Data Simulation to Optimize the GWAS Framework in Diverse Populations

Whole-genome or genome-wide association studies have become a fundamental part of modern genetic studies and methods for dissecting the genetic architecture of common traits based on common polymorphisms in random populations. It is hoped that there will be many potential uses of these identified variants, including a better understanding of the pathogenesis of traits, the discovery of biomarkers and protein targets, and the clinical prediction of drug treatments for global health. Questions have been raised on whether associations that are largely discovered in populations of European descent are replicable in diverse populations, can inform medical decision-making globally, and how efficiently current GWAS tools perform in populations of high genetic diversity, multi-wave genetic admixture, and low linkage disequilibrium (LD), such as African populations. In this study, we employ genomic data simulation to mimic structured African, European, and multi-way admixed populations to evaluate the replicability of association signals from current state-of-the-art GWAS tools in these populations. We then leverage the results to discuss an optimized framework for the analysis of GWAS data in diverse populations and outline the implications, challenges, and opportunities these studies present for populations of non-European descent. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the University of Cape Town-Africa Institute for Mathematical Sciences (UCT-AIMS) Scholarship, DAAD German Academic Exchange Service Fund No. A/91628092, the Integrative Biomedical Sciences Departmental Fund, and the NRF/RCUK Newton Grant. ### 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: The 1000 Genomes Project () and simulated data. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 The data used in this study is publicly available in the 1000 Genomes catalog. Simulated data is also available by request from the authors.