Multi-Parent Population QTL Analysis
Create a multi-parent population data object
Cross validation partition
Connected parts of a MPP design
Determine EC effects
IBD coding for mppData objects
IBS coding for mppData objects
QTL incidence matrix
Backward elimination on QTL candidates
MPP Composite Interval Mapping
MPP cross-validation
MPP QTL analysis using forward regression
QTL significance threshold by permutation
MPP QTL analysis
MPP Simple Interval Mapping
Add new phenotypic values to a mppData object
Modify the phenotypic values of a mppData object
MPP GxE Composite Interval Mapping
MPP GxE QTL analysis
MPP GxE Simple Interval Mapping
QTL genetic effects multi-QTL effect model
Multi-QTL effect MPP analysis
Parent clustering for mppData objects
plot QTL profile
plot of genome wide QTL allelic effect significance
plot QTLxEC effect
Print summary.mppData object
Print summary.QeffRes object
Print summary.QR2Res object
Quality control for mppData objects
MPP GxE QTL genetic effects
Main and QTL by environment interaction model
Estimation of a model with main and QTL by environmental sensitivity t...
Forward regression QTL model
QTL genetic effects
Predicted QTL global and partial R squared
QTL global and partial R squared
MPP GxE QTL R2
QTL candidates selection
Subset mppData object
Summary of mppData object
Summary of QeffRes object
Summary of QR2Res object
Analysis of experimental multi-parent populations to detect regions of the genome (called quantitative trait loci, QTLs) influencing phenotypic traits measured in unique and multiple environments. The population must be composed of crosses between a set of at least three parents (e.g. factorial design, 'diallel', or nested association mapping). The functions cover data processing, QTL detection, and results visualization. The implemented methodology is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3>, in Garin, Malosetti and van Eeuwijk (2020) <doi: 10.1007/s00122-020-03621-0>, and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) <doi: 10.1093/genetics/iyae003>.