Saturating an existing genetic map using markers derived from phenotype data.
cross.saturate(population, cross, map=c("genetic","physical"), placeUsing=c("qtl",
"correlation"), flagged = c("remove","warn","ignore"), threshold=3, chr, env,
use.orderMarkers=FALSE, verbose=FALSE, debugMode=0)
An object of class population
. See create.population
for details.
An object of class cross
. See read.cross
for details. If not supplied, it will be created using data from the population object
Which map should be used for comparison:
genetic - genetic map from cross$maps$genetic.
physical - physical map from cross$maps$physical.
How should the position of the new markers on the saturated map be determinated:
qtl - position the new markers between / next to markers with high LOD score (see threshold).
correlation - position the new markers on the locations with the highest correction to markers on the physical map from cross$maps$physical.
How to handle the markers influenced by epistatic or environmental interactions:
remove - warn about every marker affected and remove them.
warn - warn about every marker affected but leave them in.
ignore - leave them in.
Specifies a threshold for the selection of new phenotype markers (see markerPlacementPlot).
When specified the algorithm only saturates a subset of chromosomes. If not specified, all the chromosomes will be saturated.
Vector of environmental conditions - for each of the individuals specifies a condition. Ignored if missing.
If true the algorithm (after initial saturation) performs an orderMarkers
on the newly created map.
Be verbose.
Either use 1 or 2, this will modify the amount of information returned to the user. 1) Print out checks, 2) Print additional time information.
An object of class population
. See create.population
for details.
This function saturates an existing map with markers derived from the phenotype data provided inside either the cross or population object. A correlation matrix between those two sets of markers is made, and new markers are assigned to the 'optimal' location on the map.
reorganizeMarkersWithin
- Apply new ordering on the cross object usign ordering vector.
assignChrToMarkers
- Create ordering vector from chromosome assignment vector.
cross.denovo
- Create de novo genetic map or chromosome assignment vector.
reduceChromosomesNumber
- Functions to reduce the number of chromosomes in a cross object.
markerPlacementPlot
- Plot showing how many markers will be selected for map saturation with different thresholds.
# NOT RUN {
data(testPopulation)
cross <- cross.saturate(testPopulation,map="genetic",verbose=TRUE,debugMode=2)
# }
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