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Pociot F, Karlsen AE, Pedersen CB, et al. Novel analytical methods applied to type 1 diabetes genome-scan data. Am J Hum Genet. 2004;74(4):647-60doi: 10.1086/383095.
Pociot, F., Karlsen, A. E., Pedersen, C. B., Aalund, M., Nerup, J. (2004). Novel analytical methods applied to type 1 diabetes genome-scan data. American journal of human genetics, 74(4), 647-60. https://doi.org/10.1086/383095
Pociot, Flemming, et al. "Novel analytical methods applied to type 1 diabetes genome-scan data." American journal of human genetics vol. 74,4 (2004): 647-60. doi: https://doi.org/10.1086/383095
Pociot F, Karlsen AE, Pedersen CB, Aalund M, Nerup J. 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. PMID: 15024687; PMCID: PMC1181942.
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Am J Hum Genet. 2004 Apr;74(4):647-60. doi: 10.1086/383095. Epub 2004 Mar 11.

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

American journal of human genetics

Flemming Pociot, Allan E Karlsen, Claus B Pedersen, Mogens Aalund, Jørn Nerup,

Affiliations

  1. Steno Diabetes Center, Gentofte, Denmark. [email protected]

PMID: 15024687 PMCID: PMC1181942 DOI: 10.1086/383095

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.

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