Constructing Causal Methylation Network by Additive Noise Model (ANM)

• Activity Number: 522 - Contributed Poster Presentations: Biometrics Section
• Type: Contributed
• Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Shudi Li* and Rong Jiao and Momiao Xiong
• Companies: University of Texas School of Public Health and UT Health and University of Texas School of Public Health
• Keywords: Alzheimer's disease (AD) ; Causal inference; Additive Noise Model ; DNA Methylation

Abstract

Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes problems with memory, thinking and behavior. It contributes to 60 to 80 percent of dementia that leads to loss of memory and other cognitive abilities and worsens over time. Alzheimer’s Disease Neuroimag-ing Initiative (ADNI) is a multistudy that aim to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD). Methylation plays an important role in the development of AD. To uncover how the DNA methylation affect development of AD, we first conduct ge-nome-wide causation studies of methylation for MCI and AD using additive noise models (ANMs) to identify methylated genes that cause MCI and AD. Then, we infer methylation causal networks using the identified methylation causing genes across three time points: baseline, 12 months, and 24 months. Finally, we construct the methylation causal networks that influence the MCI and AD at three time points and look at the progression of the networks. The developed methods are applied to ADNI methylation dataset with 423 samples and 866,840 CpG cites.