Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Framework
Abstract
Trisomy 21 results in Down syndrome, a multifaceted genetic disorder with diverse clinical phenotypes, including heart defects, immune dysfunction, neurodevelopmental differences, and early-onset dementia risk. Heterogeneity and fragmented data across studies challenge comprehensive research and translational discovery. The NIH INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) initiative has assembled harmonized participant-level datasets, yet realizing their potential requires integrative analytical frameworks. We developed a knowledge graph-driven platform transforming nine INCLUDE studies, comprising 7,148 participants, 456 conditions, 501 phenotypes, and over 37,000 biospecimens, into a unified semantic infrastructure. Cross-resource enrichment with Monarch Initiative data expands coverage to 4,281 genes and 7,077 variants. The resulting knowledge graph contains over 1.6 million semantic associations, enabling AI-ready analysis with graph embeddings and path-based reasoning for hypothesis generation. Researchers can query the graph via SPARQL or natural language interfaces. This framework converts static data repositories into dynamic discovery environments, supporting cross-study pattern recognition, predictive modeling, and systematic exploration of genotype-phenotype relationships in Down syndrome.
Cite
@article{arxiv.2509.01565,
title = {Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Framework},
author = {Madan Krishnamurthy and Surya Saha and Pierrette Lo and Patricia L. Whetzel and Tursynay Issabekova and Jamed Ferreris Vargas and Jack DiGiovanna and Melissa A Haendel},
journal= {arXiv preprint arXiv:2509.01565},
year = {2025}
}