Aubrey Annis is a fourth-year PhD student in the Center for Statistical Genetics in the Department of Biostatistics. Annis studies genetic contributors to persistent opioid usage, and at the 2022 Annual Meeting of the American Society for Human Genetics, she presented a poster on research completed with Vidhya Gunaseelan, Albert Smith, Daniel B Larach, Matthew Zawistowski, Chad Brummett, and Gonçalo Abecasis (Annis’s advisor).

The poster, “New perspectives in the genetics of persistent opioid use: reexamining candidate gene studies and presenting new results from the Michigan Genomics Initiative,” reviews 69 studies in peer-reviewed publications that suggest genetic contributors to opioid-use phenotypes. Annis and her colleagues attempted to replicate these previous findings using Michigan Genomics Initiative (MGI) data. Many of the genetic associations, from small-sample candidate gene studies for opioid use, could not be replicated.

“I think it’s surprising that so few [genetic associations] have replicated,” Annis said of reexamining earlier research, but she acknowledged that “most of it has been a practicality issue with studies in the past. We know opioid dependence is a problem, but there are not large cohorts of people out there with the data that we can study.” A population with existing opioid use data is necessary to examine the genetics of an opioid-related phenotype, which often meant populations in rehab for opioid use disorder or opioid addiction, “and those populations tend to be fairly small,” said Annis. “A lot of these studies have extremely small sample sizes, from tens to hundreds, and are targeting a specific gene or a few specific variants in the genome…. When you only have a few hundred people, you have to choose what you’re going to look at,” said Annis. Researchers identified “a lot of different loci that they posited are genetic contributors to opioid phenotypes. Our concern with this is that we may be ending up with a lot of associations that are actually false associations, and not necessarily genetic contributors to opioid dependence or any other opioid phenotype,” she said.

“Now that this has become more nationally and globally recognized as a serious health crisis, the scientific community is starting to collect more data on a large number of people to be able to expand into whole-genome-wide association studies,” said Annis. “Previous studies have been trying to attack this problem in any way that they can,” said Annis, but she notes that many of these early, small studies may be the “growing pains” of a field that’s just beginning to get the recognition it merits.

“We have one of the largest datasets out there that’s doing opioid studies, so if there are significant genetic contributors to opioid dependence, we would be expecting to replicate those signals in our own association studies. And we’re not seeing a lot of them, so this is confirming to us that we might be having a problem with false associations in these other studies,” Annis explained. “Conversely, we did find a lot of signals in the OPRM1 gene, which is well recorded as an opioid contributor.”

In addition to examining data from a much-larger cohort, applying a new opioid-related phenotype is another hallmark of this research. “We defined a new phenotype, which is ‘persistent opioid usage,’ and that differs from a lot of other phenotypes,” and they are studying the phenotype “in a medically approved setting,” Annis said.  “There’s been a lot of research done on genetic contributors to opioid addiction, but recently there’s been a move toward looking at not just opioid addiction, but opioid dependence, or proclivity to opioid dependence or long-term opioid use, specifically in regard to prescription data,” she said. For this study, researchers drew on prescription data in the MGI cohort to see how the prescribing of opioids is contributing to long-term opioid usage. Incorporating prescription data, which are difficult to access, presents “a unique opportunity” to the researchers. Not very many studies have prescription data, and if they do use them, the data are usually self-reported usage, Annis said.

“Persistent opioid usage” in MGI is when a patient has been prescribed opioid medication for a surgical event, and is using it as prescribed, but for a long period of time. It’s defined as people who fill an opioid prescription in the period right around their surgery—30 days before to a few days after—and then refill the prescription at a later date—for example, a couple months after the surgery. “What we’re focusing on is trying to isolate those genetic predictors that might give someone a proclivity for addiction,” she said. “This is a really good phenotype to see that.”

Another advantage of applying the persistent opioid usage phenotype to the MGI population is that “many of our participants have had no opioid exposure prior to the surgical event, so we’re looking at a blank slate,” said Annis, and “it’s pretty environmentally controlled.”

The persistent opioid usage phenotype aids in identifying those who will not necessarily develop an opioid misuse, but who have a greater predisposition to continued usage or misuse in the future. After identifying these individuals, doctors can take appropriate measures to prevent future opioid misuse, such as prescribing alternate pain medications, having greater clinician oversight, or informing patients of this proclivity so they can be more aware and take appropriate care of their health.

“We’re hoping to publish this research in a top medical journal,” said Annis. “I’m excited about the possibility of a medical journal because it’s connected to the work that doctors do, rather than an academic journal. We really want this to be in a journal where doctors are going to see these results and become more aware of the genetic proclivities toward opioid usage of any sort.”