QTLCartographer.pptVIP

QTL Cartographer A Program Package for finding Quantitative Trait Loci C. J. Basten Z.-B. Zeng and B. S. Weir Experimental Design Three Phases Phase I: Simulate or Reformat Data Phase II: Analyze Data Phase III: Visualize Results Data 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 Data MAPMAKER *.raw file Create *.maps file with MAPMAKER Rmap reformats *.maps file Rcross reformats *.raw file Simulate Data Rmap creates a linkage map Rqtl creates a genetic model Rcross creates a data set of marker and trait values Resample Data Prune allows resampling of data Permute traits on genotypes Bootstrap Simulate missing or dominant markers Transition 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 Analysis Qstats Calculate basic statistics on Trait Produce histogram of Trait Summarize missing data for each marker and each individual Perform tests for marker segregation LRmapqtl 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 SRmapqtl 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 Zmapqtl 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 6 Missing 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 JZmapqtl Map multiple traits using IM or CIM Simultaneous estimation of