Cited by 395Alzheimer's disease research and treatmentsDementia and Cognitive Impairment ResearchGenetic Associations and Epidemiology

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Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score

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Desikan, Rahul S., Fan, Chun Chieh, Wang, Yunpeng, Schork, Andrew J., Cabral, Howard J., Cupples, L. Adrienne, Thompson, Wesley K., Besser, Lilah, Kukull, Walter A., Holland, Dominic, Chen, Chi-Hua, Brewer, James B., Karow, David S., Kauppi, Karolina, Witoelar, Aree, Karch, Celeste M., Bonham, Luke W., Yokoyama, Jennifer S., Rosen, Howard J., Miller, Bruce L., Dillon, William P., Wilson, David M., Hess, Christopher P., Pericak-Vance, Margaret, Haines, Jonathan L., Farrer, Lindsay A., Mayeux, Richard, Hardy, John, Goate, Alison M., Hyman, Bradley T., Schellenberg, Gerard D., McEvoy, Linda K., Andreassen, Ole A., Dale, Anders M.·2017·PLoS Medicine

DOI 10.1371/journal.pmed.1002258PMC PMC5360219

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Abstract

BACKGROUND: Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction. METHODS AND FINDINGS: Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer's Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10-5). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer's Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer's Disease Center [NIA ADC], and Alzheimer's Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62-4.24, p = 1.0 × 10-22). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10-26) and longitudinal progression from normal aging to AD (NIA ADC, Cochran-Armitage trend test, p = 1.5 × 10-10), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10-6, and Consortium to Establish a Registry for Alzheimer's Disease score for neuritic plaques, p = 6.8 × 10-6) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10-6, and hippocampus, p = 7.9 × 10-5). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use. CONCLUSIONS: We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials.

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Method
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Genetics of Alzheimer’s disease
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BackgroundMethodResultInfluential
Advancing drug discovery using the power of the human genome
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“Many studies demonstrated the potential of PRS to predict risks of individuals and improve risk stratification for different diseases, such as Alzheimer’s disease [102], ischemic stroke [103], and skin cancer [104].”
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“However, there is still large amount deviance in the application of these models [9-12].”
BackgroundMethod
The Human Leukocyte Antigen (HLA) DRB1*13:02 Allele Protects against Dementia in Continental Western Europe
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“There is great potential to expand this scoring system by addition of other predictors not included in this study, such as structural MRIs, genetic information, or biofluid biomarkers.(38,39)”
Background
Validation of newly derived polygenic risk scores for dementia in a prospective study of older individuals
Yu, C., Ryan, J. L., Orchard, S. G., Robb, C. E., Woods, R. L., Wolfe, R. S., Renton, A. E., Goate, A., Brodtmann, A. G., Shah, R. C., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13113 · Cited by 24
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“47,48 However, further research is needed to establish the additional information that PRS provide in addition to biomarkers and their utility in research and clinical practice.”
Background
Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer’s disease
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“43 A study by Tan et al. 44 used a related metric, polygenic hazard scores, trained to predict AD age at onset, 45 with weights from the IGAP (International Genomics of Alzheimer’s Project) study.”
Background
Cumulative Genetic Risk and APOE ε4 Are Independently Associated With Dementia Status in a Multiethnic, Population-Based Cohort
Bakulski, K. M., Vadari, H. S., Faul, J. D., Heeringa, S. G., Kardia, S. L. R., Langa, K. M., Smith, J. A., Manly, J. J., Mitchell, C. M., Benke, K. S., et al. (2021) · Neurology Genetics · doi:10.1212/nxg.0000000000000576 · Cited by 20
“AD PGSs may also predict age at AD onset, where individuals with a top quartile AD PGS had an age at onset of 75 vs an age at onset of 95 for the lowest quartile.(31) Despite the motivation to use AD PGS in clinical practice, scientists are understandably hesitant to encourage PGS use outside of research.”
Background
Melanoma risk prediction using a multilocus genetic risk score in the Women's Health Initiative cohort
Cho, H. G., Ransohoff, K. J., Yang, L., Hedlin, H., Assimes, T. L., Han, J., Stefanick, M. L., Tang, J. Y., & Sarin, K. Y. (2018) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2018.02.052 · Cited by 23
Cognitive gene risk profile for the prediction of cognitive decline in presymptomatic Alzheimer’s disease
Porter, T., Villemagne, V. L., Savage, G. R., Milicic, L., Lim, Y. Y., Maruff, P. T., Masters, C. L., Ames, D. J., Bush, A. I., Martins, R. N., et al. (2018) · Personalized Medicine in Psychiatry · doi:10.1016/j.pmip.2018.03.001 · Cited by 24
Genetic risk for Alzheimer’s dementia predicts motor deficits through multi-omic systems in older adults
Tasaki, S., Gaiteri, C., Petyuk, V., Blizinsky, K. D., Jager, P. L. D., Buchman, A. S., & Bennett, D. A. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0577-4 · Cited by 22
Potential Therapeutic Approaches to Alzheimer’s Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools
Avram, S., Mernea, M., Limban, C., Borcan, F., & Chifiriuc, M. C. (2019) · Current Neuropharmacology · doi:10.2174/1570159x18666191230120053 · Cited by 24
The importance of increasing population diversity in genetic studies of type 2 diabetes and related glycaemic traits
Barroso, I. (2021) · Diabetologia · doi:10.1007/s00125-021-05575-4 · Cited by 22
Association of APOE -Independent Alzheimer Disease Polygenic Risk Score With Brain Amyloid Deposition in Asymptomatic Older Adults
Xicota, L., Gyorgy, B., Grenier‐Boley, B., Lecoeur, A., Fontaine, G., Danjou, F., Samper‐Gonzàlez, J., Olivier Colliot, A., Amouyel, P., Martin, G., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000200544 · Cited by 22
Lipids and lipoproteins may play a role in the neuropathology of Alzheimer’s disease
Akyol, Ö., Akyol, S., Chou, M., Chen, S., Liu, C., Selek, S., Soares, J. C., & Chen, C. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1275932 · Cited by 27
Genetic counseling, 2030: An on‐demand service tailored to the needs of a price conscious, genetically literate, and busy world
Rashkin, M. D., Bowes, J., Dunaway, K. W., Dhaliwal, J. K., Loomis, E. W., Riffle, S., Washington, N. L., Ziegler, C., Lu, J. T., & Levin, E. (2019) · Journal of Genetic Counseling · doi:10.1002/jgc4.1123 · Cited by 23
Finemap-MiXeR: A variational Bayesian approach for genetic finemapping
Akdeniz, B. C., Frei, O., Shadrin, A. A., Vetrov, D., Kropotov, D., Hovig, E. J., Andreassen, O. A., & Dale, A. M. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011372 · Cited by 21
Genetic testing in dementia‐A medical genetics perspective
Huq, A. J., Sexton, A., Lacaze, P. A., Masters, C. L., Storey, E., Velakoulis, D., James, P. A., & Winship, I. (2021) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5535 · Cited by 23
Novel DNA methylation loci and genes showing pleiotropic association with Alzheimer’s dementia: a network Mendelian randomization analysis
Liu, D., Wang, Y., Jing, H., Meng, Q., & Yang, J. (2021) · Epigenetics · doi:10.1080/15592294.2021.1959735 · Cited by 21
“The genetic risk score (GRS) is a powerful tool in assessing the risk of many diseases such as Alzheimer’s dementia [4,60].”
Method
A gene–environment interplay between omega‐3 supplementation and APOE ε4 provides insights for Alzheimer’s disease precise prevention amongst high‐genetic‐risk population
Li, L., Xu, W., Tan, C., Cao, X., Wei, B., Dong, C., Tan, L., & Initiative, A. D. N. (2021) · European Journal of Neurology · doi:10.1111/ene.15160 · Cited by 23
“A polygenic hazard score (PHS) for each subject was developed and validated based on a combination of APOE ε4 and 31 other LOAD genetic variants [21].”
Protein degradation-associated mechanisms that are affected in Alzheimer´s disease
Tecalco-Cruz, Á. C., Pedraza‐Chaverrí, J., Briones‐Herrera, A., Cruz-Ramos, E., López‐Cánovas, L., & Zepeda–Cervantes, J. (2022) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-021-04334-8 · Cited by 21
“Furthermore, in a genomewide association (GWAS) analysis, a polymorphism of the gene that encodes membralin (TMEM259) was associated with late-onset AD [119].”
Background
A Blood Test for Alzheimer’s Disease: Progress, Challenges, and Recommendations
Kiddle, S. J., Voyle, N., & Dobson, R. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-179904 · Cited by 21
“Other promising approaches at an earlier stage of development have involved increasing the GWAS sample size using an AD-by-proxy phenotype [6], and developing polygenic hazard scores to predict age of dementia onset [7].”
Body mass index is associated with smaller medial temporal lobe volume in those at risk for Alzheimer's disease
Hayes, J. P., Moody, J. N., Roca, J. G., & Hayes, S. M. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102156 · Cited by 21
Neural Network on Interval-Censored Data with Application to the Prediction of Alzheimer's Disease
Sun, T., & Ding, Y. (2022) · Biometrics · doi:10.1111/biom.13734 · Cited by 21
“For example, multiple SNPs in the CDKN2AIP gene on chromosome 4 reach the moderate significance level ( 𝑝 1 × 10 −5 ) in both GWAS.”
Calibration of polygenic risk scores is required prior to clinical implementation: results of three common cancers in UKB
Wei, J., Shi, Z., Na, R., Resurreccion, W. K., Wang, C., Duggan, D. J., Zheng, S. L., Hulick, P. J., Helfand, B. T., & Xu, J. (2020) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2020-107286 · Cited by 20
Setbacks in Alzheimer research demand new strategies, not surrender
Jobke, B., McBride, T. C., Nevin, L. M., Peiperl, L., Ross, A. J., Stone, C., Turner, R. C., & Editors, A. T. P. M. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002518 · Cited by 20
Multivariate analyses of peripheral blood leukocyte transcripts distinguish Alzheimer's, Parkinson's, control, and those at risk for developing Alzheimer's
Delvaux, E., Mastroeni, D., Nolz, J., Chow, N., Sabbagh, M. N., Caselli, R. J., Reiman, E. M., Marshall, F. J., & Coleman, P. D. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.05.012 · Cited by 19
Genetic Stratification of Age‐Dependent Parkinson's Disease Risk by Polygenic Hazard Score
Pihlstrøm, L., Fan, C. C., Frei, O., Tan, M. M. X., Karunamuni, R. A., Blauwendraat, C., Bandrés‐Ciga, S., Gan‐Or, Z., Grosset, D. G., Dale, A. M., et al. (2021) · Movement Disorders · doi:10.1002/mds.28808 · Cited by 19
From reaction kinetics to dementia: A simple dimer model of Alzheimer’s disease etiology
Lindstrom, M. R., Chavez, M. B., Gross-Sable, E. A., Hayden, E. Y., & Teplow, D. B. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009114 · Cited by 19
“We consider the incidence rate (per person) in the United States [34], the AD PLOS COMPUTATIONAL BIOLOGY From reaction kinetics to dementia: A simple dimer model of Alzheimer’s disease etiology”
BackgroundMethodInfluential
Genetic diversity promotes resilience in a mouse model of Alzheimer's disease
Soni, N., Hohsfield, L. A., Tran, K. M., Kawauchi, S., Walker, A., Javonillo, D. I., Phan, J., Matheos, D. P., Cunha, C. D., Uyar, A., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13753 · Cited by 17
“10,16,18 Efforts, including those by MODEL-AD, are under way to develop mouse models that better recapitulate LOAD and investigate the impact of these specific AD variants on disease.”
Background
The Differential Influence of Immune, Endocytotic, and Lipid Metabolism Genes on Amyloid Deposition and Neurodegeneration in Subjects at Risk of Alzheimer’s Disease
Femminella, G. D., Initiative, A. D. N., Harold, D. H., Scott, J., Williams, J. H., & Edison, P. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200578 · Cited by 19
“A PRS can improve the diagnostic accuracy of APOE alone at identifying AD cases [22], predict the age of AD onset [23] and it can generally improve risk prediction in healthy older adults [24, 25].”
Cortical proteins may provide motor resilience in older adults
Buchman, A. S., Yu, L., Oveisgharan, S., Petyuk, V., Tasaki, S., Gaiteri, C., Wilson, R. S., Grodstein, F., Schneider, J. A., Klein, H., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-90859-3 · Cited by 18
Functional co-activation of the default mode network in APOE ε4-carriers: A replication study
Mentink, L. J., Guimaraes, J., Faber, M., Sprooten, E., Rikkert, M. G. M. O., Haak, K. V., & Beckmann, C. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118304 · Cited by 16
“…to the APOE gene, multiple other genetic variants have been identified in genome-wide association studies that significantly modify the risk of AD (Desikan et al., 2015; Guerreiro et al., 2013; Jansen et al., 2019; Jonsson et al., 2012; Lambert et al., 2013; Liu et al., 2017; Sims et al., 2017).”
Polygenic score modifies risk for Alzheimer's disease in APOE ε4 homozygotes at phenotypic extremes
Huq, A. J., Fulton‐Howard, B., Riaz, M., Laws, S., Sebra, R., Ryan, J., Alzheimer's Disease Genetics Consortium, Renton, A. E., Goate, A. M., Masters, C. L., et al. (2021) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12226 · Cited by 17
A Pathway-Specific Polygenic Risk Score Is Associated with Tau Pathology and Cognitive Decline
Sun, Y., Wang, M., Zhao, Y., Hu, K., Liu, Y., Liu, B., & Initiative, A. D. N. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-215163 · Cited by 17
Global association of greenness exposure with risk of nervous system disease: A systematic review and meta-analysis
Li, F., Liu, W., Hu, C., Tang, M., Zhang, Y., Ho, H. C., Peng, S., Li, Z., Wang, Q., Li, X., et al. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.162773 · Cited by 16
Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes
Gunter, N. B., Gebre, R. K., Graff‐Radford, J., Heckman, M. G., Jack, C. R., Lowe, V. J., Knopman, D. S., Petersen, R. C., Ross, O. A., Vemuri, P., et al. (2024) · Neurology Genetics · doi:10.1212/nxg.0000000000200120 · Cited by 18
Brain change trajectories in healthy adults correlate with Alzheimer’s related genetic variation and memory decline across life
Roe, J. M., Dídac Vidal-piñeiro, A., Sørensen, Ø., Grydeland, H., Leonardsen, E. H., Iakunchykova, O. P., Pan, M., Mowinckel, A. M., Strømstad, M., Nawijn, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53548-z · Cited by 20
“…risk scores for AD (PRS-AD) calculated from risk variants in genome-wide association studies (GWAS) provide a marker to test this; in AD patients, genetic AD risk links with longitudinal outcomes including faster brain and cognitive decline, earlier AD onset, and clinical progression 17 – 19 .”
Interaction effect of alcohol consumption and Alzheimer disease polygenic risk score on the brain cortical thickness of cognitively normal subjects
Matloff, W. J., Zhao, L., Ning, K., Conti, D. V., & Toga, A. W. (2019) · Alcohol · doi:10.1016/j.alcohol.2019.11.002 · Cited by 20
“AD Polygenic Risk Score A weighted polygenic risk score (PRS) was computed for all subjects based on the 33 single nucleotide polymorphisms (SNPs) that constitute the AD polygenic hazard score developed by Desikan et al (Desikan et al., 2017).”
Method
Enriching the design of Alzheimer's disease clinical trials: Application of the polygenic hazard score and composite outcome measures
Banks, S. J., Qiu, Y., Fan, C. C., Dale, A. M., Zou, J., Askew, B., Feldman, H., & Initiative, A. D. N. (2020) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12071 · Cited by 15
A Machine Learning Method to Identify Genetic Variants Potentially Associated With Alzheimer’s Disease
Monk, B., Rajkovic, A., Petrus, S., Rajkovic, A., Gaasterland, T., & Malinow, R. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.647436 · Cited by 17
Association Between Polygenic Risk Score and the Progression from Mild Cognitive Impairment to Alzheimer’s Disease
Liu, H., Initiative, A. D. N., Lutz, M. W., & Luo, S. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210700 · Cited by 17
“Information of the 25 SNPs can be found in Supplementary Table 2 [16].”
Influential
A Phenome-wide Association and Mendelian Randomization Study for Alzheimer’s Disease: A Prospective Cohort Study of 502,493 Participants From the UK Biobank
Chen, S.-D., Zhang, W., Li, Y., Liu, Y., Huang, Y., Deng, Y., Wu, B., Suckling, J., Rolls, E. T., Feng, J., et al. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.08.002 · Cited by 18
Correlations Between Single Nucleotide Polymorphisms, Cognitive Dysfunction, and Postmortem Brain Pathology in Alzheimer’s Disease Among Han Chinese
Yang, Q., Chen, K., Zhang, H., Zhang, W., Gong, C., Zhang, Q., Liu, P., Sun, T., Xu, Y., Qian, X., et al. (2019) · Neuroscience Bulletin · doi:10.1007/s12264-019-00343-2 · Cited by 17
Common variants in Alzheimer’s disease: Novel association of six genetic variants with AD and risk stratification by polygenic risk scores
de Rojas, I., Moreno-Grau, S., Tesi, N., Grenier-Boley, B., Andrade, V., Jansen, I., Pedersen, N. L., Stringa, N., Zettergren, A., Hernández, I., et al. (2019) · doi:10.1101/19012021 · Cited by 28
The pathway to secondary prevention of Alzheimer's disease
McDade, E., Bednar, M. M., Brashear, H. R., Miller, D. S., Maruff, P., Randolph, C., Ismail, Z., Carrillo, M. C., Weber, C. J., Bain, L. J., et al. (2020) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1002/trc2.12069 · Cited by 20
“90 They went on to show thatthePHScouldbeusedprospectivelytopredictrateofprogression to AD in individuals with both preclinical AD and MCI, and that the PHS was more strongly predictive compared to APOE status alone.”
Result
Screening and predicting progression from high-risk mild cognitive impairment to Alzheimer’s disease
Ge, X., Cui, K., Liu, L., Qin, Y., Cui, J., Han, H., Luo, Y., & Yu, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-96914-3 · Cited by 15
Polygenic Effect on Tau Pathology Progression in Alzheimer's Disease
Rubinski, A., Frerich, S., Malik, R., Franzmeier, N., Ramı́rez, A., Dichgans, M., Ewers, M., & Initiative, A. D. N. (2022) · Annals of Neurology · doi:10.1002/ana.26588 · Cited by 15
SNPs in Aβ clearance proteins
Brockmann, K., Lerche, S., Dilger, S. S., Stirnkorb, J. G., Apel, A., Hauser, A., Liepelt‐Scarfone, I., Berg, D., Gasser, T., Schulte, C., et al. (2017) · Neurology · doi:10.1212/wnl.0000000000004705 · Cited by 15
“The presence of the APOE e4 allele as well as polygenic risk are associated with a younger age at onset of dementia, as outlined in a recent meta-analysis.(35) It was previously shown that deranged CSF levels of t-tau and p-tau are also associated with cognitive impairment in PD and that both proteins, Ab and tau, int…”
Background
P-tau and neurodegeneration mediate the effect of β-amyloid on cognition in non-demented elders
Ma, L., Hu, H., Wang, Z.-T., Ou, Y., Dong, Q., Tan, L., & Yu, J. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00943-z · Cited by 16
“APOE genotype was determined by genotyping two single-nucleotides (rs429358, rs7412) [20].”
Method
Clinical impact of whole-genome sequencing in patients with early-onset dementia
Huq, A. J., Thompson, B. A., Bennett, M. F., Bournazos, A., Bommireddipalli, S., Gorelik, A., Schultz, J. R., Sexton, A., Purvis, R., West, K., et al. (2022) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2021-328146 · Cited by 14
eHealth provides a novel opportunity to exploit the advantages of the Nordic countries in psychiatric genetic research, building on the public health care system, biobanks, and registries
Andreassen, O. A. (2017) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32561 · Cited by 16
“For example, newly developed statistical tools are able to predict onset of neuropsychiatric disorders based on polygenic information (Desikan et al., 2017).”
Background
Alzheimer's disease heterogeneity explained by polygenic risk scores derived from brain transcriptomic profiles
Chung, J., Sahelijo, N. J., Maruyama, T., Hu, J., Panitch, R., Xia, W., Mez, J., Stein, T. D., Initiative, T. A. D. N., Saykin, A. J., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13069 · Cited by 13
Genetic risk scores and dementia risk across different ethnic groups in UK Biobank
Mukadam, N., Giannakopoulou, O., Bass, N. J., Kuchenbaecker, K. B., & McQuillin, A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277378 · Cited by 13
“They have become increasingly important in quantifying risk for dementia [6, 7] with good ability to predict both dementia and mild cognitive impairment [8] in people of European origin.”
Background
Early‐onset Alzheimer's disease explained by polygenic risk of late‐onset disease?
Mantyh, W. G., Cochran, J. N., Taylor, J. W., Broce, I., Geier, E. G., Bonham, L. W., Anderson, A. G., Sirkis, D. W., Joie, R. L., Iaccarino, L., et al. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12482 · Cited by 12
“7,11 We chose this particular polygenic risk score as it is able to predict earlier-onset cases of late-onset AD; among APOE ε 3/ ε 3 homozygotes there is a 10-year spread between the upper and lower tenth percentiles in age of onset.”
Background
Genetic risk for Alzheimer’s disease influences neuropathology via multiple biological pathways
Hannon, E. J., Shireby, G. L., Brookes, K. J., Attems, J., Sims, R., Cairns, N. J., Love, S., Thomas, A. J., Morgan, K., Francis, P. T., et al. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa167 · Cited by 14
“, 2016), cerebrospinal biomarkers (Martiskainen et al., 2015; Louwersheimer et al., 2016; Desikan et al., 2017), and neuropathology (Desikan et al.”

Authors: Desikan, R. S., Fan, C. C., Wang, Y., Schork, A. J., Cabral, H. J., Cupples, L. A., Thompson, W. K., Besser, L., Kukull, W. A., Holland, D., Chen, C.-H., Brewer, J. B., Karow, D. S., Kauppi, K., Witoelar, A., Karch, C. M., Bonham, L. W., Yokoyama, J. S., Rosen, H. J., Miller, B. L., Dillon, W. P., Wilson, D. M., Hess, C. P., Pericak-Vance, M., Haines, J. L., Farrer, L. A., Mayeux, R., Hardy, J., Goate, A. M., Hyman, B. T., Schellenberg, G. D., McEvoy, L. K., Andreassen, O. A., & Dale, A. M.
Affiliations: University of California, San Francisco (US)
University of California San Diego (US)
Oslo University Hospital (NO)
Boston University (US)
Sankt Hans Hospital (DK)
University of Washington (US)
Washington University in St. Louis (US)
Case Western Reserve University (US)
Columbia University (US)
UCL Queen Square Institute of Neurology (GB)
Icahn School of Medicine at Mount Sinai (US)
Massachusetts General Hospital (US)
University of Pennsylvania (US)
Corresponding author: Desikan, Rahul S.
Andreassen, Ole A.
Dale, Anders M.
ORCID: Desikan, Rahul S.
Fan, Chun Chieh
Wang, Yunpeng
Schork, Andrew J.
Cabral, Howard J.
Cupples, L. Adrienne
Thompson, Wesley K.
Kukull, Walter A.
Holland, Dominic
Chen, Chi-Hua
Kauppi, Karolina
Witoelar, Aree
Karch, Celeste M.
Bonham, Luke W.
Yokoyama, Jennifer S.
Miller, Bruce L.
Dillon, William P.
Wilson, David M.
Hess, Christopher P.
Haines, Jonathan L.
Farrer, Lindsay A.
Mayeux, Richard
Hardy, John
Goate, Alison M.
Hyman, Bradley T.
Schellenberg, Gerard D.
McEvoy, Linda K.
Andreassen, Ole A.
Dale, Anders M.
Venue: PLoS Medicine
Publisher: Public Library of Science (PLoS)
Published: 2017-03-21
Volume: 14
Issue: 3
Pages: e1002258
ISSN: 1549-1277 (ISSN-L)
1549-1676
DOI: 10.1371/journal.pmed.1002258
PMC: PMC5360219
OpenAlex: W2600084629
Cited by: 395
References: 36
Citations by year: 2026: 18 · 2025: 21 · 2024: 39 · 2023: 35 · 2022: 44 · 2021: 64 · 2020: 43 · 2019: 66 · 2018: 44 · 2017: 19
FWCI: 34.82
Type: journal article
Language: en
Update notices: correction (2017-03-28)
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: PLoS Medicine (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
eScholarship (California Digital Library) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
UCL Discovery (University College London) (submittedVersion)
Figshare (submittedVersion, cc-by)
Columbia Academic Commons (Columbia University)
Funders: National Institutes of Health (NIH-AG046374, K01AG049152, R01MH100351)
National Alzheimer's Coordinating Center
Radiological Society of North America
Foundation of the American Society of Neuroradiology
Norges Forskningsråd ((#213837, #225989, #223273, #237250/EU JPND)
South East Norway Health Authority (2013-123)
Norske Kvinners Sanitetsforening
KG Jebsen Foundation
Topics: Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords: Alzheimer's disease, polygenic hazard score, genetic risk prediction, age of onset, Cox proportional hazards model, neurofibrillary tangles, neuritic plaques, entorhinal cortex atrophy, amygdala atrophy, hippocampal atrophy, APOE ε4, APOE ε2
MeSH terms: Aged, Aged, 80 and over, Alzheimer Disease, Apolipoproteins E, Female, Genotype, Humans, Longitudinal Studies, Male, Middle Aged, United States, Geriatric Assessment, Multifactorial Inheritance, Polymorphism, Single Nucleotide

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Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score

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