A Pipeline for Predicting Variant Associations with Complex Disorders via Functional Annotations

Abstract

The study of complex disorders is limited by the lack of structured data sets that integrate genetic association signals with rich functional annotations. While genome wide association studies (GWAS) provide large collections of associated variants, these are heterogeneous, studydependent, and not directly suitable for machine learning applications. Although frameworks such as Combined Annotation Dependent Depletion (CADD) offer comprehensive variant-level features, there is no standardised procedure to construct curated, balanced, and reproducible data sets for the systematic study of complex traits. We introduce a scalable and reproducible pipeline for generating CADD-annotated variant data sets associated with complex disorders defined via Experimental Factor Ontology (EFO) terms. The pipeline integrates GWAS summary statistics, genome mapping, chromosomestratified negative sampling, and large-scale annotation, while addressing class imbalance and feature redundancy. The resulting data sets provide curated, balanced, and annotation-rich representations of variant-trait associations, suitable for statistical learning, systematic model evaluation and comparison, and analysis of functional annotation contributions and interactions.