Modeling continuous genetic ancestry to improve risk prediction across diverse populations

Music City Center 

Polygenic risk scores are widely used in disease risk stratification, but their accuracy varies across diverse populations. Recent methods large-scale leverage multi-ancestry data to improve accuracy in under-represented populations but require labelling individuals by ancestry for prediction. This poses challenges for practical use, as clinical practices are typically not based on ancestry. We propose SPLENDID, a novel penalized regression framework for diverse biobank-scale data. Our method utilizes ancestry principal component interactions to model genetic ancestry as a continuum within a single prediction model for all ancestries, eliminating the need for discrete labels. In extensive simulations and analyses of 9 traits from the All of Us Research Program (N=224,364) and UK Biobank (N=340,140), SPLENDID significantly outperformed existing methods in prediction accuracy and model sparsity. By directly modeling continuous genetic ancestry, SPLENDID stands as a valuable tool for robust risk prediction across diverse populations and fairer clinical implementation.

polygenic risk scores

genetic ancestry

penalized regression

All of Us

UK Biobank

genetic interactions 

Section on Statistics in Genomics and Genetics