FASTER SEQUENTIAL GENETIC-LINKAGE COMPUTATIONS

Linkage analysis using maximum-likelihood estimation is a powerful too l for locating genes. As available data sets have grown, the computati on required for analysis has grown exponentially and become a signific ant impediment. Others have previously shown that parallel computation is applicable to linkage analysis and can yield order-of-magnitude im provements in speed. In this paper, we demonstrate that algorithmic mo difications can also yield order-of-magnitude improvements, and someti mes much more. Using the software package LINKAGE, we describe a varie ty of algorithmic improvements that we have implemented, demonstrating both how these techniques are applied and their power. Experiments sh ow that these improvements speed up the programs by an order of magnit ude, on problems of moderate and large size. All improvements were mad e only in the combinatorial part of the code, without resorting to par allel computers. These improvements synthesize biological principles w ith computer science techniques, to effectively restructure the time-c onsuming computations in genetic linkage analysis.