Proteome-wide association studies using summary pQTL data of brain, CSF, and plasma identify 30 risk genes of Alzheimer’s disease dementia

Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, Sealock J, Karlsson IK, Hägg S, Athanasiu L, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet. 2019;51:404–13. https://doi.org/10.1038/s41588-018-0311-9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wightman DP, Jansen IE, Savage JE, Shadrin AA, Bahrami S, Holland D, Rongve A, Børte S, Winsvold BS, Drange OK, et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat Genet. 2021;53:1276–82. https://doi.org/10.1038/s41588-021-00921-z.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bellenguez C, Küçükali F, Jansen IE, Kleineidam L, Moreno-Grau S, Amin N, Naj AC, Campos-Martin R, Grenier-Boley B, Andrade V, et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat Genet. 2022;54:412–36. https://doi.org/10.1038/s41588-022-01024-z.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wingo AP, Liu Y, Gerasimov ES, Gockley J, Logsdon BA, Duong DM, Dammer EB, Robins C, Beach TG, Reiman EM, et al. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis. Nat Genet. 2021;53:143–6. https://doi.org/10.1038/s41588-020-00773-z.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Brandes N, Linial N, Linial M. PWAS: proteome-wide association study—linking genes and phenotypes by functional variation in proteins. Genome Biology 21. 2020. https://doi.org/10.1186/s13059-020-02089-x.

Nagpal S, Meng X, Epstein MP, Tsoi LC, Patrick M, Gibson G, De Jager PL, Bennett DA, Wingo AP, Wingo TS, Yang J. TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits. The American Journal of Human Genetics. 2019;105:258–66. https://doi.org/10.1016/j.ajhg.2019.05.018.

Article  CAS  PubMed  Google Scholar 

Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, Eyler AE, Denny JC, Nicolae DL, Cox NJ, Im HK. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015;47:1091–8. https://doi.org/10.1038/ng.3367.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, Jansen R, de Geus EJC, Boomsma DI, Wright FA, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48:245–52. https://doi.org/10.1038/ng.3506.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA. Religious Orders Study and Rush Memory and Aging Project. J Alzheimers Dis. 2018;64:S161–89. https://doi.org/10.3233/jad-179939.

Article  PubMed  PubMed Central  Google Scholar 

Hu T, Parrish, R.L., Dai, Q., Buchman, A.S., Tasaki, S., Bennett, D.A., Seyfried, N.T., Epstein, M.P., and Yang, J. Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia. The American Journal of Human Genetics. https://doi.org/10.1016/j.ajhg.2024.07.001.

Dai, Q., Zhou, G., Zhao, H., Võsa, U., Franke, L., Battle, A., Teumer, A., Lehtimäki, T., Raitakari, O.T., Esko, T., et al. OTTERS: a powerful TWAS framework leveraging summary-level reference data. Nature Communications 14. 2023. https://doi.org/10.1038/s41467-023-36862-w.

Yang C, Farias FHG, Ibanez L, Suhy A, Sadler B, Fernandez MV, Wang F, Bradley JL, Eiffert B, Bahena JA, et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci. 2021;24:1302–12. https://doi.org/10.1038/s41593-021-00886-6.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Paraskevas GP, Kapaki E. Cerebrospinal Fluid Biomarkers for Alzheimer’s Disease in the Era of Disease-Modifying Treatments. Brain Sci. 2021;11:1258. https://doi.org/10.3390/brainsci11101258.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bouwman, F.H., Frisoni, G.B., Johnson, S.C., Chen, X., Engelborghs, S., Ikeuchi, T., Paquet, C., Ritchie, C., Bozeat, S., Quevenco, F.C., and Teunissen, C. Clinical application of CSF biomarkers for Alzheimer's disease: From rationale to ratios. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 14. 2022. https://doi.org/10.1002/dad2.12314.

Pais MV, Forlenza OV, Diniz BS. Plasma Biomarkers of Alzheimer’s Disease: A Review of Available Assays, Recent Developments, and Implications for Clinical Practice. Journal of Alzheimer’s Disease Reports. 2023;7:355–80. https://doi.org/10.3233/adr-230029.

Article  PubMed  PubMed Central  Google Scholar 

Altomare D, Stampacchia S, Ribaldi F, Tomczyk S, Chevalier C, Poulain G, Asadi S, Bancila B, Marizzoni M, Martins M, et al. Plasma biomarkers for Alzheimer’s disease: a field-test in a memory clinic. J Neurol Neurosurg Psychiatry. 2023;94:420–7. https://doi.org/10.1136/jnnp-2022-330619.

Article  PubMed  Google Scholar 

Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752. 2009. https://doi.org/10.1038/nature08185.

Mak TSH, Porsch RM, Choi SW, Zhou X, Sham PC. Polygenic scores via penalized regression on summary statistics. Genet Epidemiol. 2017;41:469–80. https://doi.org/10.1002/gepi.22050.

Article  PubMed  Google Scholar 

Tibshirani R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological). 1996;58:267–88.

Google Scholar 

Zhou G, Zhao H. A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics. PLoS Genet. 2021;17: e1009697. https://doi.org/10.1371/journal.pgen.1009697.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zeng, P., and Zhou, X. Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nature Communications 8. 2017. https://doi.org/10.1038/s41467-017-00470-2.

Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C.A., and Smoller, J.W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nature Communications 10. 2019. https://doi.org/10.1038/s41467-019-09718-5.

Tang S, Buchman AS, De Jager PL, Bennett DA, Epstein MP, Yang J. Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia. PLoS Genet. 2021;17: e1009482. https://doi.org/10.1371/journal.pgen.1009482.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liu Y, Xie J. Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structures. J Am Stat Assoc. 2020;115:393–402. https://doi.org/10.1080/01621459.2018.1554485.

Article  CAS  PubMed  Google Scholar 

Liu Y, Chen S, Li Z, Morrison AC, Boerwinkle E, Lin X. ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies. The American Journal of Human Genetics. 2019;104:410–21. https://doi.org/10.1016/j.ajhg.2019.01.002.

Article  CAS  PubMed  Google Scholar 

Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, Carter J, Dalby AB, Eaton BE, Fitzwater T, et al. Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery. PLoS ONE. 2010;5: e15004. https://doi.org/10.1371/journal.pone.0015004.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Devlin B, Roeder K, Wasserman L. Genomic Control, a New Approach to Genetic-Based Association Studies. Theor Popul Biol. 2001;60:155–66. https://doi.org/10.1006/tpbi.2001.1542.

Article  CAS  PubMed  Google Scholar 

Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc: Ser B (Methodol). 2018;57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

Article  Google Scholar 

Yuan, Z., Zhu, H., Zeng, P., Yang, S., Sun, S., Yang, C., Liu, J., and Zhou, X. Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies. Nature Communications 11. 2020. https://doi.org/10.1038/s41467-020-17668-6.

Zhao S, Crouse W, Qian S, Luo K, Stephens M, He X. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits. Nat Genet. 2024;56:336–47. https://doi.org/10.1038/s41588-023-01648-9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Berisa T, Pickrell JK. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics. 2016;32:283–5. https://doi.org/10.1093/bioinformatics/btv546.

Article  CAS  PubMed  Google Scholar 

Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13. https://doi.org/10.1093/nar/gky1131.

Article  CAS  PubMed 

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