Package: polyqtlR 0.1.1

polyqtlR: QTL Analysis in Autopolyploid Bi-Parental F1 Populations

Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in autopolyploid bi-parental F1 populations. For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated. Significance thresholds, exploring QTL allele effects and visualising results are provided. For more background and to reference the package see <doi:10.1093/bioinformatics/btab574>.

Authors:Peter Bourke [aut, cre], Christine Hackett [ctb], Chris Maliepaard [ctb], Geert van Geest [ctb], Roeland Voorrips [ctb], Johan Willemsen [ctb]

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polyqtlR.pdf |polyqtlR.html✨
polyqtlR/json (API)

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • BLUEs.pheno - Best Linear Unbiased Estimates of phenotype
  • GIC_4x - Genotypic Information Coefficient for example tetraploid
  • IBD_4x - Identical by descent probabilities for example tetraploid
  • Phenotypes_4x - Phenotypes for example tetraploid
  • Rec_Data_4x - Recombination data for example tetraploid
  • SNP_dosages.4x - SNP marker dosage data for example tetraploid
  • mr.ls - Example output of meiosis report function
  • phased_maplist.4x - Phased maplist for example tetraploid
  • qtl_LODs.4x - QTL output for example tetraploid
  • segList_2x - Expected segregation for all markers types of a diploid cross
  • segList_3x - Expected segregation for all markers types of a triploid cross
  • segList_3x_24 - Expected segregation for all markers types of a triploid cross
  • segList_4x - Expected segregation for all markers types of a tetraploid cross
  • segList_6x - Expected segregation for all markers types of a hexaploid cross

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.30 score 2 scripts 272 downloads 2 mentions 27 exports 74 dependencies

Last updated 11 months agofrom:a7317b59cc. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-win-x86_64OKNov 02 2024
R-4.5-linux-x86_64OKNov 02 2024
R-4.4-win-x86_64OKNov 02 2024
R-4.4-mac-x86_64OKNov 02 2024
R-4.4-mac-aarch64OKNov 02 2024
R-4.3-win-x86_64OKNov 02 2024
R-4.3-mac-x86_64OKNov 02 2024
R-4.3-mac-aarch64OKNov 02 2024

Exports:BLUEcheck_cofactorsconvert_mappoly_to_phased.maplistcount_recombinationsestimate_GICestimate_IBDexploreQTLfindPeakfindSupportimport_IBDimpute_dosagesmaxL_IBDmeiosis_reportplotLinearQTLplotLinearQTL_listplotQTLplotRecLSPVEQTLscansegMakersingleMarkerRegressionspline_IBDthinmapvisualiseGICvisualiseHaplovisualisePairingvisualiseQTLeffects

Dependencies:abindbackportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacedata.tabledigestdoParallelevaluatefansifarverfastmapfontawesomeforeachforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigplyrR6rappdirsRColorBrewerRcppRcppArmadilloreshape2rlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

Performing polyploid QTL analysis using polyqtlR

Peter M. Bourke

Rendered frompolyqtlR_vignette.rmdusingknitr::rmarkdownon Nov 02 2024.

Last update: 2024-01-09
Started: 2020-12-16

Readme and manuals

Help Manual

Help pageTopics
Calculate Best Linear Unbiased Estimates using linear mixed model from 'nlme' packageBLUE
Best Linear Unbiased Estimates of phenotypeBLUEs.pheno
Build a multi-QTL model using step-wise procedure of checking genetic co-factors.check_cofactors
Function to extract the phased map from a mappoly.map objectconvert_mappoly_to_phased.maplist
Predict recombination breakpoints using IBD probabilitiescount_recombinations
Estimate the Genotypic Information Coefficient (GIC)estimate_GIC
Generate IBD probabilities from marker genotypes and a phased linkage mapestimate_IBD
Explore the possible segregation type of a QTL peak using Schwarz Information CriterionexploreQTL
Function to find the position of maximum LOD on a particular linkage groupfindPeak
Function to find a LOD - x support interval around a QTL positionfindSupport
Genotypic Information Coefficient for example tetraploidGIC_4x
Identical by descent probabilities for example tetraploidIBD_4x
Import IBD probabilities as estimated by TetraOrigin or PolyOriginimport_IBD
Re-estimate marker dosages given IBD input estimated using a high error prior.impute_dosages
Wrapper function to run estimate_IBD function over multiple error priorsmaxL_IBD
Generate a 'report' of predicted meiotic behaviour in an F1 populationmeiosis_report
Example output of meiosis report functionmr.ls
Phased maplist for example tetraploidphased_maplist.4x
Phenotypes for example tetraploidPhenotypes_4x
Plot the results of QTL scan.plotLinearQTL plotLinearQTL_list plotQTL
Plot the recombination landscape across the genomeplotRecLS
Function to determine the percentage variance explained (PVE) of a (maximal) QTL model, and explore sub-models.PVE
QTL output for example tetraploidqtl_LODs.4x
General QTL function that allows for co-factors, completely randomised block designs and the possibility to derive LOD thresholds using a permutation testQTLscan
Recombination data for example tetraploidRec_Data_4x
Expected segregation for all markers types of a diploid crosssegList_2x
Expected segregation for all markers types of a triploid cross (4 x 2)segList_3x
Expected segregation for all markers types of a triploid cross (2 x 4)segList_3x_24
Expected segregation for all markers types of a tetraploid crosssegList_4x
Expected segregation for all markers types of a hexaploid crosssegList_6x
Create a list of possible QTL segregation typessegMaker
Run a single marker regression using marker dosagessingleMarkerRegression
SNP marker dosage data for example tetraploidSNP_dosages.4x
Fit splines to IBD probabilitiesspline_IBD
Thin out map datathinmap
Visualise Genotypic Information CoefficientvisualiseGIC
Visualise haplotypes in certain individuals in a certain regionvisualiseHaplo
Visualise pairing of parental homologuesvisualisePairing
Visualise QTL homologue effects around a QTL positionvisualiseQTLeffects