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DRIVE-KG: Mejora del descubrimiento de asociaciones entre variantes y fenotipos en enfermedades complejas
Ananya Rajagopalan1, Tram Anh Nguyen1, Lindsay A Guare1
1Genomics and Computational Biology Graduate Program, Philadelphia, PA, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
Resumen
La integración de datos multiómicos con DRIVE-KG avanza en la comprensión de la endometriosis. Este grafo de conocimiento identifica nuevas asociaciones genéticas y mejora la clasificación de pacientes, ofreciendo nuevas vías para la investigación de la salud de la mujer.
Área de la Ciencia:
- Genómica y Bioinformática
- Investigación de la Salud de la Mujer
- Biología Computacional
Sus antecedentes:
- La endometriosis es una afección prevalente pero subestudiada de la salud de la mujer que afecta al 10% de las mujeres en edad reproductiva.
- La limitada caracterización genética de la endometriosis, y que los GWAS actuales explican solo el 11% de la heredabilidad, requiere enfoques integradores.
- Las representaciones de grafos ofrecen un marco poderoso para armonizar diversos datos biológicos.
Objetivo del estudio:
- Presentar DRIVE-KG, un novedoso grafo de conocimiento para integrar datos multiómicos para estudiar enfermedades complejas como la endometriosis.
- Desarrollar y evaluar modelos de aprendizaje automático para la inferencia de riesgos de enfermedades y la clasificación a nivel de paciente utilizando DRIVE-KG.
- Descubrir nuevas asociaciones de polimorfismos de un solo nucleótido (SNP)-fenotipo y mejorar las capacidades de diagnóstico para la endometriosis.
Principales métodos:
- Se construyó un grafo heterogéneo (DRIVE-KG) que integra diversos conjuntos de datos multiómicos.
- Se entrenó un modelo de predicción de enlaces para identificar asociaciones SNP-fenotipo (endometriosis, obesidad).
- Se desarrolló una red convolucional de grafos (GCN) para la clasificación a nivel de paciente de endometriosis/adenomiosis.
Principales resultados:
- Se identificaron 66 asociaciones SNP-endometriosis candidatas de alta confianza, enriquecidas para rasgos de obesidad y trastorno depresivo.
- Se descubrieron nuevas señales genéticas para la endometriosis, distintas de las asociaciones de obesidad conocidas.
- Se logró una puntuación F1 de 0.752 para la clasificación de endometriosis/adenomiosis utilizando GCN, superando las puntuaciones de riesgo genético (0.698).
- GCN demostró una estratificación significativa de la gravedad de la enfermedad y la señal de adenomiosis.
Conclusiones:
- La integración heterogénea de datos multiómicos a través de DRIVE-KG es eficaz para el descubrimiento y la predicción clínica en enfermedades subestudiadas.
- DRIVE-KG facilita el descubrimiento de nuevas perspectivas genéticas sobre la etiología de la endometriosis.
- El modelo GCN desarrollado muestra potencial para mejorar el diagnóstico y la estratificación de la endometriosis/adenomiosis.
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