Post on 05-Jan-2016
description
QTL CartographerQTL Cartographer
A Program Package for finding Quantitative Trait Loci
C. J. Basten
Z.-B. Zeng and B. S. Weir
Experimental DesignExperimental Design
P1 P2
F1
B1 B2F2
Inbred Lines
Three PhasesThree Phases
Phase I: Simulate or Reformat Data Phase II: Analyze Data Phase III: Visualize Results
Data PreparationData Preparation
Simulate a genetic linkage map, genetic model and data set of marker and trait values
Reformat a MAPMAKER data set Reformat your own data set Perform a bootstrap resampling
Reformat MAPMAKER DataReformat MAPMAKER Data
MAPMAKER *.raw file
Create *.maps file with MAPMAKER
Rmap reformats *.maps file
Rcross reformats *.raw file
*.maps *.raw
Rmap Rcross
Rmap.out Rcross.out
Simulate DataSimulate Data
Rmap creates a linkage map
Rqtl creates a genetic model
Rcross creates a data set of marker and trait values
Rmap Rqtl Rcross
Rmap.out Rqtl.out Rcross.out
Resample DataResample Data
Prune allows resampling of data
Permute traits on genotypes
Bootstrap Simulate missing or
dominant markersRcross2.out
Prune
Rmap.out Rcross.out
Transition to AnalysisTransition to Analysis
At this point, we have a genetic linkage map and a data file of the proper format
All analyses will depend on these two files
Call them Rmap.out and Rcross.out
AnalysisAnalysisRmap.out Rcross.out
QstatsLRmapqtlSRmapqtl
ZmapqtlJZmapqtl
Qstats.out LRmapqtl.outSRmapqtl.out
(J)Zmapqtl.out
QstatsQstats
Calculate basic statistics on Trait Produce histogram of Trait Summarize missing data for each
marker and each individual Perform tests for marker segregation
LRmapqtlLRmapqtl
Do simple linear regression of trait on each marker in turn
Trait = Mean + Marker + Error
Estimate model parameters F statistic for Hypothesis of a Linked
QTL
yi =β0 +β1xi +ei
SRmapqtlSRmapqtl
Forward stepwise regression to rank markers
Backward elimination to rank markers Forward addition with a final backward
elimination step: Rank markers, but only add or delete subject to criteria
ZmapqtlZmapqtl
Do interval or composite interval mapping (IM or CIM)
Specify genome walk rate Choose cofactors for CIM Perform tests using the bootstrap,
jacknife or permutation
CIM Model 6CIM Model 6
Markers
Test Site
LFM RFM
Blocked Region
Top markers (as determined by stepwise regression) not in blocked regions used as cofactors
Missing DataMissing Data
Jiang and Zeng method using Markov chain to infer missing markers
Dominant markers can also be used Same algorithms for genotype at test
site in IM and CIM Many experimental designs available
JZmapqtlJZmapqtl
Map multiple traits using IM or CIM Simultaneous estimation of additive and
dominance effects Joint and single trait likelihood ratios G x E interactions Still a work in progress: not yet
integrated into Preplot
Visualization SchematicVisualization Schematic
Rqtl.out Rmap.out c#t#.?
Zmapqtl.out LRmapqtl.out
Eqtl Preplot GNUPLOT(pictures)
VisualizationVisualization
Use Zmapqtl.out, LRmapqtl.out and Rmap.out
Summarize QTL positions and effects with Eqtl
Display graphs with Preplot and GNUPLOT
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LR Statistic
Position in Morgans
Results of mletest.plt for Trait 1 and Chromosome 1
'c1t1.s''c1t1.lr''c1t1.z3''c1t1.z6'
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
LR Statistic
Position in Morgans
Results of mletest.plt for Trait 1 and Chromosome 2
'c2t1.s''c2t1.lr''c2t1.z3''c2t1.z6'
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
LR Statistic
Position in Morgans
Results of mletest.plt for Trait 1 and Chromosome 3
'c3t1.s''c3t1.lr''c3t1.z3''c3t1.z6'
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
LR Statistic
Position in Morgans
Results of mletest.plt for Trait 1 and Chromosome 4
'c4t1.s''c4t1.lr''c4t1.z3''c4t1.z6'
Computing EnvironmentComputing Environment
Programs written in C language UNIX, MS-Windows and Macintosh
versions are available Command line and menu driven
interfaces Same look and feel over all platforms
AvailabilityAvailability
Free. Source code with UNIX version, binaries for Windows and Macintosh
Anonymous ftp: in /pub/qtlcart on statgen.ncsu.edu
See also: http://statgen.ncsu.edu/ Manual in pdf and html