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Bellomo G, Bayoumy S, Megaro A, Toja A, Nardi G, Gaetani L, Blujdea ER, Paolini Paoletti F, Wojdaƚa AL, Chiasserini D, van der Flier WM, Verberk IMW, Teunissen C, Parnetti L. Fully automated measurement of plasma Aβ42/40 and p-tau181: Analytical robustness and concordance with cerebrospinal fluid profile along the Alzheimer's disease continuum in two independent cohorts. Alzheimers Dement 2024; 20:2453-2468. [PMID: 38323780 PMCID: PMC11032583 DOI: 10.1002/alz.13687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 11/30/2023] [Accepted: 12/16/2023] [Indexed: 02/08/2024]
Abstract
INTRODUCTION For routine clinical implementation of Alzheimer's disease (AD) plasma biomarkers, fully automated random-access platforms are crucial to ensure reproducible measurements. We aimed to perform an analytical validation and to establish cutoffs for AD plasma biomarkers measured with Lumipulse. METHODS Two cohorts were included. UNIPG: n = 450 paired cerebrospinal fluid (CSF)/plasma samples from subjects along the AD-continuum, subjects affected by other neurodegenerative diseases, and controls with known CSF profile; AMS: n = 40 plasma samples from AD and n = 40 controls. Plasma amyloid β (Aβ)42, Aβ40, and p-tau181 were measured with Lumipulse. We evaluated analytical and diagnostic performance. RESULTS Lumipulse assays showed high analytical performance. Plasma p-tau181 levels accurately reflected CSF A+/T+ profile in AD-dementia and mild cognitive impairment (MCI)-AD, but not in asymptomatic-AD. Plasma and CSF Aβ42/40 values were concordant across clinical AD stages. Cutoffs and probability-based models performed satisfactorily in both cohorts. DISCUSSION The identified cutoffs and probability-based models represent a significant step toward plasma AD molecular diagnosis.
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Affiliation(s)
- Giovanni Bellomo
- Center for Memory DisturbancesLab of Clinical NeurochemistrySection of NeurologyDepartment of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
| | - Sherif Bayoumy
- Neurochemistry LaboratoryDepartment of Laboratory MedicineAmsterdam Neuroscience, Amsterdam UMCAmsterdamThe Netherlands
| | - Alfredo Megaro
- Center for Memory DisturbancesLab of Clinical NeurochemistrySection of NeurologyDepartment of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
| | - Andrea Toja
- Center for Memory DisturbancesLab of Clinical NeurochemistrySection of NeurologyDepartment of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
| | - Giovanna Nardi
- Center for Memory DisturbancesLab of Clinical NeurochemistrySection of NeurologyDepartment of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
| | - Lorenzo Gaetani
- Center for Memory DisturbancesLab of Clinical NeurochemistrySection of NeurologyDepartment of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
| | - Elena R. Blujdea
- Neurochemistry LaboratoryDepartment of Laboratory MedicineAmsterdam Neuroscience, Amsterdam UMCAmsterdamThe Netherlands
| | - Federico Paolini Paoletti
- Center for Memory DisturbancesLab of Clinical NeurochemistrySection of NeurologyDepartment of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
| | - Anna Lidia Wojdaƚa
- Center for Memory DisturbancesLab of Clinical NeurochemistrySection of NeurologyDepartment of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
| | - Davide Chiasserini
- Section of BiochemistryDepartment of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
| | - Wiesje M. van der Flier
- Alzheimer CenterDepartment of NeurologyVrije Universiteit Amsterdam, Amsterdam UMCAmsterdamThe Netherlands
- Department of Epidemiology and Data ScienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamThe Netherlands
| | - Inge M. W. Verberk
- Neurochemistry LaboratoryDepartment of Laboratory MedicineAmsterdam Neuroscience, Amsterdam UMCAmsterdamThe Netherlands
| | - Charlotte Teunissen
- Neurochemistry LaboratoryDepartment of Laboratory MedicineAmsterdam Neuroscience, Amsterdam UMCAmsterdamThe Netherlands
| | - Lucilla Parnetti
- Center for Memory DisturbancesLab of Clinical NeurochemistrySection of NeurologyDepartment of Medicine and SurgeryUniversity of PerugiaPerugiaItaly
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De Meyer S, Blujdea ER, Schaeverbeke J, Reinartz M, Luckett ES, Adamczuk K, Van Laere K, Dupont P, Teunissen CE, Vandenberghe R, Poesen K. Longitudinal associations of serum biomarkers with early cognitive, amyloid and grey matter changes. Brain 2024; 147:936-948. [PMID: 37787146 DOI: 10.1093/brain/awad330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/08/2023] [Accepted: 09/12/2023] [Indexed: 10/04/2023] Open
Abstract
Blood-based biomarkers have been extensively evaluated for their diagnostic potential in Alzheimer's disease. However, their relative prognostic and monitoring capabilities for cognitive decline, amyloid-β (Aβ) accumulation and grey matter loss in cognitively unimpaired elderly require further investigation over extended time periods. This prospective cohort study in cognitively unimpaired elderly [n = 185, mean age (range) = 69 (53-84) years, 48% female] examined the prognostic and monitoring capabilities of glial fibrillary acidic protein (GFAP), neurofilament light (NfL), Aβ1-42/Aβ1-40 and phosphorylated tau (pTau)181 through their quantification in serum. All participants underwent baseline Aβ-PET, MRI and blood sampling as well as 2-yearly cognitive testing. A subset additionally underwent Aβ-PET (n = 109), MRI (n = 106) and blood sampling (n = 110) during follow-up [median time interval (range) = 6.1 (1.3-11.0) years]. Matching plasma measurements were available for Aβ1-42/Aβ1-40 and pTau181 (both n = 140). Linear mixed-effects models showed that high serum GFAP and NfL predicted future cognitive decline in memory (βGFAP×Time = -0.021, PFDR = 0.007 and βNfL×Time = -0.031, PFDR = 0.002) and language (βGFAP×Time = -0.021, PFDR = 0.002 and βNfL×Time = -0.018, PFDR = 0.03) domains. Low serum Aβ1-42/Aβ1-40 equally but independently predicted memory decline (βAβ1-42/Aβ1-40×Time = -0.024, PFDR = 0.02). Whole-brain voxelwise analyses revealed that low Aβ1-42/Aβ1-40 predicted Aβ accumulation within the precuneus and frontal regions, high GFAP and NfL predicted grey matter loss within hippocampal regions and low Aβ1-42/Aβ1-40 predicted grey matter loss in lateral temporal regions. Serum GFAP, NfL and pTau181 increased over time, while Aβ1-42/Aβ1-40 decreased only in Aβ-PET-negative elderly. NfL increases associated with declining memory (βNfLchange×Time = -0.030, PFDR = 0.006) and language (βNfLchange×Time = -0.021, PFDR = 0.02) function and serum Aβ1-42/Aβ1-40 decreases associated with declining language function (βAβ1-42/Aβ1-40×Time = -0.020, PFDR = 0.04). GFAP increases associated with Aβ accumulation within the precuneus and NfL increases associated with grey matter loss. Baseline and longitudinal serum pTau181 only associated with Aβ accumulation in restricted occipital regions. In head-to-head comparisons, serum outperformed plasma Aβ1-42/Aβ1-40 (ΔAUC = 0.10, PDeLong, FDR = 0.04), while both plasma and serum pTau181 demonstrated poor performance to detect asymptomatic Aβ-PET positivity (AUC = 0.55 and 0.63, respectively). However, when measured with a more phospho-specific assay, plasma pTau181 detected Aβ-positivity with high performance (AUC = 0.82, PDeLong, FDR < 0.007). In conclusion, serum GFAP, NfL and Aβ1-42/Aβ1-40 are valuable prognostic and/or monitoring tools in asymptomatic stages providing complementary information in a time- and pathology-dependent manner.
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Affiliation(s)
- Steffi De Meyer
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Elena R Blujdea
- Neurochemistry Laboratory, Amsterdam UMC, 1081 HZ Amsterdam, The Netherlands
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Mariska Reinartz
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Katarzyna Adamczuk
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
| | - Koen Van Laere
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
- Division of Nuclear Medicine, UZ Leuven, 3000 Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | | | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Department of Neurology, UZ Leuven, 3000 Leuven, Belgium
| | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Department of Laboratory Medicine, UZ Leuven, 3000 Leuven, Belgium
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3
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van der Ende EL, In ‘t Veld SGJG, Hanskamp I, van der Lee S, Dijkstra JIR, Hok-A-Hin YS, Blujdea ER, van Swieten JC, Irwin DJ, Chen-Plotkin A, Hu WT, Lemstra AW, Pijnenburg YAL, van der Flier WM, del Campo M, Teunissen CE, Vermunt L. CSF proteomics in autosomal dominant Alzheimer's disease highlights parallels with sporadic disease. Brain 2023; 146:4495-4507. [PMID: 37348871 PMCID: PMC10629764 DOI: 10.1093/brain/awad213] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/24/2023] Open
Abstract
Autosomal dominant Alzheimer's disease (ADAD) offers a unique opportunity to study pathophysiological changes in a relatively young population with few comorbidities. A comprehensive investigation of proteome changes occurring in ADAD could provide valuable insights into AD-related biological mechanisms and uncover novel biomarkers and therapeutic targets. Furthermore, ADAD might serve as a model for sporadic AD, but in-depth proteome comparisons are lacking. We aimed to identify dysregulated CSF proteins in ADAD and determine the degree of overlap with sporadic AD. We measured 1472 proteins in CSF of PSEN1 or APP mutation carriers (n = 22) and age- and sex-matched controls (n = 20) from the Amsterdam Dementia Cohort using proximity extension-based immunoassays (PEA). We compared protein abundance between groups with two-sided t-tests and identified enriched biological pathways. Using the same protein panels in paired plasma samples, we investigated correlations between CSF proteins and their plasma counterparts. Finally, we compared our results with recently published PEA data from an international cohort of sporadic AD (n = 230) and non-AD dementias (n = 301). All statistical analyses were false discovery rate-corrected. We detected 66 differentially abundant CSF proteins (65 increased, 1 decreased) in ADAD compared to controls (q < 0.05). The most strongly upregulated proteins (fold change >1.8) were related to immunity (CHIT1, ITGB2, SMOC2), cytoskeletal structure (MAPT, NEFL) and tissue remodelling (TMSB10, MMP-10). Significant CSF-plasma correlations were found for the upregulated proteins SMOC2 and LILR1B. Of the 66 differentially expressed proteins, 36 had been measured previously in the sporadic dementias cohort, 34 of which (94%) were also significantly upregulated in sporadic AD, with a strong correlation between the fold changes of these proteins in both cohorts (rs = 0.730, P < 0.001). Twenty-nine of the 36 proteins (81%) were also upregulated among non-AD patients with suspected AD co-pathology. This CSF proteomics study demonstrates substantial biochemical similarities between ADAD and sporadic AD, suggesting involvement of the same biological processes. Besides known AD-related proteins, we identified several relatively novel proteins, such as TMSB10, MMP-10 and SMOC2, which have potential as novel biomarkers. With shared pathophysiological CSF changes, ADAD study findings might be translatable to sporadic AD, which could greatly expedite therapy development.
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Affiliation(s)
- Emma L van der Ende
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Sjors G J G In ‘t Veld
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Iris Hanskamp
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Sven van der Lee
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Janna I R Dijkstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Yanaika S Hok-A-Hin
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Elena R Blujdea
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - John C van Swieten
- Alzheimer Center and Department of Neurology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - David J Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alice Chen-Plotkin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - William T Hu
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30307, USA
| | - Afina W Lemstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Marta del Campo
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, 28003 Madrid, Spain
- Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Lisa Vermunt
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
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4
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van Bree EJ, Guimarães RLFP, Lundberg M, Blujdea ER, Rosenkrantz JL, White FTG, Poppinga J, Ferrer-Raventós P, Schneider AFE, Clayton I, Haussler D, Reinders MJT, Holstege H, Ewing AD, Moses C, Jacobs FMJ. A hidden layer of structural variation in transposable elements reveals potential genetic modifiers in human disease-risk loci. Genome Res 2022; 32:656-670. [PMID: 35332097 PMCID: PMC8997352 DOI: 10.1101/gr.275515.121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 01/28/2022] [Indexed: 11/24/2022]
Abstract
Genome-wide association studies (GWAS) have been highly informative in discovering disease-associated loci but are not designed to capture all structural variations in the human genome. Using long-read sequencing data, we discovered widespread structural variation within SINE-VNTR-Alu (SVA) elements, a class of great ape-specific transposable elements with gene-regulatory roles, which represents a major source of structural variability in the human population. We highlight the presence of structurally variable SVAs (SV-SVAs) in neurological disease-associated loci, and we further associate SV-SVAs to disease-associated SNPs and differential gene expression using luciferase assays and expression quantitative trait loci data. Finally, we genetically deleted SV-SVAs in the BIN1 and CD2AP Alzheimer's disease-associated risk loci and in the BCKDK Parkinson's disease-associated risk locus and assessed multiple aspects of their gene-regulatory influence in a human neuronal context. Together, this study reveals a novel layer of genetic variation in transposable elements that may contribute to identification of the structural variants that are the actual drivers of disease associations of GWAS loci.
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Affiliation(s)
- Elisabeth J van Bree
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Rita L F P Guimarães
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.,Genomics of Neurodegenerative Diseases and Aging, Department of Human Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands.,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Mischa Lundberg
- Mater Research Institute-University of Queensland, Woolloongabba, QLD 4102, Australia
| | - Elena R Blujdea
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Jimi L Rosenkrantz
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Fred T G White
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Josse Poppinga
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Paula Ferrer-Raventós
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Anne-Fleur E Schneider
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Isabella Clayton
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - David Haussler
- UC Santa Cruz Genomics Institute, and Howard Hughes Medical Institute, UC Santa Cruz, Santa Cruz, California 95064, USA
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
| | - Henne Holstege
- Genomics of Neurodegenerative Diseases and Aging, Department of Human Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands.,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands.,Delft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, The Netherlands.,Amsterdam Neuroscience, Complex Trait Genetics, University of Amsterdam, Amsterdam, The Netherlands
| | - Adam D Ewing
- Mater Research Institute-University of Queensland, Woolloongabba, QLD 4102, Australia
| | - Colette Moses
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Frank M J Jacobs
- Evolutionary Neurogenomics, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.,Amsterdam Neuroscience, Complex Trait Genetics, University of Amsterdam, Amsterdam, The Netherlands
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