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Inferring the molecular mechanisms of noncoding Alzheimer's disease-associated genetic variants

By Alexandre Amlie-Wolf, Mitchell Tang, Jessica Way, Beth Dombroski, Ming Jiang, Nicholas Vrettos, Yi-Fan Chou, Yi Zhao, Amanda Kuzma, Elisabeth E. Mlynarski, Yuk Yee Leung, Christopher D Brown, Li-San Wang, Gerard D. Schellenberg

Posted 27 Aug 2018
bioRxiv DOI: 10.1101/401471 (published DOI: 10.3233/JAD-190568)

INTRODUCTION: We set out to characterize the causal variants, regulatory mechanisms, tissue contexts, and target genes underlying noncoding late-onset Alzheimer's Disease (LOAD)-associated genetic signals. METHODS: We applied our INFERNO method to the IGAP genome-wide association study (GWAS) data, annotating all potentially causal variants with tissue-specific regulatory activity. Bayesian co-localization analysis of GWAS summary statistics and eQTL data was performed to identify tissue-specific target genes. RESULTS: INFERNO identified enhancer dysregulation in all 19 tag regions analyzed, significant enrichments of enhancer overlaps in the immune-related blood category, and co-localized eQTL signals overlapping enhancers from the matching tissue class in ten regions (ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, EPHA1, FERMT2, ZCWPW1). We validated the allele-specific effects of several variants on enhancer function using luciferase expression assays. DISCUSSION: Integrating functional genomics with GWAS signals yielded insights into the regulatory mechanisms, tissue contexts, and genes affected by noncoding genetic variation associated with LOAD risk.

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