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Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Benjamin A. Logsdon, Thanneer M. Perumal, Vivek Swarup, Minghui Wang, Cory Funk, Chris Gaiteri, Mariet Allen, Xue Wang, Eric Dammer, Gyan Srivastava, Sumit Mukherjee, Solveig K. Sieberts, Larsson Omberg, Kristen D. Dang, James A. Eddy, Phil Snyder, Yooree Chae, Sandeep Amberkar, Wenbin Wei, Winston Hide, Christoph Preuss, Ayla Ergun, Phillip J Ebert, David C. Airey, Gregory W. Carter, Sara Mostafavi, Lei Yu, Hans-Ulrich Klein, the AMP-AD Consortium, David A. Collier, Todd Golde, Allan Levey, David A. Bennett, Karol Estrada, Michael Decker, Zhandong Liu, Joshua M. Shulman, Bin Zhang, Eric Schadt, Phillip L. De Jager, Nathan D. Price, Nilüfer Ertekin-Taner, Lara M. Mangravite
doi: https://doi.org/10.1101/510420
Benjamin A. Logsdon
1Sage Bionetworks, Seattle, WA, 98121, USA
2Lead contact
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  • For correspondence: lara.mangravite@sagebionetworks.org ben.logsdon@sagebionetworks.org
Thanneer M. Perumal
1Sage Bionetworks, Seattle, WA, 98121, USA
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Vivek Swarup
3Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
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Minghui Wang
4Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
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Cory Funk
5Institute for Systems Biology, Seattle, WA, USA
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Chris Gaiteri
6Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
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Mariet Allen
7Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
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Xue Wang
7Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
8Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, 32224, USA
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Eric Dammer
9Emory University, Atlanta, GA, USA
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Gyan Srivastava
10AbbVie, Boston, MA, USA
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Sumit Mukherjee
1Sage Bionetworks, Seattle, WA, 98121, USA
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Solveig K. Sieberts
1Sage Bionetworks, Seattle, WA, 98121, USA
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Larsson Omberg
1Sage Bionetworks, Seattle, WA, 98121, USA
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Kristen D. Dang
1Sage Bionetworks, Seattle, WA, 98121, USA
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James A. Eddy
1Sage Bionetworks, Seattle, WA, 98121, USA
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Phil Snyder
1Sage Bionetworks, Seattle, WA, 98121, USA
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Yooree Chae
11Genentech, South San Francisco, CA, 94080, USA
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Sandeep Amberkar
12Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
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Wenbin Wei
12Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
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Winston Hide
12Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
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Christoph Preuss
13The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA 04609
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Ayla Ergun
14Biogen, Cambridge, MA, USA
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Phillip J Ebert
15Eli Lilly, Indianapolis, IN, USA
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David C. Airey
15Eli Lilly, Indianapolis, IN, USA
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Gregory W. Carter
14Biogen, Cambridge, MA, USA
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Sara Mostafavi
16University of British Columbia, Vancouver, BC, Canada
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Lei Yu
6Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
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Hans-Ulrich Klein
17Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
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18Full list of consortia authors and affiliations (doi:10.7303/syn17114455)
David A. Collier
15Eli Lilly, Indianapolis, IN, USA
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Todd Golde
19University of Florida, Gainesville, FL, USA
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Allan Levey
9Emory University, Atlanta, GA, USA
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David A. Bennett
6Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
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Karol Estrada
20Translational Genome Sciences, Biogen, Cambridge, MA 02142 USA
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Michael Decker
10AbbVie, Boston, MA, USA
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Zhandong Liu
21Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
22Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA
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Joshua M. Shulman
22Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA
23Departments of Neurology, Neuroscience, and Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA
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Bin Zhang
4Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
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Eric Schadt
4Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
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Phillip L. De Jager
17Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
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Nathan D. Price
5Institute for Systems Biology, Seattle, WA, USA
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Nilüfer Ertekin-Taner
7Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA
24Department of Neurology, Mayo Clinic, Jacksonville, FL, 32224, USA
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Lara M. Mangravite
1Sage Bionetworks, Seattle, WA, 98121, USA
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  • For correspondence: lara.mangravite@sagebionetworks.org ben.logsdon@sagebionetworks.org
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SUMMARY

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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach
Benjamin A. Logsdon, Thanneer M. Perumal, Vivek Swarup, Minghui Wang, Cory Funk, Chris Gaiteri, Mariet Allen, Xue Wang, Eric Dammer, Gyan Srivastava, Sumit Mukherjee, Solveig K. Sieberts, Larsson Omberg, Kristen D. Dang, James A. Eddy, Phil Snyder, Yooree Chae, Sandeep Amberkar, Wenbin Wei, Winston Hide, Christoph Preuss, Ayla Ergun, Phillip J Ebert, David C. Airey, Gregory W. Carter, Sara Mostafavi, Lei Yu, Hans-Ulrich Klein, the AMP-AD Consortium, David A. Collier, Todd Golde, Allan Levey, David A. Bennett, Karol Estrada, Michael Decker, Zhandong Liu, Joshua M. Shulman, Bin Zhang, Eric Schadt, Phillip L. De Jager, Nathan D. Price, Nilüfer Ertekin-Taner, Lara M. Mangravite
bioRxiv 510420; doi: https://doi.org/10.1101/510420
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Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach
Benjamin A. Logsdon, Thanneer M. Perumal, Vivek Swarup, Minghui Wang, Cory Funk, Chris Gaiteri, Mariet Allen, Xue Wang, Eric Dammer, Gyan Srivastava, Sumit Mukherjee, Solveig K. Sieberts, Larsson Omberg, Kristen D. Dang, James A. Eddy, Phil Snyder, Yooree Chae, Sandeep Amberkar, Wenbin Wei, Winston Hide, Christoph Preuss, Ayla Ergun, Phillip J Ebert, David C. Airey, Gregory W. Carter, Sara Mostafavi, Lei Yu, Hans-Ulrich Klein, the AMP-AD Consortium, David A. Collier, Todd Golde, Allan Levey, David A. Bennett, Karol Estrada, Michael Decker, Zhandong Liu, Joshua M. Shulman, Bin Zhang, Eric Schadt, Phillip L. De Jager, Nathan D. Price, Nilüfer Ertekin-Taner, Lara M. Mangravite
bioRxiv 510420; doi: https://doi.org/10.1101/510420

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