A Pipeline for Predicting Variant Associations with Complex Disorders via Functional Annotations
- 1Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI, Lugano, Switzerland.
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.
Related
- Genomic foundation models do not yet boost common variant discovery beyond standard functional annotations
- Predicting miRNA–Disease Associations Based on Contrastive Learning of Linear and Nonlinear Features
- Associations between markers of cellular ageing and of inflammation in bipolar disorders: Identification of IL-16 as an indicator of “inflamm-ageing”
- A Graph-based QSAR Modeling Pipeline for Predicting In vitro PubChem Assays and In vivo Human Hepatotoxicity: Mechanistic Analysis of Caspase-3/7 Activation
- An auditable agentic pipeline for variant-to-gene prioritisation, validated against orthogonal functional evidence
BibTeX
@misc{gualdi2026pipeline,
title = {{A Pipeline for Predicting Variant Associations with Complex Disorders via Functional Annotations}},
author = {F. Gualdi and Z. Darani and D. Malpetti and M. Scutari and F. Mangili},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.21.760618},
url = {https://www.biorxiv.org/content/10.64898/2026.10.21.760618v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A Pipeline for Predicting Variant Associations with Complex Disorders via Functional Annotations
AU - F. Gualdi
AU - Z. Darani
AU - D. Malpetti
AU - M. Scutari
AU - F. Mangili
PY - 2026
DA - 2026/10/21
PB - bioRxiv
DO - 10.64898/2026.10.21.760618
UR - https://www.biorxiv.org/content/10.64898/2026.10.21.760618v1
AB - 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.
ER -