RT Journal Article SR Electronic T1 Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach JF bioRxiv FD Cold Spring Harbor Laboratory SP 510420 DO 10.1101/510420 A1 Benjamin A. Logsdon A1 Thanneer M. Perumal A1 Vivek Swarup A1 Minghui Wang A1 Cory Funk A1 Chris Gaiteri A1 Mariet Allen A1 Xue Wang A1 Eric Dammer A1 Gyan Srivastava A1 Sumit Mukherjee A1 Solveig K. Sieberts A1 Larsson Omberg A1 Kristen D. Dang A1 James A. Eddy A1 Phil Snyder A1 Yooree Chae A1 Sandeep Amberkar A1 Wenbin Wei A1 Winston Hide A1 Christoph Preuss A1 Ayla Ergun A1 Phillip J Ebert A1 David C. Airey A1 Gregory W. Carter A1 Sara Mostafavi A1 Lei Yu A1 Hans-Ulrich Klein A1 the AMP-AD Consortium A1 David A. Collier A1 Todd Golde A1 Allan Levey A1 David A. Bennett A1 Karol Estrada A1 Michael Decker A1 Zhandong Liu A1 Joshua M. Shulman A1 Bin Zhang A1 Eric Schadt A1 Phillip L. De Jager A1 Nathan D. Price A1 Nilüfer Ertekin-Taner A1 Lara M. Mangravite YR 2019 UL http://biorxiv.org/content/early/2019/01/03/510420.abstract AB Alzheimer’s disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related biological processes. This complexity contributes, in part, to existing difficulties in the identification of successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research studies. From this analysis, five consensus clusters were identified that described the major sources of AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. AD genetic associations, previously studied AD-related biological processes, and AD targets under active investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated strong heterogeneity between males and females in AD-related expression that was consistently observed across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, which was specific for genes that showed the strongest association with changes in AD-related gene expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been previously identified and was enriched for genes related to proteostasis. This study provides an atlas to map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery efforts.