Unravelling gene interactions to find the cause of artherosclerosis, a multigenic disease, using an artificial neural network

Dassen, W.R.M.; Spiering, W.; Leeuw, PW de; Smits, Paulien; Dijk, W.A.; Spruijt, H.; Gommer, E.; Bonnemayer, C.; Doevendans, Pieter A.

doi:10.1109/cic.2001.977670

Cited by 2 publications

(11 citation statements)

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“…These questions have received increasing attention recently (see, for example, Valdes and Thomson 1997;Cox et al 1999;Dassen et al 2001;Cordell and Clayton 2002;Bugawan et al 2003). Cordell and Clayton (2002) proposed a simple but powerful stepwise logistic-regression procedure that allows for testing the dominance effects of different combinations of polymorphisms, as well as genotype interactions in the analysis of case-control data.…”

Section: Introductionmentioning

confidence: 99%

“…As an illustration of our method, we present in this article a reanalysis of a set of genotypes that was obtained from a cohort of 89 Dutch patients with type 1 diabetes and 47 healthy control individuals, with a 65polymorphism detection assay originally designed for unraveling the multigenic cause of atherosclerosis (Dassen et al 2001). Since both diabetes mellitus and atherosclerosis can be regarded as metabolic diseases with many overlapping biochemical and clinical parameters, the variants that are susceptible to atherosclerosis may also be the cause of type 1 diabetes.…”

Section: Introductionmentioning

confidence: 99%

“…Since both diabetes mellitus and atherosclerosis can be regarded as metabolic diseases with many overlapping biochemical and clinical parameters, the variants that are susceptible to atherosclerosis may also be the cause of type 1 diabetes. Dassen et al (2001) examined whether certain types of combinations of SNPs confer susceptibility to type 1 diabetes in the cohort by logistic regression and self-learning neural networks. They found that a set of four polymorphisms could predict 79.9% of the cases correctly.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, a total of 25 potential diseasepredisposing interactions between loci are predicted, which indicates 19 gene-gene interactions among 19 candidate genes. Having found four dominant variants (Dassen et al 2001), we predicted, from the interaction network, 19 transitive variants. Our results clearly demonstrate that, by considering interactions between haplotype blocks, we may succeed in identifying diseasepredisposing genetic variants that might otherwise have remained undetected.…”

Section: Introductionmentioning

confidence: 99%

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Search for Haplotype Interactions That Influence Susceptibility to Type 1 Diabetes, through Use of Unphased Genotype Data

Zhang

Liang

Dassen

et al. 2003

The American Journal of Human Genetics

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Type 1 diabetes is a T-cell-mediated chronic disease characterized by the autoimmune destruction of pancreatic insulin-producing beta cells and complete insulin deficiency. It is the result of a complex interrelation of genetic and environmental factors, most of which have yet to be identified. Simultaneous identification of these genetic factors, through use of unphased genotype data, has received increasing attention in the past few years. Several approaches have been described, such as the modified transmission/disequilibrium test procedure, the conditional extended transmission/disequilibrium test, and the stepwise logistic-regression procedure. These approaches are limited either by being restricted to family data or by ignoring so-called "haplotype interactions" between alleles. To overcome this limit, the present study provides a general method to identify, on the basis of unphased genotype data, the haplotype blocks that interact to define the risk for a complex disease. The principle underpinning the proposal is minimal entropy. The performance of our procedure is illustrated for both simulated and real data. In particular, for a set of Dutch type 1 diabetes data, our procedure suggests some novel evidence of the interactions between and within haplotype blocks that are across chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 15, 16, 17, 19, and 21. The results demonstrate that, by considering interactions between potential disease haplotype blocks, we may succeed in identifying disease-predisposing genetic variants that might otherwise have remained undetected.

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