Polygenic Score for the Prediction of Postoperative Nausea and Vomiting: A Retrospective Derivation and Validation Cohort Study.

BACKGROUND: Postoperative nausea and vomiting (PONV) is a key driver of unplanned admission and patient satisfaction following surgery. Because traditional risk factors do not completely explain variability in risk, we hypothesize that genetics may contribute to the overall risk for this complication. The objective of this research is to perform a genome-wide association study of PONV, derive a polygenic risk score for PONV, assess associations between the risk score and PONV in a validation cohort, and compare any genetic contributions to known clinical risks for PONV. METHODS: Surgeries with integrated genetic and perioperative data performed under general anesthesia at Michigan Medicine and Vanderbilt University Medical Center were studied. PONV was defined as nausea or emesis occurring and documented in the PACU. In the Discovery Phase, genome-wide association studies were performed on each genetic cohort and the results were meta-analyzed. Next, in the Polygenic Phase, we assessed whether a polygenic score, derived from genome-wide association study in a derivation cohort from Vanderbilt University Medical Center, improved prediction within a validation cohort from Michigan Medicine, as quantified by discrimination (C-statistic) and net reclassification index. RESULTS: Of 64,523 total patients, 5,703 developed PONV (8.8%). We identified 46 genetic variants exceeding P<1x10-5 threshold, occurring with minor allele frequency > 1%, and demonstrating concordant effects in both cohorts. Standardized polygenic score was associated with PONV in a basic model, controlling for age and sex, (aOR 1.027 per standard deviation increase in overall genetic risk, 95% CI 1.001-1.053, P=0.044), a model based on known clinical risks (aOR 1.029, 95% CI 1.003-1.055, P=0.030), and a full clinical regression, controlling for 21 demographic, surgical, and anesthetic factors, (aOR 1.029, 95% CI 1.002-1.056, P=0.033). The addition of polygenic score improved overall discrimination in models based on known clinical risk factors (c-statistic: 0.616 compared to 0.613, P=0.028) and improved net reclassification of 4.6% of cases. CONCLUSION: Standardized polygenic risk was associated with PONV in all three of our models, but the genetic influence was smaller than exerted by clinical risk factors. Specifically, a patient with a polygenic risk score > 1 standard deviation above the mean, has 2-3% greater odds of developing PONV when compared to the baseline population, which is at least an order of magnitude smaller than the increase associated with having prior PONV/motion sickness (55%), having a history of migraines (17%), or being female (83%), and is not clinically significant. Furthermore, the use of a polygenic risk score does not meaningfully improve discrimination compared to clinical risk factors and is not clinically useful.

Epigenetic drift underlies epigenetic clock signals, but displays distinct responses to lifespan interventions, development, and cellular dedifferentiation.

Changes in DNA methylation with age are observed across the tree of life. The stereotypical nature of these changes can be modeled to produce epigenetic clocks capable of predicting chronological age with unprecedented accuracy. Despite the predictive ability of epigenetic clocks and their utility as biomarkers in clinical applications, the underlying processes that produce clock signals are not fully resolved, which limits their interpretability. Here, we develop a computational approach to spatially resolve the within read variability or "disorder" in DNA methylation patterns and test if age-associated changes in DNA methylation disorder underlie signals comprising epigenetic clocks. We find that epigenetic clock loci are enriched in regions that both accumulate and lose disorder with age, suggesting a link between DNA methylation disorder and epigenetic clocks. We then develop epigenetic clocks that are based on regional disorder of DNA methylation patterns and compare their performance to other epigenetic clocks by investigating the influences of development, lifespan interventions, and cellular dedifferentiation. We identify common responses as well as critical differences between canonical epigenetic clocks and those based on regional disorder, demonstrating a fundamental decoupling of epigenetic aging processes. Collectively, we identify key linkages between epigenetic disorder and epigenetic clocks and demonstrate the multifaceted nature of epigenetic aging in which stochastic processes occurring at non-random loci produce predictable outcomes.

The rate of epigenetic drift scales with maximum lifespan across mammals.

Epigenetic drift or "disorder" increases across the mouse lifespan and is suggested to underlie epigenetic clock signals. While the role of epigenetic drift in determining maximum lifespan across species has been debated, robust tests of this hypothesis are lacking. Here, we test if epigenetic disorder at various levels of genomic resolution explains maximum lifespan across four mammal species. We show that epigenetic disorder increases with age in all species and at all levels of genomic resolution tested. The rate of disorder accumulation occurs faster in shorter lived species and corresponds to species adjusted maximum lifespan. While the density of cytosine-phosphate-guanine dinucleotides ("CpGs") is negatively associated with the rate of age-associated disorder accumulation, it does not fully explain differences across species. Our findings support the hypothesis that the rate of epigenetic drift explains maximum lifespan and provide partial support for the hypothesis that CpG density buffers against epigenetic drift.