Predicting Weight Loss After Vertical Sleeve Gastrectomy Using a Whole-genome Sequencing–derived Polygenic Risk Score in the All of Us Cohort

Objective: To create a genome-wide polygenic risk score (PRS) to improve prediction of a 12-month percent weight-loss (WL) following vertical sleeve gastrectomy (VSG). Background: Variability in post VSG WL is not well explained by clinical factors. The All of Us program provides access to a 414,830 short-read whole-genome sequencing (srWGS) resource enabling unbiased discovery of genetic predictors following VSG. Methods: VSG counts, demographic, anthropomorphic and vital sign information were obtained from the linked electronic health record (EHR). The discovery cohort (DC) included participants from version 7 carried into version 8 while the validation cohort (VC) included those newly added to v8. We defined good responders and non-responders as having WL±1SD from the mean. Following quality filtering we applied a two-stage penalized-regression followed by elastic-net logistic regression to identify 1,583 stable variants and derive β-weights. We then tested this PRS on the DC into a prediction model. Results: We identified 395 participants in the DC and 336 participants in the VC, respectively. Of these VSG, 44 were classified as good responders (≥37% WL) and 55 as non-responders (≤19% WL). In the VC, 55 were classified as good responders and 48 as non-responders. Adding the PRS to models to clinical predictors increased the AUC following logistic regression by 0.03; P <4.3×10⁻¹⁴, random forest by 0.03; P <9.1×10⁻⁷, decision tree by 0.05; P =1.2×10⁻³, and gradient boosting by 0.08; P <8.3×10⁻¹⁰. Conclusion: Use of srWGS from AoU can be effectively used to generate PRS to enhance predictive WL accuracy. This work has implications for outcomes of both bariatric surgery and other surgical procedures.