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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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GWAS meta-analysis provides new insights into uveal melanoma risk.

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Identificación de factores genómicos de alta dimensionalidad que modulan redes biológicas a través de datos

Samuel Anyaso-Samuel1, Shilan Li2, Giovanny Herrera Ossa1

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, Maryland 20892, USA.

bioRxiv : the preprint server for biology
|January 7, 2026
PubMed
Resumen
Este resumen es generado por máquina.

GFBioNet es un nuevo método computacional que identifica factores genómicos que influyen en las interacciones de rasgos biológicos en redes complejas. Esta herramienta ayuda a descubrir la arquitectura genómica de las redes biológicas en estudios multiómicos.

Palabras clave:
genómicaredes biológicasdatos multiómicosfactores genómicosarquitectura genómica

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Área de la Ciencia:

  • Genómica
  • Biología de Sistemas
  • Bioinformática

Sus antecedentes:

  • Los rasgos biológicos interactúan en redes complejas, pero la influencia genómica en estas interacciones no está clara.
  • Comprender estas interacciones es crucial para descifrar los sistemas biológicos.

Objetivo del estudio:

  • Introducir GFBioNet, un método computacional eficiente para identificar factores genómicos que modulan las asociaciones de rasgos en redes biológicas.
  • Permitir el análisis escalable de datos multiómicos de alta dimensionalidad controlando la tasa de falsos descubrimientos (FDR).

Principales métodos:

  • Una estrategia de dos etapas: 1. Estimación de la red de referencia utilizando modelos gráficos gaussianos. 2. Prueba de la modulación del factor genómico de los bordes de la red (relaciones rasgo-rasgo).
  • Control explícito de la tasa de falsos descubrimientos (FDR) para obtener resultados sólidos.

Principales resultados:

  • Las simulaciones confirman un control fiable de la tasa de falsos descubrimientos (FDR) y una alta potencia estadística.
  • GFBioNet identificó variantes genéticas del huésped que afectan las redes del microbioma oral.
  • Se descubrió que los microbios intestinales modulan las redes de metabolitos en el cáncer colorrectal.
  • Se demostró que las mutaciones somáticas y las alteraciones del número de copias remodelan las redes de expresión génica en el adenocarcinoma de pulmón.

Conclusiones:

  • GFBioNet es una herramienta versátil para descubrir la arquitectura genómica de las redes biológicas.
  • El método amplía el análisis de redes para evaluar los modificadores de las relaciones rasgo-rasgo en estudios multiómicos.