Trans‐ethnic polygenic risk scores for body mass index - Hui‐Qi Qu; John J Connolly; Hakon Hakonarson (Wiley, 2023)
Abstract Background Obesity is a complex trait caused by a combination of genetic, environmental and lifestyle factors that contributes to the risks of numerous serious diseases. Predictive measures of body mass index (BMI) hold significant promise, with implications for the prevention and early intervention of obesity, promoting overall improvement in health. Main body An effective BMI polygenic risk score (PRS) model can assist with the prediction and early detection of obesity at the individual level, aligning with the objectives of precision medicine in the management of obesity. However, potential health disparities may emerge among under‐represented populations with a high prevalence of obesity, primarily due to the lack of genomic data available for these populations. The development of a trans‐ethnic (TE) PRS for BMI necessitates collective action from the research community, and requires genomic data from diverse populations. Conclusion The current BMI‐PRS model exhibits moderate performance, which could be improved in several key areas: integrating genomic information through TE GWAS studies, including admixed populations, considering gene-environment interactions, implementing advanced machine learning techniques, and incorporating genomic‐informed risk assessment (GIRA) with the inclusion of family history, environmental and lifestyle factors for the risk prediction.