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Approved Research

Polygenic risk score for prediction of tuberculosis susceptibility

Principal Investigator: Professor Marlo Moller
Approved Research ID: 77066
Approval date: September 28th 2021

Lay summary

Researchers are working to improve risk prediction for many common diseases, including infectious diseases such as tuberculosis, using genetic data. Named a "polygenic risk score" (PRS), this measure can also help to personalise medicine and the idea is that it will be used in routine health care. Although it is still a work in progress, the idea of PRS remains feasible in populations that are well-represented in genetic studies. The reason behind it is that the more genetic data there is on a population, the more accurate that data will be; therefore, the more predictive will the PRS be. However, the same cannot be said for populations that are severely under-represented, such as the South African population in our study. In that light, our study aims to calculate a genetic risk prediction score to determine the predisposition to  tuberculosis in a South African population (under-represented in genetic studies) using genetic data obtained from the UK biobank (well-represented European populations in genetic studies). Although the two groups are seemingly different, there are still similarities that can provide useful information. Despite the apparent challenges, developments in this area could generate a tool that would identify "at-risk" individuals from "healthy" individuals as well as distinguish  "most-at-risk" individuals from "least-at-risk" individuals. In turn, knowing in which group an individual lies could contribute to the development of targeted treatments (also referred to as personalised medicine) and therapies that would produce the best medical outcome per patient.