PT  - JOURNAL ARTICLE
AU  - Safiye Celik
AU  - Josh C. Russell
AU  - Cezar R. Pestana
AU  - Ting-I Lee
AU  - Shubhabrata Mukherjee
AU  - Paul K. Crane
AU  - C. Dirk Keene
AU  - Jennifer F. Bobb
AU  - Matt Kaeberlein
AU  - Su-In Lee
TI  - DECODER: A probabilistic approach to integrate big data reveals mitochondrial Complex I as a potential therapeutic target for Alzheimer’s disease
AID  - 10.1101/302737
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 302737
4099  - http://biorxiv.org/content/early/2018/07/23/302737.short
4100  - http://biorxiv.org/content/early/2018/07/23/302737.full
AB  - Identifying gene expression markers for Alzheimer’s disease (AD) neuropathology through meta-analysis is a complex undertaking because available data are often from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. Here we introduce a novel probabilistic model-based framework, DECODER, leveraging these discrepancies to identify robust biomarkers for complex phenotypes. Our experiments present: (1) DECODER’s potential as a general meta-analysis framework widely applicable to various diseases (e.g., AD and cancer) and phenotypes (e.g., Amyloid-β (Aβ) pathology, tau pathology, and survival), (2) our results from a meta-analysis using 1,746 human brain tissue samples from nine brain regions in three studies — the largest expression meta-analysis for AD, to our knowledge —, and (3) in vivo validation of identified modifiers of Aβ toxicity in a transgenic Caenorhabditis elegans model expressing AD-associated Aβ, which pinpoints mitochondrial Complex I as a critical mediator of proteostasis and a promising pharmacological avenue toward treating AD.