ArticleAmerican journal of human genetics2003
Search for haplotype interactions that influence susceptibility to type 1 diabetes, through use of unphased genotype data.
Article in American journal of human genetics, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Article
- Multilocus association testing with penalized regression.Genetic epidemiology · 2011Article
- Detecting genetic interactions for quantitative traits with U-statistics.Genetic epidemiology · 2011Article
- Mapping haplotype-haplotype interactions with adaptive LASSO.BMC genetics · 2010Article
- Effects of interacting networks of cardiovascular risk genes on the risk of type 2 diabetes mellitus (the CODAM study).BMC medical genetics · 2008Article
Corrections and comments
- Erratum issuedVeldman, Bart A J [added]
Authors and funding
6 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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