The complexity of linkage analysis with neural networks

As the focus of genome-wide scans for disease loci have shifted from simple Mendelian traits to genetically complex traits, researchers have begun to consider new alternative ways to detect linkage that will consider more tha n the marginal effects of a single disease locus at a time, One interesting new method is to train a neural network on a genome-wide data set in order to search for the best non-linear relationship between identity-by-descent sharing among affected siblings at markers and their disease status. We in vestigate here the repeatability of the neural network results from run to run, and show that the results obtained by multiple runs of the neural netw ork method may differ quite a bit, This is most likely due to the fact that training a neural network involves minimizing an error function with a mul titude of local minima. Copyright (C) 2001 S. Karger AG, Basel.