Use of variable marker density, principal components, and neural networks in the dissection of disease etiology

Several approaches were taken to identify the loci contributing to the quan titative and qualitative phenotypes in the Genetic Analysis Workshop 12 sim ulated data set. To identify possible quantitative trait loci (QTL), the qu antitative traits were analyzed using SOLAR. The four replicates identified as the "best replicates" by the simulators, 42, 25, 33, and 38, were analy zed separately. Each of the five quantitative phenotypes was analyzed indiv idually in the four replicates. To increase the power to detect QTL with pl eiotropic effects, principal component analysis was performed and one new m ultivariate phenotype was estimated. In each instance, after performing a 1 0-cM genome screen, fine mapping was completed in the initially identified linked regions to further evaluate the evidence for linkage. This approach of initially performing a coarse marker screen followed by analyses using m uch higher marker density successfully identified all the QTL playing a rol e in the quantitative phenotypes. The principal component phenotype did not substantially improve the power of QTL detection or localization. A neural network approach was utilized to identify loci contributing to disease sta tus. The neural network technique identified the strongest gene influencing disease status as well as a locus contributing to quantitative traits 3 an d 4; however, the inputs that contributed the greatest information were mar kers not in QTL regions. (C) 2001 Wiley-Liss, Inc.