Novel analytical methods applied to type 1 diabetes genome-scan data.
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.
Pociot
Flemming
F
Steno Diabetes Center, Gentofte, Denmark. fpoc@steno.dk
Karlsen
Allan E
AE
Pedersen
Claus B
CB
Aalund
Mogens
M
Nerup
Jørn
J
D003663:Decision Trees; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D015894:Genome, Human; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D016571:Neural Networks, Computer
D005819:Genetic Markers