Using the GAW11 Problem 2 data set, we compared the performance of two automated map construction algorithms, MultiMap and GMS (Gene Mapping System). The MultiMap algorithm iteratively adds markers ...in a stepwise manner to the map, while the GMS algorithm seeks to find the best order of the whole set of markers by selective permutations of logically formed subgroups of the markers. While it is difficult to compare these two rather different algorithms, we found that, on these data, GMS performed better than MultiMap, placing more markers in their true order on average, with little order ambiguity. In addition, as the number of markers increased, GMS was less computationally demanding than MultiMap. However, if MultiMap placed a marker, it was almost always in the correct order. In contrast, GMS often placed a group of markers on the wrong end of the map; such incorrect placements occur when the evidence for placement on one end or the other is not strong. Thus, there is room for further algorithmic developments that combine the strengths of both the MultiMap and GMS approaches.
We have developed a version of the CRI-MAP computer program for genetic likelihood computations that runs the FLIPS and ALL functions of CRI-MAP in parallel on a distributed network of workstations. ...The performance of CRI-MAP-PVM was assessed in several linkage analyses using the FLIPS option of CRI-MAP on a map of 85 microsatellite markers for human chromosome 1. These analyses showed excellent speedup and efficiency and low distribution overhead. In addition, we have adapted the MultiMap program for automated construction of linkage maps to use CRI-MAP-PVM. These improvements significantly reduce the time required to compare likelihoods of different marker orders. Thus, the construction of linkage maps can proceed in a more timely fashion, in keeping with recent advances in genotyping technology.
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