Gene Data Tool Advances Prospects for Personalize...(applying a machine-learning algorithm ...),Health

Date:10/9/2009

applying a "machine-learning" algorithm that finds interactions among data points, say the authors, they were able to identify a large ensemble of genes that interact together. After applying their algorithm to a GWAS dataset for type 1 diabetes, they generated a model and then validated that model in two independent datasets. The model was highly accurate in separating type 1 diabetes cases from control subjects, achieving AUC scores in the mid-80s.

The authors say it is crucial to choose a target disease carefully. Type 1 diabetes is known to be highly heritable, with many risk-conferring genes concentrated in one region -- the major histocompatibility complex. For other complex diseases, such as psychiatric disorders, which do not have major-effect genes in concentrated locations, this approach might not be as effective.

Furthermore, the authors' risk assessment model might not be applicable to mass population-level screening, but rather could be most useful in evaluating siblings of affected patients, who already are known to have a higher risk for the specific disease. The authors say that their approach is more effective, and costs less, than human leukocyte antigen (HLA) testing, currently used to assess type 1 diabetes risk in clinical settings.

The researchers used data provided by the Wellcome Trust Case Control Consortium and the Genetics of Kidneys in Diabetes study. Hakonarson's co-authors from The Children's Hospital of Philadelphia were Kai Wang, Ph.D., Struan Grant, Ph.D., Haitao Zhang, Jonathan Bradfield, Cecilia Kim, Edward Frackleton, Cuiping Hou, Joseph T. Glessner, and Rosetta Chiavacci, all of the Center for Applied Genomics; Charles Stanley, M.D., of the Division of Endocrinology; and Dimitri Monos, Ph.D., of the Department of Pathology and Laboratory Medicine. Other co-authors were Constantin Polychronakos, M.D., and Hui Qi Qu, of McGill University, Montreal; and Zhi Wei, of the New Jersey Institute of Techn
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SOURCE The Children's Hospital of Philadelphia Copyright©2009 PR Newswire. All rights reserved

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