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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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
polyqtlR/json (API)

# Install 'polyqtlR' in R:

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

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
mr.ls - Example output of meiosis report function
phased_maplist.4x - Phased maplist for example tetraploid
Phenotypes_4x - Phenotypes for example tetraploid
qtl_LODs.4x - QTL output for example tetraploid
Rec_Data_4x - Recombination data 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
SNP_dosages.4x - SNP marker dosage data for example tetraploid

On CRAN:

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

cpp

2.30 score 10 scripts 341 downloads 2 mentions 27 exports 66 dependencies

Last updated from:a7317b59cc. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	200
linux-devel-x86_64	OK	199
source / vignettes	OK	259
linux-release-arm64	OK	220
linux-release-x86_64	OK	187
macos-release-arm64	OK	105
macos-release-x86_64	OK	200
macos-oldrel-arm64	OK	144
macos-oldrel-x86_64	OK	407
windows-devel	OK	169
windows-release	OK	154
windows-oldrel	OK	187
wasm-release	OK	158

Exports:BLUE check_cofactors convert_mappoly_to_phased.maplist count_recombinations estimate_GIC estimate_IBD exploreQTL findPeak findSupport import_IBD impute_dosages maxL_IBD meiosis_report plotLinearQTL plotLinearQTL_list plotQTL plotRecLS PVE QTLscan segMaker singleMarkerRegression spline_IBD thinmap visualiseGIC visualiseHaplo visualisePairing visualiseQTLeffects

Dependencies:abind backports base64enc bslib cachem checkmate cli cluster codetools colorspace cpp11 data.table digest doParallel evaluate farver fastmap fontawesome foreach foreign Formula fs ggplot2 glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets isoband iterators jquerylib jsonlite knitr labeling lattice lifecycle magrittr memoise mime nlme nnet plyr R6 rappdirs RColorBrewer Rcpp RcppArmadillo reshape2 rlang rmarkdown rpart rstudioapi S7 sass scales stringi stringr tinytex vctrs viridisLite withr xfun yaml

Performing polyploid QTL analysis using polyqtlR

Rendered frompolyqtlR_vignette.rmdusingknitr::rmarkdown

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

Help page	Topics
Calculate Best Linear Unbiased Estimates using linear mixed model from 'nlme' package	BLUE
Best Linear Unbiased Estimates of phenotype	BLUEs.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 object	convert_mappoly_to_phased.maplist
Predict recombination breakpoints using IBD probabilities	count_recombinations
Estimate the Genotypic Information Coefficient (GIC)	estimate_GIC
Generate IBD probabilities from marker genotypes and a phased linkage map	estimate_IBD
Explore the possible segregation type of a QTL peak using Schwarz Information Criterion	exploreQTL
Function to find the position of maximum LOD on a particular linkage group	findPeak
Function to find a LOD - x support interval around a QTL position	findSupport
Genotypic Information Coefficient for example tetraploid	GIC_4x
Identical by descent probabilities for example tetraploid	IBD_4x
Import IBD probabilities as estimated by TetraOrigin or PolyOrigin	import_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 priors	maxL_IBD
Generate a 'report' of predicted meiotic behaviour in an F1 population	meiosis_report
Example output of meiosis report function	mr.ls
Phased maplist for example tetraploid	phased_maplist.4x
Phenotypes for example tetraploid	Phenotypes_4x
Plot the results of QTL scan.	plotLinearQTL plotLinearQTL_list plotQTL
Plot the recombination landscape across the genome	plotRecLS
Function to determine the percentage variance explained (PVE) of a (maximal) QTL model, and explore sub-models.	PVE
QTL output for example tetraploid	qtl_LODs.4x
General QTL function that allows for co-factors, completely randomised block designs and the possibility to derive LOD thresholds using a permutation test	QTLscan
Recombination data for example tetraploid	Rec_Data_4x
Expected segregation for all markers types of a diploid cross	segList_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 cross	segList_4x
Expected segregation for all markers types of a hexaploid cross	segList_6x
Create a list of possible QTL segregation types	segMaker
Run a single marker regression using marker dosages	singleMarkerRegression
SNP marker dosage data for example tetraploid	SNP_dosages.4x
Fit splines to IBD probabilities	spline_IBD
Thin out map data	thinmap
Visualise Genotypic Information Coefficient	visualiseGIC
Visualise haplotypes in certain individuals in a certain region	visualiseHaplo
Visualise pairing of parental homologues	visualisePairing
Visualise QTL homologue effects around a QTL position	visualiseQTLeffects

Package: polyqtlR 0.1.1

polyqtlR: QTL Analysis in Autopolyploid Bi-Parental F1 Populations

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