A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease

Yanting Huang, Xiaobo Sun (), Huige Jiang, Shaojun Yu, Chloe Robins, Matthew J. Armstrong, Ronghua Li, Zhen Mei, Xiaochuan Shi, Ekaterina Sergeevna Gerasimov, Philip L. Jager, David A. Bennett, Aliza P. Wingo, Peng Jin, Thomas S. Wingo () and Zhaohui S. Qin ()
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Yanting Huang: Emory University
Xiaobo Sun: Zhongnan University of Economics and Laws
Huige Jiang: Emory University
Shaojun Yu: Emory University
Chloe Robins: Emory University School of Medicine
Matthew J. Armstrong: Emory University School of Medicine
Ronghua Li: Emory University School of Medicine
Zhen Mei: Emory University School of Medicine
Xiaochuan Shi: University of Washington
Ekaterina Sergeevna Gerasimov: Emory University School of Medicine
Philip L. Jager: Columbia University Medical Center
David A. Bennett: Rush University Medical Center
Aliza P. Wingo: Atlanta VA Medical Center
Peng Jin: Emory University School of Medicine
Thomas S. Wingo: Emory University School of Medicine
Zhaohui S. Qin: Emory University

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Alzheimer’s disease (AD) is influenced by both genetic and environmental factors; thus, brain epigenomic alterations may provide insights into AD pathogenesis. Multiple array-based Epigenome-Wide Association Studies (EWASs) have identified robust brain methylation changes in AD; however, array-based assays only test about 2% of all CpG sites in the genome. Here, we develop EWASplus, a computational method that uses a supervised machine learning strategy to extend EWAS coverage to the entire genome. Application to six AD-related traits predicts hundreds of new significant brain CpGs associated with AD, some of which are further validated experimentally. EWASplus also performs well on data collected from independent cohorts and different brain regions. Genes found near top EWASplus loci are enriched for kinases and for genes with evidence for physical interactions with known AD genes. In this work, we show that EWASplus implicates additional epigenetic loci for AD that are not found using array-based AD EWASs.

Date: 2021
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DOI: 10.1038/s41467-021-24710-8

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