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Objective 2.3 CWS MOSEL

Molecular Selection Tool (MoSel)

The Molecular Selection Tool is required to facilitate the selection of the most promising lines in terms of closeness to the target genotype and likelihood of reaching the target according to a proposed development strategy. Main functions include:

  • Quality assurance
    • Verification that genotypes of test lines are compatible with parental genotypes.
  • Display genotype and phenotype information
    • Display graphical genotypes of targets, parents and test lines so that test lines can be compared with parents and targets.
    • Graphical genotypes of test lines can be displayed in terms of segment origin or distance from a target genotype. For the classical diploid MAB situation segment origin is useful:

                                       M1           M2                                   M3           M4
                           AAAAAAAAAAHHHHHHHHHHHHHHHHHHHHHHHHHHHHBBBB

                           M1 - last homozygous recurrent marker in a segment
                           M2 - first heterozygous marker in a segment
                           M3 - last heterozygous marker in a segment
                           M4 - first homozygous donor marker (not possible in MAB without a selfing generation)
                           A - color 1 indicating homozygous recurrent
                           H - color 2 indicating heterozygous regions
                           B - color 3 indicating homozygous donor

                Markers should be spaced according to map distance if possible
                Colors should merge between markers of different origins to indicate unknown crossover points
                A fourth color is necessary to display segments of unknown origin

A display based on genetic distances to target genotypes will be useful in extending the tool to polyploid species and to more complex breeding strategies such as marker assisted recurrent selection schemes where there may be numerous donors and recipients and target genotypes are complex mixtures. Schemes for computing genetic distances can incorporate probabilities of origin by descent from individual founders.

    • Allow partitioned display of foreground and background markers. Problems with the generalized approach include how, or whether, to distinguish foreground and background markers. How to indicate relative importance of different segments (perhaps importance can be coded and displayed on target genotypes by line thickness or color intensity).
  • Order, group and filter test genotypes
    • Compute ranking scores of test genotypes using weighted averages of distances to target genotypes. A problem is to select weights for different loci. Critical loci to be retained or introgressed should have high weights while background loci should have lower weights. One possibility is to compute separate distances for background and foreground markers then filter test lines on an acceptable threshold of foreground proximity and rank remaining lines on distance to background markers.
    • One could compute pairwise distances between lines (including test lines, target genotypes and parental lines) based on genetic and phenotypic values and show ordination plots or dendrograms.
    • Filter, sort and scroll genotype displays for test lines in proximity to target and parental lines.
  • Choose lines and crossing schemes for further development
    • If the filtered set of test lines is relatively small these can be inspected individually to select a few for backcrossing, intercrossing or selfing. Many factors are used in the final choice including any available phenotypic data, as well as the number and specificity of crossovers required to approach the target genotype. It will be important to capture these decision rules from users and build them into the analytical tools.
    • Users could mark regions requiring crossover to approach the target and select a crossing scheme then the tool could compute the (map based) probability of observing the required crossover events.
    • Best lines are selected and sent to the Molecular LIMS as crossing lists for the next cycle.
    • Markers fixed in recipient populations should also be identified so that they do not need to be processed for subsequent generations.
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