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  • Original Article
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FASTKD2 is associated with memory and hippocampal structure in older adults

Molecular Psychiatry volume 20, pages 1197–1204 (2015)Cite this article

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Abstract

Memory impairment is the cardinal early feature of Alzheimer’s disease, a highly prevalent disorder whose causes remain only partially understood. To identify novel genetic predictors, we used an integrative genomics approach to perform the largest study to date of human memory (n=14 781). Using a genome-wide screen, we discovered a novel association of a polymorphism in the pro-apoptotic gene FASTKD2 (fas-activated serine/threonine kinase domains 2; rs7594645-G) with better memory performance and replicated this finding in independent samples. Consistent with a neuroprotective effect, rs7594645-G carriers exhibited increased hippocampal volume and gray matter density and decreased cerebrospinal fluid levels of apoptotic mediators. The MTOR (mechanistic target of rapamycin) gene and pathways related to endocytosis, cholinergic neurotransmission, epidermal growth factor receptor signaling and immune regulation, among others, also displayed association with memory. These findings nominate FASTKD2 as a target for modulating neurodegeneration and suggest potential mechanisms for therapies to combat memory loss in normal cognitive aging and dementia.

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References

  1. Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D, Jones E . Alzheimer's disease. Lancet 2011; 377: 1019–1031.

    Article  PubMed  Google Scholar 

  2. Sweatt JD. Mechanisms of Memory, 2nd edn. Academic Press: Waltham, MA, USA, 2009.

    Google Scholar 

  3. Hurd MD, Martorell P, Delavande A, Mullen KJ, Langa KM . Monetary costs of dementia in the United States. N Engl J Med 2013; 368: 1326–1334.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Stranahan AM, Mattson MP . Recruiting adaptive cellular stress responses for successful brain ageing. Nat Rev Neurosci 2012; 13: 209–216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Reitz C, Brayne C, Mayeux R . Epidemiology of Alzheimer disease. Nat Rev Neurol 2011; 7: 137–152.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Papassotiropoulos A, de Quervain DJ . Genetics of human episodic memory: dealing with complexity. Trends Cogn Sci 2011; 15: 381–387.

    Article  PubMed  Google Scholar 

  7. Harris SE, Deary IJ . The genetics of cognitive ability and cognitive ageing in healthy older people. Trends Cogn Sci 2011; 15: 388–394.

    PubMed  Google Scholar 

  8. Gandhi S, Wood NW . Genome-wide association studies: the key to unlocking neurodegeneration? Nat Neurosci 2010; 13: 789–794.

    Article  CAS  PubMed  Google Scholar 

  9. Ramanan VK, Shen L, Moore JH, Saykin AJ . Pathway analysis of genomic data: concepts, methods, and prospects for future development. Trends Genet 2012; 28: 323–332.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Schadt EE . Molecular networks as sensors and drivers of common human diseases. Nature 2009; 461: 218–223.

    Article  CAS  PubMed  Google Scholar 

  11. Shen L, Thompson PM, Potkin SG, Bertram L, Farrer LA, Foroud TM et al. Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers. Brain Imaging Behav 2014; 8: 183–207.

    Article  CAS  PubMed  Google Scholar 

  12. Juster FT, Suzman R . An overview of the Health and Retirement Study. J Hum Resour 1995; 30: S7.

    Article  Google Scholar 

  13. Lovestone S, Francis P, Kloszewska I, Mecocci P, Simmons A, Soininen H et al. AddNeuroMed–the European collaboration for the discovery of novel biomarkers for Alzheimer's disease. Ann NY Acad Sci 2009; 1180: 36–46.

    Article  CAS  PubMed  Google Scholar 

  14. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC et al. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 2013; 9: e111–e194.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Nho K, Corneveaux JJ, Kim S, Lin H, Risacher SL, Shen L et al. Whole-exome sequencing and imaging genetics identify functional variants for rate of change in hippocampal volume in mild cognitive impairment. Mol Psychiatry 2013; 18: 781–787.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bennett DA, Schneider JA, Buchman AS, Barnes LL, Boyle PA, Wilson RS . Overview and findings from the rush Memory and Aging Project. Curr Alzheimer Res 2012; 9: 646–663.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bennett DA, Schneider JA, Arvanitakis Z, Wilson RS . Overview and findings from the religious orders study. Curr Alzheimer Res 2012; 9: 628–645.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. St. Clair P, Bugliari D, Campbell N, Chien S, Hayden O, Hurd MD et al. RAND HRS Data Documentation, Version L. RAND Center for the Study of Aging 2011. RAND Corporation Santa Monica, CA, USA; Accessed via http://www.rand.org/content/dam/rand/www/external/labor/aging/dataprod/randhrsL.pdf.

    Google Scholar 

  19. Bush WS, Moore JH . Chapter 11: Genome-wide association studies. PLoS Comput Biol 2012; 8: e1002822.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wallace RB, Herzog AR, DR Weir, Ofstedal MB, Langa KM, Fisher GG et al. Documentation of cognitive functioning measures in the Health and Retirement Study. HRS Documentation Report DR-006, 2005. Accessed via http://hrsonline.isr.umich.edu/sitedocs/userg/dr-006.pdf.

  21. Risacher SL, Saykin AJ, West JD, Shen L, Firpi HA, McDonald BC et al. Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res 2009; 6: 347–361.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Trojanowski JQ, Vandeerstichele H, Korecka M, Clark CM, Aisen PS, Petersen RC et al. Update on the biomarker core of the Alzheimer's Disease Neuroimaging Initiative subjects. Alzheimers Dement 2010; 6: 230–238.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kim S, Swaminathan S, Inlow M, Risacher SL, Nho K, Shen L et al. Influence of genetic variation on plasma protein levels in older adults using a multi-analyte panel. PLoS One 2013; 8: e70269.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Weir DR, Faul JD, Kardia SL, Smith JD, Doheny KF, Romm J et al. Quality control report for genotypic data. Health and Retirement Study, 2012. Accessed via http://hrsonline.isr.umich.edu/sitedocs/genetics/HRS_QC_REPORT_MAR2012.pdf.

  25. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS . A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 2012; 28: 3326–3328.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR . Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 2012; 44: 955–959.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ramanan VK, Risacher SL, Nho K, Kim S, Swaminathan S, Shen L et al. APOE and BCHE as modulators of cerebral amyloid deposition: a florbetapir PET genome-wide association study. Mol Psychiatry 2014; 19: 351–357.

    Article  CAS  PubMed  Google Scholar 

  29. Chibnik LB, Shulman JM, Leurgans SE, Schneider JA, Wilson RS, Tran D et al. CR1 is associated with amyloid plaque burden and age-related cognitive decline. Ann Neurol 2011; 69: 560–569.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cruchaga C, Kauwe JSK, Harari O, Jin SC, Cai YF, Karch CM et al. GWAS of Cerebrospinal Fluid Tau Levels Identifies Risk Variants for Alzheimer's Disease. Neuron 2013; 78: 256–268.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mukherjee S, Kim S, Gibbons LE, Nho K, Risacher SL, Glymour MM et al. Genetic architecture of resilience of executive functioning. Brain Imaging Behav 2012; 6: 621–633.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Pe'er I, Yelensky R, Altshuler D, Daly MJ . Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol 2008; 32: 381–385.

    Article  PubMed  Google Scholar 

  33. Barrett JC, Fry B, Maller J, Daly MJ . Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005; 21: 263–265.

    Article  CAS  PubMed  Google Scholar 

  34. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 2010; 26: 2336–2337.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ, de Bakker PI . SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 2008; 24: 2938–2939.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Li MX, Kwan JS, Sham PC . HYST: a hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis. Am J Hum Genet 2012; 91: 478–488.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Nam D, Kim J, Kim S-Y, Kim S . GSA-SNP: a general approach for gene set analysis of polymorphisms. Nucleic Acids Res 2010; 38(Suppl 2): W749–W754.

    Article  Google Scholar 

  38. Willer CJ, Li Y, Abecasis GR . METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26: 2190–2191.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, Small GW et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science 1993; 261: 921–923.

    Article  CAS  PubMed  Google Scholar 

  40. Ghezzi D, Saada A, D'Adamo P, Fernandez-Vizarra E, Gasparini P, Tiranti V et al. FASTKD2 nonsense mutation in an infantile mitochondrial encephalomyopathy associated with cytochrome c oxidase deficiency. Am J Hum Genet 2008; 83: 415–423.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ramanathan A, Schreiber SL . Direct control of mitochondrial function by mTOR. Proc Natl Acad Sci USA 2009; 106: 22229–22232.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Pierce SB, Gersak K, Michaelson-Cohen R, Walsh T, Lee MK, Malach D et al. Mutations in LARS2, encoding mitochondrial leucyl-tRNA synthetase, lead to premature ovarian failure and hearing loss in Perrault syndrome. Am J Hum Genet 2013; 92: 614–620.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Higgins JP, Thompson SG, Deeks JJ, Altman DG . Measuring inconsistency in meta-analyses. Bmj 2003; 327: 557–560.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Yang TP, Beazley C, Montgomery SB, Dimas AS, Gutierrez-Arcelus M, Stranger BE et al. Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies. Bioinformatics 2010; 26: 2474–2476.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Grundberg E, Small KS, Hedman AK, Nica AC, Buil A, Keildson S et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 2012; 44: 1084–1089.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Williams RW, Mulligan MK . Genetic and molecular network analysis of behavior. Int Rev Neurobiol 2012; 104: 135–157.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Webster JA, Gibbs JR, Clarke J, Ray M, Zhang W, Holmans P et al. Genetic control of human brain transcript expression in Alzheimer disease. Am J Hum Genet 2009; 84: 445–458.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Myers AJ, Gibbs JR, Webster JA, Rohrer K, Zhao A, Marlowe L et al. A survey of genetic human cortical gene expression. Nat Genet 2007; 39: 1494–1499.

    Article  CAS  PubMed  Google Scholar 

  49. Yeung KT, Das S, Zhang J, Lomniczi A, Ojeda SR, Xu CF et al. A novel transcription complex that selectively modulates apoptosis of breast cancer cells through regulation of FASTKD2. Mol Cell Biol 2011; 31: 2287–2298.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Beier CP, Kolbl M, Beier D, Woertgen C, Bogdahn U, Brawanski A . CD95/Fas mediates cognitive improvement after traumatic brain injury. Cell Res 2007; 17: 732–734.

    Article  CAS  PubMed  Google Scholar 

  51. Su JH, Anderson AJ, Cribbs DH, Tu C, Tong L, Kesslack P et al. Fas and Fas ligand are associated with neuritic degeneration in the AD brain and participate in beta-amyloid-induced neuronal death. Neurobiol Dis 2003; 12: 182–193.

    Article  CAS  PubMed  Google Scholar 

  52. Jayanthi S, Deng X, Ladenheim B, McCoy MT, Cluster A, Cai NS et al. Calcineurin/NFAT-induced up-regulation of the Fas ligand/Fas death pathway is involved in methamphetamine-induced neuronal apoptosis. Proc Natl Acad Sci USA 2005; 102: 868–873.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hu WT, Chen-Plotkin A, Grossman M, Arnold SE, Clark CM, Shaw LM et al. Novel CSF biomarkers for frontotemporal lobar degenerations. Neurology 2010; 75: 2079–2086.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Papassotiropoulos A, Stefanova E, Vogler C, Gschwind L, Ackermann S, Spalek K et al. A genome-wide survey and functional brain imaging study identify CTNNBL1 as a memory-related gene. Mol Psychiatry 2011; 18: 255–263.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Kapranov P, Willingham AT, Gingeras TR . Genome-wide transcription and the implications for genomic organization. Nat Rev Genet 2007; 8: 413–423.

    Article  CAS  PubMed  Google Scholar 

  56. Consortium EP . An integrated encyclopedia of DNA elements in the human genome. Nature 2012; 489: 57–74.

    Article  Google Scholar 

  57. Lewis A, Tomlinson I . Cancer. The utility of mouse models in post-GWAS research. Science 2012; 338: 1301–1302.

    Article  CAS  PubMed  Google Scholar 

  58. Hirschhorn JN . Genomewide association studies–illuminating biologic pathways. N Engl J Med 2009; 360: 1699–1701.

    Article  CAS  PubMed  Google Scholar 

  59. Bartel DP . MicroRNAs: target recognition and regulatory functions. Cell 2009; 136: 215–233.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Hirayama T, Tarusawa E, Yoshimura Y, Galjart N, Yagi T . CTCF is required for neural development and stochastic expression of clustered Pcdh genes in neurons. Cell Rep 2012; 2: 345–357.

    Article  CAS  PubMed  Google Scholar 

  61. Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M et al. Spatio-temporal transcriptome of the human brain. Nature 2011; 478: 483–489.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Frankland PW, Bontempi B . The organization of recent and remote memories. Nat Rev Neurosci 2005; 6: 119–130.

    Article  CAS  PubMed  Google Scholar 

  63. Papassotiropoulos A, Stephan DA, Huentelman MJ, Hoerndli FJ, Craig DW, Pearson JV et al. Common Kibra alleles are associated with human memory performance. Science 2006; 314: 475–478.

    Article  CAS  PubMed  Google Scholar 

  64. Thomson PA, Parla JS, McRae AF, Kramer M, Ramakrishnan K, Yao J et al. 708 Common and 2010 rare DISC1 locus variants identified in 1542 subjects: analysis for association with psychiatric disorder and cognitive traits. Mol Psychiatry 2014; 19: 668–675.

    Article  CAS  PubMed  Google Scholar 

  65. Zuccato C, Cattaneo E . Brain-derived neurotrophic factor in neurodegenerative diseases. Nat Revs Neurol 2009; 5: 311–322.

    Article  CAS  Google Scholar 

  66. Huentelman MJ, Papassotiropoulos A, Craig DW, Hoerndli FJ, Pearson JV, Huynh KD et al. Calmodulin-binding transcription activator 1 (CAMTA1) alleles predispose human episodic memory performance. Hum Mol Genet 2007; 16: 1469–1477.

    Article  CAS  PubMed  Google Scholar 

  67. Schiepers OJ, Harris SE, Gow AJ, Pattie A, Brett CE, Starr JM et al. APOE E4 status predicts age-related cognitive decline in the ninth decade: longitudinal follow-up of the Lothian Birth Cohort 1921. Mol Psychiatry 2012; 17: 315–324.

    Article  CAS  PubMed  Google Scholar 

  68. Liu CC, Kanekiyo T, Xu H, Bu G . Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat Rev Neurol 2013; 9: 106–118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Caselli RJ, Dueck AC, Osborne D, Sabbagh MN, Connor DJ, Ahern GL et al. Longitudinal modeling of age-related memory decline and the APOE epsilon4 effect. N Engl J Med 2009; 361: 255–263.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Ramanan VK, Saykin AJ . Pathways to neurodegeneration: mechanistic insights from GWAS in Alzheimer's disease, Parkinson's disease, and related disorders. Am J Neurodegener Dis 2013; 2: 145–175.

    PubMed  PubMed Central  Google Scholar 

  71. Papassotiropoulos A, Gerhards C, Heck A, Ackermann S, Aerni A, Schicktanz N et al. Human genome-guided identification of memory-modulating drugs. Proc Natl Acad Sci USA 2013; 110: E4369–E4374.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Spilman P, Podlutskaya N, Hart MJ, Debnath J, Gorostiza O, Bredesen D et al. Inhibition of mTOR by rapamycin abolishes cognitive deficits and reduces amyloid-beta levels in a mouse model of Alzheimer's disease. PLoS One 2010; 5: e9979.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Maiese K, Chong ZZ, Shang YC, Wang S . mTOR: on target for novel therapeutic strategies in the nervous system. Trends Mol Med 2013; 19: 51–60.

    Article  CAS  PubMed  Google Scholar 

  74. Wang L, Chiang HC, Wu W, Liang B, Xie Z, Yao X et al. Epidermal growth factor receptor is a preferred target for treating amyloid-beta-induced memory loss. Proc Natl Acad Sci USA 2012; 109: 16743–16748.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Ramanan VK, Kim S, Holohan K, Shen L, Nho K, Risacher SL 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: 634–648.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Lucin KM, Wyss-Coray T . Immune activation in brain aging and neurodegeneration: too much or too little? Neuron 2009; 64: 110–122.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Martins IJ, Hone E, Foster JK, Sunram-Lea SI, Gnjec A, Fuller SJ et al. Apolipoprotein E, cholesterol metabolism, diabetes, and the convergence of risk factors for Alzheimer's disease and cardiovascular disease. Mol Psychiatry 2006; 11: 721–736.

    Article  CAS  PubMed  Google Scholar 

  78. Bras J, Guerreiro R, Hardy J . Use of next-generation sequencing and other whole-genome strategies to dissect neurological disease. Nat Rev Neurosci 2012; 13: 453–464.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The HRS is sponsored by the National Institute on Aging (grants U01AG009740, RC2AG036495 and RC4AG039029) and is conducted by the University of Michigan. Further information can be found at http://hrsonline.isr.umich.edu/index.php. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica; Biogen Idec; Bristol-Myers Squibb Company; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; F. Hoffmann-La Roche and its affiliated company Genentech; GE Healthcare; Innogenetics; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Medpace; Merck; Meso Scale Diagnostics; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Synarc; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego, CA, USA. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Additional ADNI support comes from the NIH grants P30AG010129, K01AG030514 and U24AG21886. Funding for whole genome sequencing in ADNI participants was provided by the Alzheimer’s Association and the Brin Wojcicki Foundation. For IMAS, we acknowledge the support of the Indiana CTSI (NIH grants U54 RR025761, RR027710-01 and RR020128). AddNeuroMed was funded through the EU FP6 Programme. Data management and the specific analyses reported here were supported by NIH R01AG19771, P30AG10133, R01LM011360 and R00LM011384, as well as NSF IIS-1117335.

Author Contributions

All authors contributed substantively to this report. VKR, LS and AJS were involved in study conception and design. HS, IK, PM, MT, BV and SL were involved in coordination and data collection and processing for AddNeuroMed. PSA, RCP, CRJ, LMS, JQT, MWW, RCG, AWT and AJS were involved in coordination and data collection and processing for ADNI. BCM, MRF, TMF, SG and AJS were involved in coordination and data collection and processing for IMAS. PLDJ, LY and DAB were involved in coordination and data collection and processing for ROS and MAP. VKR, KN, LS, SLR, SK, TMF, SG and AJS were involved in data organization and planning and execution of statistical analyses. VKR and AJS drafted the report and prepared all figures and tables. All authors were involved in reviewing and editing of the report.

Author information

Author notes

  1. V K Ramanan, K Nho, L Shen, S L Risacher, S Kim, B C McDonald, M R Farlow, T M Foroud, S Gao, H Soininen, I Kłoszewska, P Mecocci, M Tsolaki, B Vellas, S Lovestone, P S Aisen, R C Petersen, C R Jack, L M Shaw, J Q Trojanowski, M W Weiner, R C Green, A W Toga, P L De Jager, L Yu, D A Bennett and A J Saykin: Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. For additional details and up-to-date information, see http://www.adni-info.org.

  2. H Soininen: On behalf of the AddNeuroMed Consortium.

Authors and Affiliations

  1. Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA

    V K Ramanan, K Nho, L Shen, S L Risacher, S Kim, B C McDonald, T M Foroud & A J Saykin

  2. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

    V K Ramanan, T M Foroud & A J Saykin

  3. Medical Scientist Training Program, Indiana University School of Medicine, Indianapolis, IN, USA

    V K Ramanan

  4. Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA

    L Shen, T M Foroud & A J Saykin

  5. Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA

    B C McDonald & M R Farlow

  6. Indiana Alzheimer’s Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA

    B C McDonald, M R Farlow, T M Foroud, S Gao & A J Saykin

  7. Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA

    S Gao

  8. Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland

    H Soininen

  9. Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lodz, Poland

    I Kłoszewska

  10. Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy

    P Mecocci

  11. Third Department of Neurology, Aristotle University, Thessaloniki, Greece

    M Tsolaki

  12. INSERM U 558, University of Toulouse, Toulouse, France

    B Vellas

  13. Department of Psychiatry, University of Oxford, Oxford, UK

    S Lovestone

  14. Department of Neuroscience, University of California-San Diego, San Diego, CA, USA

    P S Aisen

  15. Department of Neurology, Mayo Clinic Minnesota, Rochester, MN, USA

    R C Petersen

  16. Department of Radiology, Mayo Clinic Minnesota, Rochester, MN, USA

    C R Jack Jr

  17. Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

    L M Shaw & J Q Trojanowski

  18. Institute on Aging, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

    L M Shaw & J Q Trojanowski

  19. Departments of Radiology, Medicine, and Psychiatry, University of California-San Francisco, San Francisco, CA, USA

    M W Weiner

  20. Department of Veterans Affairs Medical Center, San Francisco, CA, USA

    M W Weiner

  21. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    R C Green

  22. Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    A W Toga

  23. Departments of Neurology and Psychiatry, Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Brigham and Women’s Hospital, Boston, MA, USA

    P L De Jager

  24. Department of Neurology, Harvard Medical School, Boston, MA, USA

    P L De Jager

  25. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    P L De Jager

  26. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    L Yu & D A Bennett

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  1. V K Ramanan
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  8. T M Foroud
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for the Alzheimers Disease Neuroimaging Initiative (ADNI)

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Correspondence to A J Saykin.

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Ramanan, V., Nho, K., Shen, L. et al. FASTKD2 is associated with memory and hippocampal structure in older adults. Mol Psychiatry 20, 1197–1204 (2015). https://doi.org/10.1038/mp.2014.142

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  • DOI: https://doi.org/10.1038/mp.2014.142

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