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1. Using path signatures to predict a diagnosis of Alzheimer's disease

Author

Moore, P. J., Gallacher, J., and Lyons, T. J.

Subjects
Quantitative Biology - Quantitative Methods, Statistics - Applications, 62J12, 92D30
Abstract

The path signature is a means of feature generation that can encode nonlinear interactions in the data as well as the usual linear features. It can distinguish the ordering of time-sequenced changes: for example whether or not the hippocampus shrinks fast, then slowly or the converse. It provides interpretable features and its output is a fixed length vector irrespective of the number of input points so it can encode longitudinal data of varying length and with missing data points. In this paper we demonstrate the path signature in providing features to distinguish a set of people with Alzheimer's disease from a matched set of healthy individuals. The data used are volume measurements of the whole brain, ventricles and hippocampus from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The path signature method is shown to be a useful tool for the processing of sequential data which is becoming increasingly available as monitoring technologies are applied., Comment: 5 pages, 3 figures. arXiv admin note: text overlap with arXiv:1808.03273

Published
2018
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2. Random forest prediction of Alzheimer’s disease using pairwise selection from time series data

Author

Moore, PJ, Lyons, TJ, Gallacher, J, and Initiative, for the Alzheimer’s Disease Neuroimaging

Subjects
Mathematical Sciences, Biological Sciences, Statistics, Acquired Cognitive Impairment, Aging, Brain Disorders, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Dementia, Alzheimer's Disease, Neurosciences, Aged, Aged, 80 and over, Alzheimer Disease, Disease Progression, Female, Humans, Image Interpretation, Computer-Assisted, Machine Learning, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Pattern Recognition, Automated, Alzheimer’s Disease Neuroimaging Initiative, General Science & Technology
Abstract

Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is a summary of the time series history and it includes both demographic and non-time varying variables such as genetic data. To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer's disease using demographic, physical and cognitive input data. The task is to predict diagnosis, ADAS-13 score and normalised ventricles volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73 compared with a benchmark SVM predictor which gives mAUC = 0.62 and BCA = 0.52. The results show that the method is effective and comparable with other methods.

Published
2019

4. From Parnassus to the scaffold : movements of an Irish Bohemia

Author

Gallacher, J. O. and Shovlin, Frank

Subjects
709
Abstract

This thesis will undertake a critical examination of Dublin literary culture during the middle decades of the twentieth-century with a view to the identification and definition of a distinct movement of writers and artists whose commonality of output and shared vision has yet to be academically recognised. In doing so it will highlight the development of a coherent, sustained approach to the production of literature and art that was espoused by the group throughout the 1950s and beyond, and enjoyed subsequent manifestations within wider literary and artistic circles of Northern Ireland and England. Drawing on both an extensive reinterpretation of existing sources, and a significant cache of newly discovered manuscripts, it will come to show how the oftderided period of the post-War decade in Ireland actually represented the embryonic form of a subsequently robust and markedly distinctive literary and artistic philosophy that transcended its contextual origins and came to influence cultural affairs on the island of Ireland for much of the late twentieth century.

Published
2018
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6. Business models and higher education

Author

Gallacher, J.

Subjects
658
Abstract

The researcher believes that the wide ranging use of the term business model in academic and practitioner arenas suggests that the clarification of its use rhetorically, strategically or managerially or lack of use would be of interest to both academics and management practitioners. Universities face increasingly challenging economic conditions and the identification of the use within higher education of business models using interview data, cross-referenced to institutional success, measured by a variety of metrics, may allow inferences to be drawn about the relationship of the relative success of institutions and the role, if any, of business models which may then inform future decision making. The researcher suggests that a business model approach, whilst not introducing new concepts is a useful descriptive and analytical tool for both practitioners and academics. In a discursive sense the term can act as a useful short hand whilst as a framework for value propositions it can aid the identification and development of the underlying economic reality of business activity. Furthermore, business models when viewed in the context of competing value propositions and business model innovation provide a link to and an aid in, the development of strategy. The researcher found that the term business model was frequently described in interview as inappropriate and no evidence of significant explicit usage of the term business model was found in university strategic plans. Further evidence from interview and the analysis of strategic plan documentation highlights the use of business-like language and practices suggesting that implicit, rather than explicit, business model approaches are being adopted in universities for decision making purposes. No evidence of a correlation between the use of business models, business like terms or reference to Hefce strategic aims and financial or league table success was noted. Whilst relative institutional income levels remained largely static over the period 1994-95 – 2006-07, the income patterns of institutions interviewed indicated that as the level of success as measured by researcher increased, as a proportion of total income grant and fee income reduced and research and other income increased. In addition the top twenty-five universities measured by average surplus over the period 1994-5 to 2003-04 tended to have a higher than average proportion of their total income represented by research and other income. To aid the realisation of potential benefits to be gained by universities from the adoption of a more explicit business model approach the research findings suggest the consideration of three factors. Firstly the clarification of the location of business models in relation to the more traditional management tools of strategy development and resource planning. Secondly, the lack of appetite for the explicit adoption of a business model approach, in at least some parts of the sector, suggests that the language of business models needs adapting to a higher education context. The researcher suggests the language of social enterprise which would explicitly recognise the societal dimension of university activity. Finally, but related to the second point is that the constrained economy of higher education needs to be recognised in the development of a business model approach with complex stakeholder relations recognised in value propositions and outcomes and financial sustainability as a necessary facilitator rather than primary driver.

Published
2010

8. Cochlear implant services for children, young people and adults. Quality standard

Author

Cullington, H. E., primary, Jiang, D., additional, Broomfield, S. J., additional, Chung, M., additional, Craddock, L. C., additional, Driver, S., additional, Edwards, D., additional, Gallacher, J. M., additional, Jones, L. Ll., additional, Koleva, T., additional, Martin, J., additional, Meakin, H., additional, Nash, R., additional, Rocca, C., additional, Schramm, D. R., additional, Willmott, N. S., additional, and Vanat, Z. H., additional

Published
2023
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9. A multi-disciplinary commentary on preclinical research to investigate vascular contributions to dementia

Author

Sri, S., Greenstein, A., Granata, A., Collcutt, A., Jochems, A.C.C., McColl, B.W., Castro, B.D., Webber, C., Reyes, C.A., Hall, C., Lawrence, C.B., Hawkes, C., Pegasiou-Davies, C.-M., Gibson, C., Crawford, C.L., Smith, C., Vivien, D., McLean, F.H., Wiseman, F., Brezzo, G., Lalli, G., Pritchard, H.A.T., Markus, H.S., Bravo-Ferrer, I., Taylor, J., Leiper, J., Berwick, J., Gan, J., Gallacher, J., Moss, J., Goense, J., McMullan, L., Work, L., Evans, L., Stringer, M.S., Ashford, M.L.J., Abulfadl, M., Conlon, N., Malhotra, P., Bath, P., Canter, R., Brown, R., Ince, S., Anderle, S., Young, S., Quick, S., Szymkowiak, S., Hill, S., Allan, S., Wang, T., Quinn, T., Procter, T., Farr, T.D., Zhao, X., Yang, Z., Hainsworth, A.H., Wardlaw, J.M., Sri, S., Greenstein, A., Granata, A., Collcutt, A., Jochems, A.C.C., McColl, B.W., Castro, B.D., Webber, C., Reyes, C.A., Hall, C., Lawrence, C.B., Hawkes, C., Pegasiou-Davies, C.-M., Gibson, C., Crawford, C.L., Smith, C., Vivien, D., McLean, F.H., Wiseman, F., Brezzo, G., Lalli, G., Pritchard, H.A.T., Markus, H.S., Bravo-Ferrer, I., Taylor, J., Leiper, J., Berwick, J., Gan, J., Gallacher, J., Moss, J., Goense, J., McMullan, L., Work, L., Evans, L., Stringer, M.S., Ashford, M.L.J., Abulfadl, M., Conlon, N., Malhotra, P., Bath, P., Canter, R., Brown, R., Ince, S., Anderle, S., Young, S., Quick, S., Szymkowiak, S., Hill, S., Allan, S., Wang, T., Quinn, T., Procter, T., Farr, T.D., Zhao, X., Yang, Z., Hainsworth, A.H., and Wardlaw, J.M.

Abstract

Although dementia research has been dominated by Alzheimer's disease (AD), most dementia in older people is now recognised to be due to mixed pathologies, usually combining vascular and AD brain pathology. Vascular cognitive impairment (VCI), which encompasses vascular dementia (VaD) is the second most common type of dementia. Models of VCI have been delayed by limited understanding of the underlying aetiology and pathogenesis. This review by a multidisciplinary, diverse (in terms of sex, geography and career stage), cross-institute team provides a perspective on limitations to current VCI models and recommendations for improving translation and reproducibility. We discuss reproducibility, clinical features of VCI and corresponding assessments in models, human pathology, bioinformatics approaches, and data sharing. We offer recommendations for future research, particularly focusing on small vessel disease as a main underpinning disorder.

Published
2023

11. UK Biobank retinal imaging grading: methodology, baseline characteristics and findings for common ocular diseases

Author

Warwick, Alasdair N., Curran, Katie, Hamill, Barbra, Stuart, Kelsey, Khawaja, A., Foster, Paul J., Lotery, Andrew J., Quinn, Michael, Madhusudhan, Savita, Balaskas, Konstantinos, Peto, Tunde, Allen, N., Aslam, T., Atan, D., Barman, S., Barrett, J., Bishop, P., Black, G., Braithwaite, T., Carare, R., Chakravarthy, U., Chan, M., Chua, S., Day, A., Desai, P., Dhillon, B., Dick, A., Doney, A., Egan, C., Ennis, S., Foster, P., Fruttiger, M., Gallacher, J., Garway-Heath, D., Gibson, J., Hammond, C., Hardcastle, A., Harding, S., Moore, T., Morgan, J., Owen, C., Patel, P., Self, J., Stratton, I., Sun, Z., Thomas, D., Williams, K., Williams, C., Zheng, Y., Neurology, Ophthalmology, APH - Mental Health, APH - Methodology, and Amsterdam Neuroscience - Neuroinfection & -inflammation

Subjects
Ophthalmology, SDG 3 - Good Health and Well-being
Abstract

Background/objectives This study aims to describe the grading methods and baseline characteristics for UK Biobank (UKBB) participants who underwent retinal imaging in 2009–2010, and to characterise individuals with retinal features suggestive of age-related macular degeneration (AMD), glaucoma and retinopathy. Methods Non-mydriatic colour fundus photographs and macular optical coherence tomography (OCT) scans were manually graded by Central Administrative Research Facility certified graders and quality assured by clinicians of the Network of Ophthalmic Reading Centres UK. Captured retinal features included those associated with AMD (≥1 drusen, pigmentary changes, geographic atrophy or exudative AMD; either imaging modality), glaucoma (≥0.7 cup-disc ratio, ≥0.2 cup-disc ratio difference between eyes, other abnormal disc features; photographs only) and retinopathy (characteristic features of diabetic retinopathy with or without microaneurysms; either imaging modality). Suspected cases of these conditions were characterised with reference to diagnostic records, physical and biochemical measurements. Results Among 68,514 UKBB participants who underwent retinal imaging, the mean age was 57.3 years (standard deviation 8.2), 45.7% were men and 90.6% were of White ethnicity. A total of 64,367 participants had gradable colour fundus photographs and 68,281 had gradable OCT scans in at least one eye. Retinal features suggestive of AMD and glaucoma were identified in 15,176 and 2184 participants, of whom 125 (0.8%) and 188 (8.6%), respectively, had a recorded diagnosis. Of 264 participants identified to have retinopathy with microaneurysms, 251 (95.1%) had either diabetes or hypertension. Conclusions This dataset represents a valuable addition to what is currently available in UKBB, providing important insights to both ocular and systemic health.

Published
2022
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12. Decline in cognition from mid-life improves specificity of mini-mental state examination: diagnostic test accuracy in Caerphilly Prospective Study (CaPS)

Author

Crevin, S, Fish, M, Bayer, A, Gallacher, J, and Ben-Shlomo, Y

Subjects
Male, Diagnostic Tests, Routine, General Neuroscience, General Medicine, Sensitivity and Specificity, Cohort Studies, Psychiatry and Mental health, Clinical Psychology, Cognition, Cross-Sectional Studies, Humans, Dementia, Prospective Studies, Geriatrics and Gerontology
Abstract

Background: The merit of using baseline cognitive assessments in mid-life to help interpret cross-sectional cognitive tests scores in later life is uncertain. Objective: Evaluate how accuracy for diagnosing dementia is enhanced by comparing cross-sectional results to a midlife measure. Method: Cohort study of 2,512 men with repeated measures of Mini Mental State Examination (MMSE) over approximately 10 years. Index test MMSE at threshold of 24 indicating normal, as a cross-sectional measure and in combination with decline in MMSE score from mid-life. Reference standard consensus clinical diagnosis of dementia by two clinicians according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Results: 1,150 men participated at phase 4 of whom 75 had dementia. A cross-sectional MMSE alone produced a sensitivity of 60% (50% to 70%) and specificity 95% (94% to 97%) with a threshold of ≥ 24 points indicating normal. For lower-scoring men in late life, with cross sectional scores of Conclusion: It may be helpful in practice to formally evaluate cognition in mid-life as a baseline to compare with if problems develop in future, as this may enhance diagnostic accuracy and classification of people in later life.

Published
2022
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14. An All-Optical Synchrotron Light Source

Author

Schwoerer, H., Schlenvoigt, H. P., Haupt, K., Debus, A., Rohwer, E., Gallacher, J., Shanks, R., Jaroszynski, D., Corkum, Paul, editor, Silvestri, Sandro, editor, Nelson, Keith A., editor, Riedle, Eberhard, editor, and Schoenlein, Robert W., editor

Published
2009
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23. Associations with Corneal Hysteresis in a Population Cohort: Results from 96 010 UK Biobank Participants

Author

Zhang, B, Shweikh, Y, Khawaja, AP, Gallacher, J, Bauermeister, S, Foster, PJ, and Consortium, Ukbiobank Eye And Vision

Subjects
Adult, Male, Corneal Pachymetry, genetic structures, Age Factors, Middle Aged, Health Surveys, Elasticity, United Kingdom, eye diseases, Biomechanical Phenomena, Cohort Studies, Cornea, Cross-Sectional Studies, Sex Factors, Ethnicity, Humans, Regression Analysis, Female, sense organs, Intraocular Pressure, Aged, Biological Specimen Banks
Abstract

Purpose To describe the distribution of corneal hysteresis (CH) in a large cohort and explore its associated factors and possible clinical applications. Design Cross-sectional study within the UK Biobank, a large cohort study in the United Kingdom. Participants We analyzed CH data from 93 345 eligible participants in the UK Biobank cohort, aged 40 to 69 years. Methods All analyses were performed using left eye data. Linear regression models were used to evaluate associations between CH and demographic, lifestyle, ocular, and systemic variables. Piecewise logistic regression models were used to explore the relationship between self-reported glaucoma and CH. Main Outcome Measures Corneal hysteresis (mmHg). Results The mean CH was 10.6 mmHg (10.4 mmHg in male and 10.8 mmHg in female participants). After adjusting for covariables, CH was significantly negatively associated with male sex, age, black ethnicity, self-reported glaucoma, diastolic blood pressure, and height. Corneal hysteresis was significantly positively associated with smoking, hyperopia, diabetes, systemic lupus erythematosus (SLE), greater deprivation (Townsend index), and Goldmann-correlated intraocular pressure (IOPg). Self-reported glaucoma and CH were significantly associated when CH was less than 10.1 mmHg (odds ratio, 0.86; 95% confidence interval, 0.79–0.94 per mmHg CH increase) after adjusting for covariables. When CH exceeded 10.1 mmHg, there was no significant association between CH and self-reported glaucoma. Conclusions In our analyses, CH was significantly associated with factors including age, sex, and ethnicity, which should be taken into account when interpreting CH values. In our cohort, lower CH was significantly associated with a higher prevalence of self-reported glaucoma when CH was less than 10.1 mmHg. Corneal hysteresis may serve as a biomarker aiding glaucoma case detection.

Published
2019
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24. Challenges for Optimizing Real-World Evidence in Alzheimer's Disease: The ROADMAP Project

Author

Gallacher, J, De Reydet De Vulpillieres, F, Amzal, B, Angehrn, Z, Bexelius, C, Bintener, C, Bouvy, J, Campo, L, Diaz, C, Georges, J, Gray, A, Hottgenroth, A, Jonsson, P, Mittelstadt, B, Potashman, M, Reed, C, Sudlow, C, Thompson, R, Tockhorn-Heidenreich, A, Turner, A, Van Der Lei, J, Visser, P, Consortium, Roadmap, Medical Informatics, Psychiatrie & Neuropsychologie, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, and Consortium, The ROADMAP

Subjects
0301 basic medicine, Process management, Process (engineering), Computer science, Cost-Benefit Analysis, data sharing, Clinical Decision-Making, Disease, computer.software_genre, patient outcome assessment, 03 medical and health sciences, 0302 clinical medicine, Alzheimer Disease, Humans, real-world clinical trialsq, data systems, Health policy, Aged, Aged, 80 and over, Evidence-Based Medicine, real-world clinical trials, business.industry, General Neuroscience, DEMENTIA, Usability, health policy, General Medicine, Alzheimer's disease, FRAMEWORK, systems integration, 3. Good health, Data sharing, Psychiatry and Mental health, Clinical Psychology, 030104 developmental biology, Editorial, Treatment Outcome, TRIALS, General partnership, Data Interpretation, Statistical, System integration, Geriatrics and Gerontology, business, computer, Alzheimer’s disease, 030217 neurology & neurosurgery, Data integration
Abstract

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer's disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit.

Published
2019
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27. Obtaining EQ-5D-5L utilities from the disease specific quality of life Alzheimer’s disease scale: development and results from a mapping study

Author

Rombach, I., Iftikhar, M., Jhuti, G.S., Gustavsson, A., Lecomte, P., Belger, M., Handels, R., Castro Sanchez, A.Y., Kors, J., Hopper, L., Olde Rikkert, M., Selbæk, G., Stephan, A., Sikkes, S.A.M., Woods, B., Gonçalves-Pereira, M., Zanetti, O., Ramakers, I.H.G.B., Verhey, F.R.J., Gallacher, J., Actifcare Consortium, LeARN Consortium, Landeiro, F., Gray, A.M., and ROADMAP Consortium

Abstract

Purpose\ud \ud The Quality of Life Alzheimer’s Disease Scale (QoL-AD) is commonly used to assess disease specific health-related quality of life (HRQoL) as rated by patients and their carers. For cost-effectiveness analyses, utilities based on the EQ-5D are often required. We report a new mapping algorithm to obtain EQ-5D indices when only QoL-AD data are available.\ud \ud \ud \ud Methods\ud \ud Different statistical models to estimate utility directly, or responses to individual EQ-5D questions (response mapping) from QoL-AD, were trialled for patient-rated and proxy-rated questionnaires. Model performance was assessed by root mean square error and mean absolute error.\ud \ud \ud \ud Results\ud \ud The response model using multinomial regression including age and sex, performed best in both the estimation dataset and an independent dataset.\ud \ud \ud \ud Conclusions\ud \ud The recommended mapping algorithm allows researchers for the first time to estimate EQ-5D values from QoL-AD data, enabling cost-utility analyses using datasets where the QoL-AD but no utility measures were collected.

Published
2021

29. Prediction of Alzheimer's disease biomarker status defined by the ‘ATN Framework’ among cognitively healthy individuals: Results from the EPAD longitudinal cohort study

Author

Calvin, C, de Boer, C, Raymont, V, Gallacher, J, Koychev, I, Consortium, The European Prevention of Alzheimer’s Dementia (EPAD), Neurology, and Amsterdam Neuroscience - Neurodegeneration

Subjects
medicine.medical_specialty, Cognitive Neuroscience, Apolipoprotein E4, ATN framework, Context (language use), Disease, lcsh:RC346-429, lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Alzheimer Disease, Internal medicine, Diabetes mellitus, medicine, Risk scores, Humans, Dementia, Longitudinal Studies, 030212 general & internal medicine, Family history, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, lcsh:Neurology. Diseases of the nervous system, Aged, Amyloid beta-Peptides, business.industry, Research, medicine.disease, Hyperintensity, Neurology, White matter lesions volume, Biomarker (medicine), Neurology (clinical), business, Body mass index, Biomarkers, 030217 neurology & neurosurgery
Abstract

Background The Amyloid/Tau/Neurodegeneration (ATN) framework has been proposed as a means of evidencing the biological state of Alzheimer’s disease (AD). Predicting ATN status in pre-dementia individuals therefore provides an important opportunity for targeted recruitment into AD interventional studies. We investigated the extent to which ATN-defined biomarker status can be predicted by known AD risk factors as well as vascular-related composite risk scores. Methods One thousand ten cognitively healthy older adults were allocated to one of five ATN-defined biomarker categories. Multinomial logistic regression tested risk factors including age, sex, education, APOE4, family history of dementia, cognitive function, vascular risk indices (high systolic blood pressure, body mass index (BMI), high cholesterol, physical inactivity, ever smoked, blood pressure medication, diabetes, prior cardiovascular disease, atrial fibrillation and white matter lesion (WML) volume), and three vascular-related composite scores, to predict five ATN subgroups; ROC curve models estimated their added value in predicting pathology. Results Age, APOE4, family history, BMI, MMSE and white matter lesions (WML) volume differed between ATN biomarker groups. Prediction of Alzheimer’s disease pathology (versus normal AD biomarkers) improved by 7% after adding family history, BMI, MMSE and WML to a ROC curve that included age, sex and APOE4. Risk composite scores did not add value. Conclusions ATN-defined Alzheimer’s disease biomarker status prediction among cognitively healthy individuals is possible through a combination of constitutional and cardiovascular risk factors but established dementia composite risk scores do not appear to add value in this context.

Published
2020
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30. Genome-wide association meta-analysis of corneal curvature identifies novel loci and shared genetic influences across axial length and refractive error

Author

Fan, Q. (Qiao), Pozarickij, A. (Alfred), Tan, N.Y.Q. (Nicholas Y. Q.), Guo, X. (Xiaobo), Verhoeven, V.J.M. (Virginie), Vitart, V. (Veronique), Guggenheim, J. (Jean), Miyake, M. (Masahiro), Tideman, J.W.L. (Willem), Khawaja, A.P. (Anthony), Zhang, L. (Liang), MacGregor, S. (Stuart), Höhn, R. (René), Chen, P. (Peng), Biino, G. (Ginevra), Wedenoja, J. (Juho), Saffari, S.E. (Seyed Ehsan), Tedja, M. (Milly), Xie, J. (Jing), Lanca, C. (Carla), Wang, Y.X. (Ya Xing), Sahebjada, S. (Srujana), Mazur, J. (Johanna), Mirshahi, A. (Alireza), Martin, N.G. (Nicholas), Yazar, S. (Seyhan), Pennell, C.E. (Craig), Yap, M.K.H. (Maurice K. H.), Haarman, A.E.G. (Annechien E. G.), Enthoven, C.A. (Clair A.), Polling, J.R. (Jan Roelof), Bailey-Wilson, J.E. (Joan E.), Veluchamy, A.B. (Amutha Barathi), Burdon, K.P. (Kathryn P.), Campbell, H. (Harry), Chen, L.J. (Li Jia), Chew, E.Y. (Emily Y.), Craig, J.E. (Jamie), Cumberland, P.M. (Phillippa M.), DeAngelis, M.M. (Margaret), Delcourt, C. (Cécile), Ding, X. (Xiaohu), Evans, D.M. (David M.), Gharahkhani, P. (Puya), Iglesias, A.I. (Adriana I.), Haller, T. (Toomas), Han, X. (Xikun), Hoang, Q. (Quan), Igo Jr., R.P. (Robert), Iyengar, S.K. (Sudha), Kähönen, M. (Mika), Kaprio, J. (Jaakko), Klein, B.E. (Barbara E.), Klein, R. (Ronald), Lass Jr., J.H. (Jonathan), Lee, K. (Kris), Lehtimäki, T. (Terho), Lewis, D.D. (Deyana D.), Li, Q. (Qing), Li, S.-M. (Shi-Ming), Lyytikäinen, L.-P. (Leo-Pekka), Meguro, A. (Akira), Metspalu, A. (Andres), Middlebrooks, C.D. (Candace D.), Mizuki, N. (Nobuhisa), Musolf, A.M. (Anthony M.), Nickels, S. (Stefan), Oexle, K. (Konrad), Pang, C.P. (Chi Pui), Paterson, A.D. (Andrew), Rahi, J.S. (Jugnoo S.), Raitakari, O. (Olli), Rudan, I. (Igor), Stambolian, D.E. (Dwight), Simpson, C.L. (Claire), Wang, N. (Ningli), Bin Wei, W. (Wen), Williams, K.M. (Katie M.), Wilson, J.F. (James), Wojciechowski, R. (Robert), Yamashiro, K. (Kenji), Yam, J.C.S. (Jason C. S.), Zhou, X. (Xiangtian), Aslam, T. (Tariq), Barman, S.A. (Sarah A.), Barrett, J.H. (Jenny H.), Bishop, P.N. (Paul), Blows, P. (Peter), Bunce, C. (Catey), Carare, R.O. (Roxana O.), Chakravarthy, U. (Usha), Chan, M. (Michelle), Chua, S.Y.L. (Sharon Y. L.), Crabb, D.P. (David), Cumberland, P.M. (Philippa M.), Day, A. (Alexander), Desai, P. (Parul), Dhillon, B. (Bal), Dick, A.D. (Andrew D.), Egan, C. (Cathy), Ennis, S. (Sarah), Fruttiger, M. (Marcus), Gallacher, J. (John), Garway-Heath, D.F. (David F.), Gibson, J. (Jane), Gore, D. (Dan), Hardcastle, A. (Alison), Harding, S.P. (Simon), Hogg, R. (Ruth), Keane, P.A. (Pearse A.), Khaw, S.P.T. (Sir Peng T.), Lascaratos, G. (Gerassimos), Lotery, A.J. (Andrew), Macgillivray, T. (Tom), Mackie, S. (Sarah), Martin, K. (Keith), McGaughey, M. (Michelle), McGuinness, B. (Bernadette), McKay, G.J. (Gareth), McKibbin, M. (Martin), Mitry, D. (Danny), Moore, T. (Tony), Morgan, J.E. (James E.), Muthy, Z.A. (Zaynah A.), O’Sullivan, E. (Eoin), Owen, C.G. (Chris G.), Patel, P. (Praveen), Paterson, E. (Euan), Peto, T. (Tünde), Petzold, A. (Axel), Rudnikca, A.R. (Alicja R.), Self, J. (Jay), Sivaprasad, S., Steel, D. (David), Stratton, I. (Irene), Strouthidis, N. (Nicholas), Sudlow, C. (Cathie), Thomas, D. (Dhanes), Trucco, E. (Emanuele), Tufail, A. (Adnan), Vernon, S.A. (Stephen A.), Viswanathan, A.C. (Ananth C.), Williams, K. (Katie), Woodside, J.V. (J.), Yates, M.M. (Max M.), Yip, J. (Jennifer), Zheng, Y. (Yalin), Hewit, A.W. (Alex), Jaddoe, V.W.V. (Vincent), Duijn, C.M. (Cornelia) van, Hayward, C. (Caroline), Polasek, O. (Ozren), Tai, E.S. (Shyong), Yoshikatsu, H. (Hosoda), Hysi, P.G. (Pirro G.), Young, T.L. (Terri L.), Tsujikawa, A. (Akitaka), Wang, J.J. (Jie Jing), Mitchell, P. (Paul), Pfeiffer, A.F.H. (Andreas), Pärssinen, O. (Olavi), Foster, P.J. (Paul), Fossarello, M. (Maurizio), Yip, S.P. (Shea Ping), Williams, C. (Cathy), Hammond, C.J. (Christopher), Jonas, J.B., He, M. (Mingguang), Mackey, D.A. (David), Wong, T.-Y. (Tien-Yin), Klaver, C.C.W. (Caroline), Saw, S-M. (Seang-Mei), Baird, P.N. (Paul), Cheng, C.-Y. (Ching-Yu), Fan, Q. (Qiao), Pozarickij, A. (Alfred), Tan, N.Y.Q. (Nicholas Y. Q.), Guo, X. (Xiaobo), Verhoeven, V.J.M. (Virginie), Vitart, V. (Veronique), Guggenheim, J. (Jean), Miyake, M. (Masahiro), Tideman, J.W.L. (Willem), Khawaja, A.P. (Anthony), Zhang, L. (Liang), MacGregor, S. (Stuart), Höhn, R. (René), Chen, P. (Peng), Biino, G. (Ginevra), Wedenoja, J. (Juho), Saffari, S.E. (Seyed Ehsan), Tedja, M. (Milly), Xie, J. (Jing), Lanca, C. (Carla), Wang, Y.X. (Ya Xing), Sahebjada, S. (Srujana), Mazur, J. (Johanna), Mirshahi, A. (Alireza), Martin, N.G. (Nicholas), Yazar, S. (Seyhan), Pennell, C.E. (Craig), Yap, M.K.H. (Maurice K. H.), Haarman, A.E.G. (Annechien E. G.), Enthoven, C.A. (Clair A.), Polling, J.R. (Jan Roelof), Bailey-Wilson, J.E. (Joan E.), Veluchamy, A.B. (Amutha Barathi), Burdon, K.P. (Kathryn P.), Campbell, H. (Harry), Chen, L.J. (Li Jia), Chew, E.Y. (Emily Y.), Craig, J.E. (Jamie), Cumberland, P.M. (Phillippa M.), DeAngelis, M.M. (Margaret), Delcourt, C. (Cécile), Ding, X. (Xiaohu), Evans, D.M. (David M.), Gharahkhani, P. (Puya), Iglesias, A.I. (Adriana I.), Haller, T. (Toomas), Han, X. (Xikun), Hoang, Q. (Quan), Igo Jr., R.P. (Robert), Iyengar, S.K. (Sudha), Kähönen, M. (Mika), Kaprio, J. (Jaakko), Klein, B.E. (Barbara E.), Klein, R. (Ronald), Lass Jr., J.H. (Jonathan), Lee, K. (Kris), Lehtimäki, T. (Terho), Lewis, D.D. (Deyana D.), Li, Q. (Qing), Li, S.-M. (Shi-Ming), Lyytikäinen, L.-P. (Leo-Pekka), Meguro, A. (Akira), Metspalu, A. (Andres), Middlebrooks, C.D. (Candace D.), Mizuki, N. (Nobuhisa), Musolf, A.M. (Anthony M.), Nickels, S. (Stefan), Oexle, K. (Konrad), Pang, C.P. (Chi Pui), Paterson, A.D. (Andrew), Rahi, J.S. (Jugnoo S.), Raitakari, O. (Olli), Rudan, I. (Igor), Stambolian, D.E. (Dwight), Simpson, C.L. (Claire), Wang, N. (Ningli), Bin Wei, W. (Wen), Williams, K.M. (Katie M.), Wilson, J.F. (James), Wojciechowski, R. (Robert), Yamashiro, K. (Kenji), Yam, J.C.S. (Jason C. S.), Zhou, X. (Xiangtian), Aslam, T. (Tariq), Barman, S.A. (Sarah A.), Barrett, J.H. (Jenny H.), Bishop, P.N. (Paul), Blows, P. (Peter), Bunce, C. (Catey), Carare, R.O. (Roxana O.), Chakravarthy, U. (Usha), Chan, M. (Michelle), Chua, S.Y.L. (Sharon Y. L.), Crabb, D.P. (David), Cumberland, P.M. (Philippa M.), Day, A. (Alexander), Desai, P. (Parul), Dhillon, B. (Bal), Dick, A.D. (Andrew D.), Egan, C. (Cathy), Ennis, S. (Sarah), Fruttiger, M. (Marcus), Gallacher, J. (John), Garway-Heath, D.F. (David F.), Gibson, J. (Jane), Gore, D. (Dan), Hardcastle, A. (Alison), Harding, S.P. (Simon), Hogg, R. (Ruth), Keane, P.A. (Pearse A.), Khaw, S.P.T. (Sir Peng T.), Lascaratos, G. (Gerassimos), Lotery, A.J. (Andrew), Macgillivray, T. (Tom), Mackie, S. (Sarah), Martin, K. (Keith), McGaughey, M. (Michelle), McGuinness, B. (Bernadette), McKay, G.J. (Gareth), McKibbin, M. (Martin), Mitry, D. (Danny), Moore, T. (Tony), Morgan, J.E. (James E.), Muthy, Z.A. (Zaynah A.), O’Sullivan, E. (Eoin), Owen, C.G. (Chris G.), Patel, P. (Praveen), Paterson, E. (Euan), Peto, T. (Tünde), Petzold, A. (Axel), Rudnikca, A.R. (Alicja R.), Self, J. (Jay), Sivaprasad, S., Steel, D. (David), Stratton, I. (Irene), Strouthidis, N. (Nicholas), Sudlow, C. (Cathie), Thomas, D. (Dhanes), Trucco, E. (Emanuele), Tufail, A. (Adnan), Vernon, S.A. (Stephen A.), Viswanathan, A.C. (Ananth C.), Williams, K. (Katie), Woodside, J.V. (J.), Yates, M.M. (Max M.), Yip, J. (Jennifer), Zheng, Y. (Yalin), Hewit, A.W. (Alex), Jaddoe, V.W.V. (Vincent), Duijn, C.M. (Cornelia) van, Hayward, C. (Caroline), Polasek, O. (Ozren), Tai, E.S. (Shyong), Yoshikatsu, H. (Hosoda), Hysi, P.G. (Pirro G.), Young, T.L. (Terri L.), Tsujikawa, A. (Akitaka), Wang, J.J. (Jie Jing), Mitchell, P. (Paul), Pfeiffer, A.F.H. (Andreas), Pärssinen, O. (Olavi), Foster, P.J. (Paul), Fossarello, M. (Maurizio), Yip, S.P. (Shea Ping), Williams, C. (Cathy), Hammond, C.J. (Christopher), Jonas, J.B., He, M. (Mingguang), Mackey, D.A. (David), Wong, T.-Y. (Tien-Yin), Klaver, C.C.W. (Caroline), Saw, S-M. (Seang-Mei), Baird, P.N. (Paul), and Cheng, C.-Y. (Ching-Yu)

Abstract

Corneal curvature, a highly heritable trait, is a key clinical endophenotype for myopia - a major cause of visual impairment and blindness in the world. Here we present a trans-ethnic meta-analysis of corneal curvature GWAS in 44,042 individuals of Caucasian and Asian with replication in 88,218 UK Biobank data. We identified 47 loci (of which 26 are novel), with population-specific signals as well as shared signals across ethnicities. Some identified variants showed precise scaling in corneal curvature and eye elongation (i.e. axial length) to maintain eyes in emmetropia (i.e. HDAC11/FBLN2 rs2630445, RBP3 rs11204213); others exhibited association with myopia with little pleiotropic effects on eye elongation. Implicated genes are involved in extracellular matrix organization, developmental process for body and eye, connective tissue cartilage and glycosylation protein activities. Our study provides insights into population-specific novel genes for corneal curvature, and their pleiotropic effect in regulating eye size or conferring susceptibility to myopia.

Published
2020
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31. Quantile regression analysis reveals widespread evidence for gene-environment or gene-gene interactions in myopia development

Author

Pozarickij, A., Williams, C., Hysi, P. G., Guggenheim, J. A., Aslam, T., Barman, S. A., Barrett, J. H., Bishop, P., Blows, P., Bunce, C., Carare, R. O., Chakravarthy, U., Chan, M., Chua, S. Y. L., Crabb, D. P., Cumberland, P. M., Day, A., Desai, P., Dhillon, B., Dick, A. D., Egan, C., Ennis, S., Foster, P., Fruttiger, M., Gallacher, J. E. J., Garway-Heath, D. F., Gibson, J., Gore, D., Hammond, C. J., Hardcastle, A., Harding, S. P., Hogg, R. E., Keane, P. A., Khaw, S. P. T., Khawaja, A. P., Lascaratos, G., Lotery, A. J., Mac Gillivray, T., Mackie, S., Martin, K., McGaughey, M., McGuinness, B., McKay, G. J., McKibbin, M., Mitry, D., Moore, T., Morgan, J. E., Muthy, Z. A., O’Sullivan, E., Owen, C. G., Patel, P., Paterson, E., Peto, T., Petzold, A., Rahi, J. S., Rudnikca, A. R., Self, J., Sivaprasad, S., Steel, D., Stratton, I., Strouthidis, N., Sudlow, C., Thomas, D., Trucco, E., Tufail, A., Vitart, V., Vernon, S. A., Viswanathan, A. C., Williams, K., Woodside, J. V., Yates, M. M., Yip, J., and Zheng, Y.

Subjects
RE, QH426
Abstract

© 2019, The Author(s). A genetic contribution to refractive error has been confirmed by the discovery of more than 150 associated variants in genome-wide association studies (GWAS). Environmental factors such as education and time outdoors also demonstrate strong associations. Currently however, the extent of gene-environment or gene-gene interactions in myopia is unknown. We tested the hypothesis that refractive error-associated variants exhibit effect size heterogeneity, a hallmark feature of genetic interactions. Of 146 variants tested, evidence of non-uniform, non-linear effects were observed for 66 (45%) at Bonferroni-corrected significance (P < 1.1 × 10−4) and 128 (88%) at nominal significance (P < 0.05). LAMA2 variant rs12193446, for example, had an effect size varying from −0.20 diopters (95% CI −0.18 to −0.23) to −0.89 diopters (95% CI −0.71 to −1.07) in different individuals. SNP effects were strongest at the phenotype extremes and weaker in emmetropes. A parsimonious explanation for these findings is that gene-environment or gene-gene interactions in myopia are pervasive.

Published
2019

33. Monoenergetic beams of relativistic electrons from intense laser-plasma interactions

Author

Mangles, S. P. D., Murphy, C. D., Najmudin, Z., Thomas, A. G. R., Collier, J. L., Dangor, A. E., Divall, E. J., Foster, P. S., Gallacher, J. G., Hooker, C. J., Jaroszynski, D. A., Langley, A. J., Mori, W. B., Norreys, P. A., Tsung, F. S., Viskup, R., Walton, B. R., and Krushelnick, K.

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): S. P. D. Mangles (corresponding author) [1]; C. D. Murphy [1, 2]; Z. Najmudin [1]; A. G. R. Thomas [1]; J. L. Collier [2]; A. E. Dangor [1]; E. [...]

Published
2004
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38. Meta-analysis of genetic association with diagnosed Alzheimer's disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing

Author

Kunkle, BW, Grenier-Boley, B, Sims, R, Bis, JC, Naj, AC, Boland, A, Vronskaya, M, van der Lee, SJ, Amlie-Wolf, A, Bellenguez, C, Frizatti, A, Chouraki, V, Martin, ER, Sleegers, K, Badarinarayan, N, Jakobsdottir, J, Hamilton-Nelson, KL, Aloso, R, Raybould, R, Chen, Y, Kuzma, AB, Hiltunen, M, Morgan, T, Ahmad, S, Vardarajan, BN, Epelbaum, J, Hoffmann, P, Boada, M, Beecham, GW, Garnier, JG, Harold, D, Fitzpatrick, AL, Valladares, O, Moutet, ML, Gerrish, A, Smith, AV, Qu, L, Bacq, D, Denning, N, Jian, X, Zhao, Y, Zompo, MD, Fox, NC, Grove, ML, Choi, SH, Mateo, I, Hughes, JT, Adams, HH, Malamon, J, Garcia, FS, Patel, Y, Brody, JA, Dombroski, B, Naranjo, MCD, Daniilidou, M, Eiriksdottir, G, Mukherjee, S, Wallon, D, Uphill, J, Aspelund, T, Cantwell, LB, Garzia, F, Galimberti, D, Hofer, E, Butkiewics, M, Fin, B, Scarpini, E, Sarnowski, C, Bush, W, Meslage, S, Kornhuber, J, White, CC, Song, Y, Barber, RC, Engelborghs, S, Pichler, S, Voijnovic, D, Adams, PM, Vandenberghe, R, Mayhaus, M, Cupples, LA, Albert, MS, De Deyn, PP, Gu, W, Himali, JJ, Beekly, D, Squassina, A, Hartmann, AM, Orellana, A, Blacker, D, Rodriguez-Rodriguez, E, Lovestone, S, Garcia, ME, Doody, RS, Fernadez, CM, Sussams, R, Lin, H, Fairchild, TJ, Benito, YA, Holmes, C, Comic, H, Frosch, MP, Thonberg, H, Maier, W, Roschupkin, G, Ghetti, B, Giedraitis, V, Kawalia, A, Li, S, Huebinger, RM, Kilander, L, Moebus, S, Hernández, I, Kamboh, MI, Brundin, R, Turton, J, Yang, Q, Katz, MJ, Concari, L, Lord, J, Beiser, AS, Keene, CD, Helisalmi, S, Kloszewska, I, Kukull, WA, Koivisto, AM, Lynch, A, Tarraga, L, Larson, EB, Haapasalo, A, Lawlor, B, Mosley, TH, Lipton, RB, Solfrizzi, V, Gill, M, Longstreth Jr, WT, Montine, TJ, Frisardi, V, Ortega-Cubero, S, Rivadeneira, F, Petersen, RC, Deramecourt, V, Ciaramella, A, Boerwinkle, E, Reiman, EM, Fievet, N, Caltagirone, C, Rotter, JI, Reisch, JS, Hanon, O, Cupidi, C, Uitterlinden, AG, Royall, DR, Dufouil, C, Maletta, RG, Moreno-Grau, S, Sano, M, Brice, A, Cecchetti, R, St George-Hyslop, P, Ritchie, K, Tsolaki, M, Tsuang, DW, Dubois, B, Craig, D, Wu, CK, Soininen, H, Avramidou, D, Albin, RL, Fratiglioni, L, Germanou, A, Apostolova, LG, Keller, L, Koutroumani, M, Arnold, SE, Panza, F, Gkatzima, O, Asthana, S, Hannequin, D, Whitehead, P, Atwood, CS, Caffarra, P, Hampel, H, Baldwin, CT, Lannfelt, L, Rubinsztein, DC, Barnes, LL, Pasquier, F, Frölich, L, Barral, S, McGuinness, B, Beach, TG, Johnston, JI, Becker, JT, Passmore, P, Bigio, EH, Schott, JM, Bird, TD, Warren, JD, Boeve, BF, Lupton, MK, Bowen, JD, Proitsi, P, Boxer, A, Powell, JF, Burke, JR, Kauwe, JK, Burns, JM, Mancuso, M, Buxbaum, JD, Bonuccelli, U, Cairns, NJ, McQuillin, A, Cao, C, Livingston, G, Carlson, CS, Bass, NJ, Carlsson, CM, Hardy, J, Carney, RM, Bras, J, Carrasquillo, MM, Guerreiro, R, Allen, M, Chui, HC, Fisher, E, Cribbs, DH, Masullo, C, Crocco, EA, DeCarli, C, Bisceglio, G, Dick, M, Ma, L, Duara, R, Graff-Radford, NR, Evans, DA, Hodges, A, Faber, KM, Scherer, M, Fallon, KB, Riemenschneider, M, Fardo, DW, Heun, R, Farlow, MR, Ferris, S, Leber, M, Foroud, TM, Heuser, I, Galasko, DR, Giegling, I, Gearing, M, Hüll, M, Geschwind, DH, Gilbert, JR, Morris, J, Green, RC, Mayo, K, Growdon, JH, Feulner, T, Hamilton, RL, Harrell, LE, Drichel, D, Honig, LS, Cushion, TD, Huentelman, MJ, Hollingworth, P, Hulette, CM, Hyman, BT, Marshall, R, Jarvik, GP, Meggy, A, Abner, E, Menzies, G, Jin, LW, Leonenko, G, Jun, G, Grozeva, D, Karydas, A, Russo, G, Kaye, JA, Kim, R, Jessen, F, Kowall, NW, Vellas, B, Kramer, JH, Vardy, E, LaFerla, FM, Jöckel, KH, Lah, JJ, Dichgans, M, Leverenz, JB, Mann, D, Levey, AI, Pickering-Brown, S, Lieberman, AP, Klopp, N, Lunetta, KL, Wichmann, HE, Lyketsos, CG, Morgan, K, Marson, DC, Brown, K, Martiniuk, F, Medway, C, Mash, DC, Nöthen, MM, Masliah, E, Hooper, NM, McCormick, WC, Daniele, A, McCurry, SM, Bayer, A, McDavid, AN, Gallacher, J, McKee, AC, van den Bussche, H, Mesulam, M, Brayne, C, Miller, BL, Riedel-Heller, S, Miller, CA, Miller, JW, Al-Chalabi, A, Morris, JC, Shaw, CE, Myers, AJ, Wiltfang, J, O’Bryant, S, Coto, E, Olichney, JM, Alvarez, V, Parisi, JE, Singleton, AB, Paulson, HL, Collinge, J, Perry, W, Mead, S, Peskind, E, Rosser, M, Pierce, A, Ryan, N, Poon, WW, Nacmias, B, Potter, H, Sorbi, S, Quinn, JF, Sacchinelli, E, Raj, A, Spalletta, G, Raskind, M, Bossù, P, Reisberg, B, Clarke, R, Reitz, C, Smith, AD, Ringman, JM, Warden, D, Roberson, ED, Wilcock, G, Rogaeva, E, Bruni, AC, Rosen, HJ, Gallo, M, Rosenberg, RN, Ben-Shlomo, Y, Sager, MA, Mecocci, P, Saykin, AJ, Pastor, P, Cuccaro, ML, Vance, JM, Schneider, JA, Schneider, LS, Seeley, WW, Smith, AG, Sonnen, JA, Spina, S, Stern, RA, Swerdlow, RH, Tanzi, RE, Trojanowski, JQ, Troncoso, JC, Van Deerlin, VM, Van Eldik, LJ, Vinters, HV, Vonsattel, JP, Weintraub, S, Welsh-Bohmer, KA, Wilhelmsen, KC, Williamson, J, Wingo, TS, Woltjer, RL, Wright, CB, Yu, CE, Yu, L, Crane, PK, Bennett, DA, Boccardi, V, De Jager, PL, Warner, N, Lopez, OL, McDonough, S, Ingelsson, M, Deloukas, P, Cruchaga, C, Graff, C, Gwilliam, R, Fornage, M, Goate, AM, Sanchez-Juan, P, Kehoe, PG, Amin, N, Ertekin-Taner, N, Berr, C, Debette, S, Love, S, Launer, LJ, Younkin, SG, Dartigues, JF, Corcoran, C, Ikram, MA, Dickson, DW, Campion, D, Tschanz, J, Schmidt, H, Hakonarson, H, Munger, R, Schmidt, R, Farrer, LA, Van Broeckhoven, C, O’Donovan, MC, DeStefano, AL, Jones, L, Haines, JL, Deleuze, JF, Owen, MJ, Gudnason, V, Mayeux, R, Escott-Price, V, Psaty, BM, Ruiz, A, Ramirez, A, Wang, LS, van Duijn, CM, Holmans, PA, Seshadri, S, Williams, J, Amouyel, P, Schellenberg, GD, Lambert, JC, Pericak-Vance, MA, Bis, JC [0000-0002-3409-1110], Garnier, JG [0000-0003-4991-763X], Smith, AV [0000-0001-9088-234X], Denning, N [0000-0001-8467-7382], Vandenberghe, R [0000-0001-6237-2502], Himali, JJ [0000-0003-1391-9481], Rodriguez-Rodriguez, E [0000-0001-7742-677X], Frisardi, V [0000-0003-0764-7387], Ortega-Cubero, S [0000-0003-0520-9439], Hanon, O [0000-0002-4697-122X], Brice, A [0000-0002-0941-3990], Albin, RL [0000-0002-0629-608X], Buxbaum, JD [0000-0001-8898-8313], Bass, NJ [0000-0002-4481-778X], Fisher, E [0000-0003-2850-9936], Bayer, A [0000-0002-7514-248X], Gallacher, J [0000-0002-2394-5299], Brayne, C [0000-0001-5307-663X], Riedel-Heller, S [0000-0003-4321-6090], Al-Chalabi, A [0000-0002-4924-7712], and Apollo - University of Cambridge Repository

Subjects
Aging, 4202 Epidemiology, Genome-wide association study, Disease, Neurodegenerative, Biology, 3101 Biochemistry and Cell Biology, Alzheimer's Disease, 3105 Genetics, 03 medical and health sciences, 0302 clinical medicine, Clinical Research, Acquired Cognitive Impairment, Genetics, medicine, 2.1 Biological and endogenous factors, Dementia, Gene, 030304 developmental biology, Genetic association, 2 Aetiology, 0303 health sciences, Prevention, Human Genome, 42 Health Sciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Lipid metabolism, medicine.disease, Brain Disorders, 3. Good health, Meta-analysis, Neurological, Alzheimer's disease, 030217 neurology & neurosurgery, 31 Biological Sciences
Abstract

IntroductionLate-onset Alzheimer’s disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly1, and risk is partially driven by genetics2. Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS)3–8. To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci (IQCK, ACE, ADAM10, and ADAMTS1). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7) indicating that additional rare variants remain to be identified.

Published
2018
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41. Associations with Corneal Hysteresis in a Population Cohort: Results from 96010 UK Biobank Participants

Author

Zhang, B., Shweikh, Y., Khawaja, A. P., Gallacher, J., Bauermeister, S., Foster, P. J., UKBiobank Eye and Vision Consortium, and Crabb, D. P.

Subjects
genetic structures, RA0421, RE, sense organs, eye diseases
Abstract

PURPOSE: To describe the distribution of corneal hysteresis (CH) in a large cohort and explore its associated factors and possible clinical applications. \ud \ud DESIGN: Cross-sectional study within the UK Biobank, a large cohort study in the United Kingdom. \ud \ud PARTICIPANTS: We analyzed CH data from 93 345 eligible participants in the UK Biobank cohort, aged 40 to 69 years. \ud \ud METHODS: All analyses were performed using left eye data. Linear regression models were used to evaluate associations between CH and demographic, lifestyle, ocular, and systemic variables. Piecewise logistic regression models were used to explore the relationship between self-reported glaucoma and CH. \ud \ud MAIN OUTCOME MEASURES: Corneal hysteresis (mmHg). \ud \ud RESULTS: The mean CH was 10.6 mmHg (10.4 mmHg in male and 10.8 mmHg in female participants). After adjusting for covariables, CH was significantly negatively associated with male sex, age, black ethnicity, self-reported glaucoma, diastolic blood pressure, and height. Corneal hysteresis was significantly positively associated with smoking, hyperopia, diabetes, systemic lupus erythematosus (SLE), greater deprivation (Townsend index), and Goldmann-correlated intraocular pressure (IOPg). Self-reported glaucoma and CH were significantly associated when CH was less than 10.1 mmHg (odds ratio, 0.86; 95% confidence interval, 0.79-0.94 per mmHg CH increase) after adjusting for covariables. When CH exceeded 10.1 mmHg, there was no significant association between CH and self-reported glaucoma.\ud \ud CONCLUSIONS: In our analyses, CH was significantly associated with factors including age, sex, and ethnicity, which should be taken into account when interpreting CH values. In our cohort, lower CH was significantly associated with a higher prevalence of self-reported glaucoma when CH was less than 10.1 mmHg. Corneal hysteresis may serve as a biomarker aiding glaucoma case detection.

Published
2019

42. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

Author

Angelantonio, E. di, Kaptoge, S., Pennells, L., Bacquer, D. de, Cooney, M.T., Kavousi, M., Stevens, G., Riley, L., Savin, S., Altay, S., Amouyel, P., Assmann, G., Bell, S., Ben-Shlomo, Y., Berkman, L., Beulens, J.W., Bjorkelund, C., Blaha, M.J., Blazer, D.G., Bolton, T., Bonita, R., Brenner, B.H., Brunner, E.J., Casiglia, E., Chamnan, P., Choi, Y.H., Chowdhury, R., Coady, S., Crespo, C.J., Cushman, M., Dagenais, G.R., D'Agostino, R.B., Daimon, M., Davidson, K.W., Engstrom, G., Fang, X.H., Ford, I., Gallacher, J., Gansevoort, R.T., Gaziano, T.A., Giampaoli, S., Grandits, G., Grimsgaard, S., Grobbee, D.E., Gudnason, V., Guo, Q., Humphries, S., Iso, H., Jukema, J.W., Kauhanen, J., Kengne, A.P., Khalili, D., Khan, T., Knuiman, M., Koenig, W., Kromhout, D., Krumholz, H.M., Lam, T.H., Laughlin, G., Ibanez, A.M., Moons, K.G.M., Nietert, P.J., Ninomiya, T., Nordestgaard, B.G., O'Donnell, C., Palmieri, L., Patel, A., Perel, P., Price, J.F., Costa, R.B.D.E., Ridker, P.M., Rodriguez, B., Rosengren, A., Roussel, R., Sakurai, M., Salomaa, V., Sato, S., Schottker, B., Shara, N., Shaw, J.E., Shin, H.C., Simons, L.A., Sofianopoulou, E., Sundstrom, J., Tolonen, H., Ueshima, H., Volzke, H., Wallace, R.B., Wareham, N.J., Willeit, P., Wood, D., Wood, A., Zhao, D., Onuma, O., Woodward, M., Danaei, G., Roth, G., Mendis, S., Graham, I., Varghese, C., Ezzati, M., Jackson, R., Danesh, J., Nambi, V., Matsushita, K., Couper, D., Diabetes, A., Zimmet, P.Z., Barr, E.L.M., Atkins, R., Whincup, P.H., Study, B., Kiechl, S., Willeit, J., Rungger, G., Sofat, R., Dale, C., Casas, J.P., Tikhonoff, V., Hunt, K.J., Sutherland, S.E., Psaty, B.M., Tracy, R., Frikke-Schmidt, R., Jensen, G.B., Schnohr, P., Donfrancesco, C., Vanuzzo, D., Panico, S., Balkau, B., Bonnet, F., Fumeron, F., Simons, J., McLachlan, S., Guralnik, J., Khaw, K.T., Brenner, H., Zhang, Y., Holleczek, B., Cohort, F., Vartiainen, E., Jousilahti, P., Harald, K., Massaro, J.J., Pencina, M., Ramachandran, V., Susa, S., Oizumi, T., Kayama, T., Wilhelmsen, L., Lissner, L., Hange, D., Mehlig, K., Hata, J., Yoshida, D., Hirakawa, Y., Rutters, F., Elders, P.J.M., Kyowa, I., Kiyama, M., Yamagishi, K., Tuomainen, T.P., Virtanen, J., Salonen, J.T., Meade, T.W., Nilsson, P.M., Melander, O., Boer, I.H. de, DeFilippis, A.P., Kuller, L.H., Juan, S.I., Gillum, R.F., Kirkland, S., Shimbo, D., Schwartz, J.E., Imano, H., Harst, P. van der, Hillige, J.L., Bakker, S.J., Dallongeville, J., Ferrieres, J., Moitry, M., Stott, D.J., Despres, J.P., Laughlin, G.A., Daniels, L.B., McEvoy, L.K., Aspelund, T., Thorsson, B., Gudmundsson, E.F., Aribas, E., Rueda-Ochoa, O.L., Ikram, M.K., Heshmatollah, A., Ikram, M.A., Dorr, M., Nauck, M., Howard, B., Can, G., Ishizaki, M., Wilsgaard, T., Mathiesen, E., Giedraitis, V., Ingelsson, M., Cook, N., Buring, J., Schouw, Y.T. van der, Claessen, H., Rothenbacher, D., Arndt, V., Study, W.I., Shipley, M., Packard, C., Robertson, M., Young, R., Feskens, E., Geleijnse, J.M., Fang, X., Gu, D.F., Huxley, R., Imai, Y., Kim, H.C., Pan, W.H., Rodgers, A., Suh, I., Town, A., Okayama, A., Maegawa, H., Nakamura, M., Aoki, N., Wu, Z.S., Yao, C.H., Luszcz, M., Tang, Z., Liu, L.S., Xie, J.X., Norton, R., Ameratunga, S., MacMahon, S., Whitlock, G., Knuiman, M.W., Christensen, H., Wu, X.G., Zhou, J., Yu, X.H., Tamakoshi, W.A., Wu, Z.L., Chen, L.Q., Shan, G.L., Sritara, P., Duan, X.F., Li, Y.H., Jiang, C.Q., Woo, J., Ho, S.C., Hong, Z., Huang, M.S., Zhou, B., Fuh, J.L., Kita, Y., Choudhury, S.R., Jee, S.H., Kim, Woodward, M, Cardiovascular Centre (CVC), Groningen Kidney Center (GKC), Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Epidemiology, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, and APH - Health Behaviors & Chronic Diseases

Subjects
Male, 10-YEAR RISK, BLOOD-PRESSURE, 0302 clinical medicine, Risk Factors, Medicine and Health Sciences, Medicine, Uganda, Cardiac and Cardiovascular Systems, Prospective Studies, 030212 general & internal medicine, Aged, 80 and over, Medicine(all), Kardiologi, lcsh:Public aspects of medicine, PRIMARY-CARE, Public Health, Global Health, Social Medicine and Epidemiology, General Medicine, Middle Aged, 3. Good health, Pooled analysis, Cardiovascular Diseases, Egypt, Female, Adult, PROSPECTIVE COHORTS, 030231 tropical medicine, 195 COUNTRIES, Primary care, World Health Organization, Risk Assessment, Article, World health, POOLED ANALYSIS, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Primary prevention, Environmental health, SYSTEMATIC ANALYSIS, Humans, Aged, CHINESE POPULATION, Chinese population, business.industry, Individual participant data, lcsh:RA1-1270, R1, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Disease risk, business, INDIVIDUAL PARTICIPANT DATA, PRIMARY PREVENTION
Abstract

Background: To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods: In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40–80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings: Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629–0·741) to 0·833 (0·783–0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40–64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation: We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Funding: World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research. The WHO CVD Risk Chart Working Group, for complete list of authors see http://dx.doi.org/10.1016/S2214-109X(19)30318-3

Published
2019
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45. The costs of negative affect attributable to alcohol consumption in later life: A within-between random longitudinal econometric model using UK Biobank

Author

Li, C, Moore, S, Smith, J, Bauermeister, S, and Gallacher, J

Subjects
Adult, Male, Alcohol Drinking, Economics, Science, Emotions, Happiness, Social Sciences, Wine, Beverages, Mental Health and Psychiatry, Medicine and Health Sciences, Psychology, Humans, Econometrics, Longitudinal Studies, Nutrition, Aged, Biological Specimen Banks, Neuroticism, Alcohol Consumption, Covariance, Mood Disorders, Depression, Alcoholic Beverages, Biology and Life Sciences, Random Variables, Middle Aged, Probability Theory, Economic Analysis, United Kingdom, Diet, Mathematical Economics, Affect, Cross-Sectional Studies, Logistic Models, Social Class, Physical Sciences, Medicine, Female, Mathematics, Models, Econometric, Research Article
Abstract

Aims Research demonstrates a negative relationship between alcohol use and affect, but the value of deprecation is unknown and thus cannot be included in estimates of the cost of alcohol to society. This paper aims to examine this relationship and develop econometric techniques to value the loss in affect attributable to alcohol consumption. Methods Cross-sectional (n = 129,437) and longitudinal (n = 11,352) analyses of alcohol consumers in UK Biobank data were undertaken, with depression and neuroticism as proxies of negative affect. The cross-sectional relationship between household income, negative affect and alcohol consumption were analysed using regression models, controlling for confounding variables, and using within-between random models that are robust to unobserved heterogeneity. The differential in household income required to offset alcohol’s detriment to affect was derived. Results A consistent relationship between depression and alcohol consumption (β = 0.001, z = 7.64) and neuroticism and alcohol consumption (β = 0.001, z = 9.24) was observed in cross-sectional analyses, replicated in within-between models (depression β = 0.001, z = 2.32; neuroticism β = 0.001, z = 2.33). Significant associations were found between household income and depression (cross sectional β = -0.157, z = -23.86, within-between β = -0.146, z = -9.51) and household income and neuroticism (cross sectional β = -0.166, z = -32.02, within-between β = -0.158, z = -7.44). The value of reducing alcohol consumption by one gram/day was pooled and estimated to be £209.06 (95% CI £171.84 to £246.27). Conclusions There was a robust relationship between alcohol consumption and negative affect. Econometric methods can value the intangible effects of alcohol use and may, therefore, facilitate the fiscal determination of benefit.

Published
2019

46. Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates A beta, tau, immunity and lipid processing (vol 51, pg 414, 2019)

Author

Kunkle, BW, Grenier-Boley, B, Sims, R, Bis, JC, Damotte, V, Naj, AC, Boland, A, Vronskaya, M, van der Lee, SJ, Amlie-Wolf, A, Bellenguez, C, Frizatti, A, Chouraki, V, Martin, ER, Sleegers, K, Badarinarayan, N, Jakobsdottir, J, Hamilton-Nelson, KL, Moreno-Grau, S, Olaso, R, Raybould, R, Chen, YN, Kuzma, AB, Hiltunen, M, Morgan, T, Ahmad, S, Vardarajan, BN, Epelbaum, J, Hoffmann, P, Boada, M, Beecham, GW, Garnier, JG, Harold, D, Fitzpatrick, AL, Valladares, O, Moutet, ML, Gerrish, A, Smith, AV, Qu, LM, Bacq, D, Denning, N, Jian, XQ, Zhao, Y, Del Zompo, M, Fox, NC, Choi, SH, Mateo, I, Hughes, JT, Adams, HH, Malamon, J, Sanchez-Garcia, F, Patel, Y, Brody, JA, Dombroski, BA, Naranjo, MCD, Daniilidou, M, Eiriksdottir, G, Mukherjee, S, Wallon, D, Uphill, J, Aspelund, T, Cantwell, LB, Garzia, F, Galimberti, D, Hofer, E, Butkiewicz, M, Fin, B, Scarpini, E, Sarnowski, C, Bush, WS, Meslage, S, Kornhuber, J, White, CC, Song, Y, Barber, RC, Engelborghs, S, Sordon, S, Voijnovic, D, Adams, PM, Vandenberghe, R, Mayhaus, M, Cupples, LA, Albert, MS, De Deyn, PP, Gu, W, Himali, JJ, Beekly, D, Squassina, A, Hartmann, AM, Orellana, A, Blacker, D, Rodriguez-Rodriguez, E, Lovestone, S, Garcia, ME, Doody, RS, Munoz-Fernadez, C, Sussams, R, Lin, HH, Fairchild, TJ, Benito, YA, Holmes, C, Karamujic-Comic, H, Frosch, MP, Thonberg, H, Maier, W, Roshchupkin, G, Ghetti, B, Giedraitis, V, Kawalia, A, Li, S, Huebinger, RM, Kilander, L, Moebus, S, Hernandez, I, Kamboh, MI, Brundin, R, Turton, J, Yang, Q, Katz, MJ, Concari, L, Lord, J, Beiser, AS, Keene, CD, Helisalmi, S, Kloszewska, I, Kukull, WA, Koivisto, AM, Lynch, A, Tarraga, L, Larson, EB, Haapasalo, A, Lawlor, B, Mosley, TH, Lipton, RB, Solfrizzi, V, Gill, M, Longstreth, WT, Montine, TJ, Frisardi, V, Diez-Fairen, M, Rivadeneira, F, Petersen, RC, Deramecourt, V, Alvarez, I, Salani, F, Ciaramella, A, Boerwinkle, E, Reiman, EM, Fievet, N, Rotter, JI, Reisch, JS, Hanon, O, Cupidi, C, Uitterlinden, AGA, Royall, DR, Dufouil, C, Maletta, RG, de Rojas, I, Sano, M, Brice, A, Cecchetti, R, St George-Hyslop, P, Ritchie, K, Tsolaki, M, Tsuang, DW, Dubois, B, Craig, D, Wu, CK, Soininen, H, Avramidou, D, Albin, RL, Fratiglioni, L, Germanou, A, Apostolova, LG, Keller, L, Koutroumani, M, Arnold, SE, Panza, F, Gkatzima, O, Asthana, S, Hannequin, D, Whitehead, P, Atwood, CS, Caffarra, P, Hampel, H, Quintela, I, Carracedo, A, Lannfelt, L, Rubinsztein, DC, Barnes, LL, Pasquier, F, Frolich, L, Barral, S, McGuinness, B, Beach, TG, Johnston, JA, Becker, JT, Passmore, P, Bigio, EH, Schott, JM, Bird, TD, Warren, JD, Boeve, BF, Lupton, MK, Bowen, JD, Proitsi, P, Boxer, A, Powell, JF, Burke, JR, Kauwe, JSK, Burns, JM, Mancuso, M, Buxbaum, JD, Bonuccelli, U, Cairns, NJ, McQuillin, A, Cao, CH, Livingston, G, Carlson, CS, Bass, NJ, Carlsson, CM, Hardy, J, Carney, RM, Bras, J, Carrasquillo, MM, Guerreiro, R, Allen, M, Chui, HC, Fisher, E, Masullo, C, Crocco, EA, DeCarli, C, Bisceglio, G, Dick, M, Ma, L, Duara, R, Graff-Radford, NR, Evans, DA, Hodges, A, Faber, KM, Scherer, M, Fallon, KB, Riemenschneider, M, Fardo, DW, Heun, R, Farlow, MR, Kolsch, H, Ferris, S, Leber, M, Foroud, TM, Heuser, I, Galasko, DR, Giegling, I, Gearing, M, Hull, M, Geschwind, DH, Gilbert, JR, Morris, J, Green, RC, Mayo, K, Growdon, JH, Feulner, T, Hamilton, RL, Harrell, LE, Drichel, D, Honig, LS, Cushion, TD, Huentelman, MJ, Hollingworth, P, Hulette, CM, Hyman, BT, Marshall, R, Jarvik, GP, Meggy, A, Abner, E, Menzies, GE, Jin, LW, Leonenko, G, Real, LM, Jun, GR, Baldwin, CT, Grozeva, D, Karydas, A, Russo, G, Kaye, JA, Kim, R, Jessen, F, Kowall, NW, Vellas, B, Kramer, JH, Vardy, E, LaFerla, FM, Jockel, KH, Lah, JJ, Dichgans, M, Leverenz, JB, Mann, D, Levey, AI, Pickering-Brown, S, Lieberman, AP, Klopp, N, Lunetta, KL, Wichmann, HE, Lyketsos, CG, Morgan, K, Marson, DC, Brown, K, Martiniuk, F, Medway, C, Mash, DC, Nothen, MM, Masliah, E, Hooper, NM, McCormick, WC, Daniele, A, McCurry, SM, Bayer, A, McDavid, AN, Gallacher, J, Mckee, AC, van den Bussche, H, Mesulam, M, Brayne, C, Miller, BL, Riedel-Heller, S, Miller, CA, Miller, JW, Al-Chalabi, A, Morris, JC, Shaw, CE, Myers, AJ, Wiltfang, J, O'Bryant, S, Olichney, JM, Alvarez, V, Parisi, JE, Singleton, AB, Paulson, HL, Collinge, J, Perry, WR, Mead, S, Peskind, E, Cribbs, DH, Rossor, M, Pierce, A, Ryan, NS, Poon, WW, Nacmias, B, Potter, H, Sorbi, S, Quinn, JF, Sacchinelli, E, Raj, A, Spalletta, G, Raskind, M, Caltagirone, C, Bossu, P, Orfei, MD, Reisberg, B, Clarke, R, Reitz, C, Smith, AD, Ringman, JM, Warden, D, Roberson, ED, Wilcock, G, Rogaeva, E, Bruni, AC, Rosen, HJ, Gallo, M, Rosenberg, RN, Ben-Shlomo, Y, Sager, MA, Mecocci, P, Saykin, AJ, Pastor, P, Cuccaro, ML, Vance, JM, Schneider, JA, Schneider, LS, Slifer, S, Seeley, WW, Smith, AG, Sonnen, JA, Spina, S, Stern, RA, Swerdlow, RH, Tang, M, Tanzi, RE, Trojanowski, JQ, Troncoso, JC, Van Deerlin, VM, Van Eldik, LJ, Vinters, HV, Vonsattel, JP, Weintraub, S, Welsh-Bohmer, KA, Wilhelmsen, KC, Williamson, J, Wingo, TS, Woltjer, RL, Wright, CB, Yu, CE, Yu, L, Saba, Y, Pilotto, A, Bullido, MJ, Peters, O, Crane, PK, Bennett, D, Bosco, P, Coto, E, Boccardi, V, De Jager, PL, Lleo, A, Warner, N, Lopez, OL, Ingelsson, M, Deloukas, P, Cruchaga, C, Graff, C, Gwilliam, R, Fornage, M, Goate, AM, Sanchez-Juan, P, Kehoe, PG, Amin, N, Ertekin-Taner, N, Berr, C, Debette, S, Love, S, Launer, LJ, Younkin, SG, Dartigues, JF, Corcoran, C, Ikram, MA, Dickson, DW, Nicolas, G, Campion, D, Tschanz, J, Schmidt, H, Hakonarson, H, Clarimon, J, Munger, R, Schmidt, R, Farrer, LA, Van Broeckhoven, C, O'Donovan, MC, DeStefano, AL, Jones, L, Haines, JL, Deleuze, JF, Owen, MJ, Gudnason, V, Mayeux, R, Escott-Price, V, Psaty, BM, Ramirez, A, Wang, LS, Ruiz, A, van Duijn, CM, Holmans, PA, Seshadri, S, Williams, J, Amouyel, P, Schellenberg, GD, Lambert, JC, Pericak-Vance, MA, ADGC, EADI, Cohorts Heart Aging Res Genomic, and Genetic Environm Risk AD Defining

Published
2019

47. Nat Genet

Author

KUNKLE, B. W., GRENIER-BOLEY, B., SIMS, R., BIS, J. C., DAMOTTE, V., NAJ, A. C., BOLAND, A., VRONSKAYA, M., VAN DER LEE, S. J., AMLIE-WOLF, A., BELLENGUEZ, C., FRIZATTI, A., CHOURAKI, V., MARTIN, E. R., SLEEGERS, K., BADARINARAYAN, N., JAKOBSDOTTIR, J., HAMILTON-NELSON, K. L., MORENO-GRAU, S., OLASO, R., RAYBOULD, R., CHEN, Y., KUZMA, A. B., HILTUNEN, M., MORGAN, T., AHMAD, S., VARDARAJAN, B. N., EPELBAUM, J., HOFFMANN, P., BOADA, M., BEECHAM, G. W., GARNIER, J. G., HAROLD, D., FITZPATRICK, A. L., VALLADARES, O., MOUTET, M. L., GERRISH, A., QU, L., BACQ, D., DENNING, N., JIAN, X., ZHAO, Y., DEL ZOMPO, M., FOX, N. C., CHOI, S. H., MATEO, I., HUGHES, J. T., ADAMS, H. H., MALAMON, J., SANCHEZ-GARCIA, F., PATEL, Y., BRODY, J. A., DOMBROSKI, B. A., NARANJO, M. C. D., DANIILIDOU, M., EIRIKSDOTTIR, G., MUKHERJEE, S., WALLON, D., UPHILL, J., ASPELUND, T., CANTWELL, L. B., GARZIA, F., GALIMBERTI, D., HOFER, E., BUTKIEWICZ, M., FIN, B., SCARPINI, E., SARNOWSKI, C., BUSH, W. S., MESLAGE, S., KORNHUBER, J., WHITE, C. C., SONG, Y., BARBER, R. C., ENGELBORGHS, S., SORDON, S., VOIJNOVIC, D., ADAMS, P. M., VANDENBERGHE, R., MAYHAUS, M., CUPPLES, L. A., ALBERT, M. S., DE DEYN, P. P., GU, W., HIMALI, J. J., BEEKLY, D., SQUASSINA, A., HARTMANN, A. M., ORELLANA, A., BLACKER, D., RODRIGUEZ-RODRIGUEZ, E., LOVESTONE, S., GARCIA, M. E., DOODY, R. S., MUNOZ-FERNADEZ, C., SUSSAMS, R., LIN, H., FAIRCHILD, T. J., BENITO, Y. A., HOLMES, C., KARAMUJIC-COMIC, H., FROSCH, M. P., THONBERG, H., MAIER, W., ROSCHUPKIN, G., GHETTI, B., GIEDRAITIS, V., KAWALIA, A., LI, S., HUEBINGER, R. M., KILANDER, L., MOEBUS, S., HERNANDEZ, I., KAMBOH, M. I., BRUNDIN, R., TURTON, J., YANG, Q., KATZ, M. J., CONCARI, L., LORD, J., BEISER, A. S., KEENE, C. D., HELISALMI, S., KLOSZEWSKA, I., KUKULL, W. A., KOIVISTO, A. M., LYNCH, A., TARRAGA, L., LARSON, E. B., HAAPASALO, A., LAWLOR, B., MOSLEY, T. H., LIPTON, R. B., SOLFRIZZI, V., GILL, M., LONGSTRETH, W. T. Jr., MONTINE, T. J., FRISARDI, V., DIEZ-FAIREN, M., RIVADENEIRA, F., PETERSEN, R. C., DERAMECOURT, V., ALVAREZ, I., SALANI, F., CIARAMELLA, A., BOERWINKLE, E., REIMAN, E. M., FIEVET, N., ROTTER, J. I., REISCH, J. S., HANON, O., CUPIDI, C., ANDRE UITTERLINDEN, A. G., ROYALL, D. R., DUFOUIL, Carole, MALETTA, R. G., DE ROJAS, I., SANO, M., BRICE, A., CECCHETTI, R., GEORGE-HYSLOP, P. S., RITCHIE, K., TSOLAKI, M., TSUANG, D. W., DUBOIS, B., CRAIG, D., WU, C. K., SOININEN, H., AVRAMIDOU, D., ALBIN, R. L., FRATIGLIONI, L., GERMANOU, A., APOSTOLOVA, L. G., KELLER, L., KOUTROUMANI, M., ARNOLD, S. E., PANZA, F., GKATZIMA, O., ASTHANA, S., HANNEQUIN, D., WHITEHEAD, P., ATWOOD, C. S., CAFFARRA, P., HAMPEL, H., QUINTELA, I., CARRACEDO, A., LANNFELT, L., RUBINSZTEIN, D. C., BARNES, L. L., PASQUIER, F., FROLICH, L., BARRAL, S., MCGUINNESS, B., BEACH, T. G., JOHNSTON, J. A., BECKER, J. T., PASSMORE, P., BIGIO, E. H., SCHOTT, J. M., BIRD, T. D., WARREN, J. D., BOEVE, B. F., LUPTON, M. K., BOWEN, J. D., PROITSI, P., BOXER, A., POWELL, J. F., BURKE, J. R., KAUWE, J. S. K., BURNS, J. M., MANCUSO, M., BUXBAUM, J. D., BONUCCELLI, U., CAIRNS, N. J., MCQUILLIN, A., CAO, C., LIVINGSTON, G., CARLSON, C. S., BASS, N. J., CARLSSON, C. M., HARDY, J., CARNEY, R. M., BRAS, J., CARRASQUILLO, M. M., GUERREIRO, R., ALLEN, M., CHUI, H. C., FISHER, E., MASULLO, C., CROCCO, E. A., DECARLI, C., BISCEGLIO, G., DICK, M., MA, L., DUARA, R., GRAFF-RADFORD, N. R., EVANS, D. A., HODGES, A., FABER, K. M., SCHERER, M., FALLON, K. B., RIEMENSCHNEIDER, M., FARDO, D. W., HEUN, R., FARLOW, M. R., KOLSCH, H., FERRIS, S., LEBER, M., FOROUD, T. M., HEUSER, I., GALASKO, D. R., GIEGLING, I., GEARING, M., HULL, M., GESCHWIND, D. H., GILBERT, J. R., GREEN, R. C., MAYO, K., GROWDON, J. H., FEULNER, T., HAMILTON, R. L., HARRELL, L. E., DRICHEL, D., HONIG, L. S., CUSHION, T. D., HUENTELMAN, M. J., HOLLINGWORTH, P., HULETTE, C. M., HYMAN, B. T., MARSHALL, R., JARVIK, G. P., MEGGY, A., ABNER, E., MENZIES, G. E., JIN, L. W., LEONENKO, G., REAL, L. M., JUN, G. R., BALDWIN, C. T., GROZEVA, D., KARYDAS, A., RUSSO, G., KAYE, J. A., KIM, R., JESSEN, F., KOWALL, N. W., VELLAS, B., KRAMER, J. H., VARDY, E., LAFERLA, F. M., JOCKEL, K. H., LAH, J. J., DICHGANS, M., LEVERENZ, J. B., MANN, D., LEVEY, A. I., PICKERING-BROWN, S., LIEBERMAN, A. P., KLOPP, N., LUNETTA, K. L., WICHMANN, H. E., LYKETSOS, C. G., MORGAN, K., MARSON, D. C., BROWN, K., MARTINIUK, F., MEDWAY, C., MASH, D. C., NOTHEN, M. M., MASLIAH, E., HOOPER, N. M., MCCORMICK, W. C., DANIELE, A., MCCURRY, S. M., BAYER, A., MCDAVID, A. N., GALLACHER, J., MCKEE, A. C., VAN DEN BUSSCHE, H., MESULAM, M., BRAYNE, C., MILLER, B. L., RIEDEL-HELLER, S., MILLER, C. A., MILLER, J. W., AL-CHALABI, A., MORRIS, J. C., SHAW, C. E., MYERS, A. J., WILTFANG, J., O'BRYANT, S., OLICHNEY, J. M., ALVAREZ, V., PARISI, J. E., SINGLETON, A. B., PAULSON, H. L., COLLINGE, J., PERRY, W. R., MEAD, S., PESKIND, E., CRIBBS, D. H., ROSSOR, M., PIERCE, A., RYAN, N. S., POON, W. W., NACMIAS, B., POTTER, H., SORBI, S., QUINN, J. F., SACCHINELLI, E., RAJ, A., SPALLETTA, G., RASKIND, M., CALTAGIRONE, C., BOSSU, P., ORFEI, M. D., REISBERG, B., CLARKE, R., REITZ, C., RINGMAN, J. M., WARDEN, D., ROBERSON, E. D., WILCOCK, G., ROGAEVA, E., BRUNI, A. C., ROSEN, H. J., GALLO, M., ROSENBERG, R. N., BEN-SHLOMO, Y., SAGER, M. A., MECOCCI, P., SAYKIN, A. J., PASTOR, P., CUCCARO, M. L., VANCE, J. M., SCHNEIDER, J. A., SCHNEIDER, L. S., SLIFER, S., SEELEY, W. W., SMITH, A. V., SONNEN, J. A., SPINA, S., STERN, R. A., SWERDLOW, R. H., TANG, M., TANZI, R. E., TROJANOWSKI, J. Q., TRONCOSO, J. C., VAN DEERLIN, V. M., VAN ELDIK, L. J., VINTERS, H. V., VONSATTEL, J. P., WEINTRAUB, S., WELSH-BOHMER, K. A., WILHELMSEN, K. C., WILLIAMSON, J., WINGO, T. S., WOLTJER, R. L., WRIGHT, C. B., YU, C. E., YU, L., SABA, Y., ALZHEIMER DISEASE GENETICS, Consortium, EUROPEAN ALZHEIMER'S DISEASE, Initiative, COHORTS FOR, Heart, AGING RESEARCH IN GENOMIC EPIDEMIOLOGY, Consortium, GENETIC ENVIRONMENTAL RISK IN AD/DEFINING GENETIC, Polygenic, ENVIRONMENTAL RISK FOR ALZHEIMER'S DISEASE, Consortium, PILOTTO, A., BULLIDO, M. J., PETERS, O., CRANE, P. K., BENNETT, D., BOSCO, P., COTO, E., BOCCARDI, V., DE JAGER, P. L., LLEO, A., WARNER, N., LOPEZ, O. L., INGELSSON, M., DELOUKAS, P., CRUCHAGA, C., GRAFF, C., GWILLIAM, R., FORNAGE, M., GOATE, A. M., SANCHEZ-JUAN, P., KEHOE, P. G., AMIN, N., ERTEKIN-TANER, N., BERR, C., DEBETTE, S., LOVE, S., LAUNER, L. J., YOUNKIN, S. G., DARTIGUES, Jean-Francois, CORCORAN, C., IKRAM, M. A., DICKSON, D. W., NICOLAS, G., CAMPION, D., TSCHANZ, J., SCHMIDT, H., HAKONARSON, H., CLARIMON, J., MUNGER, R., SCHMIDT, R., FARRER, L. A., VAN BROECKHOVEN, C., M, C. O' Donovan, DESTEFANO, A. L., JONES, L., HAINES, J. L., DELEUZE, J. F., OWEN, M. J., GUDNASON, V., MAYEUX, R., ESCOTT-PRICE, V., PSATY, B. M., RAMIREZ, A., WANG, L. S., RUIZ, A., VAN DUIJN, C. M., HOLMANS, P. A., SESHADRI, S., WILLIAMS, J., AMOUYEL, P., SCHELLENBERG, G. D., LAMBERT, J. C., and PERICAK-VANCE, M. A.

Published
2019

48. Author Correction: Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (Nature Genetics, (2019), 51, 3, (414-430), 10.1038/s41588-019-0358-2)

Author

Kunkle, B. W., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., Boland, A., Vronskaya, M., van der Lee, S. J., Amlie-Wolf, A., Bellenguez, C., Frizatti, A., Chouraki, V., Martin, E. R., Sleegers, K., Badarinarayan, N., Jakobsdottir, J., Hamilton-Nelson, K. L., Moreno-Grau, S., Olaso, R., Raybould, R., Chen, Y., Kuzma, A. B., Hiltunen, M., Morgan, T., Ahmad, S., Vardarajan, B. N., Epelbaum, J., Hoffmann, P., Boada, M., Beecham, G. W., Garnier, J. -G., Harold, D., Fitzpatrick, A. L., Valladares, O., Moutet, M. -L., Gerrish, A., Smith, A. V., Qu, L., Bacq, D., Denning, N., Jian, X., Zhao, Y., Del Zompo, M., Fox, N. C., Choi, S. -H., Mateo, I., Hughes, J. T., Adams, H. H., Malamon, J., Sanchez-Garcia, F., Patel, Y., Brody, J. A., Dombroski, B. A., Naranjo, M. C. D., Daniilidou, M., Eiriksdottir, G., Mukherjee, S., Wallon, D., Uphill, J., Aspelund, T., Cantwell, L. B., Garzia, F., Galimberti, D., Hofer, E., Butkiewicz, M., Fin, B., Scarpini, E., Sarnowski, C., Bush, W. S., Meslage, S., Kornhuber, J., White, C. C., Song, Y., Barber, R. C., Engelborghs, S., Sordon, S., Voijnovic, D., Adams, P. M., Vandenberghe, R., Mayhaus, M., Cupples, L. A., Albert, M. S., De Deyn, P. P., Gu, W., Himali, J. J., Beekly, D., Squassina, A., Hartmann, A. M., Orellana, A., Blacker, D., Rodriguez-Rodriguez, E., Lovestone, S., Garcia, M. E., Doody, R. S., Munoz-Fernadez, C., Sussams, R., Lin, H., Fairchild, T. J., Benito, Y. A., Holmes, C., Karamujic-Comic, H., Frosch, M. P., Thonberg, H., Maier, W., Roshchupkin, G., Ghetti, B., Giedraitis, V., Kawalia, A., Li, S., Huebinger, R. M., Kilander, L., Moebus, S., Hernandez, I., Kamboh, M. I., Brundin, R. M., Turton, J., Yang, Q., Katz, M. J., Concari, L., Lord, J., Beiser, A. S., Keene, C. D., Helisalmi, S., Kloszewska, I., Kukull, W. A., Koivisto, A. M., Lynch, A., Tarraga, L., Larson, E. B., Haapasalo, A., Lawlor, B., Mosley, T. H., Lipton, R. B., Solfrizzi, V., Gill, M., Longstreth, W. T., Montine, T. J., Frisardi, V., Diez-Fairen, M., Rivadeneira, F., Petersen, R. C., Deramecourt, V., Alvarez, I., Salani, F., Ciaramella, A., Boerwinkle, E., Reiman, E. M., Fievet, N., Rotter, J. I., Reisch, J. S., Hanon, O., Cupidi, C., Uitterlinden, A. G. A., Royall, D. R., Dufouil, C., Maletta, R. G., de Rojas, I., Sano, M., Brice, A., Cecchetti, R., George-Hyslop, P. S., Ritchie, K., Tsolaki, M., Tsuang, D. W., Dubois, B., Craig, D., C. -K., Wu, Soininen, H., Avramidou, D., Albin, R. L., Fratiglioni, L., Germanou, A., Apostolova, L. G., Keller, L., Koutroumani, M., Arnold, S. E., Panza, F., Gkatzima, O., Asthana, S., Hannequin, D., Whitehead, P., Atwood, C. S., Caffarra, P., Hampel, H., Quintela, I., Carracedo, A., Lannfelt, L., Rubinsztein, D. C., Barnes, L. L., Pasquier, F., Frolich, L., Barral, S., Mcguinness, B., Beach, T. G., Johnston, J. A., Becker, J. T., Passmore, P., Bigio, E. H., Schott, J. M., Bird, T. D., Warren, J. D., Boeve, B. F., Lupton, M. K., Bowen, J. D., Proitsi, P., Boxer, A., Powell, J. F., Burke, J. R., Kauwe, J. S. K., Burns, J. M., Mancuso, M., Buxbaum, J. D., Bonuccelli, U., Cairns, N. J., Mcquillin, A., Cao, C., Livingston, G., Carlson, C. S., Bass, N. J., Carlsson, C. M., Hardy, J., Carney, R. M., Bras, J., Carrasquillo, M. M., Guerreiro, R., Allen, M., Chui, H. C., Fisher, E., Masullo, Carlo, Crocco, E. A., Decarli, C., Bisceglio, G., Dick, M., Ma, L., Duara, R., Graff-Radford, N. R., Evans, D. A., Hodges, A., Faber, K. M., Scherer, M., Fallon, K. B., Riemenschneider, M., Fardo, D. W., Heun, R., Farlow, M. R., Kolsch, H., Ferris, S., Leber, M., Foroud, T. M., Heuser, I., Galasko, D. R., Giegling, I., Gearing, M., Hull, M., Geschwind, D. H., Gilbert, J. R., Morris, J., Green, R. C., Mayo, K., Growdon, J. H., Feulner, T., Hamilton, R. L., Harrell, L. E., Drichel, D., Honig, L. S., Cushion, T. D., Huentelman, M. J., Hollingworth, P., Hulette, C. M., Hyman, B. T., Marshall, R., Jarvik, G. P., Meggy, A., Abner, E., Menzies, G. E., Jin, L. -W., Leonenko, G., Real, L. M., Jun, G. R., Baldwin, C. T., Grozeva, D., Karydas, A., Russo, G., Kaye, J. A., Kim, R., Jessen, F., Kowall, N. W., Vellas, B., Kramer, J. H., Vardy, E., Laferla, F. M., Jockel, K. -H., Lah, J. J., Dichgans, M., Leverenz, J. B., Mann, D., Levey, A. I., Pickering-Brown, S., Lieberman, A. P., Klopp, N., Lunetta, K. L., Wichmann, H. -E., Lyketsos, C. G., Morgan, K., Marson, D. C., Brown, K., Martiniuk, F., Medway, C., Mash, D. C., Nothen, M. M., Masliah, E., Hooper, N. M., Mccormick, W. C., Daniele, Antonio, Mccurry, S. M., Bayer, A., Mcdavid, A. N., Gallacher, J., Mckee, A. C., van den Bussche, H., Mesulam, M., Brayne, C., Miller, B. L., Riedel-Heller, S., Miller, C. A., Miller, J. W., Al-Chalabi, A., Morris, J. C., Shaw, C. E., Myers, A. J., Wiltfang, J., O'Bryant, S., Olichney, J. M., Alvarez, V., Parisi, J. E., Singleton, A. B., Paulson, H. L., Collinge, J., Perry, W. R., Mead, S., Peskind, E., Cribbs, D. H., Rossor, M., Pierce, A., Ryan, N. S., Poon, W. W., Nacmias, B., Potter, H., Sorbi, S., Quinn, J. F., Sacchinelli, E., Raj, A., Spalletta, Gianfranco, Raskind, M., Caltagirone, C., Bossu, P., Orfei, M. D., Reisberg, B., Clarke, Ronald Victor, Reitz, C., Smith, A. D., Ringman, J. M., Warden, D., Roberson, E. D., Wilcock, G., Rogaeva, E., Bruni, A. C., Rosen, H. J., Gallo, Massimiliano, Rosenberg, R. N., Ben-Shlomo, Y., Sager, M. A., Mecocci, P., Saykin, A. J., Pastor, P., Cuccaro, M. L., Vance, J. M., Schneider, J. A., Schneider, L. S., Slifer, S., Seeley, W. W., Smith, A. G., Sonnen, J. A., Spina, S., Stern, R. A., Swerdlow, R. H., Tang, M., Tanzi, R. E., Trojanowski, J. Q., Troncoso, J. C., Van Deerlin, V. M., Van Eldik, L. J., Vinters, H. V., Vonsattel, J. P., Weintraub, S., Welsh-Bohmer, K. A., Wilhelmsen, K. C., Williamson, J., Wingo, T. S., Woltjer, R. L., Wright, C. B., C. -E., Yu, Yu, L., Saba, Y., Pilotto, A., Bullido, M. J., Peters, O., Crane, P. K., Bennett, D., Bosco, P., Coto, E., Boccardi, V., De Jager, P. L., Lleo, A., Warner, N., Lopez, O. L., Ingelsson, M., Deloukas, P., Cruchaga, C., Graff, C., Gwilliam, R., Fornage, M., Goate, A. M., Sanchez-Juan, P., Kehoe, P. G., Amin, N., Ertekin-Taner, N., Berr, C., Debette, S., Love, S., Launer, L. J., Younkin, S. G., Dartigues, J. -F., Corcoran, C., Ikram, M. A., Dickson, D. W., Nicolas, G., Campion, D., Tschanz, J. A., Schmidt, H., Hakonarson, H., Clarimon, J., Munger, R., Schmidt, R., Farrer, L. A., Van Broeckhoven, C., O'Donovan, M. C., Destefano, A. L., Jones, L., Haines, J. L., Deleuze, J. -F., Owen, M. J., Gudnason, V., Mayeux, R., Escott-Price, V., Psaty, B. M., Ramirez, A., Wang, L. -S., Ruiz, A., van Duijn, C. M., Holmans, P. A., Seshadri, S., Williams, J., Amouyel, P., Schellenberg, G. D., Lambert, J. -C., and Pericak-Vance, M. A.

Subjects
Author Correction: Alzheimer’s disease genetic meta-analysis new risk loci Aβ, tau, immunity and lipid processing, Settore MED/26 - NEUROLOGIA, Author Correction: Alzheimer’s disease genetic meta-analysis new risk loci Aβ, tau, immunity and lipid processing
Published
2019

49. Polygenic risk and hazard scores for Alzheimer's disease prediction

Author

Leonenko, G, Sims, R, Shoai, M, Frizzati, A, Bossù, P, Spalletta, G, Fox, Nc, Williams, J, for the GERAD consortium: Hardy, J, Escott-Price, V, Tsolaki, M, Craig, D, Avramidou, D, Germanou, A, Koutroumani, M, Gkatzima, O, Hampel, H, Kehoe, Pg, Love, S, Rubinsztein, Dc, Frölich, L, Mcguinness, B, Johnston, Ja, Passmore, P, Drichel, D, Rossor, M, Schott, Jm, Warren, Jd, Bras, J, Guerreiro, R, Kawalia, A, Hughes, Jt, Patel, Y, Lupton, Mk, Proitsi, P, Powell, J, Kauwe, Jsk, Mancuso, M, Bonuccelli, U, Uphill, J, Fisher, E, Masullo, C, Soininen, H, Bisceglio, G, Ma, L, Dickson, Dw, Graff‐radford, Nr, Carrasquillo, Mm, Younkin, Sg, Sandro Sorbi, S, Daniilidou, M, Hodges, A, Galimberti, D, Scarpini, E, Scherer, M, Peters, O, Ramirez, A, Leber, M, Pichler, S, Mayhaus, M, Gu, W, Riemenschneider, M, Wiltfang, J, Heun, R, Kölsch, H, Kornhuber, J, Heuser, I, Rujescu, D, Hartmann, Am, Giegling, I, Hüll, M, Lovestone, S, Cruchaga, C, Morris, J, Mayo, K, Feulner, T, Sussams, R, Holmes, C, Mann, D, Pickering‐brown, S, Hooper, Nm, Mcquillin, A, Livingston, G, Bass, Nj, Vronskaya, M, Morgan, T, Denning, N, Cushion, Td, Jones, L, Marshall, R, Meggy, A, Menzies, G, Grozeva, D, O'Donovan, Mc, Owen, Mj, Holmans, Pa, Salani, F, Russo, G, Maier, W, Jessen, F, Wichmann, H-E, Morgan, K, Goate, Am, Vellas, B, Vardy, E, Moebus, S, Jöckel, K-H, Dichgans, M, Klopp, N, Turton, J, Lord, J, Brown, K, Medway, C, Nöthen, Mm, Hoffmann, P, Daniele, A, Bayer, A, Gallacher, J, van den Bussche, H, Brayne, C, Riedel‐heller, S, Powell, Jf, Al‐chalabi, A, Shaw, Ce, Kloszewska, I, Pastor, P, Diez‐fairen, M, Lynch, A, Lawlor, B, Gill, M, Coto, E, Alvarez, V, Singleton, Ab, Collinge, J, Mead, S, Ryan, N, Nacmias, B, Ortega‐cubero, S, Rodriguez‐rodriguez, E, Sanchez‐juan, P, Shofany, J, Banaj, N, Ciullo, V, Sacchinelli, E, Robert, Clarke, A David Smith, Donald, Warden, Yoav, Ben‐shlomo, Chiara, Cupidi, Raffaele Giovanni Maletta, Runi, Gallo, M, Harold, D, Cecchetti, R, Mecocci, P, Boccardi, V, Warner, N, Wilcock, G, Deloukas, P, Gwilliam, R, Corcoran, C, Tschanz, J, Munger, R., and Consortium, Gerad

Subjects
Male, Multifactorial Inheritance, Polygenic risk, Alzheimer's disease, hazard score, Polymorphism, Single Nucleotide, Settore MED/26 - NEUROLOGIA, Apolipoproteins E, Alzheimer Disease, Risk Factors, Case-Control Studies, Humans, Female, Genetic Predisposition to Disease, Research Articles, Research Article, Genome-Wide Association Study, Proportional Hazards Models
Abstract

Objective: Genome‐wide association studies (GWAS) have identified over 30 susceptibility loci associated with Alzheimer's disease (AD). Using AD GWAS data from the International Genomics of Alzheimer's Project (IGAP), Polygenic Risk Score (PRS) was successfully applied to predict life time risk of AD development. A recently introduced Polygenic Hazard Score (PHS) is able to quantify individuals with age‐specific genetic risk for AD. The aim of this study was to quantify the age‐specific genetic risk for AD with PRS and compare the results generated by PRS with those from PHS. Methods: Quantification of individual differences in age‐specific genetic risk for AD identified by the PRS, was performed with Cox Regression on 9903 (2626 cases and 7277 controls) individuals from the Genetic and Environmental Risk in Alzheimer's Disease consortium (GERAD). Polygenic Hazard Scores were generated for the same individuals. The age‐specific genetic risk for AD identified by the PRS was compared with that generated by the PHS. This was repeated using varying SNPs P‐value thresholds for disease association. Results: Polygenic Risk Score significantly predicted the risk associated with age at AD onset when SNPs were preselected for association to AD at P ≤ 0.001. The strongest effect (B = 0.28, SE = 0.04, P = 2.5 × 10−12) was observed for PRS based upon genome‐wide significant SNPs (P ≤ 5 × 10−8). The strength of association was weaker with less stringent SNP selection thresholds. Interpretation: Both PRS and PHS can be used to predict an age‐specific risk for developing AD. The PHS approach uses SNP effect sizes derived with the Cox Proportional Hazard Regression model. When SNPs were selected based upon AD GWAS case/control P ≤ 10−3, we found no advantage of using SNP effects sizes calculated with the Cox Proportional Hazard Regression model in our study. When SNPs are selected for association with AD risk at P > 10−3, the age‐specific risk prediction results are not significant for either PRS or PHS. However PHS could be more advantageous than PRS of age specific AD risk predictions when SNPs are prioritized for association with AD age at onset (i.e., powerful Cox Regression GWAS study).

Published
2019

50. Author Correction: Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing

Author

KUNKLE, B. W., GRENIER-BOLEY, B., SIMS, R., BIS, J. C., DAMOTTE, V., NAJ, A. C., BOLAND, A., VRONSKAYA, M., VAN DER LEE, S. J., AMLIE-WOLF, A., BELLENGUEZ, C., FRIZATTI, A., CHOURAKI, V., MARTIN, E. R., SLEEGERS, K., BADARINARAYAN, N., JAKOBSDOTTIR, J., HAMILTON-NELSON, K. L., MORENO-GRAU, S., OLASO, R., RAYBOULD, R., CHEN, Y., KUZMA, A. B., HILTUNEN, M., MORGAN, T., AHMAD, S., VARDARAJAN, B. N., EPELBAUM, J., HOFFMANN, P., BOADA, M., BEECHAM, G. W., GARNIER, J. G., HAROLD, D., FITZPATRICK, A. L., VALLADARES, O., MOUTET, M. L., GERRISH, A., SMITH, A. V., QU, L., BACQ, D., DENNING, N., JIAN, X., ZHAO, Y., DEL ZOMPO, M., FOX, N. C., CHOI, S. H., MATEO, I., HUGHES, J. T., ADAMS, H. H., MALAMON, J., SANCHEZ-GARCIA, F., PATEL, Y., BRODY, J. A., DOMBROSKI, B. A., NARANJO, M. C. D., DANIILIDOU, M., EIRIKSDOTTIR, G., MUKHERJEE, S., WALLON, D., UPHILL, J., ASPELUND, T., CANTWELL, L. B., GARZIA, F., GALIMBERTI, D., HOFER, E., BUTKIEWICZ, M., FIN, B., SCARPINI, E., SARNOWSKI, C., BUSH, W. S., MESLAGE, S., KORNHUBER, J., WHITE, C. C., SONG, Y., BARBER, R. C., ENGELBORGHS, S., SORDON, S., VOIJNOVIC, D., ADAMS, P. M., VANDENBERGHE, R., MAYHAUS, M., CUPPLES, L. A., ALBERT, M. S., DE DEYN, P. P., GU, W., HIMALI, J. J., BEEKLY, D., SQUASSINA, A., HARTMANN, A. M., ORELLANA, A., BLACKER, D., RODRIGUEZ-RODRIGUEZ, E., LOVESTONE, S., GARCIA, M. E., DOODY, R. S., MUNOZ-FERNADEZ, C., SUSSAMS, R., LIN, H., FAIRCHILD, T. J., BENITO, Y. A., HOLMES, C., KARAMUJIC-COMIC, H., FROSCH, M. P., THONBERG, H., MAIER, W., ROSHCHUPKIN, G., GHETTI, B., GIEDRAITIS, V., KAWALIA, A., LI, S., HUEBINGER, R. M., KILANDER, L., MOEBUS, S., HERNANDEZ, I., KAMBOH, M. I., BRUNDIN, R., TURTON, J., YANG, Q., KATZ, M. J., CONCARI, L., LORD, J., BEISER, A. S., KEENE, C. D., HELISALMI, S., KLOSZEWSKA, I., KUKULL, W. A., KOIVISTO, A. M., LYNCH, A., TARRAGA, L., LARSON, E. B., HAAPASALO, A., LAWLOR, B., MOSLEY, T. H., LIPTON, R. B., SOLFRIZZI, V., GILL, M., LONGSTRETH, W. T., Jr., MONTINE, T. J., FRISARDI, V., DIEZ-FAIREN, M., RIVADENEIRA, F., PETERSEN, R. C., DERAMECOURT, V., ALVAREZ, I., SALANI, F., CIARAMELLA, A., BOERWINKLE, E., REIMAN, E. M., FIEVET, N., ROTTER, J. I., REISCH, J. S., HANON, O., CUPIDI, C., UITTERLINDEN, A. G. A., ROYALL, D. R., DUFOUIL, Carole, MALETTA, R. G., DE ROJAS, I., SANO, M., BRICE, A., CECCHETTI, R., GEORGE-HYSLOP, P. S., RITCHIE, K., TSOLAKI, M., TSUANG, D. W., DUBOIS, B., CRAIG, D., WU, C. K., SOININEN, H., AVRAMIDOU, D., ALBIN, R. L., FRATIGLIONI, L., GERMANOU, A., APOSTOLOVA, L. G., KELLER, L., KOUTROUMANI, M., ARNOLD, S. E., PANZA, F., GKATZIMA, O., ASTHANA, S., HANNEQUIN, D., WHITEHEAD, P., ATWOOD, C. S., CAFFARRA, P., HAMPEL, H., QUINTELA, I., CARRACEDO, A., LANNFELT, L., RUBINSZTEIN, D. C., BARNES, L. L., PASQUIER, F., FROLICH, L., BARRAL, S., MCGUINNESS, B., BEACH, T. G., JOHNSTON, J. A., BECKER, J. T., PASSMORE, P., BIGIO, E. H., SCHOTT, J. M., BIRD, T. D., WARREN, J. D., BOEVE, B. F., LUPTON, M. K., BOWEN, J. D., PROITSI, P., BOXER, A., POWELL, J. F., BURKE, J. R., KAUWE, J. S. K., BURNS, J. M., MANCUSO, M., BUXBAUM, J. D., BONUCCELLI, U., CAIRNS, N. J., MCQUILLIN, A., CAO, C., LIVINGSTON, G., CARLSON, C. S., BASS, N. J., CARLSSON, C. M., HARDY, J., CARNEY, R. M., BRAS, J., CARRASQUILLO, M. M., GUERREIRO, R., ALLEN, M., CHUI, H. C., FISHER, E., MASULLO, C., CROCCO, E. A., DECARLI, C., BISCEGLIO, G., DICK, M., MA, L., DUARA, R., GRAFF-RADFORD, N. R., EVANS, D. A., HODGES, A., FABER, K. M., SCHERER, M., FALLON, K. B., RIEMENSCHNEIDER, M., FARDO, D. W., HEUN, R., FARLOW, M. R., KOLSCH, H., FERRIS, S., LEBER, M., FOROUD, T. M., HEUSER, I., GALASKO, D. R., GIEGLING, I., GEARING, M., HULL, M., GESCHWIND, D. H., GILBERT, J. R., MORRIS, J., GREEN, R. C., MAYO, K., GROWDON, J. H., FEULNER, T., HAMILTON, R. L., HARRELL, L. E., DRICHEL, D., HONIG, L. S., CUSHION, T. D., HUENTELMAN, M. J., HOLLINGWORTH, P., HULETTE, C. M., HYMAN, B. T., MARSHALL, R., JARVIK, G. P., MEGGY, A., ABNER, E., MENZIES, G. E., JIN, L. W., LEONENKO, G., REAL, L. M., JUN, G. R., BALDWIN, C. T., GROZEVA, D., KARYDAS, A., RUSSO, G., KAYE, J. A., KIM, R., JESSEN, F., KOWALL, N. W., VELLAS, B., KRAMER, J. H., VARDY, E., LAFERLA, F. M., JOCKEL, K. H., LAH, J. J., DICHGANS, M., LEVERENZ, J. B., MANN, D., LEVEY, A. I., PICKERING-BROWN, S., LIEBERMAN, A. P., KLOPP, N., LUNETTA, K. L., WICHMANN, H. E., LYKETSOS, C. G., MORGAN, K., MARSON, D. C., BROWN, K., MARTINIUK, F., MEDWAY, C., MASH, D. C., NOTHEN, M. M., MASLIAH, E., HOOPER, N. M., MCCORMICK, W. C., DANIELE, A., MCCURRY, S. M., BAYER, A., MCDAVID, A. N., GALLACHER, J., MCKEE, A. C., VAN DEN BUSSCHE, H., MESULAM, M., BRAYNE, C., MILLER, B. L., RIEDEL-HELLER, S., MILLER, C. A., MILLER, J. W., AL-CHALABI, A., MORRIS, J. C., SHAW, C. E., MYERS, A. J., WILTFANG, J., O'BRYANT, S., OLICHNEY, J. M., ALVAREZ, V., PARISI, J. E., SINGLETON, A. B., PAULSON, H. L., COLLINGE, J., PERRY, W. R., MEAD, S., PESKIND, E., CRIBBS, D. H., ROSSOR, M., PIERCE, A., RYAN, N. S., POON, W. W., NACMIAS, B., POTTER, H., SORBI, S., QUINN, J. F., SACCHINELLI, E., RAJ, A., SPALLETTA, G., RASKIND, M., CALTAGIRONE, C., BOSSU, P., ORFEI, M. D., REISBERG, B., CLARKE, R., REITZ, C., SMITH, A. D., RINGMAN, J. M., WARDEN, D., ROBERSON, E. D., WILCOCK, G., ROGAEVA, E., BRUNI, A. C., ROSEN, H. J., GALLO, M., ROSENBERG, R. N., BEN-SHLOMO, Y., SAGER, M. A., MECOCCI, P., SAYKIN, A. J., PASTOR, P., CUCCARO, M. L., VANCE, J. M., SCHNEIDER, J. A., SCHNEIDER, L. S., SLIFER, S., SEELEY, W. W., SMITH, A. G., SONNEN, J. A., SPINA, S., STERN, R. A., SWERDLOW, R. H., TANG, M., TANZI, R. E., TROJANOWSKI, J. Q., TRONCOSO, J. C., VAN DEERLIN, V. M., VAN ELDIK, L. J., VINTERS, H. V., VONSATTEL, J. P., WEINTRAUB, S., WELSH-BOHMER, K. A., WILHELMSEN, K. C., WILLIAMSON, J., WINGO, T. S., WOLTJER, R. L., WRIGHT, C. B., YU, C. E., YU, L., SABA, Y., PILOTTO, A., BULLIDO, M. J., PETERS, O., CRANE, P. K., BENNETT, D., BOSCO, P., COTO, E., BOCCARDI, V., DE JAGER, P. L., LLEO, A., WARNER, N., LOPEZ, O. L., INGELSSON, M., DELOUKAS, P., CRUCHAGA, C., GRAFF, C., GWILLIAM, R., FORNAGE, M., GOATE, A. M., SANCHEZ-JUAN, P., KEHOE, P. G., AMIN, N., ERTEKIN-TANER, N., BERR, C., DEBETTE, Stephanie, LOVE, S., LAUNER, L. J., YOUNKIN, S. G., DARTIGUES, Jean-Francois, CORCORAN, C., IKRAM, M. A., DICKSON, D. W., NICOLAS, G., CAMPION, D., TSCHANZ, J., SCHMIDT, H., HAKONARSON, H., CLARIMON, J., MUNGER, R., SCHMIDT, R., FARRER, L. A., VAN BROECKHOVEN, C., O'DONOVAN, M. C., DESTEFANO, A. L., JONES, L., HAINES, J. L., DELEUZE, J. F., OWEN, M. J., GUDNASON, V., MAYEUX, R., ESCOTT-PRICE, V., PSATY, B. M., RAMIREZ, A., WANG, L. S., RUIZ, A., VAN DUIJN, C. M., HOLMANS, P. A., SESHADRI, S., WILLIAMS, J., AMOUYEL, P., SCHELLENBERG, G. D., LAMBERT, J. C., PERICAK-VANCE, M. A., Clinical sciences, Neurology, and Pathologic Biochemistry and Physiology

Subjects
Medicine(all), MEDLINE, Medizin, Disease, Computational biology, Biology, medicine.disease, Article, Immunity, Alzheimer Disease, ddc:570, Meta-analysis, Genetics, medicine, Alzheimer's disease
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2019
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