  Recent genome-wide association studies (GWAS) have uncovered the genetic
basis of complex traits, but show an under-representation of non-European
descent individuals, underscoring a critical gap in genetic research. Here, we
assess whether we can improve disease prediction across diverse ancestries
using multiomic data. We evaluate the performance of Group-LASSO
INTERaction-NET (glinternet) and pretrained lasso in disease prediction
focusing on diverse ancestries in the UK Biobank. Models were trained on data
from White British and other ancestries and validated across a cohort of over
96,000 individuals for 8 diseases. Out of 96 models trained, we report 16 with
statistically significant incremental predictive performance in terms of
ROC-AUC scores (p-value < 0.05), found for diabetes, arthritis, gall stones,
cystitis, asthma and osteoarthritis. For the interaction and pretrained models
that outperformed the baseline, the PRS score was the primary driver behind
prediction. Our findings indicate that both interaction terms and pre-training
can enhance prediction accuracy but for a limited set of diseases and moderate
improvements in accuracy
