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Wang X, Broce I, Qiu Y, Deters KD, Fan CC, Dale AM, Edland SD, Banks SJ. A simple genetic stratification method for lower cost, more expedient clinical trials in early Alzheimer's disease: A preliminary study of tau PET and cognitive outcomes. Alzheimers Dement 2023; 19:3078-3086. [PMID: 36701211 PMCID: PMC10368787 DOI: 10.1002/alz.12952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 01/27/2023]
Abstract
INTRODUCTION Identifying individuals who are most likely to accumulate tau and exhibit cognitive decline is critical for Alzheimer's disease (AD) clinical trials. METHODS Participants (N = 235) who were cognitively normal or with mild cognitive impairment from the Alzheimer's Disease Neuroimaging Initiative were stratified by a cutoff on the polygenic hazard score (PHS) at 65th percentile (above as high-risk group and below as low-risk group). We evaluated the associations between the PHS risk groups and tau positron emission tomography and cognitive decline, respectively. Power analyses estimated the sample size needed for clinical trials to detect differences in tau accumulation or cognitive change. RESULTS The high-risk group showed faster tau accumulation and cognitive decline. Clinical trials using the high-risk group would require a fraction of the sample size as trials without this inclusion criterion. DISCUSSION Incorporating a PHS inclusion criterion represents a low-cost and accessible way to identify potential participants for AD clinical trials.
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Affiliation(s)
- Xin Wang
- University of California, San Diego, California, USA
| | - Iris Broce
- University of California, San Diego, California, USA
| | - Yuqi Qiu
- East China Normal University, Shanghai, China
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Xu J, Guan X, Wen J, Zhang M, Xu X. Polygenic hazard score modified the relationship between hippocampal subfield atrophy and episodic memory in older adults. Front Aging Neurosci 2022; 14:943702. [PMID: 36389062 PMCID: PMC9659745 DOI: 10.3389/fnagi.2022.943702] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 09/30/2022] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND Understanding genetic influences on Alzheimer's disease (AD) may improve early identification. Polygenic hazard score (PHS) is associated with the age of AD onset and cognitive decline. It interacts with other risk factors, but the nature of such combined effects remains poorly understood. MATERIALS AND METHODS We examined the effect of genetic risk and hippocampal atrophy pattern on episodic memory in a sample of older adults ranging from cognitively normal to those diagnosed with AD using structural MRI. Participants included 51 memory unimpaired normal control (NC), 69 mild cognitive impairment (MCI), and 43 AD adults enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Hierarchical linear regression analyses examined the main and interaction effects of hippocampal subfield volumes and PHS, indicating genetic risk for AD, on a validated episodic memory composite score. Diagnosis-stratified models further assessed the role of PHS. RESULTS Polygenic hazard score moderated the relationship between right fimbria/hippocampus volume ratio and episodic memory, such that patients with high PHS and lower volume ratio had lower episodic memory composite scores [ΔF = 6.730, p = 0.011, ΔR 2 = 0.059]. This effect was also found among individuals with MCI [ΔF = 4.519, p = 0.038, ΔR 2 = 0.050]. In contrast, no interaction effects were present for those NC or AD individuals. A follow-up mediation analysis also indicated that the right fimbria/hippocampus volume ratio might mediate the link between PHS and episodic memory performance in the MCI group, whereas no mediation effects were present for those NC or AD individuals. CONCLUSION These findings suggest that the interaction between AD genetic risk and hippocampal subfield volume ratio increases memory impairment among older adults. Also, the results highlighted a potential pathway in which genetic risk affects memory by degrading hippocampal subfield volume ratio in cognitive decline subjects.
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Affiliation(s)
| | | | | | | | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Motazedi E, Cheng W, Thomassen JQ, Frei O, Rongve A, Athanasiu L, Bahrami S, Shadrin A, Ulstein I, Stordal E, Brækhus A, Saltvedt I, Sando SB, O’Connell KS, Hindley G, van der Meer D, Bergh S, Nordestgaard BG, Tybjærg-Hansen A, Bråthen G, Pihlstrøm L, Djurovic S, Frikke-Schmidt R, Fladby T, Aarsland D, Selbæk G, Seibert TM, Dale AM, Fan CC, Andreassen OA. Using Polygenic Hazard Scores to Predict Age at Onset of Alzheimer's Disease in Nordic Populations. J Alzheimers Dis 2022; 88:1533-1544. [PMID: 35848024 PMCID: PMC10022308 DOI: 10.3233/jad-220174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Polygenic hazard scores (PHS) estimate age-dependent genetic risk of late-onset Alzheimer's disease (AD), but there is limited information about the performance of PHS on real-world data where the population of interest differs from the model development population and part of the model genotypes are missing or need to be imputed. OBJECTIVE The aim of this study was to estimate age-dependent risk of late-onset AD using polygenic predictors in Nordic populations. METHODS We used Desikan PHS model, based on Cox proportional hazards assumption, to obtain age-dependent hazard scores for AD from individual genotypes in the Norwegian DemGene cohort (n = 2,772). We assessed the risk discrimination and calibration of Desikan model and extended it by adding new genotype markers (the Desikan Nordic model). Finally, we evaluated both Desikan and Desikan Nordic models in two independent Danish cohorts: The Copenhagen City Heart Study (CCHS) cohort (n = 7,643) and The Copenhagen General Population Study (CGPS) cohort (n = 10,886). RESULTS We showed a robust prediction efficiency of Desikan model in stratifying AD risk groups in Nordic populations, even when some of the model SNPs were missing or imputed. We attempted to improve Desikan PHS model by adding new SNPs to it, but we still achieved similar risk discrimination and calibration with the extended model. CONCLUSION PHS modeling has the potential to guide the timing of treatment initiation based on individual risk profiles and can help enrich clinical trials with people at high risk to AD in Nordic populations.
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Affiliation(s)
- Ehsan Motazedi
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Jesper Q. Thomassen
- Department of Clinical Biochemistry, Copenhagen University Hospital – Rigshospitalet, 2100 Copenhagen, Denmark
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, PO box 1080, Blindern, 0316 Oslo, Norway
| | - Arvid Rongve
- Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway
| | - Lavinia Athanasiu
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Ingun Ulstein
- Department of Geriatric Medicine, Oslo University Hospital, Ullevål, 0424 Oslo, Norway
| | - Eystein Stordal
- Department of Neuromedicine and Movement Science (INB), NTNU, Faculty of Medicine and Health Sciences, N-7491 Trondheim, Norway
- Clinic of Psychiatry, Namsos Hospital, 7801 Namsos, Norway
| | - Anne Brækhus
- Department of Geriatric Medicine, Oslo University Hospital, Ullevål, 0424 Oslo, Norway
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Ingvild Saltvedt
- Department of Neuromedicine and Movement Science (INB), NTNU, Faculty of Medicine and Health Sciences, N-7491 Trondheim, Norway
- Department of geriatric medicine, Clinic of Medicine, St. Olavs Hospital, Trondheim university hospital, Trondheim, Norway
| | - Sigrid B. Sando
- Department of Neuromedicine and Movement Science (INB), NTNU, Faculty of Medicine and Health Sciences, N-7491 Trondheim, Norway
- University Hospital of Trondheim, Department of Neurology and Clinical Neurophysiology, Postboks 3250 Torgarden, N-7006 Trondheim, Norway
| | - Kevin S. O’Connell
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
| | - Guy Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AB
| | - Dennis van der Meer
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
- School for Mental Health and Neuroscience, Maastricht University, the Netherlands
| | - Sverre Bergh
- Research center for Age-related Functional Decline and Disease, Innlandet Hospital Trust, 2381 Brumunddal, Norway
- Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, 3103 Tønsberg, Norway
| | - Børge G. Nordestgaard
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital – Herlev Gentofte, 2730 Herlev, Denmark
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Copenhagen University Hospital – Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Geir Bråthen
- Department of Neuromedicine and Movement Science (INB), NTNU, Faculty of Medicine and Health Sciences, N-7491 Trondheim, Norway
- University Hospital of Trondheim, Department of Neurology and Clinical Neurophysiology, Postboks 3250 Torgarden, N-7006 Trondheim, Norway
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ruth Frikke-Schmidt
- Department of Clinical Biochemistry, Copenhagen University Hospital – Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Tormod Fladby
- Department of Neuromedicine and Movement Science (INB), NTNU, Faculty of Medicine and Health Sciences, N-7491 Trondheim, Norway
- Klinikk for indremedisin og lab fag (AHUSKIL), Akershus University Hospital, 1478 Lørenskog, Norway
| | - Dag Aarsland
- Department of Old-Age Psychiatry, Stavanger University Hospital, 4011 Stavanger, Norway
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, PO Box P070, De Crespigny Park, London SE5 8AF
| | - Geir Selbæk
- Department of Geriatric Medicine, Oslo University Hospital, Ullevål, 0424 Oslo, Norway
- Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, 3103 Tønsberg, Norway
- Faculty of Medicine, University of Oslo, PO BOX 1078 Blindern, 0316 Oslo, Norway
| | - Tyler M. Seibert
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
- Department of Radiation Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Chun C. Fan
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
- Population Neuroscience and Genetics Lab, University of California San Diego, La Jolla, CA, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway
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Li K, Fu Z, Qi S, Luo X, Zeng Q, Xu X, Huang P, Zhang M, Calhoun VD. Polygenic Hazard Score Associated Multimodal Brain Networks Along the Alzheimer's Disease Continuum. Front Aging Neurosci 2021; 13:725246. [PMID: 34539385 PMCID: PMC8446666 DOI: 10.3389/fnagi.2021.725246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/10/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Late-onset Alzheimer's disease (AD) is a polygenic neurodegenerative disease. Identifying the neuroimaging phenotypes behind the genetic predisposition of AD is critical to the understanding of AD pathogenesis. Two major questions which previous studies have led to are: (1) should the general "polygenic hazard score" (PHS) be a good choice to identify the individual genetic risk for AD; and (2) should researchers also include inter-modality relationships in the analyses considering these may provide complementary information about the AD etiology. METHODS We collected 88 healthy controls, 77 patients with mild cognitive impairment (MCI), and 22 AD patients to simulate the AD continuum included from the ADNI database. PHS-guided multimodal fusion was used to investigate the impact of PHS on multimodal brain networks in AD-continuum by maximizing both inter-modality association and reference-modality correlation. Fractional amplitude of low frequency fluctuations, gray matter (GM) volume, and amyloid standard uptake value ratios were included as neuroimaging features. Eventually, the changes in neuroimaging features along AD continuum were investigated, and relationships between cognitive performance and identified PHS associated multimodal components were established. RESULTS We found that PHS was associated with multimodal brain networks, which showed different functional and structural impairments under increased amyloid deposits. Notably, along with AD progression, functional impairment occurred before GM atrophy, amyloid deposition started from the MCI stage and progressively increased throughout the disease continuum. CONCLUSION PHS is associated with multi-facets of brain impairments along the AD continuum, including cognitive dysfunction, pathological deposition, which might underpin the AD pathogenesis.
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Affiliation(s)
- Kaicheng Li
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Shile Qi
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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Zhou X, Li YYT, Fu AKY, Ip NY. Polygenic Score Models for Alzheimer's Disease: From Research to Clinical Applications. Front Neurosci 2021; 15:650220. [PMID: 33854414 PMCID: PMC8039467 DOI: 10.3389/fnins.2021.650220] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 03/09/2021] [Indexed: 12/13/2022] Open
Abstract
The high prevalence of Alzheimer's disease (AD) among the elderly population and its lack of effective treatments make this disease a critical threat to human health. Recent epidemiological and genetics studies have revealed the polygenic nature of the disease, which is possibly explainable by a polygenic score model that considers multiple genetic risks. Here, we systemically review the rationale and methods used to construct polygenic score models for studying AD. We also discuss the associations of polygenic risk scores (PRSs) with clinical outcomes, brain imaging findings, and biochemical biomarkers from both the brain and peripheral system. Finally, we discuss the possibility of incorporating polygenic score models into research and clinical practice along with potential challenges.
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Affiliation(s)
- Xiaopu Zhou
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen, China
| | - Yolanda Y. T. Li
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Amy K. Y. Fu
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen, China
| | - Nancy Y. Ip
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen, China
- *Correspondence: Nancy Y. Ip,
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Banks SJ, Qiu Y, Fan CC, Dale AM, Zou J, Askew B, Feldman HH. Enriching the design of Alzheimer's disease clinical trials: Application of the polygenic hazard score and composite outcome measures. Alzheimers Dement (N Y) 2020; 6:e12071. [PMID: 32999917 PMCID: PMC7507583 DOI: 10.1002/trc2.12071] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 07/09/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Selecting individuals at high risk of Alzheimer's disease (AD) dementia and using the most sensitive outcome measures are important aspects of trial design. METHODS We divided participants from Alzheimer's Disease Neuroimaging Initiative at the 50th percentile of the predicted absolute risk of the polygenic hazard score (PHS). Outcome measures were the Alzheimer's Disease Assessment Schedule-Cognitive Subscale (ADAS-Cog), ADNI-Mem, Clinical Dementia Rating-Sum of Boxes (CDR SB), and Cognitive Function Composite 2 (CFC2). In addition to modeling, we use a power analysis compare numbers needed with each technique. RESULTS Data from 188 cognitively normal and 319 mild cognitively impaired (MCI) participants were analyzed. Using the ADAS-Cog to estimate sample sizes, without stratification over 24 months, would require 930 participants with MCI, while using the CFC2 and restricting participants to those in the upper 50th percentile would require only 284 participants. DISCUSSION Combining stratification by PHS and selection of a sensitive combined outcome measure in a cohort of patients with MCI can allow trial design that is more efficient, potentially less burdensome on participants, and more cost effective.
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Affiliation(s)
- Sarah J. Banks
- University of California San DiegoSan DiegoCaliforniaUSA
| | - Yuqi Qiu
- University of California San DiegoSan DiegoCaliforniaUSA
| | - Chun Chieh Fan
- University of California San DiegoSan DiegoCaliforniaUSA
| | - Anders M. Dale
- University of California San DiegoSan DiegoCaliforniaUSA
| | - Jingjing Zou
- University of California San DiegoSan DiegoCaliforniaUSA
| | - Brianna Askew
- University of California San DiegoSan DiegoCaliforniaUSA
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Huynh-Le MP, Fan CC, Karunamuni R, Walsh EI, Turner EL, Lane JA, Martin RM, Neal DE, Donovan JL, Hamdy FC, Parsons JK, Eeles RA, Easton DF, Kote-Jarai ZS, Al Olama AA, Garcia SB, Muir K, Gronberg H, Wiklund F, Aly M, Schleutker J, Sipeky C, Tammela TLJ, Nordestgaard BG, Key TJ, Travis RC, Pharoah PDP, Pashayan N, Khaw KT, Thibodeau SN, McDonnell SK, Schaid DJ, Maier C, Vogel W, Luedeke M, Herkommer K, Kibel AS, Cybulski C, Wokolorczyk D, Kluzniak W, Cannon-Albright LA, Brenner H, Schöttker B, Holleczek B, Park JY, Sellers TA, Lin HY, Slavov CK, Kaneva RP, Mitev VI, Batra J, Clements JA, Spurdle AB, Teixeira MR, Paulo P, Maia S, Pandha H, Michael A, Mills IG, Andreassen OA, Dale AM, Seibert TM. A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data. Cancer Epidemiol Biomarkers Prev 2020; 29:1731-1738. [PMID: 32581112 PMCID: PMC7483627 DOI: 10.1158/1055-9965.epi-19-1527] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 02/25/2020] [Accepted: 06/15/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening. METHODS United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups. RESULTS The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age. CONCLUSIONS PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS. IMPACT Personalized genetic risk assessments could inform prostate cancer screening decisions.
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Affiliation(s)
- Minh-Phuong Huynh-Le
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Eleanor I. Walsh
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Emma L. Turner
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - J. Athene Lane
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Richard M. Martin
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - David E. Neal
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Oncology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge UK
| | - Jenny L. Donovan
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Freddie C. Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - J. Kellogg Parsons
- Department of Urology, University of California, San Diego, La Jolla, CA, USA
| | - Rosalind A. Eeles
- The Institute of Cancer Research, London, UK
- Royal Marsden NHS Foundation Trust, London, UK
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | | | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, UK
| | - Sara Benlloch Garcia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Oxford Road, Manchester, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
- Department of Urology, Karolinska University Hospital, Stockholm, Sweden
| | - Johanna Schleutker
- Institute of Biomedicine, University of Turku, Turku Finland
- Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, Turku, Finland
| | - Csilla Sipeky
- Institute of Biomedicine, University of Turku, Turku Finland
| | - Teuvo LJ Tammela
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, FI-33014 Tampere University, Finland
- Department of Urology, University of Tampere, Finland
| | - Børge G. Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Copenhagen, Denmark
| | | | | | - Paul D. P. Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Cambridge, UK
| | - Nora Pashayan
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Cambridge, UK
- University College London, Department of Applied Health Research, London, UK
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
| | - Stephen N. Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Shannon K. McDonnell
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Daniel J. Schaid
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA
| | | | - Walther Vogel
- Institute for Human Genetics, University Hospital Ulm, Ulm, Germany
| | | | - Kathleen Herkommer
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Urology, Munich, Germany
| | - Adam S. Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, Boston, MA, USA
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Dominika Wokolorczyk
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Wojciech Kluzniak
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Lisa A. Cannon-Albright
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Bernd Holleczek
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Saarland Cancer Registry, D-66119 Saarbrücken, Germany
| | - Jong Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Thomas A. Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Hui-Yi Lin
- School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Chavdar Kroumov Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University of Sofia, Sofia, Bulgaria
| | - Radka P. Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
| | - Vanio I. Mitev
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
| | - Jyotsna Batra
- Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre-Qld, Translational Research Institute, Brisbane, Queensland, Australia
| | - Judith A. Clements
- Australian Prostate Cancer Research Centre-Qld, Translational Research Institute, Brisbane, Queensland, Australia
- Translational Research Institute, Brisbane, Queensland, Australia
| | - Amanda B. Spurdle
- Molecular Cancer Epidemiology Laboratory, QIMR Berghofer Institute of Medical Research, Brisbane, Australia
| | | | - Manuel R. Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Cancer Genetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
| | - Sofia Maia
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Cancer Genetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
| | | | | | - Ian G. Mills
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Ole A. Andreassen
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Tyler M. Seibert
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
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8
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Altmann A, Scelsi MA, Shoai M, de Silva E, Aksman LM, Cash DM, Hardy J, Schott JM. A comprehensive analysis of methods for assessing polygenic burden on Alzheimer's disease pathology and risk beyond APOE. Brain Commun 2019; 2:fcz047. [PMID: 32226939 PMCID: PMC7100005 DOI: 10.1093/braincomms/fcz047] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Genome-wide association studies have identified dozens of loci that alter the risk to develop Alzheimer's disease. However, with the exception of the APOE-ε4 allele, most variants bear only little individual effect and have, therefore, limited diagnostic and prognostic value. Polygenic risk scores aim to collate the disease risk distributed across the genome in a single score. Recent works have demonstrated that polygenic risk scores designed for Alzheimer's disease are predictive of clinical diagnosis, pathology confirmed diagnosis and changes in imaging biomarkers. Methodological innovations in polygenic risk modelling include the polygenic hazard score, which derives effect estimates for individual single nucleotide polymorphisms from survival analysis, and methods that account for linkage disequilibrium between genomic loci. In this work, using data from the Alzheimer's disease neuroimaging initiative, we compared different approaches to quantify polygenic disease burden for Alzheimer's disease and their association (beyond the APOE locus) with a broad range of Alzheimer's disease-related traits: cross-sectional CSF biomarker levels, cross-sectional cortical amyloid burden, clinical diagnosis, clinical progression, longitudinal loss of grey matter and longitudinal decline in cognitive function. We found that polygenic scores were associated beyond APOE with clinical diagnosis, CSF-tau levels and, to a minor degree, with progressive atrophy. However, for many other tested traits such as clinical disease progression, CSF amyloid, cognitive decline and cortical amyloid load, the additional effects of polygenic burden beyond APOE were of minor nature. Overall, polygenic risk scores and the polygenic hazard score performed equally and given the ease with which polygenic risk scores can be derived; they constitute the more practical choice in comparison with polygenic hazard scores. Furthermore, our results demonstrate that incomplete adjustment for the APOE locus, i.e. only adjusting for APOE-ε4 carrier status, can lead to overestimated effects of polygenic scores due to APOE-ε4 homozygous participants. Lastly, on many of the tested traits, the major driving factor remained the APOE locus, with the exception of quantitative CSF-tau and p-tau measures.
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Affiliation(s)
- Andre Altmann
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London WC1V 6LJ, UK
| | - Marzia A Scelsi
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London WC1V 6LJ, UK
| | - Maryam Shoai
- Reta Lilla Research Laboratories, Department of Neurodegeneration, Queen Square Institute of Neurology, University College London (UCL), London WC1V 6LJ, UK.,UK Dementia Research Institute, University College London (UCL), London WC1V 6LJ, UK
| | - Eric de Silva
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London WC1V 6LJ, UK.,Institute for Health Informatics, University College London (UCL), London WC1V 6LJ, UK
| | - Leon M Aksman
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London WC1V 6LJ, UK
| | - David M Cash
- UK Dementia Research Institute, University College London (UCL), London WC1V 6LJ, UK.,Dementia Research Centre, Queen Square Institute of Neurology, University College London (UCL), London WC1V 6LJ, UK
| | - John Hardy
- Reta Lilla Research Laboratories, Department of Neurodegeneration, Queen Square Institute of Neurology, University College London (UCL), London WC1V 6LJ, UK.,UK Dementia Research Institute, University College London (UCL), London WC1V 6LJ, UK
| | - Jonathan M Schott
- UK Dementia Research Institute, University College London (UCL), London WC1V 6LJ, UK.,Dementia Research Centre, Queen Square Institute of Neurology, University College London (UCL), London WC1V 6LJ, UK
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9
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Chasioti D, Yan J, Nho K, Saykin AJ. Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases. Trends Genet 2019; 35:371-382. [PMID: 30922659 DOI: 10.1016/j.tig.2019.02.005] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 02/12/2019] [Accepted: 02/22/2019] [Indexed: 11/25/2022]
Abstract
Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies.
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Affiliation(s)
- Danai Chasioti
- Department of BioHealth Informatics, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Kwangsik Nho
- Department of BioHealth Informatics, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Andrew J Saykin
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
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10
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Tan CH, Bonham LW, Fan CC, Mormino EC, Sugrue LP, Broce IJ, Hess CP, Yokoyama JS, Rabinovici GD, Miller BL, Yaffe K, Schellenberg GD, Kauppi K, Holland D, McEvoy LK, Kukull WA, Tosun D, Weiner MW, Sperling RA, Bennett DA, Hyman BT, Andreassen OA, Dale AM, Desikan RS. Polygenic hazard score, amyloid deposition and Alzheimer's neurodegeneration. Brain 2019; 142:460-470. [PMID: 30689776 PMCID: PMC6351776 DOI: 10.1093/brain/awy327] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 11/05/2018] [Accepted: 11/06/2018] [Indexed: 12/20/2022] Open
Abstract
Mounting evidence indicates that the polygenic basis of late-onset Alzheimer's disease can be harnessed to identify individuals at greatest risk for cognitive decline. We have previously developed and validated a polygenic hazard score comprising of 31 single nucleotide polymorphisms for predicting Alzheimer's disease dementia age of onset. In this study, we examined whether polygenic hazard scores are associated with: (i) regional tracer uptake using amyloid PET; (ii) regional volume loss using longitudinal MRI; (iii) post-mortem regional amyloid-β protein and tau associated neurofibrillary tangles; and (iv) four common non-Alzheimer's pathologies. Even after accounting for APOE, we found a strong association between polygenic hazard scores and amyloid PET standard uptake volume ratio with the largest effects within frontal cortical regions in 980 older individuals across the disease spectrum, and longitudinal MRI volume loss within the entorhinal cortex in 607 older individuals across the disease spectrum. We also found that higher polygenic hazard scores were associated with greater rates of cognitive and clinical decline in 632 non-demented older individuals, even after controlling for APOE status, frontal amyloid PET and entorhinal cortex volume. In addition, the combined model that included polygenic hazard scores, frontal amyloid PET and entorhinal cortex volume resulted in a better fit compared to a model with only imaging markers. Neuropathologically, we found that polygenic hazard scores were associated with regional post-mortem amyloid load and neuronal neurofibrillary tangles, even after accounting for APOE, validating our imaging findings. Lastly, polygenic hazard scores were associated with Lewy body and cerebrovascular pathology. Beyond APOE, we show that in living subjects, polygenic hazard scores were associated with amyloid deposition and neurodegeneration in susceptible brain regions. Polygenic hazard scores may also be useful for the identification of individuals at the highest risk for developing multi-aetiological dementia.
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Affiliation(s)
- Chin Hong Tan
- Division of Psychology, Nanyang Technological University, 48 Nanyang Avenue, Singapore
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 500 Parnassus Avenue, San Francisco, CA, USA
| | - Luke W Bonham
- Department of Neurology, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, USA
| | - Chun Chieh Fan
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, USA
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University, 300 Pasteur Dr, Palo Alto, CA, USA
| | - Leo P Sugrue
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 500 Parnassus Avenue, San Francisco, CA, USA
| | - Iris J Broce
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 500 Parnassus Avenue, San Francisco, CA, USA
| | - Christopher P Hess
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 500 Parnassus Avenue, San Francisco, CA, USA
| | - Jennifer S Yokoyama
- Department of Neurology, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, USA
| | - Gil D Rabinovici
- Department of Neurology, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, USA
| | - Bruce L Miller
- Department of Neurology, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, USA
| | - Kristine Yaffe
- Department of Neurology, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, San Francisco, CA, USA
- Department of Psychiatry, University of California, San Francisco, 982 Mission St, San Francisco, CA, USA
| | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, 204 N Broad St, Philadelphia, PA, USA
| | - Karolina Kauppi
- Department of Radiology, University of California, San Diego, 8929 University Center, La Jolla, CA, USA
| | - Dominic Holland
- Department of Neurosciences, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, USA
| | - Linda K McEvoy
- Department of Radiology, University of California, San Diego, 8929 University Center, La Jolla, CA, USA
| | - Walter A Kukull
- National Alzheimer’s Coordinating Center, Department of Epidemiology, University of Washington, 1959 NE Pacific St, Seattle, WA, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 500 Parnassus Avenue, San Francisco, CA, USA
| | - Michael W Weiner
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 500 Parnassus Avenue, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, 15 Parkman St, Boston, MA, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, 1750 W Harrison St, Chicago, IL, USA
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, 15 Parkman St, Boston, MA, USA
| | - Ole A Andreassen
- NORMENT Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Boks 1072 Blindern, Oslo, Norway
| | - Anders M Dale
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego, 8929 University Center, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, USA
| | - Rahul S Desikan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 500 Parnassus Avenue, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, USA
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