Abstract
Most genetic studies concerning risk genes in Alzheimer’s disease (AD) are from Caucasian populations, whereas the data remain limited in the Chinese population. In this study, we systematically explored the relationship between AD and risk genes in mainland China. We sequenced 33 risk genes previously reported to be associated with AD in a total of 3604 individuals in the mainland Chinese population. Common variant (MAF ≥ 0.01) based association analysis and gene-based (MAF < 0.01) association test were performed by PLINK 1.9 and Sequence Kernel Association Test-Optimal, respectively. Polygenic risk score (PRS) was calculated, and receiver operating characteristic curve (AUC) was computed. Plasma Aβ42, Aβ40, total tau (T-tau), and neurofilament light chain (NFL) were tested in a subgroup, and their associations with PRS were conducted using the Spearman correlation test. Six common variants varied significantly between AD patients and cognitively normal controls after the adjustment of age, gender, and APOE ε4 status, including variants in ABCA7 (n = 5) and APOE (n = 1). Among them, four variants were novel and two were reported previously. The AUC of PRS was 0.71. The high PRS was significantly associated with an earlier age at onset (P = 4.30 × 10−4). PRS was correlated with plasma Aβ42, Aβ42/Aβ40 ratio, T-tau, and NFL levels. Gene-based association test revealed that ABCA7 and UNC5C reached statistical significance. The common variants in APOE and ABCA7, as well as rare variants in ABCA7 and UNC5C, may contribute to the etiology of AD. Moreover, the PRS, to some extent, could predict the risk, onset age, and biological changes of AD.
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References
2020 Alzheimer’s disease facts and figures. Alzheimers Dement. 2020;16:391–460.
van der Kant R, Goldstein LSB, Ossenkoppele R. Amyloid-β-independent regulators of tau pathology in Alzheimer disease. Nat Rev Neurosci. 2020;21:21–35.
Sims R, Hill M, Williams J. The multiplex model of the genetics of Alzheimer’s disease. Nat Neurosci. 2020;23:311–22.
Bellenguez C, Grenier-Boley B, Lambert JC. Genetics of Alzheimer’s disease: where we are, and where we are going. Curr Opin Neurobiol. 2020;61:40–8.
Strittmatter WJ, Saunders AM, Schmechel D, Pericak-Vance M, Enghild J, Salvesen GS, et al. Apolipoprotein E: high-avidity binding to beta-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc Natl Acad Sci USA. 1993;90:1977–81.
Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet. 2009;41:1088–93.
Lambert JC, Heath S, Even G, Campion D, Sleegers K, Hiltunen M, et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet. 2009;41:1094–9.
Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M, et al. Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA. 2010;303:1832–40.
Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC, Carrasquillo MM, et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet. 2011;43:429–35.
Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, Buros J, et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet. 2011;43:436–41.
Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45:1452–8.
Cruchaga C, Karch CM, Jin SC, Benitez BA, Cai Y, Guerreiro R, et al. Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease. Nature. 2014;505:550–4.
Steinberg S, Stefansson H, Jonsson T, Johannsdottir H, Ingason A, Helgason H, et al. Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nat Genet. 2015;47:445–7.
Hallman DM, Boerwinkle E, Saha N, Sandholzer C, Menzel HJ, Csázár A, et al. The apolipoprotein E polymorphism: a comparison of allele frequencies and effects in nine populations. Am J Hum Genet. 1991;49:338–49.
McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr., Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:263–9.
Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010;26:589–95.
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.
Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.
Li J, Zhao T, Zhang Y, Zhang K, Shi L, Chen Y, et al. Performance evaluation of pathogenicity-computation methods for missense variants. Nucleic Acids Res. 2018;46:7793–804.
den Dunnen JT, Dalgleish R, Maglott DR, Hart RK, Greenblatt MS, McGowan-Jordan J, et al. HGVS recommendations for the description of sequence variants: 2016 update. Hum Mutat. 2016;37:564–9.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.
Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–5.
Choi SW, O’Reilly PF. PRSice-2: Polygenic Risk Score software for biobank-scale data. Gigascience. 2019;8:giz082.
Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA, et al. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet. 2012;91:224–37.
Carlin DE, Fong SH, Qin Y, Jia T, Huang JK, Bao B, et al. A fast and flexible framework for network-assisted genomic association. iScience. 2019;28:155–61.
Farrer LA, Cupples LA, Haines JL, Hyman B, Kukull WA, Mayeux R, et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA. 1997;278:1349–56.
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.
Serrano-Pozo A, Das S, Hyman BT. APOE and Alzheimer’s disease: advances in genetics, pathophysiology, and therapeutic approaches. Lancet Neurol. 2021;20:68–80.
Ma FC, Wang HF, Cao XP, Tan CC, Tan L, Yu JT. Meta-analysis of the association between variants in ABCA7 and Alzheimer’s disease. J Alzheimers Dis. 2018;63:1261–7.
De Roeck A, Van Broeckhoven C, Sleegers K. The role of ABCA7 in Alzheimer’s disease: evidence from genomics, transcriptomics and methylomics. Acta Neuropathol. 2019;138:201–20.
Pathak GA, Zhou Z, Silzer TK, Barber RC, Phillips NR. Alzheimer’s Disease Neuroimaging Initiative, Breast and Prostate Cancer Cohort Consortium, and Alzheimer’s Disease Genetics Consortium Two-stage Bayesian GWAS of 9576 individuals identifies SNP regions that are targeted by miRNAs inversely expressed in Alzheimer’s and cancer. Alzheimers Dement. 2020;16:162–77.
Shao Y, Shaw M, Todd K, Khrestian M, D’Aleo G, Barnard PJ, et al. DNA methylation of TOMM40-APOE-APOC2 in Alzheimer’s disease. J Hum Genet. 2018;63:459–71.
Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–24.
Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E, et al. Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain. 2015;138:3673–84.
Li WW, Wang Z, Fan DY, Shen YY, Chen DW, Li HY, et al. Association of polygenic risk score with age at onset and cerebrospinal fluid biomarkers of Alzheimer’s disease in a Chinese cohort. Neurosci Bull 2020;36:696–704.
Jia L, Li F, Wei C, Zhu M, Qu Q, Qin W, et al. Prediction of Alzheimer’s disease using multi-variants from a Chinese genome-wide association study. Brain. 2020;144:924–37.
Leonenko G, Sims R, Shoai M, Frizzati A, Bossù P, Spalletta G, et al. Polygenic risk and hazard scores for Alzheimer’s disease prediction. Ann Clin Transl Neurol. 2019;6:456–65.
Jack CR Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14:535–62.
Simrén J, Leuzy A, Karikari TK, Hye A, Benedet AL, Lantero-Rodriguez J, et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer’s disease. Alzheimers Dement. 2021;17:1145–56.
Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Doré V, et al. High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature. 2018;554:249–54.
Preische O, Schultz SA, Apel A, Kuhle J, Kaeser SA, Barro C, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med. 2019;25:277–83.
Skoog I, Kern S, Najar J, Guerreiro R, Bras J, Waern M, et al. A non-APOE polygenic risk score for Alzheimer’s disease is associated with cerebrospinal fluid neurofilament light in a representative sample of cognitively unimpaired 70-year olds. J Gerontol A Biol Sci Med Sci. 2021;76:983–90.
Zhou X, Chen Y, Ip FCF, Lai NCH, Li YYT, Jiang Y, et al. Genetic and polygenic risk score analysis for Alzheimer’s disease in the Chinese population. Alzheimers Dement (Amst). 2020;12:e12074.
Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 2012;367:795–804.
Cuyvers E, De Roeck A, Van den Bossche T, Van Cauwenberghe C, Bettens K, Vermeulen S, et al. Mutations in ABCA7 in a Belgian cohort of Alzheimer’s disease patients: a targeted resequencing study. Lancet Neurol. 2015;14:814–22.
Le Guennec K, Nicolas G, Quenez O, Charbonnier C, Wallon D, Bellenguez C, et al. ABCA7 rare variants and Alzheimer disease risk. Neurology. 2016;86:2134–7.
Li Q, Wang BL, Sun FR, Li JQ, Cao XP, Tan L. The role of UNC5C in Alzheimer’s disease. Ann Transl Med. 2018;6:178.
Wetzel-Smith MK, Hunkapiller J, Bhangale TR, Srinivasan K, Maloney JA, Atwal JK, et al. A rare mutation in UNC5C predisposes to late-onset Alzheimer’s disease and increases neuronal cell death. Nat Med. 2014;20:1452–7.
Jiao B, Liu X, Tang B, Hou L, Zhou L, Zhang F, et al. Investigation of TREM2, PLD3, and UNC5C variants in patients with Alzheimer’s disease from mainland China. Neurobiol Aging. 2014;35:2422.e9–2422.e11.
Korvatska O, Leverenz JB, Jayadev S, McMillan P, Kurtz I, Guo X, et al. R47H variant of TREM2 associated With Alzheimer disease in a large late-onset family: clinical, genetic, and neuropathological study. JAMA Neurol. 2015;72:920–7.
Acknowledgements
The authors are grateful to all subjects for participation in our study.
Funding
This study was supported by the National Key R&D Program of China (No. 2020YFC2008500, 2017YFC0840100, 2017YFC0840104, and 2018YFC1312003), National Natural Science Foundation of China (No. 81671075, 81971029, 81701134, 82071216, and 81901171), Innovation platform and talent plan of Hunan Province (2019SK2335), and the Youth Science Foundation of Xiangya Hospital (2018Q020).
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BJ, XWX, JCL, and LS designed the experiment and analyzed the data. ZHY, LG, XXL, YFZ, LZ, XW, XXL, HL, YLJ, ZJL, YZ, QJY, and WWZ collected the data. BJ and XWX wrote this manuscript. LS edited the manuscript. All authors read and approved the final manuscript.
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Jiao, B., Xiao, X., Yuan, Z. et al. Associations of risk genes with onset age and plasma biomarkers of Alzheimer’s disease: a large case–control study in mainland China. Neuropsychopharmacol. 47, 1121–1127 (2022). https://doi.org/10.1038/s41386-021-01258-1
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DOI: https://doi.org/10.1038/s41386-021-01258-1
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