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Genetic study of multimodal imaging Alzheimer's disease progression score implicates novel loci.
Scelsi, Marzia A; Khan, Raiyan R; Lorenzi, Marco; Christopher, Leigh; Greicius, Michael D; Schott, Jonathan M; Ourselin, Sebastien; Altmann, Andre.
Afiliação
  • Scelsi MA; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, Gower Street NW1 2HE, London, UK.
  • Khan RR; Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304-5777, USA.
  • Lorenzi M; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, Gower Street NW1 2HE, London, UK.
  • Christopher L; Epione Research Project, Université Côte d'Azur, BP 93 06 902, Inria Sophia Antipolis, France.
  • Greicius MD; Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304-5777, USA.
  • Schott JM; Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304-5777, USA.
  • Ourselin S; Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304-5777, USA.
  • Altmann A; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, Gower Street NW1 2HE, London, UK.
Brain ; 141(7): 2167-2180, 2018 07 01.
Article em En | MEDLINE | ID: mdl-29860282
Identifying genetic risk factors underpinning different aspects of Alzheimer's disease has the potential to provide important insights into pathogenesis. Moving away from simple case-control definitions, there is considerable interest in using quantitative endophenotypes, such as those derived from imaging as outcome measures. Previous genome-wide association studies of imaging-derived biomarkers in sporadic late-onset Alzheimer's disease focused only on phenotypes derived from single imaging modalities. In contrast, we computed a novel multi-modal neuroimaging phenotype comprising cortical amyloid burden and bilateral hippocampal volume. Both imaging biomarkers were used as input to a disease progression modelling algorithm, which estimates the biomarkers' long-term evolution curves from population-based longitudinal data. Among other parameters, the algorithm computes the shift in time required to optimally align a subjects' biomarker trajectories with these population curves. This time shift serves as a disease progression score and it was used as a quantitative trait in a discovery genome-wide association study with n = 944 subjects from the Alzheimer's Disease Neuroimaging Initiative database diagnosed as Alzheimer's disease, mild cognitive impairment or healthy at the time of imaging. We identified a genome-wide significant locus implicating LCORL (rs6850306, chromosome 4; P = 1.03 × 10-8). The top variant rs6850306 was found to act as an expression quantitative trait locus for LCORL in brain tissue. The clinical role of rs6850306 in conversion from healthy ageing to mild cognitive impairment or Alzheimer's disease was further validated in an independent cohort comprising healthy, older subjects from the National Alzheimer's Coordinating Center database. Specifically, possession of a minor allele at rs6850306 was protective against conversion from mild cognitive impairment to Alzheimer's disease in the National Alzheimer's Coordinating Center cohort (hazard ratio = 0.593, 95% confidence interval = 0.387-0.907, n = 911, PBonf = 0.032), in keeping with the negative direction of effect reported in the genome-wide association study (ßdisease progression score = -0.07 ± 0.01). The implicated locus is linked to genes with known connections to Alzheimer's disease pathophysiology and other neurodegenerative diseases. Using multimodal imaging phenotypes in association studies may assist in unveiling the genetic drivers of the onset and progression of complex diseases.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença de Alzheimer / Imagem Multimodal Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies Limite: Aged / Female / Humans / Male Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença de Alzheimer / Imagem Multimodal Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies Limite: Aged / Female / Humans / Male Idioma: En Ano de publicação: 2018 Tipo de documento: Article