Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank

arXiv:2404.17626v1 Announce Type: new
Abstract: 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 …

abstract ancestry arxiv association cs.lg data disease diverse gap genome modeling prediction pre-training representation research show stat.ap stat.co studies training type