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Identification of novel drug targets for Alzheimer’s disease by integrating genetics and proteomes from brain and blood


Genome-wide association studies (GWASs) have discovered numerous risk genes for Alzheimer’s disease (AD), but how these genes confer AD risk is challenging to decipher. To efficiently transform genetic associations into drug targets for AD, we employed an integrative analytical pipeline using proteomes in the brain and blood by systematically applying proteome-wide association study (PWAS), Mendelian randomization (MR) and Bayesian colocalization. Collectively, we identified the brain protein abundance of 7 genes (ACE, ICA1L, TOM1L2, SNX32, EPHX2, CTSH, and RTFDC1) are causal in AD (P < 0.05/proteins identified for PWAS and MR; PPH4 >80% for Bayesian colocalization). The proteins encoded by these genes were mainly expressed on the surface of glutamatergic neurons and astrocytes. Of them, ACE with its protein abundance was also identified in significant association with AD on the blood-based studies and showed significance at the transcriptomic level. SNX32 was also found to be associated with AD at the blood transcriptomic level. Collectively, our current study results on genetic, proteomic, and transcriptomic approaches has identified compelling genes, which may provide important leads to design future functional studies and potential drug targets for AD.

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Fig. 1: The analytical pipeline for identifying genetic targets of AD using brain- and blood-based proteomics.
Fig. 2: Manhattan plots of PWAS by integrating brain pQTL and AD GWAS.
Fig. 3: MR and Bayesian colocalization analysis identified AD-associated genes.
Fig. 4: Summary of the AD significant genes with evidence for being consistent among the four approaches.

Data availability

Data of brain pQTL and eQTL from the ROS/MAP study are available through and!Synapse:syn3219045, respectively. Proteomic and transcriptomic data used in this manuscript are available via the AD Knowledge Portal ( Data are available for general research use according to the following requirements for data access and data attribution ( Data from AGES Reykjavik study can be accessed at Data from the AGES Reykjavik study are available through collaboration ( under a data usage agreement with the IHA. GTEx can be accessed at (GTEx Analysis V8). Summary statistics for the Schwartzentruber’s meta-analysis are available through the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalog under accession nos. GCST90012877 and GCST90012878 ( Summary statistics for the Jansen’s GWAS can be made available for download upon publication ( Cell-type specificity data are available at

Code availability

Codes associated with the current submission can be requested by contacting the corresponding author.


  1. 2021 Alzheimer’s disease facts and figures. Alzheimers Dement. 2021;17:327–406.

  2. Arvanitakis Z, Shah RC, Bennett DA. Diagnosis and management of dementia: review. JAMA. 2019;322:1589.

    Article  Google Scholar 

  3. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet. 2019;51:404–13.

    Article  CAS  Google Scholar 

  4. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 2019;51:414–30.

  5. Cannon ME, Mohlke KL. Deciphering the emerging complexities of molecular mechanisms at GWAS loci. Am J Hum Genet. 2018;103:637–53.

    Article  CAS  Google Scholar 

  6. Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558:73–9.

    Article  CAS  Google Scholar 

  7. Brandes N, Linial N, Linial M. PWAS: proteome-wide association study—linking genes and phenotypes by functional variation in proteins. Genome Biol. 2020;21:173.

  8. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.

  9. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2019;4:186.

    Article  Google Scholar 

  10. McGowan LM, Davey Smith G, Gaunt TR, Richardson TG. Integrating Mendelian randomization and multiple-trait colocalization to uncover cell-specific inflammatory drivers of autoimmune and atopic disease. Hum Mol Genet. 2019;28:3293–300.

    Article  CAS  Google Scholar 

  11. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.

    Article  CAS  Google Scholar 

  12. Richardson TG, Hemani G, Gaunt TR, Relton CL, Smith GD. A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome. Nat Commun. 2020;11:185.

  13. Wang M, Beckmann ND, Roussos P, Wang E, Zhou X, Wang Q, et al. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci Data. 2018;5:180185.

  14. De Jager PL, Ma Y, McCabe C, Xu J, Vardarajan BN, Felsky D, et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci Data. 2018;5:180142.

  15. Emilsson V, Ilkov M, Lamb JR, Finkel N, Gudmundsson EF, Pitts R, et al. Co-regulatory networks of human serum proteins link genetics to disease. Science. 2018;361:769–73.

  16. Yang J, Yu X, Zhu G, Wang R, Lou S, Zhu W, et al. Integrating GWAS and eQTL to predict genes and pathways for non‐syndromic cleft lip with or without palate. Oral Dis. 2020.

  17. Schwartzentruber J, Cooper S, Liu JZ, Barrio-Hernandez I, Bello E, Kumasaka N, et al. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes. Nat Genet. 2021;53:392–402.

    Article  CAS  Google Scholar 

  18. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48:245–52.

    Article  CAS  Google Scholar 

  19. Wingo AP, Dammer EB, Breen MS, Logsdon BA, Duong DM, Troncosco JC, et al. Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age. Nat Commun. 2019;10:1619.

  20. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89–98.

    Article  CAS  Google Scholar 

  21. Rasooly D, Patel CJ. Conducting a reproducible Mendelian randomization analysis using the R analytic statistical environment. Curr Protoc Hum Genet. 2019;101:e82.

    PubMed  PubMed Central  Google Scholar 

  22. Kibinge NK, Relton CL, Gaunt TR, Richardson TG. Characterizing the causal pathway for genetic variants associated with neurological phenotypes using human brain-derived proteome data. Am J Hum Genet. 2020;106:885–92.

    Article  CAS  Google Scholar 

  23. Taylor K, Davey Smith G, Relton CL, Gaunt TR, Richardson TG. Prioritizing putative influential genes in cardiovascular disease susceptibility by applying tissue-specific Mendelian randomization. Genome Med. 2019;11:6.

  24. Timshel PN, Thompson JJ, Pers TH. Genetic mapping of etiologic brain cell types for obesity. Elife. 2020;9:e55851.

  25. Wingo AP, Liu Y, Gerasimov ES, Gockley J, Logsdon BA, Duong DM, 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.

    Article  CAS  Google Scholar 

  26. Reynolds CA, Hong M-G, Eriksson UK, Blennow K, Wiklund F, Johansson B, et al. Analysis of lipid pathway genes indicates association of sequence variation near SREBF1/TOM1L2/ATPAF2 with dementia risk. Hum Mol Genet. 2010;19:2068–78.

    Article  CAS  Google Scholar 

  27. Padhy B, Hayat B, Nanda GG, Mohanty PP, Alone DP. Pseudoexfoliation and Alzheimer’s associated CLU risk variant, rs2279590, lies within an enhancer element and regulates CLU, EPHX2 and PTK2B gene expression. Hum Mol Genet. 2017;26:4519–29.

    Article  CAS  Google Scholar 

  28. Chen W, Wang M, Zhu M, Xiong W, Qin X, Zhu X. 14,15-Epoxyeicosatrienoic acid alleviates pathology in a mouse model of Alzheimer’s disease. J Neurosci. 2020;40:8188–203.

    Article  CAS  Google Scholar 

  29. Wang X, Mo X, Zhang H, Zhang Y, Shen Y. Identification of phosphorylation associated SNPs for blood pressure, coronary artery disease and stroke from genome-wide association studies. Curr Mol Med. 2019;19:731–8.

    Article  CAS  Google Scholar 

  30. Chung J, Marini S, Pera J, Norrving B, Jimenez-Conde J, Roquer J, et al. Genome-wide association study of cerebral small vessel disease reveals established and novel loci. Brain. 2019;142:3176–89.

    Article  Google Scholar 

  31. Kottemann MC, Conti BA, Lach FP, Smogorzewska A. Removal of RTF2 from stalled replisomes promotes maintenance of genome integrity. Mol Cell. 2018;69:24.e5–35.e5.

    Article  Google Scholar 

  32. Seet LF, Hong W. The Phox (PX) domain proteins and membrane traffic. Biochim Biophys Acta. 2006;1761:878–96.

    Article  CAS  Google Scholar 

  33. Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L, et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun. 2018;9;2282.

  34. Jochemsen HadassaM, Teunissen CharlotteE, Ashby EmmaL, van der Flier WiesjeM, Jones RuthE, Mirjam I Geerlings, et al. The association of angiotensin-converting enzyme with biomarkers for Alzheimer’s disease. Alzheimers Res Ther. 2014;6:27.

    Article  Google Scholar 

  35. Miners Scott, Ashby Emma, Baig Shabnam, Harrison Rachel, Tayler Hannah, Speedy Elizabeth, et al. Angiotensin-converting enzyme levels and activity in Alzheimer’s disease: differences in brain and CSF ACE and association with ACE1 genotypes. Am J Transl Res. 2009;1:163–77.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Baranello RJ, Bharani KL, Padmaraju V, Chopra N, Lahiri DK, Greig NH, et al. Amyloid-beta protein clearance and degradation (ABCD) pathways and their role in Alzheimer’s disease. Curr Alzheimer Res. 2015;12:32–46.

    Article  CAS  Google Scholar 

  37. Yasar S, Xia J, Yao W, Furberg CD, Xue QL, Mercado CI, et al. Antihypertensive drugs decrease risk of Alzheimer disease: Ginkgo Evaluation of Memory Study. Neurology. 2013;81:896–903.

    Article  CAS  Google Scholar 

  38. Koronyo-Hamaoui M, Sheyn J, Hayden EY, Li S, Fuchs DT, Regis GC, et al. Peripherally derived angiotensin converting enzyme-enhanced macrophages alleviate Alzheimer-related disease. Brain. 2020;143:336–58.

    Article  Google Scholar 

  39. Kehoe PG, Perry G, Avila J, Tabaton M, Zhu X. The coming of age of the angiotensin hypothesis in Alzheimer’s disease: progress toward disease prevention and treatment? J Alzheimers Dis. 2018;62:1443–66.

    Article  CAS  Google Scholar 

  40. Huo Y, Li S, Liu J, Li X, Luo X-J. Functional genomics reveal gene regulatory mechanisms underlying schizophrenia risk. Nat Commun 2019;10:670.

  41. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet. 2018;50:538–48.

    Article  CAS  Google Scholar 

  42. Nounu A, Greenhough A, Heesom KJ, Richmond RC, Zheng J, Weinstein SJ, et al. A combined proteomics and Mendelian randomization approach to investigate the effects of aspirin-targeted proteins on colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2020;30:564–75.

  43. Battle A, Khan Z, Wang SH, Mitrano A, Ford MJ, Pritchard JK, et al. Genomic variation. Impact of regulatory variation from RNA to protein. Science. 2015;347:664–7.

    Article  CAS  Google Scholar 

  44. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227–32.

    Article  CAS  Google Scholar 

  45. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature. 2017;550:204–13.

    Article  Google Scholar 

  46. Giambartolomei C, Zhenli Liu J, Zhang W, Hauberg M, Shi H, Boocock J, et al. A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics. 2017;34:2538–45.

    Article  Google Scholar 

  47. Yang C, Farias FHG, Ibanez L, Suhy A, Sadler B, Fernandez MV, et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci. 2021.

  48. Prokopenko D, Morgan SL, Mullin K, Hofmann O, Chapman B, Kirchner R, et al. Whole‐genome sequencing reveals new Alzheimer’s disease–associated rare variants in loci related to synaptic function and neuronal development. Alzheimers Dement. 2021.

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This study was supported by grants from the National Natural Science Foundation of China (82071201, 91849126), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University. This work was made possible by the generous sharing of statistics from the public databases. We thank the participants of the ROS and MAP for their time and effort. Study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. We thank the AGES Reykjavik study, GTEx project, and Cell Types database for their kind dedication. We thank the Schwartzentruber’s GWAS meta-analysis and Jansen’s GWAS. Statistics were made possible by their generous sharing of GWAS summary statistics. Access to data is shown below. The full list of acknowledgements can be found in the Supplementary Text.

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J-TY conceptualized the study and revised the manuscript. Y-NO, Y-XY, B-SW, Y-TD, and YL analyzed and interpreted the data. Y-NO prepared all the figures and tables. Y-NO and Y-XY drafted and revised the manuscript. HH, LT, JS, CZ, YZ, and Y-JW revised the manuscript. All authors contributed to the writing and revisions of the paper and approved the final version.

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Correspondence to Jin-Tai Yu.

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Ou, YN., Yang, YX., Deng, YT. et al. Identification of novel drug targets for Alzheimer’s disease by integrating genetics and proteomes from brain and blood. Mol Psychiatry 26, 6065–6073 (2021).

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