DeepEXPOKE: A Deep Learning Framework with Polygenic Risk Scores as Knockoffs for Deconvoluting Genetic and Non-Genetic Exposure Risks in Sepsis and Coronary Heart Disease

ABSTRACT The exposome refers to the totality of environmental, behavioral, and lifestyle exposures an individual experiences throughout one’s lifetime. Due to the modifiability of exposures, identifying the risk exposures on a disease is crucial for effective intervention and prevention of the disease. However, traditional analytical methods struggle to capture the complexities of exposome data: nonlinear effects, correlated exposures, and potential interplay with genetic effects. To address these challenges and accurately estimate exposure effects on complex diseases, we developed DeepEXPOKE, a deep learning framework integrating two types of knockoff features: statistical knockoffs (statKO) and polygenic risk score as knockoffs (PRSKO). DeepEXPOKE-statKO controls exposure correlation and DeepEXPOKE-PRSKO isolates genetic effects, while both can capture nonlinear effects. We applied DeepEXPOKE to predict outcomes of two significant diseases with distinct etiology and clinical presentation: sepsis and coronary heart disease (CHD), demonstrating its performance in comparison to existing machine learning methods. Furthermore, both DeepEXPOKE-PRSKO and DeepEXPOKE-statKO identified metabolites such as glucose and triglycerides as risk factors for sepsis and suggested that their effects are primarily at the non-genetic level, consistent with the role of metabolites in responding to environmental factors. Additionally, DeepEXPOKE-PRSKO uniquely identified asthma as a sepsis risk factor and suggested its effect is partially at the genetic level, offering insights into the conflicting associations observed between the genome data studies and patient data analysis regarding asthma and sepsis risk. Overall, DeepEXPOKE offers a novel DNN approach for identifying and interpreting exposure risk factors, advancing our understanding of complex diseases. Competing Interest Statement The authors have declared no competing interest. Funding Statement Funding is through Dr. HJ Park's and Dr. Joseph A. Carcillo's grant and the University of Pittsburgh. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: UK Biobank data use (Project Application Number 83829) was approved by the UK Biobank according to their established access procedures. UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB), and as such researchers using UK Biobank data do not require separate ethical clearance and can operate under the RTB approval. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are from the UK Biobank. Polygenic risk score scoring files can be found online at: https://www.pgscatalog.org/