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polyqtlR (version 0.1.1)

QTL Analysis in Autopolyploid Bi-Parental F1 Populations

Description

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 .

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Version

Install

install.packages('polyqtlR')

Monthly Downloads

248

Version

0.1.1

License

GPL-3

Maintainer

Peter Bourke

Last Published

January 9th, 2024

Functions in polyqtlR (0.1.1)

Estimate the Genotypic Information Coefficient (GIC)

Function to find a LOD - x support interval around a QTL position

Re-estimate marker dosages given IBD input estimated using a high error prior.

Function to find the position of maximum LOD on a particular linkage group

Import IBD probabilities as estimated by TetraOrigin or PolyOrigin

convert_mappoly_to_phased.maplist

Function to extract the phased map from a mappoly.map object

Explore the possible segregation type of a QTL peak using Schwarz Information Criterion

count_recombinations

Predict recombination breakpoints using IBD probabilities

Wrapper function to run estimate_IBD function over multiple error priors

Generate IBD probabilities from marker genotypes and a phased linkage map

QTL output for example tetraploid

Plot the results of QTL scan.

Generate a 'report' of predicted meiotic behaviour in an F1 population

Example output of meiosis report function

Expected segregation for all markers types of a diploid cross

phased_maplist.4x

Phased maplist for example tetraploid

Expected segregation for all markers types of a triploid cross (4 x 2)

Expected segregation for all markers types of a triploid cross (2 x 4)

Plot the recombination landscape across the genome

polyqtlR-package

QTL analysis in polyploid species using posterior genotype probabilities

singleMarkerRegression

Run a single marker regression using marker dosages

Visualise Genotypic Information Coefficient

Expected segregation for all markers types of a hexaploid cross

Create a list of possible QTL segregation types

Thin out map data

visualiseQTLeffects

Visualise QTL homologue effects around a QTL position

visualisePairing

Visualise pairing of parental homologues

Visualise haplotypes in certain individuals in a certain region

Fit splines to IBD probabilities

Expected segregation for all markers types of a tetraploid cross

SNP marker dosage data for example tetraploid

Calculate Best Linear Unbiased Estimates using linear mixed model from nlme package

check_cofactors

Build a multi-QTL model using step-wise procedure of checking genetic co-factors.

General QTL function that allows for co-factors, completely randomised block designs and the possibility to derive LOD thresholds using a permutation test

Function to determine the percentage variance explained (PVE) of a (maximal) QTL model, and explore sub-models.

Recombination data for example tetraploid

Genotypic Information Coefficient for example tetraploid

Phenotypes for example tetraploid

Identical by descent probabilities for example tetraploid

Best Linear Unbiased Estimates of phenotype