Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks

• Published in: PLoS computational biology Vol. 16; no. 4; p. e1007771
• Main Authors: Klein, Hans-Ulrich, Schäfer, Martin, Bennett, David A., Schwender, Holger, De Jager, Philip L.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2020; Public Library of Science (PLoS)
• Subjects: Acetylation; Aging; Algorithms; Alzheimer Disease - genetics; Alzheimer Disease - metabolism; Alzheimer's disease; Astrocytes; Autoregressive models; Bayes Theorem; Bayesian analysis; Biological activity; Biology and life sciences; Brain research; Case-Control Studies; Cell differentiation; Cell Differentiation - genetics; Computational Biology - methods; Computer and Information Sciences; Databases, Genetic; Datasets; Deoxyribonucleic acid; Differentiation (biology); DNA; DNA methylation; Epigenetics; Epigenomics - methods; Funding; Gene Expression Profiling - methods; Gene Regulatory Networks - genetics; Genes; Genetic aspects; Genomes; Genomics; Humans; Identification and classification; Information sharing; Klein, Martin; Medical research; Medicine and Health Sciences; Methods; Multilevel analysis; Neurodegenerative diseases; Parameter estimation; Phosphorylation; Physical Sciences; Proteins; Regulatory genes; Ribonucleic acid; Risk factors; RNA; Software; Statistical inference; Statistical methods; Studies; Supervision; Tau protein; Transcriptome - genetics; United States; New York; United States > US; Germany

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer's disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measure...