-x86_64)
NAM_1.8.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
NAM/json (API)

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

Bug tracker:https://github.com/alenxav/nam/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • chr - Tetra-seed Pods
  • fam - Tetra-seed Pods
  • gen - Tetra-seed Pods
  • Gen - Multi-environmental trial
  • Obs - Multi-environmental trial
  • simu - Pseudo-Expectation Gauss-Seidel for Multivariate Models
  • X - Pseudo-Expectation Gauss-Seidel for Multivariate Models
  • y - Tetra-seed Pods
  • Y - Pseudo-Expectation Gauss-Seidel for Multivariate Models

On CRAN:

Conda:

openblascpp

5.84 score 2 stars 1 packages 57 scripts 604 downloads 18 mentions 76 exports 1 dependencies

Last updated from:cc936ca82f. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK116
linux-devel-x86_64OK128
source / vignettesOK168
linux-release-arm64OK147
linux-release-x86_64OK128
macos-release-arm64OK131
macos-release-x86_64OK318
macos-oldrel-arm64OK127
macos-oldrel-x86_64OK192
windows-devel-arm64OK328
windows-devel-x86_64OK166
windows-release-arm64OK212
windows-release-x86_64OK141
windows-oldrel-x86_64OK107
wasm-releaseOK104

Exports:BCpibenBRR2calcSizecleanREPCNTcovareigXemBAemBBemBCemBDemBLemCVemDEemENemGWAemMLemML2emMXemRRFstfunIfunXG2A_KernelsGAUGdistgibbsgibbs2gmmGRMgsgwasgwas2gwas3gwasGEIMPImport_datainputRowKMUPKMUP2LDmarkovMCremlmeta3mkrmkr2Xmlmrrmrr2XmrrFastmrrV2MSXNNcovNNsrcNORPedMatPedMat2pegsPEGS_lapackplot.fstplot.gibbsplot.H2plot.NAMpressreferenceremlSAMPSAMP2snpH2snpQCSPCSPMtimesMatrixtimesVecwgr

Dependencies:Rcpp

Introduction to NAM
1. Installing the NAM package | 2. Loading and formatting data | 3. Genome-wide association studies | 4. Marker quality control | 5. Signatures of selection | 6. BLUPs and GEBVs | 7. Finding substructures | 8. Other structured populations | 9. Further background

Last update: 2019-09-23
Started: 2017-10-27

Meta-analysis
Meta-analysis of multiple populations along with GxE | Genome-wide association | AMMI term | Hypothesis testing | Woodbury's matrix identities

Last update: 2018-09-12
Started: 2017-10-27

Readme and manuals

Help Manual

Help pageTopics
Nested Association MappingNAM-package NAM
Tetra-seed Podschr fam gen tpod y
Multi-environmental trialGen met Obs
Fixation IndexFst plot.fst
Empirical Bayes Genome Wide Association Mappinggwas gwas2 gwas3 gwasGE meta3
Genome-wide predictionben wgr
Manhattan plot for Association Studiesplot.NAM
Bayesian Mixed Modelgibbs gibbs2 ml plot.gibbs
Pseudo-Expectation Gauss-Seidel for Multivariate Modelspegs PEGS_lapack simu X Y
Restricted Maximum LikelihoodMCreml reml
Genomic mixed modelgmm
Internal functionsBCpi BRR2 calcSize CNT covar eigX emBA emBB emBC emBD emBL emCV emDE emEN emGWA emML emML2 emMX emRR funI funX G2A_Kernels GAU Gdist GRM gs IMP Import_data inputRow KMUP KMUP2 LD markov mkr mkr2X mrr mrr2X mrrFast mrrV2 MSX NNcov NNsrc NOR PedMat PedMat2 press RcppExports SAMP SAMP2 SPC SPM timesMatrix timesVec
SNP heritabilityplot.H2 snpH2
SNP Quality ControlcleanREP reference snpQC