Work

Calculating genetic risk scores directly from summary statistics with an application to type 1 diabetes

Year: 2024

Type: article

Abstract: 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 benefit 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 calculate 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 calculate GRSs with an example of a type 1 diabetes (T1D) GRS. An example on European populations of the mean T1D GRS for those calculated from genetic data and from summary statistics (our method) was 10.31 (10.12-10.48) and 10.38 (10.24-10.53), respectively. An example of a case-control set for T1D has an area under the receiver operating characteristic curve of 0.917 (0.903-0.93) for those calculated from genetic data and 0.914 (0.898-0.929) for those calculated from summary statistics. Availability: The code is available at https://github.com/stevensquires/simulating_genetic_risk_scores.

Source: Bioinformatics Advances

DOI: 10.1093/bioadv/vbaf158

Authors: Steven Squires, Michael N Weedon, Richard A Oram

Institutions: University of Exeter, Royal Devon University Healthcare NHS Foundation Trust

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,917