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.
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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 |
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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 |
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Constructing and displaying 2-D and 3-D maps of markers based on genetic distance |
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Drawing a graphical representation of the differences and similarities of genetic markers and their positions in two linkage maps |
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Importing a series of linkage maps from distinct genetic studies and using them to estimate (via linear programming optimization) a single consensus linkage map. |
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Quickly scanning the whole genome for evidence of QTL signals |
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Building a genotype probability SAS data set that can be used by the QTL IM, CIM and MIM Analysis process |
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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 |
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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.