Novel analytical methods applied to type 1 diabetes genome-scan data

Am J Hum Genet. 2004 Apr;74(4):647-60. doi: 10.1086/383095. Epub 2004 Mar 11.


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.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Decision Trees*
  • Diabetes Mellitus, Type 1 / genetics*
  • Genetic Markers / genetics
  • Genetic Predisposition to Disease / genetics*
  • Genome, Human*
  • Humans
  • Linkage Disequilibrium
  • Models, Genetic
  • Neural Networks, Computer*


  • Genetic Markers

Associated data

  • OMIM/222100