Using quality measures to facilitate allele calling in high-throughput genotyping

Currently, the main limitation in high-throughput microsatellite genotyping is the required manual editing of allele calls. Even though programs for automated allele calling have been available for several years, they have limited capability because accurate data could only be assured by manual inspection of the electropherograms for confirmation. Here we describe the development of a parametric approach to allele call quality control that eliminates much of the time required for manual editing of the data. This approach was implemented in an editing tool, Decode-GT, that works downstream of the allele calling program, TrueAllele (TA). Decode-GT reads the output data from TA, displays the underlying electropherograms for the genotypes, and sorts the allele calls into three categories: good, bad, and ambiguous. It discards the bad calls, accepts the good calls, and suggests that the user inspect the ambiguous calls, thereby reducing dependence on manual editing. For the categorization we use the following parameters: (1) the quality value for each allele call from TrueAllele; (2) the peak height of the alleles; and (3) the size of the peak shift needed to move peaks into the nearest bin. Here we report how we optimized the parameters such that the size of the ambiguous category was minimized, and both the number of miscalled genotypes in the good category and the useable genotypes in the bad category were negligible. This approach reduces the manual editing time and results in <1% miscalls.