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. These findings suggest that advanced statistical methods that
borrow information across multiple ancestries may improve disease risk
prediction, but with limited benefit.

通过使用多组学数据，我们评估了在不同祖源中改善疾病预测的可行性，研究结果表明，使用群体 LASSO INTERaction-NET 和预训练的 LASSO 在多种祖源中的疾病预测性能有所提高，但效果有限。