Using genomic context informed genotype data and within-model ancestry adjustment to classify type 2 diabetes.

ABSTRACT:

Background

Despite high heritability estimates, complex genetic disorders have proven difficult to predict with genetic data. Genomic research has documented polygenic inheritance, cross-disorder genetic correlations, and enrichment of risk by functional genomic annotation, but the vast potential of that combined knowledge has not yet been leveraged to build optimal risk models. Additional methods are likely required to progress genetic risk models of complex genetic disorders towards clinical utility.

Methods

We developed a modeling framework that uses annotations providing genomic context alongside genotype data as input to convolutional neural networks. To test this framework, we used a matched-pairs dataset of individuals with and without type 2 diabetes. We compared the

SUBMITTER: Barnett EJ 

PROVIDER: S-EPMC12627480 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature