Simulated Populations Used To Probe Gene Mapping For Studying Complex Diseases

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Simulated Populations Used To Probe Gene Mapping For Studying

Complex Diseases

http://www.medicalnewstoday.com/medicalnews.php?newsid=65993

More powerful computers are allowing scientists and engineers to

conduct simulations that grow more realistic each year. While

companies are using these tools to slash the costs of producing

everything from airliners to antibiotics, researchers in Houston are

using them to refine their search for the genetic causes of disease.

In a new study published today in the journal PLoS Genetics,

statisticians and genetic epidemiologists from Rice University and

The University of Texas M. D. Cancer Center used computer

simulations to trace genetic changes over thousands of generations

in a simulated population of hundreds of thousands of people. The

goal: find out whether the tools that statistical geneticists use to

pinpoint disease genes are up to the task of identifying multiple

genes that cause complex diseases like cancer.

" In a real population, you never have the complete genetic picture,

particularly for complex diseases where more than one gene is

implicated and where environmental factors play a role, " said lead

author Bo Peng of M. D. . " If we only see the people who get

sick, we can never be sure how many people with the disease variant

of the gene avoided getting sick. And there's always the question

about how many people got the disease even though they didn't carry

the variant. "

In order to simulate the evolution of complex human diseases, Peng

developed a computer program called simuPOP that generates genetic

profiles for large multi-generation populations. The program, which

Peng developed during his doctoral studies at Rice, allows

researchers to sample individuals from a simulated population and

test whether statistical methods are up to the task of accurately

identifying genes that interact to cause complex diseases.

" Though they have much in common, the disciplines of statistical

genetics, population genetics, molecular genetics and genetic

epidemiology have traditionally used their own tools and

techniques, " said co-author Marek Kimmel, professor in the

Statistics Department at Rice. " simuPOP is one of the first examples

of a new paradigm where the tools of the various disciplines are

being used in concert to create a clearer picture of genetic health

effects. "

" Complex diseases like hypertension and cancer are usually caused by

multiple disease-susceptibility genes, environmental factors and

interactions between environmental and genetic factors, " said co-

author Amos, professor of epidemiology at M. D.

. " In the current study, we show that our method of

simulating populations as they move forward in time, over multiple

generations, is a practical and useful approach for simulating

complex diseases. "

Peng said the latest findings are preliminary but they confirm that

known statistical genetic methods are limited in their ability to

accurately identify the genetic interactions implicated in complex

diseases. Peng said the findings are useful because they identify

which methods work best with particular types of populations. He

said simuPOP could be useful in developing and testing new methods

for gene mapping, and he's already gotten interest from more than 20

research groups that are interested in using the program.

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Peng developed this research when he was a doctoral student of

Kimmel at Rice. His doctoral committee also included Amos

and Francois Balloux, who are both on the faculty at Cambridge

University in the United Kingdom and who were brought to Houston

using support from the Texas-UK Collaborative Research Initiative.

The research was also supported by the National Institutes of

Health, the Keck Center for Computational and Structural Biology,

the National Cancer Institute, the Polish Committee for Scientific

Research, the National Science Foundation, Intel and HP.