Spatially-integrated Ancestry Polygenic Risk Mapping for Diabetes: A Geographic-adjusted Genetic Scoring Framework
Rana Abdulmohsen Alotaibi, Sarah Abdulaziz Alsaggaf, Somaya Salem Alaofi, Rahaf Faleh Alanazi, Sadeen Khalid Fallatah, Ibrahim Ahmed Alawi, Lujain Mansour Alhejeili, Refan Ahmed Al-Hujairi, Faisal Abdulqader Almuzaini, Fai Waleed Jayyar, Turki Hathal Alfehaidi, Abdulaziz Fahad Alsaud, Mohsin Hashim AlmaghrabiAbstract
Background:
Conventional polygenic risk scores (PRSs) often fail to capture geographic and ancestry-specific variation, limiting their predictive accuracy in admixed and globally diverse populations. This study aims to develop a spatially-aware PRS (SA-PRS) framework that integrates ancestry-specific genetic contributions with geographic admixture patterns to enhance diabetes risk prediction.
Methods:
The SA-PRS framework incorporates ancestry-aware variant weighting with spatial modelling approaches. It utilises admixture-spatial decomposition to estimate individual ancestry proportions and geographic distribution through a transformer-based variational autoencoder. Hierarchical Bayesian spatial weighting is applied to model region-specific allele frequency distributions, while a three-dimensional spatial risk weighting tensor to dynamically adjust variant effects across geographic contexts. In addition, an attention-based fusion network integrates genetic risk with clinical risk factors to ensure compatibility with conventional models.
Results:
The proposed model demonstrated a statistically significant improvement in diabetes risk stratification in admixed populations (
Conclusion:
The SA-PRS framework provides an interpretable and scalable approach for precision medicine, although further validation in larger multiethical studies is required to confirm generalisability.