Gene Data Tool Advances Prospects for Personalized Medicine

--Applied to type 1 diabetes, widens net while increasing accuracy of
individual risk assessments--

PHILADELPHIA, Oct. 9 /PRNewswire-USNewswire/ -- A sophisticated computational
algorithm, applied to a large set of genetic markers, has achieved greater
accuracy than conventional methods in assessing individual risk for type 1
diabetes.

A research team led by Hakon Hakonarson, M.D., Ph.D., 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 October 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.

The authors say that 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.

Hakonarson's team broadened their 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.

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 Technology.

About The Children's Hospital of Philadelphia: The Children's Hospital of
Philadelphia was founded in 1855 as the nation's first pediatric hospital.
Through its long-standing commitment to providing exceptional patient care,
training new generations of pediatric healthcare professionals and pioneering
major research initiatives, Children's Hospital has fostered many discoveries
that have benefited children worldwide. Its pediatric research program is
among the largest in the country, ranking second in National Institutes of
Health funding. In addition, its unique family-centered care and public
service programs have brought the 430-bed hospital recognition as a leading
advocate for children and adolescents. For more information, visit
http://www.chop.edu.

CONTACT:
John Ascenzi
The Children's Hospital of Philadelphia
267-426-6055
Ascenzi@email.chop.edu

SOURCE The Children's Hospital of Philadelphia

John Ascenzi of The Children's Hospital of Philadelphia, +1-267-426-6055,
Ascenzi@email.chop.edu