Novel analytical methods applied to type 1 diabetes genome-scan data.
Am J Hum Genet
; 74(4): 647-60, 2004 Apr.
Article
em En
| MEDLINE
| ID: mdl-15024687
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Árvores de Decisões
/
Genoma Humano
/
Redes Neurais de Computação
/
Predisposição Genética para Doença
/
Diabetes Mellitus Tipo 1
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Am J Hum Genet
Ano de publicação:
2004
Tipo de documento:
Article