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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

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Published on: November 3, 2010

NEpiC: a network-assisted algorithm for epigenetic studies using mean and variance combined signals.

Peifeng Ruan¹, Jing Shen², Regina M Santella²

¹School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.

Nucleic Acids Research

|June 16, 2016

Summary

This study introduces NEpiC, a novel algorithm for epigenome-wide association studies (EWAS). NEpiC integrates biological networks and variance signals to identify disease-associated genes more effectively.

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Last Updated: Mar 19, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

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Published on: November 3, 2010

Pattern-based Search of Epigenomic Data Using GeNemo

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

DNA methylation is crucial for biological processes and disease.
Epigenome-wide association studies (EWAS) often analyze methylation sites individually.
Integrating biological networks and variance signals in EWAS is underexplored.

Purpose of the Study:

To develop a network-assisted algorithm (NEpiC) for identifying differentially methylated subnetworks.
To incorporate both mean and variance signals of DNA methylation data.
To leverage protein-protein interaction (PPI) networks for enhanced analysis.

Main Methods:

Developed NEpiC, a network-assisted algorithm combining mean and variance signals.
Utilized protein-protein interaction (PPI) networks for subnetwork analysis.
Performed simulation studies to evaluate algorithm performance.

Main Results:

NEpiC demonstrated significant power gain by incorporating biological networks and variance signals.
Simulations showed improved results compared to methods using only mean signals or no network information.
Applied NEpiC to TCGA and CUMC datasets for hepatocellular carcinoma (HCC) analysis.

Conclusions:

NEpiC effectively identifies cancer-related genes by integrating network and variance information.
The algorithm provides better replication results in real-world datasets.
NEpiC advances EWAS methodology by incorporating crucial biological context and statistical signals.