PDF
(2572KB)
Abstract
Background: Genome wide association studies (GWAS) have identified many genetic variants associated with increased risk of Alzheimer’s disease (AD). These susceptibility loci may effect AD indirectly through a combination of physiological brain changes. Many of these neuropathologic features are detectable via magnetic resonance imaging (MRI).
Methods: In this study, we examine the effects of such brain imaging derived phenotypes (IDPs) with genetic etiology on AD, using and comparing the following methods: two-sample Mendelian randomization (2SMR), generalized summary statistics based Mendelian randomization (GSMR), transcriptome wide association studies (TWAS) and the adaptive sum of powered score (aSPU) test. These methods do not require individual-level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics.
Results: Using publicly available GWAS datasets from the International Genomics of Alzheimer’s Project (IGAP) and UK Biobank’s (UKBB) brain imaging initiatives, we identify 35 IDPs possibly associated with AD, many of which have well established or biologically plausible links to the characteristic cognitive impairments of this neurodegenerative disease.
Conclusions: Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP-based methods, i.e., aSPU, GSMR and TWAS, over MR methods based on independent SNPs (as instrumental variables).
Availability: Example code is available at https://github.com/kathalexknuts/ADIDP.
Graphical abstract
Keywords
aSPU test
/
Mendelian randomization
/
MRI
/
SPU tests
/
Sum test
/
TWAS
Cite this article
Download citation ▾
Katherine A. Knutson, Wei Pan.
Integrating brain imaging endophenotypes with GWAS for Alzheimer’s disease.
Quant. Biol., 2021, 9(2): 185-200 DOI:10.1007/s40484-020-0202-9
| [1] |
Alzheimer’s Association (2016) 2016 Alzheimer’s disease facts and figures. Alzheimers Dement., 12, 459–509
|
| [2] |
Frisoni, G. B., Fox, N. C., Jack, Jr, C. R., Scheltens, P. and Thompson, P. M. (2010) The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol., 6, 67–77
|
| [3] |
Greicius, M. D., Srivastava, G., Reiss, A. L. and Menon, V. (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. USA, 101, 4637–4642
|
| [4] |
Zhang, Y., Schuff, N., Du, A. T., Rosen, H. J., Kramer, J. H., Gorno-Tempini, M. L., Miller, B. L. and Weiner, M. W. (2009) White matter damage in frontotemporal dementia and Alzheimer’s disease measured by diffusion MRI. Brain, 132, 2579–2592
|
| [5] |
Lambert, J.-C., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., Jun, G., DeStefano, A. L., Bis, J. C., Beecham, G. W., (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet., 45, 1452–1458
|
| [6] |
Pierce, B. L. and Burgess, S. (2013) Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am. J. Epidemiol., 178, 1177–1184
|
| [7] |
Zhu, Z., Zheng, Z., Zhang, F., Wu, Y., Trzaskowski, M., Maier, R., Robinson, M. R., McGrath, J. J., Visscher, P. M., Wray, N. R., (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun., 9, 224
|
| [8] |
Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., (2015) A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet., 47, 1091–1098
|
| [9] |
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W., Jansen, R., de Geus, E. J., Boomsma, D. I., Wright, F. A., (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet., 48, 245–252
|
| [10] |
Xu, Z., Wu, C., Wei, P. and Pan, W. (2017) A powerful framework for integrating eQTL and GWAS summary data. Genetics, 207, 893–902
|
| [11] |
Yang, C., Wan, X., Lin, X., Chen, M., Zhou, X. and Liu, J. (2019) CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information. Bioinformatics, 35, 1644–1652
|
| [12] |
Barbeira, A. N., Pividori, M., Zheng, J., Wheeler, H. E., Nicolae, D. L. and Im, H. K. (2019) Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet., 15, e1007889
|
| [13] |
Hu, Y., Li, M., Lu, Q., Weng, H., Wang, J., Zekavat, S. M., Yu, Z., Li, B., Gu, J., Muchnik, S., (2019) A statistical framework for cross-tissue transcriptome-wide association analysis. Nat. Genet., 51, 568–576
|
| [14] |
Yang, Y., Shi, X., Jiao, Y., Huang, J., Chen, M., Zhou, X., Sun, L., Lin, X., Yang, C. and Liu, J. (2019) CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies. Bioinformatics, 36, 2009–2016
|
| [15] |
Xu, Z., Wu, C. and Pan, W., and the Alzheimer’s Disease Neuroimaging Initiative. (2017) Imaging-wide association study: integrating imaging endophenotypes in GWAS. Neuroimage, 159, 159–169
|
| [16] |
Zhao, B., Luo, T., Li, T., Li, Y., Zhang, J., Shan, Y., Wang, X., Yang, L., Zhou, F., Zhu, Z., (2019) Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet., 51, 1637–1644
|
| [17] |
Zhao, B., Shan, Y., Yang, Y., Li, T., Luo, T., Zhu, Z., Li, Y. and Zhu, H. (2019b) Transcriptome-wide association analysis of 211 neuroimaging traits identifies new genes for brain structures and yields insights into the gene-level pleiotropy with other complex traits. bioRxiv, 842872
|
| [18] |
Nicholas, M., Freund, M. K., Johnson, R., Shi, H., Kichaev, G., Gusev, A. and Pasaniuc, B. (2019) Probabilistic fine-mapping of transcriptomewide association studies. Nat. Genet., 51, 675–682
|
| [19] |
Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A. N., Knowles, D. A., Golan, D., Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., (2019) Opportunities and challenges for transcriptome-wide association studies. Nat. Genet., 51, 592–599
|
| [20] |
Pan, W. (2009) Asymptotic tests of association with multiple SNPs in linkage disequilibrium. Genet. Epidemiol., 33, 497–507
|
| [21] |
Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) A powerful and adaptive association test for rare variants. Genetics, 197, 1081–1095
|
| [22] |
Kwak, I. Y. and Pan, W. (2016) Adaptive gene- and pathway-trait association testing with GWAS summary statistics. Bioinformatics, 32, 1178–1184
|
| [23] |
Yan D., Hu B., Darst B., Mukherjee S., Kunkle B., Deming Y., Dumitrescu L., Wang Y., Naj A., Kuzma A., (2019) Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes. bioRxiv, 468306
|
| [24] |
Elliott, L. T., Sharp, K., Alfaro-Almagro, F., Shi, S., Miller, K. L., Douaud, G., Marchini, J. and Smith, S. M. (2018) Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature, 562, 210–216
|
| [25] |
1000 Genomes Project Consortium, 2010. A map of human genome variation from population-scale sequencing. Nature, 467, 1061–1073
|
| [26] |
Li, X., Xia, J., Ma, C., Chen, K., Xu, K., Zhang, J., Chen, Y., Li, H., Wei, D., and Zhang, Z. (2020) Accelerating structural degeneration in temporal regions and their effects on cognition in aging of MCI patients. Cereb., Cortex, 30, 326–338
|
| [27] |
Silbert, L. C., Nelson, C., Howieson, D. B., Moore, M. M. and Kaye, J. A. (2008) Impact of white matter hyperintensity volume progression on rate of cognitive and motor decline. Neurology, 71, 108–113
|
| [28] |
Bozzali, M., Giulietti, G., Basile, B., Serra, L., Spanò B., Perri, R., Giubilei, F., Marra, C., Caltagirone, C. and Cercignani, M. (2012) Damage to the cingulum contributes to Alzheimer’s disease pathophysiology by deafferentation mechanism. Hum. Brain Mapp., 33, 1295–1308
|
| [29] |
Brickman, A. M., Meier, I. B., Korgaonkar, M. S., Provenzano, F. A., Grieve, S. M., Siedlecki, K. L., Wasserman, B. T., Williams, L. M. and Zimmerman, M. E. (2012) Testing the white matter retrogenesis hypothesis of cognitive aging. Neurobiol. Aging, 33, 1699–1715
|
| [30] |
de Jong, L. W., van der Hiele, K., Veer, I. M., Houwing, J. J., Westendorp, R. G., Bollen, E. L., de Bruin, P. W., Middelkoop, H. A., van Buchem, M. A. and van der Grond, J. (2008) Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: an MRI study. Brain, 131, 3277–3285
|
| [31] |
Li, X., Wang, H., Tian, Y., Zhou, S., Li, X., Wang, K. and Yu, Y. (2016) Impaired white matter connections of the limbic system networks associated with impaired emotional memory in Alzheimer’s disease. Front. Aging Neurosci., 8, 250
|
| [32] |
Mayo, C. D., Garcia-Barrera, M. A., Mazerolle, E. L., Ritchie, L. J., Fisk, J. D. and Gawryluk, J. R., and the Alzheimer’s Disease Neuroimaging Initiative. (2019) Relationship between DTI metrics and cognitive function in Alzheimer’s disease. Front. Aging Neurosci., 10, 436
|
| [33] |
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., (2018) The MR-base platform supports systematic causal inference across the human phenome. eLife, 7, e34408
|
RIGHTS & PERMISSIONS
Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature