Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology
Autoři:
Nikhil Milind aff001; Christoph Preuss aff001; Annat Haber aff001; Guruprasad Ananda aff001; Shubhabrata Mukherjee aff003; Cai John aff001; Sarah Shapley aff001; Benjamin A. Logsdon aff005; Paul K. Crane aff003; Gregory W. Carter aff001
Působiště autorů:
The Jackson Laboratory, Bar Harbor, Maine, United States of America
aff001; Program in Genetics, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
aff002; Department of Medicine, School of Medicine, University of Washington, Seattle, Washington, United States of America
aff003; Program in Neuroscience, Department of Biology and Geology, Baldwin Wallace University, Berea, Ohio, United States of America
aff004; Sage Bionetworks, Seattle, Washington, United States of America
aff005
Vyšlo v časopise:
Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology. PLoS Genet 16(6): e32767. doi:10.1371/journal.pgen.1008775
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008775
Souhrn
Late-Onset Alzheimer’s disease (LOAD) is a common, complex genetic disorder well-known for its heterogeneous pathology. The genetic heterogeneity underlying common, complex diseases poses a major challenge for targeted therapies and the identification of novel disease-associated variants. Case-control approaches are often limited to examining a specific outcome in a group of heterogenous patients with different clinical characteristics. Here, we developed a novel approach to define relevant transcriptomic endophenotypes and stratify decedents based on molecular profiles in three independent human LOAD cohorts. By integrating post-mortem brain gene co-expression data from 2114 human samples with LOAD, we developed a novel quantitative, composite phenotype that can better account for the heterogeneity in genetic architecture underlying the disease. We used iterative weighted gene co-expression network analysis (WGCNA) to reduce data dimensionality and to isolate gene sets that are highly co-expressed within disease subtypes and represent specific molecular pathways. We then performed single variant association testing using whole genome-sequencing data for the novel composite phenotype in order to identify genetic loci that contribute to disease heterogeneity. Distinct LOAD subtypes were identified for all three study cohorts (two in ROSMAP, three in Mayo Clinic, and two in Mount Sinai Brain Bank). Single variant association analysis identified a genome-wide significant variant in TMEM106B (p-value < 5×10−8, rs1990620G) in the ROSMAP cohort that confers protection from the inflammatory LOAD subtype. Taken together, our novel approach can be used to stratify LOAD into distinct molecular subtypes based on affected disease pathways.
Klíčová slova:
Alzheimer's disease – Cognitive impairment – Gene expression – Gene mapping – Genetic loci – Genetics of disease – RNA sequencing – Transcriptome analysis
Zdroje
1. Cummings JL. Alzheimer’s Disease. Wood AJJ, editor. N Engl J Med. 2004;351: 56–67. doi: 10.1056/NEJMra040223 15229308
2. Bertram L, Tanzi RE. Thirty years of Alzheimer’s disease genetics: The implications of systematic meta-analyses. Nat Rev Neurosci. 2008;9: 768–778. doi: 10.1038/nrn2494 18802446
3. Kilpinen H, Barrett JC. How next-generation sequencing is transforming complex disease genetics. Trends in Genetics. 2013. doi: 10.1016/j.tig.2012.10.001 23103023
4. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996. doi: 10.1126/science.273.5281.1516 8801636
5. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet. 2019;51: 404–413. doi: 10.1038/s41588-018-0311-9 30617256
6. Verheijen J, Sleegers K. Understanding Alzheimer Disease at the Interface between Genetics and Transcriptomics. Trends Genet. 2018;34: 434–447. doi: 10.1016/j.tig.2018.02.007 29573818
7. Mostafavi S, Gaiteri C, Sullivan SE, White CC, Tasaki S, Xu J, et al. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease. Nat Neurosci. 2018;21. doi: 10.1038/s41593-018-0154-9 29802388
8. Mukherjee S, Mez J, Trittschuh E, Saykin AJ, Gibbons LE, Fardo DW, et al. Genetic data and cognitively-defined late-onset Alzheimer’s disease subgroups. Mol Psychiatry. 2018; 1–10. doi: 10.1101/367615
9. Ferreira D, Verhagen C, Hernández-Cabrera JA, Cavallin L, Guo CJ, Ekman U, et al. Distinct subtypes of Alzheimer’s disease based on patterns of brain atrophy: longitudinal trajectories and clinical applications. Sci Rep. 2017;7: 1–13. doi: 10.1038/s41598-016-0028-x 28127051
10. Langfelder P, Horvath S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol. 2007; doi: 10.1186/1752-0509-1-54 18031580
11. Zhang B, Gaiteri C, Bodea LG, Wang Z, McElwee J, Podtelezhnikov AA, et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell. 2013; doi: 10.1016/j.cell.2013.03.030 23622250
12. De Jager PL, Ma Y, McCabe C, Xu J, Vardarajan BN, Felsky D, et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci Data. 2018;5: 180142. Available: doi: 10.1038/sdata.2018.142 30084846
13. Allen M, Carrasquillo MM, Funk C, Heavner BD, Zou F, Younkin CS, et al. Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases. Sci Data. 2016;3: 1–10. doi: 10.1038/sdata.2016.89 27727239
14. Wang M, Beckmann ND, Roussos P, Wang E, Zhou X, Wang Q, et al. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci Data. 2018;5: 1–16. doi: 10.1038/s41597-018-0002-5 30482902
15. Logsdon BA, Perumal TM, Swarup V, Wang M, Funk C, Gaiteri C, et al. Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach. bioRxiv. 2019; doi: 10.7303/syn17114455
16. Greenfest-Allen E, Cartailler J-P, Magnuson MA, Stoeckert CJ. iterativeWGCNA: iterative refinement to improve module detection from WGCNA co-expression networks. bioRxiv. 2017; 234062. doi: 10.1101/234062
17. McKenzie AT, Wang M, Hauberg ME, Fullard JF, Kozlenkov A, Keenan A, et al. Brain Cell Type Specific Gene Expression and Co-expression Network Architectures. Sci Rep. 2018;8: 1–19. doi: 10.1038/s41598-017-17765-5 29311619
18. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S-Y, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42: 348–354. doi: 10.1038/ng.548 20208533
19. DeTure MA, Dickson DW. The neuropathological diagnosis of Alzheimer disease. Mol Neurodegener. 2019;14: 1–18. doi: 10.1186/s13024-018-0301-5 30630532
20. Charrad M, Ghazzali N, Boiteau V, Niknafs A. NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. J Stat Softw. 2014;61: 1–36. doi: 10.18637/jss.v061.i06
21. White CC, Yang HS, Yu L, Chibnik LB, Dawe RJ, Yang J, et al. Identification of genes associated with dissociation of cognitive performance and neuropathological burden: Multistep analysis of genetic, epigenetic, and transcriptional data. PLoS Med. 2017;14: 1–23. doi: 10.1371/journal.pmed.1002287 28441426
22. Klein ZA, Takahashi H, Ma M, Stagi M, Zhou M, Lam TKT, et al. Loss of TMEM106B Ameliorates Lysosomal and Frontotemporal Dementia-Related Phenotypes in Progranulin-Deficient Mice. Neuron. 2017;95: 281–296.e6. doi: 10.1016/j.neuron.2017.06.026 28728022
23. Gallagher MD, Posavi M, Huang P, Unger TL, Berlyand Y, Gruenewald AL, et al. A Dementia-Associated Risk Variant near TMEM106B Alters Chromatin Architecture and Gene Expression. Am J Hum Genet. 2017;101: 643–663. doi: 10.1016/j.ajhg.2017.09.004 29056226
24. Furney SJ, Simmons A, Breen G, Pedroso I, Lunnon K, Proitsi P, et al. Genome-wide association with MRI atrophy measures as a quantitative trait locus for Alzheimer’s disease. Mol Psychiatry. 2011;16: 1130–1138. doi: 10.1038/mp.2010.123 21116278
25. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018; doi: 10.1038/s41588-018-0090-3 29700475
26. Nagel M, Jansen PR, Stringer S, Watanabe K, De Leeuw CA, Bryois J, et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat Genet. 2018; doi: 10.1038/s41588-018-0151-7 29942085
27. Howard DM, Adams MJ, Clarke TK, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019; doi: 10.1038/s41593-018-0326-7 30718901
28. Nagel M, Watanabe K, Stringer S, Posthuma D, Van Der Sluis S. Item-level analyses reveal genetic heterogeneity in neuroticism. Nat Commun. 2018;9. doi: 10.1038/s41467-018-03242-8 29500382
29. Luciano M, Hagenaars SP, Davies G, Hill WD, Clarke TK, Shirali M, et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat Genet. 2018; doi: 10.1038/s41588-017-0013-8 29255261
30. Baselmans BML, Jansen R, Ip HF, van Dongen J, Abdellaoui A, van de Weijer MP, et al. Multivariate genome-wide analyses of the well-being spectrum. Nat Genet. 2019; doi: 10.1038/s41588-018-0320-8 30643256
31. Hill WD, Weiss A, Liewald DC, Davies G, Porteous DJ, Hayward C, et al. Genetic contributions to two special factors of neuroticism are associated with affluence, higher intelligence, better health, and longer life. Mol Psychiatry. 2019; doi: 10.1038/s41380-019-0387-3 30867560
32. Van Der Harst P, Verweij N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ Res. 2018; doi: 10.1161/CIRCRESAHA.117.312086 29212778
33. Pottier C, Zhou X, Perkerson RB, Baker M, Jenkins GD, Serie DJ, et al. Potential genetic modifiers of disease risk and age at onset in patients with frontotemporal lobar degeneration and GRN mutations: a genome-wide association study. Lancet Neurol. 2018; doi: 10.1016/S1474-4422(18)30126–1
34. Chang D, Nalls MA, Hallgrímsdóttir IB, Hunkapiller J, Brug M van der, Cai F, et al. A meta-analysis of genome-wide association studies identifies 17 new Parkinson’s disease risk loci. Nat Genet. 2017; doi: 10.1038/ng.3955 28892059
35. MacGregor S, Ong JS, An J, Han X, Zhou T, Siggs OM, et al. Genome-wide association study of intraocular pressure uncovers new pathways to glaucoma. Nature Genetics. 2018. doi: 10.1038/s41588-018-0176-y 30054594
36. Gharahkhani P, Burdon KP, Cooke Bailey JN, Hewitt AW, Law MH, Pasquale LR, et al. Analysis combining correlated glaucoma traits identifies five new risk loci for open-angle glaucoma. Sci Rep. 2018; doi: 10.1038/s41598-018-20435-9 29449654
37. Choquet H, Paylakhi S, Kneeland SC, Thai KK, Hoffmann TJ, Yin J, et al. A multiethnic genome-wide association study of primary open-angle glaucoma identifies novel risk loci. Nat Commun. 2018; doi: 10.1038/s41467-018-04555-4 29891935
38. Futch HS, Croft CL, Truong VQ, Krause EG, Golde TE. Targeting psychologic stress signaling pathways in Alzheimer’s disease. Mol Neurodegener. 2017;12: 49. doi: 10.1186/s13024-017-0190-z 28633663
39. Vogl T, Gharibyan AL, Morozova-Roche LA. Pro-Inflammatory S100A8 and S100A9 Proteins: Self-Assembly into Multifunctional Native and Amyloid Complexes. Int J Mol Sci. 2012;13: 2893. doi: 10.3390/ijms13032893 22489132
40. De Strooper B, Karran E. The Cellular Phase of Alzheimer’s Disease. Cell. 2016;164: 603–615. doi: 10.1016/j.cell.2015.12.056 26871627
41. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45: 1452–1458. doi: 10.1038/ng.2802 24162737
42. Li Z, Farias FG, Dube U, Del-Aguila JL, Mihindukulasuriya KA, Fernandez MV, et al. The TMEM106B rs1990621 protective variant is also associated with increased neuronal proportion. bioRxiv. 2019; doi: 10.1101/583286
43. Schott JM, Crutch SJ, Carrasquillo MM, Uphill J, Shakespeare TJ, Ryan NS, et al. Genetic risk factors for the posterior cortical atrophy variant of Alzheimer’s disease. Alzheimer’s Dement. 2016;12: 862–871. doi: 10.1016/j.jalz.2016.01.010 26993346
44. Kichaev G, Bhatia G, Loh P-R, Gazal S, Burch K, Freund MK, et al. Leveraging Polygenic Functional Enrichment to Improve GWAS Power. Am J Hum Genet. 2019;104: 65–75. doi: 10.1016/j.ajhg.2018.11.008 30595370
45. Chibnik LB, White CC, Mukherjee S, Raj T, Yu L, Larson EB, et al. Susceptibility to neurofibrillary tangles: role of the PTPRD locus and limited pleiotropy with other neuropathologies. Mol Psychiatry. 2018;23: 1521–1529. doi: 10.1038/mp.2017.20 28322283
46. Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1986;20: 53–65. doi: 10.1177/003754977702900403
47. Clifford H, Wessely F, Pendurthi S, Emes RD. Comparison of clustering methods for investigation of genome-wide methylation array data. Front Genet. 2011;2: 1–11. doi: 10.3389/fgene.2011.00001 22303300
48. Braak H, Thal DR, Ghebremedhin E, Del Tredici K. Stages of the Pathologic Process in Alzheimer Disease. J Neuropathol Exp Neurol. 2011;70: 960–969. doi: 10.1097/NEN.0b013e318232a379 22002422
49. Wilson RS, Arnold SE, Schneider JA, Li Y, Bennett DA. Chronic Distress, Age-Related Neuropathology, and Late-Life Dementia. Psychosom Med. 2007;69. Available: https://journals.lww.com/psychosomaticmedicine/Fulltext/2007/01000/Chronic_Distress,_Age_Related_Neuropathology,_and.9.aspx
50. Mukherjee S, Mez J, Trittschuh EH, Saykin AJ, Gibbons LE, Fardo DW, et al. Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups. Mol Psychiatry. 2018; doi: 10.1038/s41380-018-0298-8 30514930
51. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43: e47–e47. doi: 10.1093/nar/gkv007 25605792
52. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. Omi A J Integr Biol. 2012;16: 284–287. doi: 10.1089/omi.2011.0118 22455463
53. Buniello A, Macarthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47: D1005–D1012. doi: 10.1093/nar/gky1120 30445434
54. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models. Genome Res. 2003;13: 2498–2504. doi: 10.1101/gr.1239303 14597658
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 6
- Může hubnutí souviset s vyšším rizikem nádorových onemocnění?
- Raději si zajděte na oční! Jak souvisí citlivost zraku s rozvojem demence?
- Co způsobuje pooperační infekce? Na vině může být i naše vlastní mikrobiota
- Čeká nás průlom v diagnostice karcinomu pankreatu?
- Polibek, který mi „vzal nohy“ aneb vzácný výskyt EBV u 70leté ženy – kazuistika
Nejčtenější v tomto čísle
- AXR1 affects DNA methylation independently of its role in regulating meiotic crossover localization
- Osteocalcin promotes bone mineralization but is not a hormone
- Super-resolution imaging of RAD51 and DMC1 in DNA repair foci reveals dynamic distribution patterns in meiotic prophase
- Steroid hormones regulate genome-wide epigenetic programming and gene transcription in human endometrial cells with marked aberrancies in endometriosis