Flexibly encoded GWAS identifies novel nonadditive SNPs in individuals of African and European ancestry

Most genome-wide association studies (GWASs) assume an additive inheritance model, where heterozygous genotypes (HET) are coded with half the risk of homozygous alternate genotypes (HA), leading to less explained nonadditive genetic effects for complex diseases. Yet, growing evidence indicates that with flexible modeling, many single-nucleotide polymorphisms (SNPs) show nonadditive effects, including dominant and recessive, which will be missed using only the additive model. We developed Elastic Data-Driven Encoding (EDGE) to determine the HET to HA ratio of risk. Simulation results demonstrated that EDGE outperformed traditional methods across all simulated models for power while maintaining a conserved false positive rate. This research lays the necessary groundwork for integrating nonadditive genetic effects into GWAS workflows to identify novel disease-risk SNPs, which may ultimately improve polygenic risk prediction in diverse populations and springboard future applications to thousands of disease phenotypes and other omic domains to improve disease-prediction capability. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work is supported by the USDA National Institute of Food and Agriculture and Hatch Appropriations under Project #PEN04275 and Accession #1018544, startup funds from the College of Agricultural Sciences, Pennsylvania State University (https://agsci.psu.edu/), and the Dr. Frances Keesler Graham Early Career Professorship from the Social Science Research Institute, Pennsylvania State University (https://ssri.psu.edu/) to MAH. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 All data produced are available online at https://github.com/HallLab. All results from the simulations could be re-generated by using the same parameters and designed software.