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A genetic variant might be associated with opioid addiction. But it can’t yet predict outcomes for individual patients.
A new genome-wide association study has identified a genetic variation that appears to be linked to opioid dependency. The report highlights the powerful new frontier of genetic research, but it also shows how big data remains limited in predicting outcomes for individual patients.
Researchers from Yale University, Boston University, and the University of Pennsylvania completed a genome-wide association study (GWAS) of 3058 European-American patients who had been exposed to opioids. Of those, 1290 met the DSM-IV’s criteria for opioid dependency.
Zhongshan Cheng, PhD, a postdoctoral associate at Yale University and the study’s first author, said there was already evidence suggesting a genetic factor in opioid dependence, though few associations been reported in the European-American population. He and his colleagues designed their analysis to factor in severity of dependence, not just a binary diagnosis.
“By using the criterion counts of opioid dependence, we have more power to detect genetic factors associated with opioid dependence severity,” he told Healthcare Analytics News™.
The analysis suggested that a variant near the gene RGMA was associated with a greater likelihood of opioid dependence. That same variant has also been implicated in other mental disorders, such as schizophrenia and Alzheimer’s disease.
The researchers used mouse models to gauge the effect of RGMA in mice treated with morphine. That experimentation suggested that opioid dependence might be reducible if researchers block RGMa, the protein product of RGMA. However, Cheng said more research, in both mice and humans, is needed to confirm that hypothesis.
The study highlights the relatively new technology of GWAS, which dates to the mid-aughts. The approach leverages massive amounts of data to perform a comprehensive analysis of the human genome. Cheng said there are a lot of advantages to using GWAS to uncover genetic associations.
“[GWAS] is an unbiased method that doesn’t require biological hypothesis to search for genetic factors associated with binary phenotypes or quantitative phenotypes,” he said. “The replication of the association in independent cohorts is the key. If we find a variant that is validated among different GWASs, it indicates the marker is a real signal.”
If GWAS provides a kind of data-enabled conclusivity, it still has its limits, at least for now. Co-author Lindsay Farrar, PhD, a professor and chief of biomedical genetics at Boston University, said GWAS can’t yet act as a means of evaluating risk in individual patients.
“Our finding, like most genetic association findings, is predictive at the population level but not on an individual level,” he said. “Even if the findings were highly predictive at the individual level, genetically driven clinical trials would need to be performed first to establish the predictive value of such a test.​”
But while Farrar said the studies aren’t predictive enough to be used in a clinical setting, they can provide valuable insights to physicians.
“That said, I do believe that in the not-so-distant future, it will be common practice for treatment decisions to be guided by one's genotype,” he said. “It is happening now for certain diseases, especially many types of cancer.”
The study, titled “Genome-wide Association Study Identifies a Regulatory Variant of RGMA Associated With Opioid Dependence in European Americans,” was published this month in Biological Psychiatry.