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Gene data tool advances prospects for diabetes, personalized medicine
10-12-2009
by David Hutton  |  Email the author

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PHILADELPHIA, Pa.—Researchers have discovered a sophisticated computational algorithm that, when applied to a large set of gene markers, has achieved greater accuracy than conventional methods in assessing individual risk for type 1 diabetes.
 
A research team led by Dr. Hakon Hakonarson, director of the Center for Applied Genomics at The Children’s Hospital of Philadelphia, suggests that their technique, applied to appropriate complex multigenic diseases, improves the prospects for personalizing medicine to an individual’s genetic profile. The study appears in the Oct. 9 issue of the online journal PLoS Genetics.
 
Genome-wide association studies (GWAS), in which automated genotyping tools scan the entire human genome seeking gene variants that contribute to disease risk, have yet to fulfill their potential in allowing physicians to accurately predict a person’s individual risk for a disease, and thus guide prevention and treatment strategies.  
 
“For type 1 diabetes (TID), it means that we could identify a high-risk group of individuals who would be followed and monitored closely for earliest signs of T1D (before any islet cell destruction) and intervention would begin with anti-T cell drugs—or other immunosuppressive drugs—to prevent T1D from developing,” Hakonarson says. “We believe also that development of a new drugs that block the CLEC16A (formerly KIAA0350) signaling pathway in NK cells will become a specific future preventive therapy for T1D.” 
 
Hakonarson points out that this approach can also be applied in other disease areas, with inflammation and autoimmunity most effective.
 
“We are currently applying this algorithm on other GWAS data we have and we see marked improvement in disease prediction of other inflammatory/autoimmune disorders,” he says. “This method is likely to work well on diseases that are highly heritable.”
 
According to Hakonarson, for many diseases, the majority of contributory genes remain undiscovered, and studies that make selective use of a limited number of selected, validated gene variants yield very limited results.  
 
“For many of the recent studies, the area under the curve (AUC), a method of measuring the accuracy of risk assessment, amounts to 0.55 to 0.60, little better than chance (0.50), and thus falling short of clinical usefulness,” he says.  
 
Hakonarson’s team broadened its net, going beyond cherry-picked susceptibility genes to searching a broader collection of markers, including many that have not yet been confirmed, but which reach a statistical threshold for gene interactions or association with a disease. Although this approach embraces some false positives, its overall statistical power produces robust predictive results.  
 
By 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.  
 
Hakonarson points out that 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,” he notes.  
 
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 researchers’ 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.
 
Hakonarson says the team’s approach is more effective, and costs less, than human leukocyte antigen (HLA) testing, currently used to assess type 1 diabetes risk in clinical settings.  
 
“We would like to see this test reach the market so we can inform subjects at high risk in a better way and give them more options,” notes Harkonarson. “We will measure the impact we will have on clinical care in the future.”  
 
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, Struan Grant, Haitao Zhang, Jonathan Bradfield, Cecilia Kim, Edward Frackleton, Cuiping Hou, Joseph T. Glessner and Rosetta Chiavacci, all of the Center for Applied Genomics; Dr. Charles Stanley of the Division of Endocrinology; and Dr. Dimitri Monos of the Department of Pathology and Laboratory Medicine. Other co-authors were Constantin Polychronakos and Hui Qi Qu of McGill University in Montreal; and Zhi Wei of the New Jersey Institute of Technology.

Code: E10140903

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