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Gene-based aggregate SNP associations between candidate AD genes and cognitive decline

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Abstract

Single nucleotide polymorphisms (SNPs) in and near ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, complement receptor 1 (CR1), EPHA1, EXOC3L2, FERMT2, HLA cluster (DRB5-DQA), INPP5D, MEF2C, MS4A cluster (MS4A3-MS4A6E), NME8, PICALM, PTK2B, SLC24A4, SORL1, and ZCWPW1 have been associated with Alzheimer’s disease (AD) in large meta-analyses. We aimed to determine whether established AD-associated genes are associated with longitudinal cognitive decline by examining aggregate variation across these gene regions. In two single-sex cohorts of older, community-dwelling adults, we examined the association between SNPs in previously implicated gene regions and cognitive decline (age-adjusted person-specific cognitive slopes) using a Sequence Kernel Association Test (SKAT). In regions which showed aggregate significance, we examined the univariate association between individual SNPs in the region and cognitive decline. Only two of the original AD-associated SNPs were significantly associated with cognitive decline in our cohorts. We identified significant aggregate-level associations between cognitive decline and the gene regions BIN1, CD33, CELF1, CR1, HLA cluster, and MEF2C in the all-female cohort and significant associations with ABCA7, HLA cluster, MS4A6E, PICALM, PTK2B, SLC24A4, and SORL1 in the all-male cohort. We also identified a block of eight correlated SNPs in CD33 and several blocks of correlated SNPs in CELF1 that were significantly associated with cognitive decline in univariate analysis in the all-female cohort.

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References

  • Barker WW, Luis CA, Kashuba A, Luis M, Harwood DG, Loewenstein D, … Duara R (2002) Relative frequencies of Alzheimer disease, Lewy body, vascular and frontotemporal dementia, and hippocampal sclerosis in the State of Florida Brain Bank. Alzheimer Dis Assoc Disord 16(4): 203–212.

  • Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57:289–300

    Google Scholar 

  • Blank JB, Cawthon PM, Carrion-Petersen ML, Harper L, Johnson JP, Mitson E, Delay RR (2005) Overview of recruitment for the osteoporotic fractures in men study (MrOS). Contemp Clin Trials 26(5):557–568. doi:10.1016/j.cct.2005.05.005

    Article  PubMed  Google Scholar 

  • Bradshaw EM, Chibnik LB, Keenan BT, Ottoboni L, Raj T, Tang A, … De Jager PL (2013) CD33 Alzheimer’s disease locus: altered monocyte function and amyloid biology. Nat Neurosci 16(7):848–850. doi:10.1038/nn.3435

  • Cargill R, Kohama SG, Struve J, Su W, Banine F, Witkowski E, … Sherman LS (2012) Astrocytes in aged nonhuman primate brain gray matter synthesize excess hyaluronan. Neurobiol Aging 33(4):830. e813–824. doi:10.1016/j.neurobiolaging.2011.07.006

  • Crehan H, Holton P, Wray S, Pocock J, Guerreiro R, Hardy J (2012) Complement receptor 1 (CR1) and Alzheimer’s disease. Immunobiology 217(2):244–250. doi:10.1016/j.imbio.2011.07.017

    Article  CAS  PubMed  Google Scholar 

  • Crocker PR, McMillan SJ, Richards HE (2012) CD33-related siglecs as potential modulators of inflammatory responses. Ann N Y Acad Sci 1253:102–111. doi:10.1111/j.1749-6632.2011.06449.x

    Article  CAS  PubMed  Google Scholar 

  • Crocker PR, Paulson JC, Varki A (2007) Siglecs and their roles in the immune system. Nat Rev Immunol 7(4):255–266. doi:10.1038/nri2056

    Article  CAS  PubMed  Google Scholar 

  • Cummings SR, Black DM, Nevitt MC, Browner WS, Cauley JA, Genant HK et al (1990) Appendicular bone density and age predict hip fracture in women. The Study of Osteoporotic Fractures Research Group. JAMA 263(5):665–668

    Article  CAS  PubMed  Google Scholar 

  • Dasgupta T, Ladd AN (2012) The importance of CELF control: molecular and biological roles of the CUG-BP, Elav-like family of RNA-binding proteins. Wiley Interdiscip Rev RNA 3(1):104–121. doi:10.1002/wrna.107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ebbert MT, Ridge PG, Wilson AR, Sharp AR, Bailey M, Norton MC, …. Kauwe JS (2014) Population-based analysis of Alzheimer’s disease risk alleles implicates genetic interactions. Biol Psychiatry 75(9):732–737. doi: 10.1016/j.biopsych.2013.07.008

  • Gould R, Abramson I, Galasko D, Salmon D (2001) Rate of cognitive change in Alzheimer’s disease: methodological approaches using random effects models. J Int Neuropsychol Soc 7(7):813–824

    CAS  PubMed  Google Scholar 

  • Griciuc A, Serrano-Pozo A, Parrado AR, Lesinski AN, Asselin CN, Mullin K, … Tanzi RE (2013) Alzheimer’s disease risk gene CD33 inhibits microglial uptake of amyloid beta. Neuron 78(4):631–643. doi: 10.1016/j.neuron.2013.04.014

  • Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML,…. Williams J (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 41(10):1088–1093. doi: 10.1038/ng.440

  • Harris SE, Davies G, Luciano M, Payton A, Fox HC, Haggarty P, … Deary IJ (2014) Polygenic risk for Alzheimer’s disease is not associated with cognitive ability or cognitive aging in non-demented older people. J Alzheimers Dis 39(3):565–574. doi: 10.3233/JAD-131058

  • Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC, Carrasquillo MM, … Williams J (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet 43(5):429–435. doi: 10.1038/ng.803

  • Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44(8):955–959. doi:10.1038/ng.2354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, …. Amouyel P (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45(12):1452–1458. doi: 10.1038/ng.2802

  • Li Y, Abecasis GR (2006) Mach 1.0: rapid haplotype reconstruction and missing genotype inference. Am J Hum Genet S 79(3):2290

    Google Scholar 

  • Malik M, Simpson JF, Parikh I, Wilfred BR, Fardo DW, Nelson PT, Estus S (2013) CD33 Alzheimer’s risk-altering polymorphism, CD33 expression, and exon 2 splicing. J Neurosci 33(33):13320–13325. doi:10.1523/JNEUROSCI.1224-13.2013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, Buros J, … Schellenberg GD (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet 43(5):436–441. doi: 10.1038/ng.801

  • Orwoll E, Blank JB, Barrett-Connor E, Cauley J, Cummings S, Ensrud K, … Stone K (2005) Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study—a large observational study of the determinants of fracture in older men. Contemp Clin Trials 26(5):569–585. doi: 10.1016/j.cct.2005.05.006

  • Pillai S, Netravali IA, Cariappa A, Mattoo H (2012) Siglecs and immune regulation. Annu Rev Immunol 30:357–392. doi:10.1146/annurev-immunol-020711-075018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pruim R, Welch R, Sanna S, Teslovich T, Chines P, Gliedt T, … Willer C (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics (Oxford, England) 26(18):2336–2337.

  • Ramasamy A, Trabzuni D, Guelfi S, Varghese V, Smith C, Walker R, … Weale ME (2014) Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci 17(10):1418–1428. doi: 10.1038/nn.3801

  • Schork NJ, Murray SS, Frazer K a, Topol EJ (2009) Common vs. rare allele hypotheses for complex diseases. Curr Opin Genet Dev 19:212–219. doi:10.1016/j.gde.2009.04.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M, …. Consortium E (2010) Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 303(18):1832–1840. doi: 10.1001/jama.2010.574

  • Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29(1):308–311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Torkamani A, Topol EJ, Schork NJ (2008) Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 92(5):265–272. doi:10.1016/j.ygeno.2008.07.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Verhaaren BF, Vernooij MW, Koudstaal PJ, Uitterlinden AG, van Duijn CM, Hofman A, … Ikram MA (2013) Alzheimer’s disease genes and cognition in the nondemented general population. Biol Psychiatry 73(5):429–434. doi: 10.1016/j.biopsych.2012.04.009

  • von Gunten S, Bochner BS (2008) Basic and clinical immunology of Siglecs. Ann N Y Acad Sci 1143:61–82. doi:10.1196/annals.1443.011

    Article  Google Scholar 

  • Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, Lin X (2010) Powerful SNP-set analysis for case–control genome-wide association studies. Am J Hum Genet 86(6):929–942. doi:10.1016/j.ajhg.2010.05.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89(1):82–93. doi:10.1016/j.ajhg.2011.05.029

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The Study of Osteoporotic Fractures (SOF) is supported by the National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: R01 AG005407, R01 AR35582, R01 AR35583, R01 AR35584, R01 AG005394, R01 AG027574, and R01 AG027576. The Osteoporotic Fractures in Men (MrOS) Study is supported by the National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. The NIAMS provides funding for the MrOS ancillary study “GWAS in MrOS and SOF” under the grant number RC2 AR058973. TheNIAMS provides funding for the MrOS ancillary study “Replication of candidate gene associations and bone strength phenotype in MrOS” under the grant number R01 AR051124. Dr. Yaffe is supported in part by a National Institute of Aging Grant (K24AG031155). Dr. Yokoyama is supported in part by Larry L. Hillblom Foundation 2012-A-015-FEL, National Institute on Aging K01 AG049152 and Diversity Supplement to P50 AG023501, and AFTD Susan Marcus Memorial Fund Clinical Research Grant.

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Authors and Affiliations

  1. Department of Psychiatry, University of San Francisco - California, 4150 Clement Street, Box VAMC-116H, San Francisco, CA, 94121, USA

    Jasmine Nettiksimmons

  2. California Pacific Medical Center Research Institute, Department of Epidemiology and Biostatistics, University of California - San Francisco, Mission Hall: Global Health & Clinical Sciences Building, 550 16th Street, 2nd floor, Box #0560, San Francisco, CA, 94158-2549, USA

    Gregory Tranah

  3. California Pacific Medical Center Research Institute, Mission Hall: Global Health & Clinical Sciences Building, 550 16th Street, 2nd floor, Box #0560, San Francisco, CA, 94158-2549, USA

    Daniel S. Evans

  4. Memory and Aging Center, University of California - San Francisco, Sandler Neurosciences Center, 675 Nelson Rising Lane, Suite 190, San Francisco, CA, USA

    Jennifer S. Yokoyama

  5. Departments of Psychiatry, Neurology, and Epidemiology and Biostatistics, University of California - San Francisco, San Francisco Veterans Affairs Medical Center, 4150 Clement Street, Box 181, San Francisco, CA, 94121, USA

    Kristine Yaffe

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  1. Jasmine Nettiksimmons
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  2. Gregory Tranah
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Corresponding author

Correspondence to Jasmine Nettiksimmons.

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Supplementary Fig. 1

Locus plot of nominal p values for SNPs examined in CD33 gene region (Pruim et al. 2010). The purple diamond indicates the sentinel SNP in CD33 (rs3865444). The color bar indicates LD structure with the sentinel SNP as the reference. The colored circles represent the other SNPs examined in the CD33 gene region. The LD block of SNPs which were significant in univariate analysis occurs in the promoter region of CD33. (PDF 10 kb)

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Nettiksimmons, J., Tranah, G., Evans, D.S. et al. Gene-based aggregate SNP associations between candidate AD genes and cognitive decline. AGE 38, 41 (2016). https://doi.org/10.1007/s11357-016-9885-2

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