Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility


Here we use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLPFC) of 450 subjects from two aging cohorts. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimer’s disease. We also generate a catalog of splicing quantitative trait loci (sQTL) effects: splicing of 3,006 genes is influenced by genetic variation. We report that altered splicing is the mechanism for the effects of the PICALM, CLU and PTK2B susceptibility alleles. Furthermore, we performed a transcriptome-wide association study and identified 21 genes with significant associations with Alzheimer’s disease, many of which are found in known loci, whereas 8 are in novel loci. These results highlight the convergence of old and new genes associated with Alzheimer’s disease in autophagy–lysosomal-related pathways. Overall, this study of the transcriptome of the aging brain provides evidence that dysregulation of mRNA splicing is a feature of Alzheimer’s disease and is, in some cases, genetically driven.

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Fig. 1: Overview of the study.
Fig. 2: Differential splicing analysis in relation to Alzheimer’s disease diagnosis and neuropathology.
Fig. 3: Enrichment of sQTLs in epigenomic marks and in Alzheimer’s disease GWAS.
Fig. 4: Enrichment of RBP binding sites among sQTLs.
Fig. 5: TWAS of Alzheimer’s disease.
Fig. 6: TWAS prioritizes Alzheimer’s disease genes in endocytosis- and autophagy-related pathways.

Data availability

The ROS/MAP sQTL visualization (Shiny App) browser is available at The ROS/MAP data are available at the RADC Research Resource Sharing Hub at The ROS/MAP and MSBB mapped RNA-seq data that support the findings of this study are available from the AMP-AD Knowledge Portal (!Synapse:syn2580853) upon authentication by the Consortium. The CMC data are available from the CMC Knowledge Portal (!Synapse:syn4923029).


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We thank the participants of ROS and MAP for their essential contributions and gift to these projects; A. Gusev for helpful discussions and for sharing the source code and scripts for TWAS; the International Genomics of Alzheimer’s Project (IGAP) for providing summary results data for these analyses. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. T.R. is supported by grants from the NIH National Institute on Aging (R01AG054005) and the Alzheimer’s Association. P.L.D. is supported by NIH R01AG036836. D.A.B. is supported by NIH P30AG10161, R01AG015819, R01AG017917. P.L.D. and D.A.B. are supported by NIH U01AG046152. B.Z. is supported by NIH R01AG046170, RF1AG054014, RF1AG057440 and R01AG057907. We thank the patients and families who donated material for CommonMind Consortium data. The CommonMind Consortium data are available in CMC Knowledge Portal:!Synapse:syn4923029. Data were generated as part of the CMC supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881 and R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories and the NIMH Human Brain Collection Core. CMC Leadership: P.S., J. Buxbaum (Icahn School of Medicine at Mount Sinai), B. Devlin, D. Lewis (University of Pittsburgh), R. Gur, C.-G. Hahn (University of Pennsylvania), K. Hirai, H. Toyoshiba (Takeda Pharmaceuticals Company Limited), E. Domenici, L. Essioux (F. Hoffman-La Roche Ltd), L. Mangravite, M. Peters (Sage Bionetworks), T. Lehner and B. Lipska (NIMH).

Author information




T.R. and P.L.D. conceived the project and planned the experiments. T.R. and Y.I.L. analyzed and interpreted the data with support from G.W., S.R., J.H., Y.W., I.G., B.N. and S.M. P.L.D., D.A.B., M.W., P.S., E.E.S., V.H. and B.Z. contributed samples and/or data. T.Y.P. performed the tau overexpression in iPSC neurons. T.R. and P.L.D. prepared the first draft of the manuscript. All authors contributed to the final manuscript.

Corresponding authors

Correspondence to Towfique Raj or Philip L. De Jager.

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The authors declare no competing interests.

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Supplementary Information

Supplementary Text and Figures

Supplementary Figures 1–17 and Supplementary Note

Reporting Summary

Supplementary Table 1

Demographic characteristics of the ROS and MAP cohort

Supplementary Table 2

List of significantly differentially spliced introns associated with neuropathologies

Supplementary Table 3

List of significantly differentially spliced introns associated with clinical AD status

Supplementary Tables 4–7

Supplementary Tables 4–7

Supplementary Table 8

List of differentially spliced introns associated with clinical AD status in ROS/MAP that replicate in the MSBB dataset

Supplementary Table 9

List of differentially spliced introns from overexpressing Tau in iPSC-derived neurons

Supplementary Table 10

List of splicing QTLs at FDR 0.05 identified in the ROS/MAP dataset

Supplementary Table 11

Significant TWAS genes with association to IGAP AD GWAS

Supplementary Table 12

Significant TWAS genes with association to meta-analysis of IGAP and UKBB AD GWAS

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Raj, T., Li, Y.I., Wong, G. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat Genet 50, 1584–1592 (2018).

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