@article {Logsdon510420, author = {Benjamin A. Logsdon and Thanneer M. Perumal and Vivek Swarup and Minghui Wang and Cory Funk and Chris Gaiteri and Mariet Allen and Xue Wang and Eric Dammer and Gyan Srivastava and Sumit Mukherjee and Solveig K. Sieberts and Larsson Omberg and Kristen D. Dang and James A. Eddy and Phil Snyder and Yooree Chae and Sandeep Amberkar and Wenbin Wei and Winston Hide and Christoph Preuss and Ayla Ergun and Phillip J Ebert and David C. Airey and Gregory W. Carter and Sara Mostafavi and Lei Yu and Hans-Ulrich Klein and the AMP-AD Consortium and David A. Collier and Todd Golde and Allan Levey and David A. Bennett and Karol Estrada and Michael Decker and Zhandong Liu and Joshua M. Shulman and Bin Zhang and Eric Schadt and Phillip L. De Jager and Nathan D. Price and Nil{\"u}fer Ertekin-Taner and Lara M. Mangravite}, title = {Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach}, elocation-id = {510420}, year = {2019}, doi = {10.1101/510420}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Alzheimer{\textquoteright}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.}, URL = {https://www.biorxiv.org/content/early/2019/01/03/510420}, eprint = {https://www.biorxiv.org/content/early/2019/01/03/510420.full.pdf}, journal = {bioRxiv} }