期刊
AMERICAN JOURNAL OF HUMAN GENETICS
卷 74, 期 4, 页码 647-660出版社
CELL PRESS
DOI: 10.1086/383095
关键词
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Complex traits like type 1 diabetes mellitus (T1DM) are generally taken to be under the influence of multiple genes interacting with each other to confer disease susceptibility and/or protection. Although novel methods are being developed, analyses of whole-genome scans are most often performed with multipoint methods that work under the assumption that multiple trait loci are unrelated to each other; that is, most models specify the effect of only one locus at a time. We have applied a novel approach, which includes decision-tree construction and artificial neural networks, to the analysis of T1DM genome-scan data. We demonstrate that this approach ( 1) allows identification of all major susceptibility loci identified by nonparametric linkage analysis, ( 2) identifies a number of novel regions as well as combinations of markers with predictive value for T1DM, and ( 3) may be useful in characterizing markers in linkage disequilibrium with protective-gene variants. Furthermore, the approach outlined here permits combined analyses of genetic-marker data and information on environmental and clinical covariates.
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