Understanding Alzheimer Disease at the Interface between Genetics and Transcriptomics
Authors: Jan Verheijen, Kristel Sleegers
Journal: Trends in Genetics (2018)

Due to risk gene pleiotropy, difficulty in finding functional variants, and poor reflection of physiological complexity in genetic analysis, translation of new genetic findings for Alzheimer disease (AD) into functional mechanisms has been difficult. Transcriptomic analysis has provided additional support for previously identified risk genes while also identifying novel associated genes, helping to elucidate mechanisms of disease. Refinement of transcriptomics through 2nd and 3rd generation sequencing, single-cell sequencing and bioinformatics is revealing mechanisms involved in AD in previously unattainable detail, including brain region- and cell-type-specific expression changes and molecular processes such as transcript rescue events, challenging the direct interpretation of an association between genetic variant and phenotype. Transcriptome analysis in postmortem brain has uncovered central biological pathways and central regulator 'hub' genes in disease, for example, SPI.1 and TYROBP in the brain immune response. Over 25 genes are known to affect the risk of developing Alzheimer disease (AD), the most common neurodegenerative dementia. However, mechanistic insights and improved disease management remains limited, due to difficulties in determining the functional consequences of genetic associations. Transcriptomics is increasingly being used to corroborate or enhance interpretation of genetic discoveries. These approaches, which include second and third generation sequencing, single-cell sequencing, and bioinformatics, reveal allele-specific events connecting AD risk genes to expression profiles, and provide converging evidence of pathophysiological pathways underlying AD. Simultaneously, they highlight brain region- and cell-type-specific expression patterns, and alternative splicing events that affect the straightforward relation between a genetic variant and AD, re-emphasizing the need for an integrated approach of genetics and transcriptomics in understanding AD. Over 25 genes are known to affect the risk of developing Alzheimer disease (AD), the most common neurodegenerative dementia. However, mechanistic insights and improved disease management remains limited, due to difficulties in determining the functional consequences of genetic associations. Transcriptomics is increasingly being used to corroborate or enhance interpretation of genetic discoveries. These approaches, which include second and third generation sequencing, single-cell sequencing, and bioinformatics, reveal allele-specific events connecting AD risk genes to expression profiles, and provide converging evidence of pathophysiological pathways underlying AD. Simultaneously, they highlight brain region- and cell-type-specific expression patterns, and alternative splicing events that affect the straightforward relation between a genetic variant and AD, re-emphasizing the need for an integrated approach of genetics and transcriptomics in understanding AD. Alzheimer's disease (AD) is a genetically complex, multifactorial disease that leads to neurodegenerative dementia. There is no cure for AD yet, and due to a high prevalence and continuously increasing incidence it poses a major threat to personal health as well as to the health care system. Patients display a progressive decline of cognitive capabilities, with characteristic early loss of episodic memory, eventually resulting in complete dependency and death. The disease is preceded by a long prodromal phase [1Jack Jr, C.R. et al.Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease.Alzheimers Dement. 2011; 7: 257-262Abstract Full Text Full Text PDF PubMed Scopus (1259) Google Scholar, 2McKhann G.M. et al.The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease.Alzheimers Dement. 2011; 7: 263-269Abstract Full Text Full Text PDF PubMed Scopus (9313) Google Scholar]. Neuropathological changes in the AD brain include progressive hippocampal and cortical atrophy, visible upon neuroimaging and macroscopic examination. Characteristic microscopic features are intracellular neurofibrillary tangles (NFTs) of hyperphosphorylated tau protein and extracellular depositions of Amyloid-β (Aβ)1–42 peptide, accompanied by neuronal and synapse loss and reactive gliosis [3Braak H. Braak E. Neuropathological stageing of Alzheimer-related changes.Acta Neuropathol. 1991; 82: 239-259Crossref PubMed Scopus (11595) Google Scholar]. Initial evidence of a genetic etiology of AD was presented by the observation of families with multiple generations affected by a rare early onset form of AD (EOAD, <65 years). Molecular genetic investigation of these pedigrees resulted in the identification of the amyloid precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2) genes as disease genes in AD. Pathogenic mutations in these genes converge to a general mechanism of increased Aβ1-42 accumulation or increased Aβ1–42/Aβ1–40 ratio. Hundreds of dominantly inherited pathogenic mutations have since been described for these genes, mostly in EOAD patients, although only explaining up to 10% of EOAD (reviewed in [4Cacace R. et al.Molecular genetics of early-onset Alzheimer's disease revisited.Alzheimers Dement. 2016; 12: 733-748Abstract Full Text Full Text PDF PubMed Scopus (301) Google Scholar]). Most patients have late-onset AD (LOAD). While a pathogenic mutation in APP, PSEN1 or PSEN2 is infrequently identified in LOAD patients, LOAD is typically considered multifactorial, with a strong polygenic component and an estimated heritability of up to 80%. The most well-known genetic risk factor for AD is the APOE ε4 allele (see Glossary), explaining approximately 25% of the heritability of liability [5Cuyvers E. et al.Genetic variations underlying Alzheimer's disease: evidence from genome-wide association studies and beyond.Lancet Neurol. 2016; 15: 857-868Abstract Full Text Full Text PDF PubMed Scopus (171) Google Scholar]. Over the past decade, complex genetic research on AD has been successful in identifying additional genetic risk factors for AD, both low-penetrant common risk factors (e.g., [6Lambert J.C. et al.Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease.Nat. Genet. 2013; 45: 1452-1458Crossref PubMed Scopus (2713) Google Scholar]) and rare alleles with intermediate to high penetrance (e.g., [7Guerreiro R. et al.TREM2 variants in Alzheimer's disease.N. Engl. J. Med. 2013; 368: 117-127Crossref PubMed Scopus (1896) Google Scholar, 8Steinberg S. et al.Loss-of-function variants in ABCA7 confer risk of Alzheimer's disease.Nat. Genet. 2015; 47: 445-447Crossref PubMed Scopus (221) Google Scholar, 9Sims R. et al.Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease.Nat. Genet. 2017; 49: 1373-1384Crossref PubMed Scopus (519) Google Scholar]). While the amyloid cascade hypothesis has long dominated the efforts towards development of diagnostics and therapeutics for AD, gene discovery studies and pathway-based analysis of genome-wide studies shed light on a range of additional biological processes contributing to AD, proposing new targets for therapy development. Translational impact of these findings is still limited however, owing to – amongst others – pleiotropy of risk genes, limited understanding which genetic variants in identified risk genes or loci actually affect AD risk and how, and the physiological complexity of tissues which is insufficiently represented in genetic analysis. While the field of AD genetics is still in active pursuit of additional genetic risk loci through genome-wide association studies (GWAS) and next generation sequencing studies on increasingly large AD cohorts, a trend is emerging of simultaneous interrogation of transcriptomics data to study the effect of newly identified genetic risk factors at the level of the transcriptome (e.g., [10Jones L. et al.Convergent genetic and expression data implicate immunity in Alzheimer's disease.Alzheimers Dement. 2015; 11: 658-671Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar]). In parallel, refinement of transcriptomics through second and third generation sequencing methodology, single-cell sequencing and bioinformatics analysis allows investigating functional mechanisms involved in AD in previously unattainable detail. Here, we review the state-of-the-art of genetic discovery and transcriptome investigations in multifactorial AD, with emphasis on the insights emerging at their interface. The search for genetic risk factors for polygenic AD was initially dominated by studies querying common genetic variation, most successfully through GWAS. At least 42 genes/loci have been associated with LOAD at genome-wide significance in at least one GWAS [6Lambert J.C. et al.Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease.Nat. Genet. 2013; 45: 1452-1458Crossref PubMed Scopus (2713) Google Scholar, 11Lambert J.C. et al.Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease.Nat. 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Neurol. 2011; 68: 320-328Crossref PubMed Scopus (151) Google Scholar, 17Miyashita A. et al.SORL1 is genetically associated with late-onset Alzheimer's disease in Japanese, Koreans and Caucasians.PLoS One. 2013; 8e58618Crossref PubMed Scopus (112) Google Scholar, 18Bertram L. et al.Genome-wide association analysis reveals putative Alzheimer's disease susceptibility loci in addition to APOE.Am. J. Hum. Genet. 2008; 83: 623-632Abstract Full Text Full Text PDF PubMed Scopus (381) Google Scholar, 19Jun G. et al.PLXNA4 is associated with Alzheimer disease and modulates tau phosphorylation.Ann. Neurol. 2014; 76: 379-392Crossref PubMed Scopus (46) Google Scholar, 20Wijsman E.M. et al.Genome-wide association of familial late-onset Alzheimer's disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE.PLoS Genet. 2011; 7e1001308Crossref PubMed Scopus (191) Google Scholar], of which ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, CR1, EPHA1, FERMT2, HLA-cluster, INPP5D, MEF2C, MS4A6A, NME8, PICALM, PTK2B, SLC24A4/RIN3, SORL1, CELF1, NME8, FERMT2, CASS4, DGS2, and ZCWPW1 have been identified or confirmed in GWAS meta-analysis, and are since regarded as established risk genes/loci for LOAD. CD33 and DSG2 did not show genome-wide significance in the replication phase of the largest AD GWAS meta-analysis published to date [6Lambert J.C. et al.Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease.Nat. Genet. 2013; 45: 1452-1458Crossref PubMed Scopus (2713) Google Scholar]. Family-based GWAS approaches reported significant loci overlapping with those in case-control GWAS, including APOE, BIN1, CLU, and CD33. In addition, PLXNA4, CUGBP2, TRPC4AP, ATXN1, and APOC1 as well as uncharacterized chromosome 14 locus 14q31.2 were reported [18Bertram L. et al.Genome-wide association analysis reveals putative Alzheimer's disease susceptibility loci in addition to APOE.Am. J. Hum. Genet. 2008; 83: 623-632Abstract Full Text Full Text PDF PubMed Scopus (381) Google Scholar, 19Jun G. et al.PLXNA4 is associated with Alzheimer disease and modulates tau phosphorylation.Ann. Neurol. 2014; 76: 379-392Crossref PubMed Scopus (46) Google Scholar, 20Wijsman E.M. et al.Genome-wide association of familial late-onset Alzheimer's disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE.PLoS Genet. 2011; 7e1001308Crossref PubMed Scopus (191) Google Scholar] but not replicated in case-control approaches. Alternative analytical approaches of case-control GWAS detected additional risk loci such as FRMD4A in a sliding window haplotype-based approach [21Lambert J.C. et al.Genome-wide haplotype association study identifies the FRMD4A gene as a risk locus for Alzheimer's disease.Mol. Psychiatry. 2013; 18: 461-470Crossref PubMed Scopus (75) Google Scholar], and TP53INP1 and IGHV1-67 in gene-wide analysis [22Escott-Price V. et al.Gene-wide analysis detects two new susceptibility genes for Alzheimer's disease.PLoS One. 2014; 9e94661Crossref PubMed Scopus (88) Google Scholar]. Expanding analysis of genome-wide variant data beyond single-variant or single-gene level to pathway-based analysis revealed common pathways associated with AD, despite differences in analytical approach and pathway definition between studies. Common biological pathways emerging from these studies are immune response, lipid metabolism, endocytosis, and cell adhesion molecule (CAM) pathways [10Jones L. et al.Convergent genetic and expression data implicate immunity in Alzheimer's disease.Alzheimers Dement. 2015; 11: 658-671Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar, 23Jones L. et al.Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease.PLoS One. 2010; 5e13950Crossref PubMed Scopus (299) Google Scholar, 24Lambert J.C. et al.Implication of the immune system in Alzheimer's disease: evidence from genome-wide pathway analysis.J. Alzheimers Dis. 2010; 20: 1107-1118Crossref PubMed Scopus (112) Google Scholar, 25Hong M.G. et al.Genome-wide pathway analysis implicates intracellular transmembrane protein transport in Alzheimer disease.J. Hum. Genet. 2010; 55: 707-709Crossref PubMed Scopus (42) Google Scholar, 26Liu G. et al.Cell adhesion molecules contribute to Alzheimer's disease: multiple pathway analyses of two genome-wide association studies.J. Neurochem. 2012; 120: 190-198Crossref PubMed Scopus (69) Google Scholar, 27Ramanan V.K. et al.Genome-wide pathway analysis of memory impairment in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort implicates gene candidates, canonical pathways, and networks.Brain. Imaging Behav. 2012; 6: 634-648Crossref PubMed Scopus (58) Google Scholar, 28Perez-Palma E. et al.Overrepresentation of glutamate signaling in Alzheimer's disease: network-based pathway enrichment using meta-analysis of genome-wide association studies.PLoS One. 2014; 9e95413Crossref PubMed Scopus (45) Google Scholar, 29Xiang Z. et al.Integrating genome-wide association study and brain expression data highlights cell adhesion molecules and purine metabolism in Alzheimer's disease.Mol. Neurobiol. 2015; 52: 514-521Crossref PubMed Scopus (23) Google Scholar] (Table 1). A study using the methodologically distinct approach of genome-wide heritability partitioning confirmed enrichment of the adaptive and innate immune response in AD [30Gagliano S.A. et al.Genomics implicates adaptive and innate immunity in Alzheimer's and Parkinson's diseases.Ann. Clin. Transl. Neurol. 2016; 3: 924-933Crossref PubMed Scopus (51) Google Scholar].Table 1Significantly Enriched Pathways in GWAS Meta-analysis Studies on ADaP values indicate multiple-testing corrected values according to respective studies for terms showing strongest interaction within each pathway. Abbreviations: ADGC, Alzheimer's Disease Genetics Consortium; CHARGE, Cohorts for Heart and Aging in Genomic Epidemiology; EADI, European Alzheimer's Disease Initiative; GERAD, Genetic and Environmental Risk in Alzheimer's Disease; IGAP, International Genomics of Alzheimer's Project.GWAS data sets analyzedConsulted databaseLipid metabolismImmune responseEndocytosisSynaptic transmissionCell adhesion moleculesMiscellaneousGERAD/EADI 23Jones L. et al.Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease.PLoS One. 2010; 5e13950Crossref PubMed Scopus (299) Google ScholarALIGATOR/GSA:KEGG, GO databasesSterol transport(P = 0.0079), cholesterol transport(P = 0.0079)Immunoglobulin mediated immune response(P = 4 × 10−3), immune response(P = 3 × 10−3)Synaptic transmission, cholinergic(P = 5.0 × 10−3)EADI 24Lambert J.C. et al.Implication of the immune system in Alzheimer's disease: evidence from genome-wide pathway analysis.J. Alzheimers Dis. 2010; 20: 1107-1118Crossref PubMed Scopus (112) Google ScholarKEGG, GO databasesRIG-I-like receptor signaling(P = 3 × 10−2) Antigen processing and presentation(P = 2.0 × 10−2)Regulation of autophagy(P = 0.007)EADI 25Hong M.G. et al.Genome-wide pathway analysis implicates intracellular transmembrane protein transport in Alzheimer disease.J. Hum. Genet. 2010; 55: 707-709Crossref PubMed Scopus (42) Google ScholarGencodis/DAVID: GO databaseIntracellular transmembrane protein transport(P = 7.2 × 10−6)Discovery data EADI, replication data ADNI 26Liu G. et al.Cell adhesion molecules contribute to Alzheimer's disease: multiple pathway analyses of two genome-wide association studies.J. Neurochem. 2012; 120: 190-198Crossref PubMed Scopus (69) Google ScholarIGSEA: KEGG database. WebGestalt/DAVID: GO databaseRIG-I-like receptor signaling(P = 7.00 × 10−4), Natural killer cell mediated cytotoxicity(P = 8.56 × 10−5), Antigen processing and presentation(P = 3.50 × 10−7)Cell adhesion molecules KEGG(P = 1.84 × 10−6)Regulation of autophagy(P = 6.22 × 10−5)ADNI, composite measure of memory as phenotype 27Ramanan V.K. et al.Genome-wide pathway analysis of memory impairment in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort implicates gene candidates, canonical pathways, and networks.Brain. Imaging Behav. 2012; 6: 634-648Crossref PubMed Scopus (58) Google ScholarGSA-SNP: BioCarta, KEGG, GO, Reactome databasesAllograft rejection(P = 3.9 × 10−2)Transmission across chemical synapses(P = 1.77 × 10−4)Focal adhesion(P = 0.006), Cell adhesion molecules (CAMs)(P = 2.9 × 10−2)Calcium signaling pathway(P = 1.17 × 10−4), Viral myocarditis (P = 0.039), Long-term depression/potentiation(P = 8.0 × 10−3)/(P = 1.9 × 10−2)Combined TGen1, NIA-LOAD/NCRAD, ADNI replication data 28Perez-Palma E. et al.Overrepresentation of glutamate signaling in Alzheimer's disease: network-based pathway enrichment using meta-analysis of genome-wide association studies.PLoS One. 2014; 9e95413Crossref PubMed Scopus (45) Google ScholarKEGG, GO databasesLipid transport(P = 8.12 × 10−9)Endocytosis(P = 2.24 × 10−6)Glutamate receptor signaling pathway(P=1.86 × 10−11), axon guidance(P = 1.20 × 10−9)Focal adhesion(P = 2.63 × 10−10)Protein autophosphorylation(P = 2.30 × 10−12), transmembrane receptor protein kinase activity(P = 1.18 × 10−12),Calcium signaling pathway(P = 1.27 × 10−11)IGAP 10Jones L. et al.Convergent genetic and expression data implicate immunity in Alzheimer's disease.Alzheimers Dement. 2015; 11: 658-671Abstract Full Text Full Text PDF PubMed Scopus (42) Google ScholarALIGATOR/GSEA: KEGG, GO databasesCholesterol transport(P = 2.96 × 10−9), sterol transport(P = 3.91 × 10−9)Humoral immune response mediated by circulating immunoglobulin(P = 3.27 × 10−12), regulation of immune response(P = 3.27 × 10−12)Regulation of endocytosis(P = 1.31 × 10−11)Clathrin adaptor complex(P = 1.20 × 10−3), protein folding(P = 1.60 × 10−3)GERAD integrated with transcriptome profiling data of temporal cortex 29Xiang Z. et al.Integrating genome-wide association study and brain expression data highlights cell adhesion molecules and purine metabolism in Alzheimer's disease.Mol. Neurobiol. 2015; 52: 514-521Crossref PubMed Scopus (23) Google Scholar.KEGGAxon guidance(P = 3.03 × 10−3)Cell adhesion molecules (CAMs)(P = 1.04 × 10−5)Calcium signaling pathway(P = 2.08 × 10−3), Viral myocarditis (P = 4.00 × 10−4), Purine metabolism(P = 4.00 × 10−4)a P values indicate multiple-testing corrected values according to respective studies for terms showing strongest interaction within each pathway. Abbreviations: ADGC, Alzheimer's Disease Genetics Consortium; CHARGE, Cohorts for Heart and Aging in Genomic Epidemiology; EADI, European Alzheimer's Disease Initiative; GERAD, Genetic and Environmental Risk in Alzheimer's Disease; IGAP, International Genomics of Alzheimer's Project. Open table in a new tab The investigation of rare variants in susceptibility of AD has gained significant momentum after the identification through combined whole genome (WGS) and whole exome sequencing (WES) of a rare non-synonymous mutation, p.R47H, in TREM2 that increases risk of AD [7Guerreiro R. et al.TREM2 variants in Alzheimer's disease.N. Engl. J. Med. 2013; 368: 117-127Crossref PubMed Scopus (1896) Google Scholar, 31Jonsson T. et al.Variant of TREM2 associated with the risk of Alzheimer's disease.N. Engl. J. Med. 2013; 368: 107-116Crossref PubMed Scopus (1649) Google Scholar], which has since been widely replicated. Numerous independent studies further reported an enrichment of rare coding variants among AD patients in SORL1 and ABCA7, which were already known to be implicated in AD risk through common risk factors. In SORL1, both heterozygous missense and premature termination codon (PTC) mutations were found, most notably in patients with EOAD and/or familial disease [32Pottier C. et al.High frequency of potentially pathogenic SORL1 mutations in autosomal dominant early-onset Alzheimer disease.Mol. Psychiatry. 2012; 17: 875-879Crossref PubMed Scopus (195) Google Scholar]. For ABCA7, strongest evidence of association was found for heterozygous PTC mutations, with widely varying onset age among carriers but an increased proportion of positive family history [8Steinberg S. et al.Loss-of-function variants in ABCA7 confer risk of Alzheimer's disease.Nat. Genet. 2015; 47: 445-447Crossref PubMed Scopus (221) Google Scholar, 33Cuyvers E. et al.Mutations in ABCA7 in a Belgian cohort of Alzheimer's disease patients: a targeted resequencing study.Lancet Neurol. 2015; 14: 814-822Abstract Full Text Full Text PDF PubMed Scopus (105) Google Scholar]. Large-scale sequencing efforts, such as the Alzheimer Disease Sequencing Project (ADSP) which includes ∼11 000 participants, are providing further evidence of rare risk variants in genes initially identified in GWAS [34Beecham, G. (2017) Whole-genome sequencing in familial late-onset Alzheimer's disease identifies rare variation in AD candidate genes, Alzheimer's Association International Conference | July 16-20, 2017, Alzheimer's & Dementia: The Journal of the Alzheimer's Association. London, EnglandGoogle Scholar]. These studies are likely to provide insight into novel risk genes for AD in the near future. An exome-wide association analysis on rare and low-frequency variants using exome chip genotyping proposed rare variants in two additional genes, that is, ABI3 and PLGC2, the latter being protective for AD [9Sims R. et al.Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease.Nat. Genet. 2017; 49: 1373-1384Crossref PubMed Scopus (519) Google Scholar]. Both classical gene discovery and pathway-based analysis of AD GWAS shed light on the range of biological processes that contribute to AD in addition to amyloid pathology. The straightforward interpretation and functional investigation of the association between a genetic variant and AD remains challenging, however, due to difficulty in identifying disease variants, pleiotropy of risk genes, and pathophysiological complexity. Hundreds of molecular factors putatively interact in multiple networks at different time points and at distinct biological levels including subcellular, cellular, tissue, and organic level. Increasingly, studies address this complexity by combining genetic data with gene expression studies. This allows studying the effect of genetic risk factors at the level of the transcriptome in a tissue, topology, time and cell-type-dependent manner, to identify similarities between disease networks, and uncover molecular interactors that relate to hub nodes connecting pathways. Transcriptome profiling is commonly performed by either microarray hybridization or next-generation RNA sequencing. Although both methods enable large-scale investigation of gene expression, the underlying principles of expression profiling differ fundamentally (Box 1).Box 1Common Methods for Transcriptome ProfilingMicroarray transcriptome profiling provides a cost-efficient approach to quantification of thousands of transcripts in parallel. Complementary DNA (cDNA) libraries reverse transcribed from RNA samples are introduced to the array prior to further analysis such as differential gene expression. However, the necessity of correcting for nonbiological effects on signal output represents a drawback. Microarray profiling is further confounded by variations in probe hybridization sensitivity within and between array platforms. Additionally, quantification of very low or very highly expressed transcripts proves problematic [77Shendure J. The beginning of the end for microarrays?.Nat. Methods. 2008; 5: 585-587Crossref PubMed Scopus (247) Google Scholar]. As probes cannot be designed for unknown sequences, microarray profiling is unable to identify novel transcripts. Probe design is restricted to knowledge of the genome at the time of array design. Human reference genome builds have been updated multiple times over the last ten years [78Tyner C. et al.The UCSC Genome Browser database: 2017 update.Nucleic Acids Res. 2017; 45: D626-D634PubMed Google Scholar], which should be kept in mind when interpreting meta-analyses of several independent datasets.By contrast, RNA sequencing (RNA-Seq) represents a hybridization-free method allowing massive parallel sequencing of cDNA using next-generation sequencing platforms. Selection of subset of RNAs from a total RNA sample is performed prior to reverse transcription to obtain a cDNA library enriched for RNAs, such as miRNA or (poly-adenylated) mRNA. Common procedures for enrichment involve removal of highly abundant ribosomal RNA by depletion of ribosomal RNA or pull-down of poly-adenylated (poly-A) RNAs by oligo-dT beads. Of note, RNAs lacking a poly-A tail including small mRNAs and non-coding RNAs (ncRNAs) are not retained with poly-A pulldown, while these molecules have been shown relevant to AD. For example, the poly-A tail lacking ncRNA 51A maps to intron 1 of the SORL1 gene in antisense direction. ncRNA 51A is upregulated in AD frontal cortex and regulates SORL1 expression and splicing [79Ciarlo E. et al.An intronic ncRNA-dependent regulation of SORL1 expression affecting Aβ formation is upregulated in post-mortem Alzheimer's disease brain samples.Dis. Model Mech. 2013; 6: 424-433Crossref PubMed Scopus (134) Google Scholar]. Known degradation of poly-A sequences in postmortem tissue presents a limitation for use of poly-A selected RNA-Seq in the context of AD, which can be overcome by 3′ mRNA-sequencing, where oligo-dT annealing is performed on the 3′ UTR, circumventing loss of non-polyadenylated transcripts. A disadvantage of this method is the inability to discriminate between different transcript isoforms. Quantification of transcripts generated by RNA-Seq involves read alignment and quality control filtering, where the number of reads aligned to each transcript represents a measure of transcript abundance. The RNA-Seq approach enables directional sequencing of cDNA, generating reads spanning multiple exons while maintaining directional information of reads. This allows for identification of novel splice events, which are of specific interest in the understanding of complex diseases [80Sutherland G.T. et al.Understanding the pathogenesis of Alzheimer's disease: will RNA-Seq realize the promise of transcriptomics?.J. Neurochem. 2011; 116: 937-946Crossref PubMed Scopus (52) Google Scholar]. Microarray transcriptome profiling provides a cost-efficient approach to quantification of thousands of transcripts in parallel. Complementary DNA (cDNA) libraries reverse transcribed from RNA samples are introduced to the array prior to further analysis such as differential gene expression. However, the necessity of correcting for nonbiological effects on signal output represents a drawback. Microarray profiling is further confounded by variations in probe hybridization sensitivity within and between array platforms. Additionally, quantification of very low or very highly expressed transcripts proves problematic [77Shendure J. The beginning of the end for microarrays?.Nat. Methods. 2008; 5: 585-587Crossref PubMed Scopus (247) Google Scholar]. As probes cannot be designed for unknown sequences, microarray profiling is unable to identify novel transcripts. Probe design is restricted to knowledge of the genome at the time of array design. Human reference genome builds have been updated multiple times over the last ten years [78Tyner C. et al.The UCSC Genome Browser database: 2017 update.Nucleic Acids Res. 2017; 45: D626-D634PubMed Google Scholar], which should be kept in mind when interpreting meta-analyses of several independent datasets. By contrast, RNA sequ