Using Genome Sequence Data to Predict SARS-CoV-2 Detection Cycle Threshold Values

The continuing emergence of SARS-CoV-2 variants of concern (VOCs) presents a serious public health threat, exacerbating the effects of the COVID19 pandemic. Although millions of genomes have been deposited in public archives since the start of the pandemic, predicting SARS-CoV-2 clinical characteristics from the genome sequence remains challenging. In this study, we used a collection of over 29,000 high quality SARS-CoV-2 genomes to build machine learning models for predicting clinical detection cycle threshold (Ct) values, which correspond with viral load. After evaluating several machine learning methods and parameters, our best model was a random forest regressor that used 10-mer oligonucleotides as features and achieved an R2 score of 0.521 ± 0.010 (95% confidence interval over 5 folds) and an RMSE of 5.7 ± 0.034, demonstrating the ability of the models to detect the presence of a signal in the genomic data. In an attempt to predict Ct values for newly emerging variants, we predicted Ct values for Omicron variants using models trained on previous variants. We found that approximately 5% of the data in the model needed to be from the new variant in order to learn its Ct values. Finally, to understand how the model is working, we evaluated the top features and found that the model is using a multitude of k-mers from across the genome to make the predictions. However, when we looked at the top k-mers that occurred most frequently across the set of genomes, we observed a clustering of k-mers that span spike protein regions corresponding with key variations that are hallmarks of the VOCs including G339, K417, L452, N501, and P681, indicating that these sites are informative in the model and may impact the Ct values that are observed in clinical samples. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement LD was funded by the Northwestern-Argonne Institute of Science and Engineering (NAISE) Summer Research Experience Program supported by Northwestern University's Office for Research. JJD and MN were supported by the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services [75N93019C00076 to PI Rick Stevens]. This work was also supported by Discovery Partners Institute award [PRJ1009544] to JD. PC, SWL, RJO, and JMM were supported by the Houston Methodist Academic Institute Infectious Diseases Fund. ### 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: All samples were collected with patient consent and Institutional Review Board approval from the Houston Methodist Research Institute (IRB1010-0199). 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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present work are contained in the manuscript * BV-BRC : Bacterial and Viral Bioinformatics Resource Center Ct : cycle threshold VOC : variant of concern RMSE : root mean square error RT-PCR : reverse transcription-polymerase chain reaction XGBoost : extreme gradient boosting