Linkage Maps and QTL

Click on a button corresponding to a linkage map or quantitative trait locus (QTL) process. Refer to the table below for guidance.

Process	Choose this process for...
Recombination and Linkage Groups	Creating a matrix of pairwise recombination rates based on an experimental cross design, clustering that matrix to identify linkage groups for linkage mapping and QTL analysis, and calculating segregation ratios for each marker
Linkage Map Order	Determining the probable order of genetic markers within linkage groups based on recombination frequencies, and calculating the genetic distances between markers to produce a linkage map
Linkage Map Viewer	Constructing and displaying 2-D and 3-D maps of markers based on genetic distance
Compare Linkage Maps	Drawing a graphical representation of the differences and similarities of genetic markers and their positions in two linkage maps
Build Consensus Linkage Map	Importing a series of linkage maps from distinct genetic studies and using them to estimate (via linear programming optimization) a single consensus linkage map.
QTL Single Marker Analysis	Quickly scanning the whole genome for evidence of QTL signals
Build QTL Genotype Probability Data Set	Building a genotype probability SAS data set that can be used by the QTL IM, CIM and MIM Analysis process
QTL IM, CIM and MIM Analysis	Performing both Interval Mapping (IM) (an extension of single marker analysis) and Composite Interval Mapping (CIM) (an extension of IM analysis) to scan for QTLs
Compare QTL Plots	Comparing results from multiple analyses. Output data sets from the QTL IM, CIM and MIM Analysis process are merged into one data set containing variables from the specified studies. The QTL Test Size and QTL Effect Size variables in the merged data set are overlaid on a single plot to facilitate the comparison of QTL mapping results from different methods and/or traits .

See Genetics for other subcategories.