Motivation: Genetic risk scores (GRS) summarise genetic data into a single number and allow for discrimination between cases and controls. Many applications of GRSs would benefitt from comparisons with multiple datasets to assess quality of the GRS across different groups. However, genetic data is often unavailable. If summary statistics of the genetic data could be used to simulate GRSs more comparisons could be made, potentially leading to improved research. Results: We present a methodology that utilises only summary statistics of genetic data to simulate GRSs with an example of a type 1 diabetes (T1D) GRS. An example on European populations of the mean T1D GRS for real and simulated data are 10.31 (10.12-10.48) and 10.38 (10.24-10.53) respectively. An example of a case-control set for T1D has a area under the receiver operating characteristic curve of 0.917 (0.903-0.93) for real data and 0.914 (0.898-0.929) for simulated data. Availability: The code is available at https://github.com/stevensquires/simulating_genetic_risk_scores.

The authors have declared no competing interest.

This study was supported by a grant from the Randox Corporation. This study was supported by the National Institute for Health and Care Research Exeter Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

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The study used only openly available human data. The three datasets used were: 1000G available at https://www.internationalgenome.org/; UK Biobank available at https://www.ukbiobank.ac.uk/; T1DGC available at https://repository.niddk.nih.gov/studies/t1dgc/.

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