Connecticut: When it comes to diagnosing substance abuse, health care professionals are mainly limited to relying on patient honesty. But a new study aims to find the genetic causes of addiction, which could lead to a more nuanced way of treating complex medical and social problems. The study, led by Jinbo Bi, associate professor of computer science and engineering, recently received a $1.12 million grant from the National Institutes of Health (NIH). Bi and her fellow researchers aim to develop new statistical tools and techniques to better classify the many variations of substance dependence. The hope is that a better understanding of the role genes play in these disorders will lead to more effective treatment.
Researchers have made great progress in using genetic information to
diagnose and treat other diseases in patients. Genome sequencing, for
instance, is leading to customized treatment for certain cancers.
Substance addiction, with its multiple causes, has been a tougher nut
to crack for genetics researchers. Currently doctors rely on the
symptoms listed in the Diagnostic and Statistical Manual (DSM) of Mental
Disorders. The current edition provides several criteria, based mostly
on behavior. Examples include whether a patient worries about stopping,
or spends a lot of time trying to obtain drugs or alcohol.
While previous studies have looked at the genes associated with the
diagnoses of substance abuse, Bi’s study will take a more specific
approach by looking at the genes associated with the clinical symptoms
that lead to abuse.
For instance, two people diagnosed with alcohol dependence can have
different symptoms. Perhaps only one has trouble sleeping, or one cites a
diminished social life while the other goes to so many parties that
it’s affecting work. Bi says the study will look at whether genetic
variances can account for these differences.
To integrate multiple clinical symptoms with multiple genetic
variants, however, requires more sophisticated algorithms than what
researchers have now. Developing new ones is the first task in the
The study will make use of a database of more than 11,000 subjects
who were identically assessed in genetic studies of cocaine, opioid, and
alcohol dependence, the largest sample of its kind.
With such a robust sample, the researchers expect to put a much finer
point on diagnosing different addictions. For instance, the criteria
provided in the DSM doesn’t discern between different types of cocaine
dependence. Preliminary studies by Bi’s team, though, show that a
variant in what’s known as the CLOCK gene could be the difference
between addicts who inject the drug and those who consume it in other
ways. And the more that is known about which genes are associated with
specific subtypes of drug dependence, the closer it brings researchers
to developing more effective treatments. The fact that the CLOCK gene
regulates our circadian rhythms, for instance, could be significant.
“Maybe then we can design something to control the circadian rhythm,”
Bi says. “If we know a specific property of a particular sub-population
of the patients, then we can design something to target it.”
Bi’s collaborators include Victor Hesselbrock of UConn Health, Henry
R. Kranzler of the University of Pennsylvania, and Joel Gelernter of