{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T20:44:21Z","timestamp":1793825061382,"version":"4.6.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["UKDRI-3003"],"award-info":[{"award-number":["UKDRI-3003"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["UKDRI-3003"],"award-info":[{"award-number":["UKDRI-3003"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["UKDRI-3003"],"award-info":[{"award-number":["UKDRI-3003"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Alz Res Therapy"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The success of selecting high risk or early-stage Alzheimer\u2019s disease individuals for the delivery of clinical trials depends on the design and the appropriate recruitment of participants. Polygenic risk scores (PRS) show potential for identifying individuals at risk for Alzheimer\u2019s disease (AD). Our study comprehensively examines AD PRS utility using various methods and models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We compared the PRS prediction accuracy in ADNI (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009568) and BioFINDER (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009766) cohorts using five disease risk modelling approaches, three PRS derivation methods, two AD genome-wide association study (GWAS) statistics and two sets of SNPs: the whole genome and microglia-selective regions only.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The best prediction accuracy was achieved when modelling genetic risk by using two predictors:\n                      <jats:italic>APOE<\/jats:italic>\n                      and remaining PRS (AUC\u2009=\u20090.72\u20130.76). Microglial PRS showed comparable accuracy to the whole genome (AUC\u2009=\u20090.71\u20130.74). The individuals\u2019 risk scores differed substantially, with the largest discrepancies (up to 70%) attributable to the GWAS statistics used.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our work benchmarks the best PRS derivation and modelling strategies for AD genetic prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13195-024-01664-9","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T03:08:11Z","timestamp":1736132891000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Benchmarking Alzheimer\u2019s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies"],"prefix":"10.1186","volume":"17","author":[{"given":"Eftychia","family":"Bellou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woori","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ganna","family":"Leonenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily","family":"Simmonds","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niklas","family":"Mattsson-Carlgren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oskar","family":"Hansson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael W.","family":"Nagle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Escott-Price","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"key":"1664_CR1","unstructured":"The State of Ageing in England is getting worse. Centre for Ageing Better. 2022. Available from: https:\/\/ageingbetter.org.uk\/summary-state-ageing-2022."},{"key":"1664_CR2","unstructured":"NIA statement on report of lecanemab reducing cognitive decline in Alzheimer\u2019s clinical trial | National Institute on Aging. Available from: https:\/\/www.nia.nih.gov\/news\/nia-statement-report-lecanemab-reducing-cognitive-decline-alzheimers-clinical-trial.\u00a0Cited 2023 Nov 8."},{"key":"1664_CR3","first-page":"31","volume":"1","author":"JM Novitzke","year":"2008","unstructured":"Novitzke JM. The significance of clinical trials. J Vasc Interv Neurol. 2008;1:31 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/22518214\/. Cited 2023 Nov 8.","journal-title":"J Vasc Interv Neurol"},{"key":"1664_CR4","unstructured":"Enrichment Strategies for Clinical Trials to Support Approval of Human. Drugs and Biological Products | FDA. Available from: https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/enrichment-strategies-clinical-trials-support-approval-human-drugs-and-biological-products.\u00a0Cited 2023 Nov 8."},{"key":"1664_CR5","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1038\/s41591-021-01382-x","volume":"27","author":"O Hansson","year":"2021","unstructured":"Hansson O. Biomarkers for neurodegenerative diseases. Nat Med. 2021;27:954\u201363 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/34083813\/.\u00a0Cited 2024 Apr 19.","journal-title":"Nat Med"},{"key":"1664_CR6","first-page":"164","volume":"5","author":"C Ballard","year":"2019","unstructured":"Ballard C, Atri A, Boneva N, Cummings JL, Fr\u00f6lich L, Molinuevo JL, et al. Enrichment factors for clinical trials in mild-to-moderate Alzheimer\u2019s disease. Alzheimer\u2019s Dementia\u202f: Transl Res Clin Interv. 2019;5:164 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/31193334\/.\u00a0Cited 2023 Nov 8.","journal-title":"Alzheimer\u2019s Dementia : Transl Res Clin Interv"},{"key":"1664_CR7","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.1001\/jama.298.14.1682","volume":"298","author":"W Burke","year":"2007","unstructured":"Burke W, Psaty BM. Personalized medicine in the era of genomics. JAMA. 2007;298:1682\u20134 Available from: https:\/\/jamanetwork.com\/journals\/jama\/fullarticle\/209088.\u00a0Cited 2023 Nov 9.","journal-title":"JAMA"},{"key":"1664_CR8","doi-asserted-by":"publisher","first-page":"3673","DOI":"10.1093\/brain\/awv268","volume":"138","author":"V Escott-Price","year":"2015","unstructured":"Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E, et al. Common polygenic variation enhances risk prediction for Alzheimer\u2019s disease. Brain. 2015;138:3673\u201384.","journal-title":"Brain"},{"key":"1664_CR9","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/s41588-019-0358-2","volume":"51","author":"BW Kunkle","year":"2019","unstructured":"Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, et al. Genetic meta-analysis of diagnosed Alzheimer\u2019s disease identifies new risk loci and implicates A\u03b2, tau, immunity and lipid processing. Nat Genet. 2019;51:414\u201330.","journal-title":"Nat Genet"},{"key":"1664_CR10","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1038\/s41588-022-01024-z","volume":"54","author":"C Bellenguez","year":"2022","unstructured":"Bellenguez C, K\u00fc\u00e7\u00fckali F, Jansen IE, Kleineidam L, Moreno-Grau S, Amin N, et al. New insights into the genetic etiology of Alzheimer\u2019s disease and related dementias. Nat Genet. 2022;54:412\u201336.","journal-title":"Nat Genet"},{"key":"1664_CR11","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1038\/nature08185","volume":"460","author":"SM Purcell","year":"2009","unstructured":"Purcell SM, Wray NR, Stone JL, Visscher PM, O\u2019Donovan MC, Sullivan PF, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748\u201352. https:\/\/doi.org\/10.1038\/nature08185. Cited 2013 Sep 19.","journal-title":"Nature"},{"key":"1664_CR12","doi-asserted-by":"crossref","unstructured":"\u00a0Leonenko G, Baker E, Stevenson-Hoare J, Sierksma A, Fiers M, Williams J, et al. Identifying individuals with high risk of Alzheimer\u2019s disease using polygenic risk scores. Nat Commun. 2021;12. Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/34301930\/.","DOI":"10.1038\/s41467-021-24082-z"},{"key":"1664_CR13","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1101\/gr.6665407","volume":"17","author":"NR Wray","year":"2007","unstructured":"Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 2007;17:1520\u20138 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/17785532\/.\u00a0Cited 2023 Nov 9.","journal-title":"Genome Res"},{"key":"1664_CR14","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1093\/bioinformatics\/btu848","volume":"31","author":"J Euesden","year":"2015","unstructured":"Euesden J, Lewis CM, O\u2019Reilly PF. PRSice: Polygenic Risk Score software. Bioinformatics. 2015;31:1466\u20138 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/25550326\/. Cited 2023 Nov 9.","journal-title":"Bioinformatics"},{"key":"1664_CR15","doi-asserted-by":"crossref","unstructured":"Ge T, Chen CY, Ni Y, Feng YCA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019; 10. Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/30992449\/.","DOI":"10.1038\/s41467-019-09718-5"},{"key":"1664_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-20314-w","volume":"12","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Priv\u00e9 F, Vilhj\u00e1lmsson B, Speed D. Improved genetic prediction of complex traits from individual-level data or summary statistics. Nat Commun. 2021;12:1\u20139 Available from: https:\/\/www.nature.com\/articles\/s41467-021-24485-y. Cited 2023 Nov 9.","journal-title":"Nat Commun"},{"key":"1664_CR17","doi-asserted-by":"crossref","unstructured":"Gosselin D, Skola D, Coufal NG, Holtman IR, Schlachetzki JCM, Sajti E, et al. An environment-dependent transcriptional network specifies human microglia identity. Science. 2017;356(6344). Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/28546318\/.","DOI":"10.1126\/science.aal3222"},{"key":"1664_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13073-018-0523-8","volume":"10","author":"KE Tansey","year":"2018","unstructured":"Tansey KE, Cameron D, Hill MJ. Genetic risk for Alzheimer\u2019s disease is concentrated in specific macrophage and microglial transcriptional networks. Genome Med. 2018;10:1\u201310 Available from: https:\/\/genomemedicine.biomedcentral.com\/articles\/10.1186\/s13073-018-0523-8.\u00a0Cited 2023 Nov 10.","journal-title":"Genome Med"},{"key":"1664_CR19","doi-asserted-by":"publisher","first-page":"e0281440","DOI":"10.1371\/journal.pone.0281440","volume":"18","author":"E Baker","year":"2023","unstructured":"Baker E, Leonenko G, Schmidt KM, Hill M, Myers AJ, Shoai M, et al. What does heritability of Alzheimer\u2019s disease represent? PLoS ONE. 2023;18:e0281440.","journal-title":"PLoS ONE"},{"key":"1664_CR20","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1038\/ng.2802","volume":"45","author":"JC Lambert","year":"2013","unstructured":"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\u2019s disease. Nat Genet. 2013;45:1452\u20138.","journal-title":"Nat Genet"},{"key":"1664_CR21","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","volume":"74","author":"RC Petersen","year":"2010","unstructured":"Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, et al. Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology. 2010;74:201\u20139.","journal-title":"Neurology"},{"key":"1664_CR22","doi-asserted-by":"crossref","unstructured":"Marees AT, de Kluiver H, Stringer S, Vorspan F, Curis E, Marie-Claire C, et al. A tutorial on conducting genome-wide association studies: Quality control and statistical analysis. Int J Methods Psychiatr Res. 2018;27(2). Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/29484742\/.","DOI":"10.1002\/mpr.1608"},{"key":"1664_CR23","doi-asserted-by":"crossref","unstructured":"Chang CC, Chow CC, Tellier LCCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. Available from:\u00a0http:\/\/gigascience.biomedcentral.com\/articles\/10.1186\/s13742-015-0047-8.","DOI":"10.1186\/s13742-015-0047-8"},{"key":"1664_CR24","unstructured":"The 1000 Genomes Project Consortium, Auton A, Abecasis GR, Altshuler DM, Durbin RM, Bentley DR et al. A global reference for human genetic variation. Nature. 2015:68\u201374. Available from: http:\/\/www.nature.com\/nature\/journal\/v526\/n7571\/full\/nature15393."},{"key":"1664_CR25","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1038\/s41591-021-01348-z","volume":"27","author":"S Palmqvist","year":"2021","unstructured":"Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Dage JL, et al. Prediction of future Alzheimer\u2019s disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med. 2021;27:1034\u201342 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/34031605\/.\u00a0Cited 2024 Apr 19.","journal-title":"Nat Med"},{"key":"1664_CR26","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1038\/s41591-023-02450-0","volume":"29","author":"S Palmqvist","year":"2023","unstructured":"Palmqvist S, Rossi M, Hall S, Quadalti C, Mattsson-Carlgren N, Dellavalle S, et al. Cognitive effects of Lewy body pathology in clinically unimpaired individuals. Nat Med. 2023;29:1971\u20138 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/37464059\/. Cited 2024 Apr 19.","journal-title":"Nat Med"},{"key":"1664_CR27","doi-asserted-by":"publisher","first-page":"2867","DOI":"10.1093\/bioinformatics\/btq559","volume":"26","author":"A Manichaikul","year":"2010","unstructured":"Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26:2867\u201373 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/20926424\/.\u00a0Cited 2024 Jan 4.","journal-title":"Bioinformatics"},{"key":"1664_CR28","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1038\/s41588-020-0600-y","volume":"52","author":"D Speed","year":"2020","unstructured":"Speed D, Holmes J, Balding DJ. Evaluating and improving heritability models using summary statistics. Nat Genet. 2020;52:458\u201362 Available from: https:\/\/www.nature.com\/articles\/s41588-020-0600-y.\u00a0Cited 2023 Nov 9.","journal-title":"Nat Genet"},{"issue":"1","key":"1664_CR29","doi-asserted-by":"publisher","first-page":"5086","DOI":"10.1038\/s41467-019-12653-0","volume":"10","author":"LR Lloyd-Jones","year":"2019","unstructured":"Lloyd-Jones LR, Zeng J, Sidorenko J, Yengo L, Moser G, Kemper KE, et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat Commun. 2019;10(1):5086.\u00a0Available from:\u00a0https:\/\/pubmed.ncbi.nlm.nih.gov\/31704910\/.","journal-title":"Nat Commun"},{"key":"1664_CR30","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1038\/ng.3865","volume":"7","author":"D Speed","year":"2017","unstructured":"Speed D, Cai N, Johnson MR, Nejentsev S, Balding DJ. Reevaluation of SNP heritability in complex human traits. Nat Genet. 2017;7:986\u201392. Available from:\u00a0https:\/\/pubmed.ncbi.nlm.nih.gov\/28530675\/.","journal-title":"Nat Genet"},{"key":"1664_CR31","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.ajhg.2017.03.004","volume":"100","author":"AR Martin","year":"2017","unstructured":"Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, et al. Human demographic history impacts genetic risk prediction across diverse populations. Am J Hum Genet. 2017;100:635\u201349 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/28366442\/.\u00a0Cited 2024 Jan 22.","journal-title":"Am J Hum Genet"},{"key":"1664_CR32","doi-asserted-by":"crossref","unstructured":"Scutari M, Mackay I, Balding D. Using genetic distance to infer the accuracy of genomic prediction. PLoS Genet. 2016;12. Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/27589268\/.\u00a0Cited 2024 Jan 22.","DOI":"10.1371\/journal.pgen.1006288"},{"key":"1664_CR33","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1038\/s41588-022-01054-7","volume":"54","author":"Y Ruan","year":"2022","unstructured":"Ruan Y, Lin YF, Feng YCA, Chen CY, Lam M, Guo Z, et al. Improving polygenic prediction in ancestrally diverse populations. Nat Genet. 2022;54:573\u201380 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/35513724\/.\u00a0Cited 2024 Jan 23.","journal-title":"Nat Genet"},{"key":"1664_CR34","doi-asserted-by":"publisher","unstructured":"Escott-Price V, Hardy J. Genome-wide association studies for Alzheimer\u2019s disease: bigger is not always better. Brain Commun. 2022;4.\u00a0https:\/\/doi.org\/10.1093\/braincomms\/fcac125. Cited 2023 Dec 12.","DOI":"10.1093\/braincomms\/fcac125"},{"key":"1664_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-30675-z","volume":"13","author":"AC Fahed","year":"2022","unstructured":"Fahed AC, Philippakis AA, Khera AV. The potential of polygenic scores to improve cost and efficiency of clinical trials. Nat Commun. 2022;13:1\u20134. Available from: https:\/\/www.nature.com\/articles\/s41467-022-30675-z. Cited 2023 Nov 15.","journal-title":"Nat Commun"},{"key":"1664_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/JAD-220025","volume":"89","author":"K Clark","year":"2022","unstructured":"Clark K, Leung YY, Lee WP, Voight B, Wang LS. Polygenic risk scores in Alzheimer\u2019s disease genetics: methodology, applications, inclusion, and diversity. J Alzheimer\u2019s Dis. 2022;89:1.\u00a0Available from:\u00a0https:\/\/pubmed.ncbi.nlm.nih.gov\/35848019\/. Cited 2023 Nov 21.","journal-title":"J Alzheimer\u2019s Dis"},{"key":"1664_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/gigascience\/giad043","volume":"12","author":"SW Choi","year":"2022","unstructured":"Choi SW, Mak TSH, Hoggart CJ, O\u2019reilly PF. EraSOR: a software tool to eliminate inflation caused by sample overlap in polygenic score analyses. Gigascience. 2022;12:1\u201311 Available from: https:\/\/doi.org\/10.1093\/gigascience\/giad043.\u00a0Cited 2023 Dec 6.","journal-title":"Gigascience"},{"key":"1664_CR38","doi-asserted-by":"publisher","first-page":"2759","DOI":"10.1038\/s41596-020-0353-1","volume":"15","author":"SW Choi","year":"2020","unstructured":"Choi SW, Mak TSH, O\u2019Reilly PF. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc. 2020;15:2759\u201372 Available from: https:\/\/www.nature.com\/articles\/s41596-020-0353-1.\u00a0Cited 2024 Apr 17.","journal-title":"Nat Protoc"},{"key":"1664_CR39","doi-asserted-by":"crossref","unstructured":"Ganna Leonenko, Maryam Shoai, Eftychia Bellou, Rebecca Sims, Julie Williams, John Hardy VE-P, Leonenko G, Shoai M, Bellou E, Sims R, Williams J, et al. Genetic risk for alzheimer disease is distinct from genetic risk for amyloid deposition. Ann Neurol. 2019;86(3):427-35.\u00a0Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/31199530.","DOI":"10.1002\/ana.25530"},{"key":"1664_CR40","doi-asserted-by":"publisher","first-page":"631827","DOI":"10.3389\/fnagi.2021.631827","volume":"13","author":"Y Chen","year":"2021","unstructured":"Chen Y, Hong T, Chen F, Sun Y, Wang Y, Cui L. Interplay between Microglia and Alzheimer\u2019s Disease\u2014Focus on the most relevant risks: APOE genotype, sex and age. Front Aging Neurosci. 2021;13:631827.","journal-title":"Front Aging Neurosci"},{"key":"1664_CR41","doi-asserted-by":"publisher","first-page":"2696","DOI":"10.1038\/s41588-024-01963-9","volume":"56","author":"Y Wu","year":"2024","unstructured":"Wu Y, Sun Z, Zheng Q, Miao J, Dorn S, Mukherjee S, et al. Pervasive biases in proxy genome-wide association studies based on parental history of Alzheimer\u2019s disease. Nat Genet. 2024;56:2696\u2013703 Available from: https:\/\/www.nature.com\/articles\/s41588-024-01963-9. Cited 2024 Dec 11.","journal-title":"Nat Genet"},{"key":"1664_CR42","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1038\/s41588-021-00961-5","volume":"54","author":"Y Ding","year":"2022","unstructured":"Ding Y, Hou K, Burch KS, Lapinska S, Priv\u00e9 F, Vilhj\u00e1lmsson B, et al. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet. 2022;54:30\u20139 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/34931067\/. Cited 2024 Apr 17.","journal-title":"Nat Genet"},{"key":"1664_CR43","doi-asserted-by":"publisher","first-page":"243.e1","DOI":"10.1016\/j.neurobiolaging.2019.02.022","volume":"84","author":"MT Lo","year":"2019","unstructured":"Lo MT, Kauppi K, Fan CC, Sanyal N, Reas ET, Sundar VS, et al. Identification of genetic heterogeneity of Alzheimer\u2019s disease across age. Neurobiol Aging. 2019;84:243.e1-243.e9 Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/30979435\/. Cited 2024 Jul 18.","journal-title":"Neurobiol Aging"}],"container-title":["Alzheimer's Research &amp; Therapy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13195-024-01664-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13195-024-01664-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13195-024-01664-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T15:04:28Z","timestamp":1736175868000},"score":1,"resource":{"primary":{"URL":"https:\/\/alzres.biomedcentral.com\/articles\/10.1186\/s13195-024-01664-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,6]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1664"],"URL":"https:\/\/doi.org\/10.1186\/s13195-024-01664-9","relation":{},"ISSN":["1758-9193"],"issn-type":[{"value":"1758-9193","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,6]]},"assertion":[{"value":"8 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"As per ADNI protocols, all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. More details can be found at adni.loni.usc.edu. BioFINDER is approved by the Regional Ethics Committee in Lund, Sweden.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"EB was supported jointly by UK-DRI and by Eisai Inc; WK, FT, YW and MWN are employees of the Eisai Inc.OH has acquired research support (for the institution) from AVID Radiopharmaceuticals, Biogen, C2N Diagnostics, Eli Lilly, Eisai, Fujirebio, GE Healthcare, and Roche. In the past 2 years, he has received consultancy\/speaker fees from AC Immune, Alzpath, BioArctic, Biogen, Bristol Meyer Squibb, Cerveau, Eisai, Eli Lilly, Fujirebio, Merck, Novartis, Novo Nordisk, Roche, Sanofi and Siemens.GL, VE-P, ES, NM-C-nothing to declare.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}