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.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The ROS/MAP sQTL visualization (Shiny App) browser is available at https://rajlab.shinyapps.io/sQTLviz_ROSMAP/. The ROS/MAP data are available at the RADC Research Resource Sharing Hub at http://www.radc.rush.edu/. The ROS/MAP and MSBB mapped RNA-seq data that support the findings of this study are available from the AMP-AD Knowledge Portal (https://www.synapse.org/#!Synapse:syn2580853) upon authentication by the Consortium. The CMC data are available from the CMC Knowledge Portal (https://www.synapse.org/#!Synapse:syn4923029).
Wang, E. T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).
Kornblihtt, A. R. et al. Alternative splicing: a pivotal step between eukaryotic transcription and translation. Nat. Rev. Mol. Cell Biol. 14, 153–165 (2013).
Barbosa-Morais, N. L. et al. The evolutionary landscape of alternative splicing in vertebrate species. Science 338, 1587–1593 (2012).
Wang, G. S. & Cooper, T. A. Splicing in disease: disruption of the splicing code and the decoding machinery. Nat. Rev. Genet. 8, 749–761 (2007).
Dredge, B. K., Polydorides, A. D. & Darnell, R. B. The splice of life: alternative splicing and neurological disease. Nat. Rev. Neurosci. 2, 43–50 (2001).
Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016).
Arai, T. et al. TDP-43 is a component of ubiquitin-positive tau-negative inclusions in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Biochem. Biophys. Res. Commun. 351, 602–611 (2006).
Trabzuni, D. et al. MAPT expression and splicing is differentially regulated by brain region: relation to genotype and implication for tauopathies. Hum. Mol. Genet. 21, 4094–4103 (2012).
Rockenstein, E. M. et al. Levels and alternative splicing of amyloid beta protein precursor (APP) transcripts in brains of APP transgenic mice and humans with Alzheimer’s disease. J. Biol. Chem. 270, 28257–28267 (1995).
Buee, L., Bussiere, T., Buee-Scherrer, V., Delacourte, A. & Hof, P. R. Tau protein isoforms, phosphorylation and role in neurodegenerative disorders. Brain Res. Rev. 33, 95–130 (2000).
Valenca, G. T. et al. The role of MAPT haplotype H2 and isoform 1N/4R in Parkinsonism of older adults. PLoS ONE 11, e0157452 (2016).
Bai, B. et al. U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer’s disease. Proc. Natl Acad. Sci. USA 110, 16562–16567 (2013).
Vaquero-Garcia, J. et al. A new view of transcriptome complexity and regulation through the lens of local splicing variations. eLife 5, e11752 (2016).
Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).
Raj, T. et al. CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer’s disease susceptibility. Hum. Mol. Genet. 23, 2729–2736 (2014).
Bennett, D. A., Schneider, J. A., Arvanitakis, Z. & Wilson, R. S. Overview and findings from the Religious Orders Study. Curr. Alzheimer. Res. 9, 628–645 (2012).
Bennett, D. A. et al. Selected findings from the Religious Orders Study and Rush Memory and Aging Project. J. Alzheimers. Dis. 33, S397–S403 (2013).
Mostafavi, S. et al. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease. Nat. Neurosci. 21, 811–819 (2018).
Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).
Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600–604 (2016).
Tollervey, J. R. et al. Analysis of alternative splicing associated with aging and neurodegeneration in the human brain. Genome Res. 21, 1572–1582 (2011).
Mitchelmore, C. et al. NDRG2: a novel Alzheimer’s disease associated protein. Neurobiol. Dis. 16, 48–58 (2004).
Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, e17 (2005).
Wang, M. et al. Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease. Genome Med. 8, 104 (2016).
Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).
Bernstein, B. E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat. Neurosci. 20, 1418–1426 (2017).
Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).
Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 (2016).
Malik, M. et al. CD33 Alzheimer’s risk-altering polymorphism, CD33 expression, and exon 2 splicing. J. Neurosci. 33, 13320–13325 (2013).
Sibley, C. R., Blazquez, L. & Ule, J. Lessons from non-canonical splicing. Nat. Rev. Genet. 17, 407–421 (2016).
Yang, Y. C. et al. CLIPdb: a CLIP-seq database for protein–RNA interactions. BMC Genomics 16, 51 (2015).
Scheckel, C. et al. Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain. eLife 5, e10421 (2016).
Borreca, A., Gironi, K., Amadoro, G. & Ammassari-Teule, M. Opposite dysregulation of fragile-X mental retardation protein and heteronuclear ribonucleoprotein C protein associates with enhanced APP translation in Alzheimer disease. Mol. Neurobiol. 53, 3227–3234 (2016).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
Seyfried, N. T. et al. A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease. Cell Syst. 4, 60–72 (2017).
Liu, J. Z., Erlich, Y. & Pickrell, J. K. Case–control association mapping by proxy using family history of disease. Nat. Genet. 49, 325–331 (2017).
Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
Li, T. et al. GeNets: a unified web platform for network-based genomic analyses. Nat. Methods 15, 543–546 (2018).
Raj, T. et al. Alzheimer disease susceptibility loci: evidence for a protein network under natural selection. Am. J. Hum. Genet. 90, 720–726 (2012).
Nixon, R. A. New perspectives on lysosomes in ageing and neurodegenerative disease. Ageing Res. Rev. 32, 1 (2016).
Emmett, M. J. et al. Histone deacetylase 3 prepares brown adipose tissue for acute thermogenic challenge. Nature 546, 544–548 (2017).
Tian, Y., Chang, J. C., Fan, E. Y., Flajolet, M. & Greengard, P. Adaptor complex AP2/PICALM, through interaction with LC3, targets Alzheimer’s APP-CTF for terminal degradation via autophagy. Proc. Natl Acad. Sci. USA 110, 17071–17076 (2013).
Ingelsson, M. et al. Early Aβ accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology 62, 925–931 (2004).
Guillozet, A. L., Weintraub, S., Mash, D. C. & Mesulam, M. M. Neurofibrillary tangles, amyloid, and memory in aging and mild cognitive impairment. Arch. Neurol. 60, 729–736 (2003).
The GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).
Bennett, D. A. et al. Overview and findings from the Rush Memory and Aging Project. Curr. Alzheimer. Res. 9, 646–663 (2012).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Patterson, N., Price, A. L. & Reich, D. Population structure and Eigenanalysis. PLoS Genet. 2, e190 (2006).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
The Haplotype Reference Consortium. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).
van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J. K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).
Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).
Nica, A. C. et al. The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet. 7, e1002003 (2011).
Giulietti, M. et al. SpliceAid-F: a database of human splicing factors and their RNA-binding sites. Nucleic Acids Res. 41, D125–D131 (2013).
Schmidt, E. M. et al. GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31, 2601–2606 (2015).
Lage, K. et al. A human phenome–interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).
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: https://www.synapse.org/#!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).
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figures 1–17 and Supplementary Note
Demographic characteristics of the ROS and MAP cohort
List of significantly differentially spliced introns associated with neuropathologies
List of significantly differentially spliced introns associated with clinical AD status
Supplementary Tables 4–7
List of differentially spliced introns associated with clinical AD status in ROS/MAP that replicate in the MSBB dataset
List of differentially spliced introns from overexpressing Tau in iPSC-derived neurons
List of splicing QTLs at FDR 0.05 identified in the ROS/MAP dataset
Significant TWAS genes with association to IGAP AD GWAS
Significant TWAS genes with association to meta-analysis of IGAP and UKBB AD GWAS
About this article
Cite this article
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). https://doi.org/10.1038/s41588-018-0238-1
Integrating whole-genome sequencing with multi-omic data reveals the impact of structural variants on gene regulation in the human brain
Nature Neuroscience (2022)
Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder
Molecular Psychiatry (2022)
Allele-specific analysis reveals exon- and cell-type-specific regulatory effects of Alzheimer’s disease-associated genetic variants
Translational Psychiatry (2022)
Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies
Nature Genetics (2022)