Joint analysis of genetic and epigenetic data using a conditional autoregressive model.

Area of Science:

• Genomics and Epigenetics
• Biomarker Discovery
• Statistical Genetics

Background:

• High-throughput technologies enable cost-effective collection of multilevel omic data.
• Integrating omic data enhances the identification of disease-associated biomarkers.
• Existing methods may not fully capture the joint effects of genetic and epigenetic factors.

Purpose of the Study:

• To propose and evaluate a novel joint conditional autoregressive (JCAR) model.
• To model the joint effect of genetic markers and DNA methylation on phenotypes.
• To identify novel disease-associated biomarkers by integrating omic data.

Main Methods:

• Developed a joint conditional autoregressive (JCAR) model.
• Employed a linear score test for hypothesis testing.
• Utilized the Davies method for p-value calculation.
• Applied the JCAR model to GAW20 data from the GOLDN study, analyzing genetic and DNA methylation data from multiple visits.

Main Results:

• The JCAR model was applied to baseline and full models considering various combinations of genetic and methylation data.
• The gene MYO3B was consistently identified as significant when DNA methylation data was incorporated.
• Top 10 significant genes were reported for each model scenario.

Conclusions:

• The JCAR model is a valuable tool for joint association analysis of genetic and epigenetic data.
• The model is easy to implement and computationally efficient.
• The JCAR framework can be extended to incorporate other omic data types.