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Genetic architecture of resilience of executive functioning

  • ADNI: Friday Harbor 2011 Workshop SPECIAL ISSUE
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Abstract

The genetic basis of resilience, defined as better cognitive functioning than predicted based on neuroimaging or neuropathology, is not well understood. Our objective was to identify genetic variation associated with executive functioning resilience. We computed residuals from regression models of executive functioning, adjusting for age, sex, education, Hachinski score, and MRI findings (lacunes, cortical thickness, volumes of white matter hyperintensities and hippocampus). We estimated heritability and analyzed these residuals in models for each SNP. We further evaluated our most promising SNP result by evaluating cis-associations with brain levels of nearby (±100 kb) genes from a companion data set, and comparing expression levels in cortex and cerebellum from decedents with AD with those from other non-AD diseases. Complete data were available for 750 ADNI participants of European descent. Executive functioning resilience was highly heritable (H2 = 0.76; S.E. = 0.44). rs3748348 on chromosome 14 in the region of RNASE13 was associated with executive functioning resilience (p-value = 4.31 × 10-7). rs3748348 is in strong linkage disequilibrium (D′ of 1.00 and 0.96) with SNPs that map to TPPP2, a member of the α-synuclein family of proteins. We identified nominally significant associations between rs3748348 and expression levels of three genes (FLJ10357, RNASE2, and NDRG2). The strongest association was for FLJ10357 in cortex, which also had the most significant difference in expression between AD and non-AD brains, with greater expression in cortex of decedents with AD (p-value = 7 × 10-7). Further research is warranted to determine whether this signal can be replicated and whether other loci may be associated with cognitive resilience.

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References

  • Allen, M., Zou, F., Chai, H. S., Younkin, C. S., Crook, J., Pankratz, V. S., et al. (2012 in press). Novel late-onset Alzheimer’s disease loci variants associate with brain region expression. Neurology.

  • Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. (2005). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21(2), 263–265. doi:10.1093/bioinformatics/bth457.

    Article  PubMed  CAS  Google Scholar 

  • Carmichael, O., Schwarz, C., Drucker, D., Fletcher, E., Harvey, D., Beckett, L., et al. (2010). Longitudinal changes in white matter disease and cognition in the first year of the Alzheimer disease neuroimaging initiative. Archives of Neurology, 67(11), 1370–1378. doi:10.1001/archneurol.2010.284.

    Article  PubMed  Google Scholar 

  • Carrasquillo, M. M., Zou, F., Pankratz, V. S., Wilcox, S. L., Ma, L., Walker, L. P., et al. (2009). Genetic variation in PCDH11X is associated with susceptibility to late-onset Alzheimer’s disease. Nature Genetics, 41(2), 192–198. doi:10.1038/ng.305.

    Article  PubMed  CAS  Google Scholar 

  • Centers for Disease Control and Prevention, & Alzheimer’s Association. (2007). The healthy brain initiative: a national public health road map to maintaining cognitive health. Chicago: Alzheimer’s Association.

    Google Scholar 

  • Crane, P. K., Carle, A., Gibbons, L. E., Insel, P., Mackin, R. S., Gross, A., et al. (2012). Development and assessment of a composite score for memory in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Brain Imaging and Behavior. doi:10.1007/s11682-012-9186-z

  • Desikan, R., Lee, I., & Thundat, T. (2006). Effect of nanometer surface morphology on surface stress and adsorption kinetics of alkanethiol self-assembled monolayers. Ultramicroscopy, 106(8–9), 795–799. doi:10.1016/j.ultramic.2005.11.012.

    Article  PubMed  CAS  Google Scholar 

  • Ertekin-Taner, N. (2011). Gene expression endophenotypes: a novel approach for gene discovery in Alzheimer’s disease. Molecular Neurodegeneration, 6, 31. doi:10.1186/1750-1326-6-31.

    Article  PubMed  CAS  Google Scholar 

  • Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.

    Article  PubMed  CAS  Google Scholar 

  • Gibbons, L. E., Carle, A. C., Mackin, R. S., Harvey, D., Mukherjee, S., Insel, P., et al. (2012). A composite score for executive functioning, validated in Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment. Brain Imaging and Behavior. doi:10.1007/s11682-012-9176-1

  • Goodglass, H., & Kaplan, D. (1983). The assessment of aphasia and related disorders (2nd ed.). Philadelphia: Lea & Febiger.

    Google Scholar 

  • Hertzog, C., Kramer, A. F., Wilson, R. S., & Lindenberger, U. (2009). Enrichment effects on adult cognitive development: can the functional capacity of older adults be preserved and enhanced? Psychological Science in the Public Interest, 9(1), 1–65.

    Google Scholar 

  • Ikram, M. A., Vrooman, H. A., Vernooij, M. W., van der Lijn, F., Hofman, A., van der Lugt, A., et al. (2007). Brain tissue volumes in the general elderly population The Rotterdam Scan Study. Neurobiology of Aging. 29(6): 882–890.

    Google Scholar 

  • Jack, C. R., Jr., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., et al. (2008). The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691.

    Article  PubMed  Google Scholar 

  • Lewin Group, & Alzheimer’s Association. (2003). Saving lives, saving money: dividends for Americans investing in Alzheimer’s research. Washington, D.C.: Alzheimer’s Association.

    Google Scholar 

  • Longstreth, W. T., Jr., Manolio, T. A., Arnold, A., Burke, G. L., Bryan, N., Jungreis, C. A., et al. (1996). Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke; A Journal of Cerebral Circulation, 27(8), 1274–1282.

    Article  Google Scholar 

  • Longstreth, W. T., Jr., Bernick, C., Manolio, T. A., Bryan, N., Jungreis, C. A., & Price, T. R. (1998). Lacunar infarcts defined by magnetic resonance imaging of 3660 elderly people: the Cardiovascular Health Study. Archives of Neurology, 55(9), 1217–1225.

    Article  PubMed  Google Scholar 

  • Longstreth, W. T., Jr., Dulberg, C., Manolio, T. A., Lewis, M. R., Beauchamp, N. J., Jr., O’Leary, D., et al. (2002). Incidence, manifestations, and predictors of brain infarcts defined by serial cranial magnetic resonance imaging in the elderly: the Cardiovascular Health Study. Stroke, 33(10), 2376–2382.

    Article  PubMed  Google Scholar 

  • McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34(7), 939–944.

    Article  PubMed  CAS  Google Scholar 

  • Mohs, R. C., Knopman, D., Petersen, R. C., Ferris, S. H., Ernesto, C., Grundman, M., et al. (1997). Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the Alzheimer’s Disease Assessment Scale that broaden its scope. The Alzheimer’s Disease Cooperative Study. Alzheimer Dis Assoc Disord, 11(Suppl 2), S13–21.

    Article  Google Scholar 

  • Morris, J. C., Heyman, A., Mohs, R. C., Hughes, J. P., van Belle, G., Fillenbaum, G., et al. (1989). The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology, 39(9), 1159–1165.

    Article  PubMed  CAS  Google Scholar 

  • Mungas, D., Harvey, D., Reed, B. R., Jagust, W. J., DeCarli, C., Beckett, L., et al. (2005). Longitudinal volumetric MRI change and rate of cognitive decline. Neurology, 65(4), 565–571. doi:10.1212/01.wnl.0000172913.88973.0d.

    Article  PubMed  CAS  Google Scholar 

  • Muthén, L., & Muthén, B. (2006). Mplus users guide. Version 4.1 ed. Los Angeles: Muthen and Muthen.

    Google Scholar 

  • Potkin, S. G., Guffanti, G., Lakatos, A., Turner, J. A., Kruggel, F., Fallon, J. H., et al. (2009). Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer’s disease. PLoS One, 4(8), e6501. doi:10.1371/journal.pone.0006501.

    Article  PubMed  Google Scholar 

  • Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38(8), 904–909.

    Article  PubMed  CAS  Google Scholar 

  • Pruim, R. J., Welch, R. P., Sanna, S., Teslovich, T. M., Chines, P. S., Gliedt, T. P., et al. (2010). LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics, 26(18), 2336–2337. doi:10.1093/bioinformatics/btq419.

    Article  PubMed  CAS  Google Scholar 

  • Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559–575.

    Article  PubMed  CAS  Google Scholar 

  • R Development Core Team. (2005). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

    Google Scholar 

  • Reed, B. R., Mungas, D., Farias, S. T., Harvey, D., Beckett, L., Widaman, K., et al. (2010). Measuring cognitive reserve based on the decomposition of episodic memory variance. Brain: A Journal of Neurology, 133(Pt 8), 2196–2209. doi:10.1093/brain/awq154.

    Article  Google Scholar 

  • Reitan, R. M. (1958). Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills, 8, 271–276.

    Google Scholar 

  • Rey, A. (1964). L’examen clinique en psychologie. Paris: Presses Universitaires de France.

    Google Scholar 

  • Satz, P. (1993). Brain reserve capacity on symptom onset after brain injury: a formulation and review of evidence for threshold theory. Neuropsychology, 7(3), 273–295.

    Article  Google Scholar 

  • Saykin, A. J., Shen, L., Foroud, T. M., Potkin, S. G., Swaminathan, S., Kim, S., et al. (2010). Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimer’s & Dementia, 6(3), 265–273. doi:S1552-5260(10)00082-8 [pii]10.1016/j.jalz.2010.03.013.

    Article  CAS  Google Scholar 

  • Scarmeas, N., & Stern, Y. (2003). Cognitive reserve and lifestyle. Journal of Clinical and Experimental Neuropsychology, 25(5), 625–633.

    Article  PubMed  Google Scholar 

  • Schwarz, C., Fletcher, E., DeCarli, C., & Carmichael, O. (2009). Fully-automated white matter hyperintensity detection with anatomical prior knowledge and without FLAIR. Information Processing in Medical Imaging, 21, 239–251.

    Article  PubMed  Google Scholar 

  • Shen, L., Kim, S., Risacher, S. L., Nho, K., Swaminathan, S., West, J. D., et al. (2010). Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. NeuroImage, 53(3), 1051–1063. doi:S1053-8119(10)00064-9.

    Article  PubMed  CAS  Google Scholar 

  • Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(3), 448–460.

    Article  PubMed  Google Scholar 

  • The International HapMap Project. (2003). Nature, 426(6968), 789–796. doi:10.1038/nature02168.

    Article  Google Scholar 

  • Visscher, P. M., Hill, W. G., & Wray, N. R. (2008). Heritability in the genomics era–concepts and misconceptions. Nature Reviews. Genetics, 9(4), 255–266. doi:10.1038/nrg2322.

    Article  PubMed  CAS  Google Scholar 

  • Visscher, P. M., Yang, J., & Goddard, M. E. (2010). A commentary on ‘common SNPs explain a large proportion of the heritability for human height’ by Yang et al. (2010). Twin Research and Human Genetics: The Official Journal of the International Society for Twin Studies, 13(6), 517–524. doi:10.1375/twin.13.6.517.

    Google Scholar 

  • Weiner, M. W., Aisen, P. S., Jack, C. R., Jr., Jagust, W. J., Trojanowski, J. Q., Shaw, L., et al. (2010). The Alzheimer’s Disease Neuroimaging Initiative: progress report and future plans. Alzheimer’s & Dementia, 6(3), 202–211. e207.

    Article  Google Scholar 

  • Weschler, D. (1981). Weschler Adult Intelligence Scale-Revised. NY: Psychological Corporation.

    Google Scholar 

  • Weschler, D. (1987). WMS-R: Weschler Memory Scale - Revised manual. NY: Psychological Corporation / HBJ.

    Google Scholar 

  • Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: a tool for genome-wide complex trait analysis. American Journal of Human Genetics, 88(1), 76–82. doi:10.1016/j.ajhg.2010.11.011.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgment

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; 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. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. Data management and the specific analyses reported here were supported by NIH grant R01 AG029672 (Paul Crane, PI), R13 AG030995 (Mungas), and P50 AG05136 (Raskind), from the NIA as well as NIA R01 AG19771 (Saykin) and P30 AG10133 (Saykin/Ghetti).

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Correspondence to Paul K. Crane.

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*Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.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.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Appendices

APPENDIX A: Regression results for the phenotypes of interest

Table 5 Regression results for executive functioning residual when adjusting for demographics, Hachinski score and MRI variables
Table 6 Regression results for executive functioning residual when adjusting for ADNI-Mem, demographics, Hachinski score and MRI variables

APPENDIX B: Sensitivity GWAS analyses results

Table 7 Comparison of GWAS results of our phenotype of interest when a) not adjusting for principal components, b) adjusting for the first three principal components and c) adjusting for the first three principal components and APOE status
Table 8 Results from single-step GWAS analysis for ADNI-EF when a) not adjusting for principal components, b) adjusting for the first three principal components

APPENDIX C: Regional association plot

Fig. 5
figure 5

Regional association plot of the locus (rs3748348) nominally associated with resilience

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Mukherjee, S., Kim, S., Gibbons, L.E. et al. Genetic architecture of resilience of executive functioning. Brain Imaging and Behavior 6, 621–633 (2012). https://doi.org/10.1007/s11682-012-9184-1

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