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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Identifying differentially methylated genes using mixed effect and generalized least square models.

Shuying Sun¹, Pearlly S Yan, Tim H M Huang

¹Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106, USA. shuying.sun@case.edu

BMC Bioinformatics

|December 17, 2009

Summary

This summary is machine-generated.

Identifying differentially methylated genes is crucial for understanding cancer. This study introduces new statistical models to analyze DNA methylation data, accounting for sample heterogeneity and probe correlations, leading to more robust results.

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Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

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Area of Science:

Genomics
Cancer Biology
Bioinformatics

Background:

DNA methylation is a key factor in tumorigenesis.
Identifying differentially methylated genes and CpG islands (CGIs) between tumor subtypes is biologically significant.
Differential methylation hybridization (DMH) microarrays assay genome-wide CGI methylation but face challenges from sample heterogeneity and probe correlations.

Purpose of the Study:

To propose and evaluate statistical models for identifying differentially methylated (DM) genes using associated DM CGIs.
To investigate the impact of random effects and correlation structures on DM gene analysis.
To compare proposed models against a simple least squares regression.

Main Methods:

Developed a novel method to identify DM genes by analyzing associated DM CGIs.
Implemented four mixed-effects and generalized least squares models at each CGI.
Compared these models with a standard least squares regression model.

Main Results:

The inclusion or exclusion of random effects significantly influences the analysis outcomes.
The choice of correlation structures among probes also demonstrably affects the results.
Four proposed models were compared against a simple least squares regression.

Conclusions:

Statistical modeling choices, including random effects and correlation structures, critically impact DM gene identification.
The study assesses the false discovery rate of different models using CGIs linked to housekeeping genes.
Findings highlight the importance of appropriate statistical approaches for analyzing complex DMH data.