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Updated: Sep 9, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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Un marco Lasso eficiente para las puntuaciones poligénicas conscientes de las mezclas

Franklin Ockerman, Brian Chen, Quan Sun

bioRxiv : the preprint server for biology

|September 5, 2025

Resumen

Este resumen es generado por máquina.

HAUDI mejora las puntuaciones poligénicas (PGS) para diversas poblaciones mediante el manejo eficiente de la mezcla genética. Este nuevo método ofrece una mayor precisión y velocidad que los enfoques existentes para aplicaciones de medicina personalizada.

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Área de la Ciencia:

La genética
La bioinformática
Biología computacional

Sus antecedentes:

Las puntuaciones poligénicas (PGS) son valiosas para la medicina personalizada, pero a menudo funcionan mal en poblaciones no europeas debido a sesgos de entrenamiento.
Los métodos existentes de PGS entre poblaciones luchan con individuos de mezcla reciente, lo que limita su utilidad clínica.
El método GAUDI aborda la ascendencia local, pero es computacionalmente intensivo y limitado a la mezcla de dos vías.

Objetivo del estudio:

Introducir HAUDI, un marco eficiente basado en LASSO para la construcción de puntuaciones poligénicas en poblaciones mezcladas.
Mejorar la eficiencia computacional y el manejo de mezclas de múltiples vías del método GAUDI.
Mejorar la generalización y la precisión de las puntuaciones poligénicas en diversos antecedentes ancestrales.

Principales métodos:

Re-parametrizado el modelo GAUDI en un problema LASSO estándar para la eficiencia computacional.
HAUDI desarrollado para acomodar ajustes de mezcla de múltiples vías.
HAUDI validado a través de simulaciones extensas y datos clínicos del mundo real.

Principales resultados:

HAUDI demostró un rendimiento superior en comparación con GAUDI en simulaciones, con un tiempo de cálculo significativamente reducido.
HAUDI superó a GAUDI en 18 fenotipos clínicos en análisis de datos reales.
HAUDI proporcionó beneficios sustanciales sobre la PGS agnóstica de la ascendencia para rasgos como el recuento de glóbulos blancos y la enfermedad renal crónica.

Conclusiones:

HAUDI ofrece una solución eficiente y efectiva para la construcción de puntuaciones poligénicas en poblaciones mezcladas.
El método mejora la portabilidad y la precisión de la PGS, avanzando en la medicina personalizada.
El rendimiento mejorado y la velocidad computacional de HAUDI lo convierten en una herramienta valiosa para la predicción de riesgos genéticos en diversos grupos.