Use of Variable Marker Density, Principal Components, and Neural Networks in the Dissection of Disease Etiology

• Published in: Genetic epidemiology 2001, Vol.21 (S1), p.S732-S737
• Main Authors: Pankratz, Nathan, Kirkwood, Sandra C., Flury, Leah, Koller, Daniel L., Foroud, Tatiana
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Chromosome Mapping - statistics & numerical data; Genetic Markers - genetics; Genetic Predisposition to Disease - genetics; Genetic Testing; genetics; Humans; Lod Score; machine learning; Models, Genetic; multipoint linkage; Neural Networks (Computer); Phenotype; Principal Component Analysis; Quantitative Trait, Heritable; Risk Factors
• ISSN: 0741-0395; 1098-2272
• DOI: 10.1002/gepi.2001.21.s1.s732

Several approaches were taken to identify the loci contributing to the quantitative and qualitative phenotypes in the Genetic Analysis Workshop 12 simulated data set. To identify possible quantitative trait loci (QTL), the quantitative traits were analyzed using SOLAR. The four replicates identified as the “best replicates” by the simulators, 42, 25, 33, and 38, were analyzed separately. Each of the five quantitative phenotypes was analyzed individually in the four replicates. To increase the power to detect QTL with pleiotropic effects, principal component analysis was performed and one new multivariate phenotype was estimated. In each instance, after performing a 10‐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 much higher marker density successfully identified all the QTL playing a role 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 status. The neural network technique identified the strongest gene influencing disease status as well as a locus contributing to quantitative traits 3 and 4; however, the inputs that contributed the greatest information were markers not in QTL regions. © 2001 Wiley‐Liss, Inc.