There is a need for new therapeutic targets with which to prevent Alzheimer’s disease (AD), a major contributor to aging-related cognitive decline. Here we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first build a molecular network using modules of coexpressed genes and then relate these modules to AD and its neuropathologic and cognitive endophenotypes. We confirm these associations in two independent AD datasets. We also illustrate the use of the network in prioritizing amyloid- and cognition-associated genes for in vitro validation in human neurons and astrocytes. These analyses based on unique cohorts enable us to resolve the role of distinct cortical modules that have a direct effect on the accumulation of AD pathology from those that have a direct effect on cognitive decline, exemplifying a network approach to complex diseases.

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We thank the participants of ROS and MAP for their essential contributions and the gifts of their brains to these projects. All subjects gave informed consent. This work has been supported by many different NIH grants: U01AG046152, R01AG036836, P30AG10161, R01AG015819, R01AG017917, R01AG036547. This work was done as part of the National Institute of Aging’s Accelerating Medicines Partnership for AD (AMP-AD).

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Author notes

  1. These authors contributed equally: Sara Mostafavi, Chris Gaiteri.

  2. These authors jointly supervised this work: David A. Bennett, Philip L. De Jager.


  1. Department of Statistics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada

    • Sara Mostafavi
  2. Canadian Institute for Advanced Research, Toronto, ON, Canada

    • Sara Mostafavi
  3. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    • Chris Gaiteri
    • , Shinya Tasaki
    • , Vitalina Komashko
    • , Lei Yu
    • , Julie A. Schneider
    •  & David A. Bennett
  4. Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA

    • Sarah E. Sullivan
    • , Robert Smith
    •  & Tracy L. Young-Pearse
  5. Broad Institute, Cambridge, MA, USA

    • Charles C. White
    • , Jishu Xu
    • , Mariko Taga
    • , Hans-Ulrich Klein
    • , Cristin McCabe
    • , Elizabeth M. Bradshaw
    • , David E. Root
    • , Aviv Regev
    • , Lori B. Chibnik
    •  & Philip L. De Jager
  6. Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA

    • Mariko Taga
    • , Hans-Ulrich Klein
    • , Elizabeth M. Bradshaw
    •  & Philip L. De Jager
  7. University of Sydney, Sydney, NSW, Australia

    • Ellis Patrick
  8. Harvard Medical School, Boston, MA, USA

    • Lori B. Chibnik
    •  & Tracy L. Young-Pearse
  9. Harvard T.H. Chan School of Public Health, Boston, MA, USA

    • Lori B. Chibnik


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D.A.B. and P.L.D.J. designed and funded the study. S.M. and C.G. designed the computational and statistical methods, performed the analysis, and generated the predictions. J.A.S. and D.A.B. collected the biological samples and phenotypic data. S.E.S., J.X., M.T., C.M., R.S., D.E.R., T.L.Y.-P. and P.L.D.J. contributed to the design and execution of data generation with the experimental pipeline. S.M., C.G., C.C.W., S.T., H.-U.K., E.P., V.K., L.Y., L.B.C., D.A.B. and P.L.D.J. contributed to designing and executing the analyses. S.M., C.G., E.M.B., A.R., T.L.Y.-P., D.A.B. and P.L.D.J. reviewed and interpreted results. All authors critically reviewed the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to David A. Bennett or Philip L. De Jager.

Integrated Supplementary Information

  1. Supplementary Figure 1 Effect of principal components adjustment on gene association with AD.

    Figure shows the association strength (negative log10 pvalue) between 31 previously identified AD genes (in columns)(21 from GWAS and the remainder from smaller scale functional studies) and clinical AD. Rows report the results of the association analyses to clinical AD after removing the stated number of principal components (PCs) derived from the RNA-Seq data. The color scale reporting the level of significance is displayed to the right of the figure. This figure demonstrates that removing expression PCs typically does not improve the association strength between gene expression level and AD status.

  2. Supplementary Figure 2 Overlap of association results for our five traits.

    For each of the five AD-related traits shown in the figure, a univariate transcriptome-wide association study (TWAS) was conducted. To quantify the proportion of genes whose expression is associated with each pair of AD traits, the following procedure was used: for each trait i, we used the 0.05 FDR threshold to identify genes whose expression associate with trait i. In this case, trait i is the “discovery” sample. Then, we used the pi1 statistic to quantify the proportion of the 0.05 FDR genes that are deemed as true positives for trait j, and so trait j is the “replication” sample. In summary, element (i,j) in this figure represents the pi1 statistic when assessing the 0.05 FDR genes (identified in the discovery sample) from trait i (rows) in the replication sample (trait j)(columns).

  3. Supplementary Figure 3 Coexpression pattern in our cortical RNA-seq data.

    This heatmap illustrates the expression patterns of all measured genes (columns), grouped by module, in all analyzed DLPFC samples (rows). Module membership is shown at the bottom of the image. The color scale for relative expression is shown to be the right of the figure.

  4. Supplementary Figure 4 Module preservation and replication.

    (A) Module preservation (z-summary, x axis) as assessed in the 4 test datasets. Each row reports the preservation of a module in the 4 datasets. The red dashed line marks the “strongly preserved” threshold and the green dotted line marks the “moderately preserved” threshold (as defined empirically by Langfelder, PLOS Computational Biology 2011). (B) We assessed the replication of the trait to transcriptional measure associations, at the gene-level and module-level (see Supplementary material). To do so, we computed the correlation between vectors of module-to-trait association in our study and in the Zhang et al. study (shown by the red dotted line). We compared the observed correlation between module-trait vectors with gene level associations through resampling. The histogram shows the empirical distribution of correlation coefficient between vectors of gene-to-trait associations in the Zhang study and this study, where we select 47 random genes 10,000 times.

  5. Supplementary Figure 5 Effect of cell population frequency in cortical tissue.

    (A) This figure shows the correlation strength (signed, negative log10 p-value: positive denotes a direct correlation) between 7 known gene markers of common brain cell types and the AD-related traits analyzed in this study. As shown, GFAP, which is a marker of astrocytes, is most strongly associated with amyloid load. (B) This figure shows the correlation strength between expression level of all genes with an AD-related trait before (x-axis) and after (y-axis) adjusting for 7 standard cell type markers (shown in Figure S3A).

  6. Supplementary Figure 6 Expression of module 109 meta-feature categorized in order of AD risk, by APOE genotype status.

    Due to small sample sizes, genotypes e2/e2 and e2/e4 were collapsed into larger categories. APOE genotype status is defined as: e2 (rs7412-T, rs429358-T), e3 (rs7412-C, rs429358-T), e4 (rs7412-C, rs429358-C). Each dot represents one subject.

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