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He XY, Wu BS, Kuo K, Zhang W, Ma Q, Xiang ST, Li YZ, Wang ZY, Dong Q, Feng JF, Cheng W, Yu JT. Association between polygenic risk for Alzheimer's disease and brain structure in children and adults. Alzheimers Res Ther 2023; 15:109. [PMID: 37312172 DOI: 10.1186/s13195-023-01256-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND The correlations between genetic risk for Alzheimer's disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages. METHODS This study used large existing genome-wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ~ 23 000) and Adolescent Brain Cognitive Development Study (N ~ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed-effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life. RESULTS Compared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle-aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD). CONCLUSIONS In conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age-specific change is consistent with the classical pattern of brain impairment observed in AD patients.
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Affiliation(s)
- Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zi-Yi Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China.
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
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2
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Topiwala A, Mankia K, Bell S, Webb A, Ebmeier KP, Howard I, Wang C, Alfaro-Almagro F, Miller K, Burgess S, Smith S, Nichols TE. Association of gout with brain reserve and vulnerability to neurodegenerative disease. Nat Commun 2023; 14:2844. [PMID: 37202397 PMCID: PMC10195870 DOI: 10.1038/s41467-023-38602-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/09/2023] [Indexed: 05/20/2023] Open
Abstract
Studies of neurodegenerative disease risk in gout are contradictory. Relationships with neuroimaging markers of brain structure, which may offer insights, are uncertain. Here we investigated associations between gout, brain structure, and neurodegenerative disease incidence. Gout patients had smaller global and regional brain volumes and markers of higher brain iron, using both observational and genetic approaches. Participants with gout also had higher incidence of all-cause dementia, Parkinson's disease, and probable essential tremor. Risks were strongly time dependent, whereby associations with incident dementia were highest in the first 3 years after gout diagnosis. These findings suggest gout is causally related to several measures of brain structure. Lower brain reserve amongst gout patients may explain their higher vulnerability to multiple neurodegenerative diseases. Motor and cognitive impairments may affect gout patients, particularly in early years after diagnosis.
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Affiliation(s)
- Anya Topiwala
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK.
| | - Kulveer Mankia
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds and NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Chapel Allerton Hospital, Leeds, UK
| | - Steven Bell
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Alastair Webb
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Klaus P Ebmeier
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Isobel Howard
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Shanghai, China
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Stephen Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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3
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Ali M, Sung YJ, Wang F, Fernández MV, Morris JC, Fagan AM, Blennow K, Zetterberg H, Heslegrave A, Johansson PM, Svensson J, Nellgård B, Lleó A, Alcolea D, Clarimon J, Rami L, Molinuevo JL, Suárez-Calvet M, Morenas-Rodríguez E, Kleinberger G, Haass C, Ewers M, Levin J, Farlow MR, Perrin RJ, Cruchaga C. Leveraging large multi-center cohorts of Alzheimer disease endophenotypes to understand the role of Klotho heterozygosity on disease risk. PLoS One 2022; 17:e0267298. [PMID: 35617280 PMCID: PMC9135221 DOI: 10.1371/journal.pone.0267298] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/06/2022] [Indexed: 11/18/2022] Open
Abstract
Two genetic variants in strong linkage disequilibrium (rs9536314 and rs9527025) in the Klotho (KL) gene, encoding a transmembrane protein, implicated in longevity and associated with brain resilience during normal aging, were recently shown to be associated with Alzheimer disease (AD) risk in cognitively normal participants who are APOE ε4 carriers. Specifically, the participants heterozygous for this variant (KL-SVHET+) showed lower risk of developing AD. Furthermore, a neuroprotective effect of KL-VSHET+ has been suggested against amyloid burden for cognitively normal participants, potentially mediated via the regulation of redox pathways. However, inconsistent associations and a smaller sample size of existing studies pose significant hurdles in drawing definitive conclusions. Here, we performed a well-powered association analysis between KL-VSHET+ and five different AD endophenotypes; brain amyloidosis measured by positron emission tomography (PET) scans (n = 5,541) or cerebrospinal fluid Aβ42 levels (CSF; n = 5,093), as well as biomarkers associated with tau pathology: the CSF Tau (n = 5,127), phosphorylated Tau (pTau181; n = 4,778) and inflammation: CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2; n = 2,123) levels. Our results found nominally significant associations of KL-VSHET+ status with biomarkers for brain amyloidosis (e.g., CSF Aβ positivity; odds ratio [OR] = 0.67 [95% CI, 0.55-0.78], β = 0.72, p = 0.007) and tau pathology (e.g., biomarker positivity for CSF Tau; OR = 0.39 [95% CI, 0.19-0.77], β = -0.94, p = 0.007, and pTau; OR = 0.50 [95% CI, 0.27-0.96], β = -0.68, p = 0.04) in cognitively normal participants, 60-80 years old, who are APOE e4-carriers. Our work supports previous findings, suggesting that the KL-VSHET+ on an APOE ε4 genotype background may modulate Aβ and tau pathology, thereby lowering the intensity of neurodegeneration and incidence of cognitive decline in older controls susceptible to AD.
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Affiliation(s)
- Muhammad Ali
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Neurogenomics and Informatics Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Neurogenomics and Informatics Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Fengxian Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Neurogenomics and Informatics Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Maria V. Fernández
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Neurogenomics and Informatics Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Anne M. Fagan
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Department of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Department of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom
- UK Dementia Research Institute at UCL, London, United Kingdom
| | - Amanda Heslegrave
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom
- UK Dementia Research Institute at UCL, London, United Kingdom
| | - Per M. Johansson
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom
- UK Dementia Research Institute at UCL, London, United Kingdom
- Department of Anesthesiology and Intensive Care Medicine, Sahlgrenska University Hospital, Mölndal, Sweden
- Institute of Clinical Sciences, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Johan Svensson
- Department of Internal Medicine, Institute of Medicine, The Sahlgrenska Academy at the University of Gothenburg, Göteborg, Sweden
| | - Bengt Nellgård
- Department of Internal Medicine, Institute of Medicine, The Sahlgrenska Academy at the University of Gothenburg, Göteborg, Sweden
| | - Alberto Lleó
- Neurology Department, Hospital de Sant Pau, Barcelona, Spain
| | - Daniel Alcolea
- Neurology Department, Hospital de Sant Pau, Barcelona, Spain
| | - Jordi Clarimon
- Neurology Department, Hospital de Sant Pau, Barcelona, Spain
| | - Lorena Rami
- IDIBAPS, Alzheimer´s Disease and Other Cognitive Disorders Unit, Neurology Service, ICN Hospital Clinic, Barcelona, Spain
| | - José Luis Molinuevo
- IDIBAPS, Alzheimer´s Disease and Other Cognitive Disorders Unit, Neurology Service, ICN Hospital Clinic, Barcelona, Spain
- Alzheimer´s Disease and Other Cognitive Disorders Unit, Neurology Service, ICN Hospital Clinic i Universitari, Barcelona, Spain
- BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Marc Suárez-Calvet
- BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- Biomedical Center (BMC), Biochemistry, Ludwig‐Maximilians‐Universität München, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Estrella Morenas-Rodríguez
- Biomedical Center (BMC), Biochemistry, Ludwig‐Maximilians‐Universität München, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Gernot Kleinberger
- Biomedical Center (BMC), Biochemistry, Ludwig‐Maximilians‐Universität München, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Christian Haass
- Biomedical Center (BMC), Biochemistry, Ludwig‐Maximilians‐Universität München, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Martin R. Farlow
- Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Richard J. Perrin
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | | | | | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Neurogenomics and Informatics Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, Missouri, United States of America
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4
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Duan J, Zhang J, Liu L, Wen Y. A guidance of model selection for genomic prediction based on linear mixed models for complex traits. Front Genet 2022; 13:1017380. [PMID: 36276959 PMCID: PMC9581223 DOI: 10.3389/fgene.2022.1017380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/20/2022] [Indexed: 11/27/2022] Open
Abstract
Brain imaging outcomes are important for Alzheimer's disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer's Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model.
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Affiliation(s)
- Jiefang Duan
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jiayu Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Long Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yalu Wen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.,Department of Statistics, University of Auckland, Auckland, New Zealand
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5
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Li Y, Nan B, Zhu J. A Structured Brain-wide and Genome-wide Association Study Using ADNI PET Images. CAN J STAT 2021; 49:182-202. [PMID: 34566241 DOI: 10.1002/cjs.11605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A multi-stage variable selection method is introduced for detecting association signals in structured brain-wide and genome-wide association studies (brain-GWAS). Compared to conventional single-voxel-to-single-SNP approaches, our approach is more efficient and powerful in selecting the important signals by integrating anatomic and gene grouping structures in the brain and the genome, respectively. It avoids large number of multiple comparisons while effectively controls the false discoveries. Validity of the proposed approach is demonstrated by both theoretical investigation and numerical simulations. We apply the proposed method to a brain-GWAS using ADNI PET imaging and genomic data. We confirm previously reported association signals and also find several novel SNPs and genes that either are associated with brain glucose metabolism or have their association significantly modified by Alzheimer's disease status.
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Affiliation(s)
- Yanming Li
- Department of Biotatistics & Data Science, University of Kansas Medical Center Kansas City, KS 66160
| | - Bin Nan
- Department of Statistics, University of California at Irvine Irvine, CA 92697
| | - Ji Zhu
- Department of Statistics, University of Michigan Ann Arbor, MI 48109
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6
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Lin E, Lin CH, Lane HY. Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease. Int J Mol Sci 2021; 22:7911. [PMID: 34360676 PMCID: PMC8347529 DOI: 10.3390/ijms22157911] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/17/2021] [Accepted: 07/22/2021] [Indexed: 12/21/2022] Open
Abstract
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40447, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
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7
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Kim BH, Nho K, Lee JM. Genome-wide association study identifies susceptibility loci of brain atrophy to NFIA and ST18 in Alzheimer's disease. Neurobiol Aging 2021; 102:200.e1-200.e11. [PMID: 33640202 DOI: 10.1016/j.neurobiolaging.2021.01.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 01/08/2021] [Accepted: 01/25/2021] [Indexed: 02/04/2023]
Abstract
To identify genetic variants influencing cortical atrophy in Alzheimer's disease (AD), we performed genome-wide association studies (GWAS) of mean cortical thicknesses in 17 AD-related brain. In this study, we used neuroimaging and genetic data of 919 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, which include 268 cognitively normal controls, 488 mild cognitive impairment, 163 AD individuals. We performed GWAS with 3,041,429 single nucleotide polymorphisms (SNPs) for cortical thickness. The results of GWAS indicated that rs10109716 in ST18 (ST18 C2H2C-type zinc finger transcription factor) and rs661526 in NFIA (nuclear factor I A) genes are significantly associated with mean cortical thicknesses of the left inferior frontal gyrus and left parahippocampal gyrus, respectively. The rs661526 regulates the expression levels of NFIA in the substantia nigra and frontal cortex and rs10109716 regulates the expression levels of ST18 in the thalamus. These results suggest a crucial role of identified genes for cortical atrophy and could provide further insights into the genetic basis of AD.
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Affiliation(s)
- Bo-Hyun Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
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8
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Macedo A, Gómez C, Rebelo MÂ, Poza J, Gomes I, Martins S, Maturana-Candelas A, Pablo VGD, Durães L, Sousa P, Figueruelo M, Rodríguez M, Pita C, Arenas M, Álvarez L, Hornero R, Lopes AM, Pinto N. Risk Variants in Three Alzheimer's Disease Genes Show Association with EEG Endophenotypes. J Alzheimers Dis 2021; 80:209-223. [PMID: 33522999 PMCID: PMC8075394 DOI: 10.3233/jad-200963] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: Dementia due to Alzheimer’s disease (AD) is a complex neurodegenerative disorder, which much of heritability remains unexplained. At the clinical level, one of the most common physiological alterations is the slowing of oscillatory brain activity, measurable by electroencephalography (EEG). Relative power (RP) at the conventional frequency bands (i.e., delta, theta, alpha, beta-1, and beta-2) can be considered as AD endophenotypes. Objective: The aim of this work is to analyze the association between sixteen genes previously related with AD: APOE, PICALM, CLU, BCHE, CETP, CR1, SLC6A3, GRIN2
β, SORL1, TOMM40, GSK3
β, UNC5C, OPRD1, NAV2, HOMER2, and IL1RAP, and the slowing of the brain activity, assessed by means of RP at the aforementioned frequency bands. Methods: An Iberian cohort of 45 elderly controls, 45 individuals with mild cognitive impairment, and 109 AD patients in the three stages of the disease was considered. Genomic information and brain activity of each subject were analyzed. Results: The slowing of brain activity was observed in carriers of risk alleles in IL1RAP (rs10212109, rs9823517, rs4687150), UNC5C (rs17024131), and NAV2 (rs1425227, rs862785) genes, regardless of the disease status and situation towards the strongest risk factors: age, sex, and APOE ɛ4 presence. Conclusion: Endophenotypes reduce the complexity of the general phenotype and genetic variants with a major effect on those specific traits may be then identified. The found associations in this work are novel and may contribute to the comprehension of AD pathogenesis, each with a different biological role, and influencing multiple factors involved in brain physiology.
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Affiliation(s)
- Ana Macedo
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,JTA: The Data Scientists, Porto, Portugal
| | - Carlos Gómez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
| | - Miguel Ângelo Rebelo
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Jesús Poza
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
| | - Iva Gomes
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Sandra Martins
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | | | | | - Luis Durães
- Associação Portuguesa de Familiares e Amigos de Doentes de Alzheimer, Lavra, Portugal
| | - Patrícia Sousa
- Associação Portuguesa de Familiares e Amigos de Doentes de Alzheimer, Lavra, Portugal
| | - Manuel Figueruelo
- Asociación de Familiares y Amigos de Enfermos de Alzheimer y otras demencias de Zamora, Zamora, Spain
| | - María Rodríguez
- Asociación de Familiares y Amigos de Enfermos de Alzheimer y otras demencias de Zamora, Zamora, Spain
| | - Carmen Pita
- Asociación de Familiares y Amigos de Enfermos de Alzheimer y otras demencias de Zamora, Zamora, Spain
| | - Miguel Arenas
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,CINBIO (Biomedical Research Center), University of Vigo, Vigo, Spain.,Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain
| | - Luis Álvarez
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,Adeneas, Valencia, Spain
| | - Roberto Hornero
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
| | - Alexandra M Lopes
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Nádia Pinto
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,Centro de Matemática da Universidade do Porto, Porto, Portugal
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9
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Golriz Khatami S, Domingo-Fernández D, Mubeen S, Hoyt CT, Robinson C, Karki R, Iyappan A, Kodamullil AT, Hofmann-Apitius M. A Systems Biology Approach for Hypothesizing the Effect of Genetic Variants on Neuroimaging Features in Alzheimer's Disease. J Alzheimers Dis 2021; 80:831-840. [PMID: 33554913 PMCID: PMC8075382 DOI: 10.3233/jad-201397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/07/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Neuroimaging markers provide quantitative insight into brain structure and function in neurodegenerative diseases, such as Alzheimer's disease, where we lack mechanistic insights to explain pathophysiology. These mechanisms are often mediated by genes and genetic variations and are often studied through the lens of genome-wide association studies. Linking these two disparate layers (i.e., imaging and genetic variation) through causal relationships between biological entities involved in the disease's etiology would pave the way to large-scale mechanistic reasoning and interpretation. OBJECTIVE We explore how genetic variants may lead to functional alterations of intermediate molecular traits, which can further impact neuroimaging hallmarks over a series of biological processes across multiple scales. METHODS We present an approach in which knowledge pertaining to single nucleotide polymorphisms and imaging readouts is extracted from the literature, encoded in Biological Expression Language, and used in a novel workflow to assist in the functional interpretation of SNPs in a clinical context. RESULTS We demonstrate our approach in a case scenario which proposes KANSL1 as a candidate gene that accounts for the clinically reported correlation between the incidence of the genetic variants and hippocampal atrophy. We find that the workflow prioritizes multiple mechanisms reported in the literature through which KANSL1 may have an impact on hippocampal atrophy such as through the dysregulation of cell proliferation, synaptic plasticity, and metabolic processes. CONCLUSION We have presented an approach that enables pinpointing relevant genetic variants as well as investigating their functional role in biological processes spanning across several, diverse biological scales.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
| | - Christine Robinson
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Anandhi Iyappan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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10
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Murray AN, Chandler HL, Lancaster TM. Multimodal hippocampal and amygdala subfield volumetry in polygenic risk for Alzheimer's disease. Neurobiol Aging 2020; 98:33-41. [PMID: 33227567 PMCID: PMC7886309 DOI: 10.1016/j.neurobiolaging.2020.08.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/28/2020] [Accepted: 08/02/2020] [Indexed: 11/29/2022]
Abstract
Preclinical models of Alzheimer's disease (AD) suggest that volumetric reductions in medial temporal lobe (MTL) structures manifest before clinical onset. AD polygenic risk scores (PRSs) are further linked to reduced MTL volumes (the hippocampus/amygdala); however, the relationship between the PRS and specific subregions remains unclear. We determine the relationship between the AD-PRSs and MTL subregions in a large sample of young participants (N = 730, aged 22–35 years) using a multimodal (T1w/T2w) approach. We first demonstrate that the PRSs for the hippocampus/amygdala predict their respective volumes and specific hippocampal subregions (pFDR < 0.05). We further observe negative relationships between the AD-PRSs and whole hippocampal/amygdala volumes. Critically, we demonstrate novel associations between the AD-PRSs and specific hippocampal subfields such as CA1 (β = −0.096, pFDR = 0.045) and the fissure (β = −0.101, pFDR = 0.041). We provide evidence that the AD-PRS is linked to specific MTL subfields decades before AD onset. This may help inform preclinical models of AD risk, providing additional specificity for intervention and further insight into mechanisms by which common AD variants confer susceptibility. Polygenic risk for Alzheimer's disease (AD-PRS) explains significant proportion of AD. AD-PRS also linked to hippocampus and amygdala volume. AD-PRS is negatively associated with specific hippocampal subfields. Polygenic AD models help us understand genetic contributions to medial temporal lobe nuclei.
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Affiliation(s)
- Amy N Murray
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Hannah L Chandler
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Thomas M Lancaster
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; Dementia Research Institute at Cardiff University, School of Medicine, Cardiff University, Cardiff, United Kingdom; School of Psychology, Bath University, Bath, United Kingdom.
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11
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Sims R, Hill M, Williams J. The multiplex model of the genetics of Alzheimer's disease. Nat Neurosci 2020; 23:311-322. [PMID: 32112059 DOI: 10.1038/s41593-020-0599-5] [Citation(s) in RCA: 240] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 01/24/2020] [Indexed: 12/25/2022]
Abstract
Genes play a strong role in Alzheimer's disease (AD), with late-onset AD showing heritability of 58-79% and early-onset AD showing over 90%. Genetic association provides a robust platform to build our understanding of the etiology of this complex disease. Over 50 loci are now implicated for AD, suggesting that AD is a disease of multiple components, as supported by pathway analyses (immunity, endocytosis, cholesterol transport, ubiquitination, amyloid-β and tau processing). Over 50% of late-onset AD heritability has been captured, allowing researchers to calculate the accumulation of AD genetic risk through polygenic risk scores. A polygenic risk score predicts disease with up to 90% accuracy and is an exciting tool in our research armory that could allow selection of those with high polygenic risk scores for clinical trials and precision medicine. It could also allow cellular modelling of the combined risk. Here we propose the multiplex model as a new perspective from which to understand AD. The multiplex model reflects the combination of some, or all, of these model components (genetic and environmental), in a tissue-specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell and synaptic loss observed in AD.
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Affiliation(s)
- Rebecca Sims
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Matthew Hill
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
- UK Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, UK
| | - Julie Williams
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
- UK Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, UK.
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12
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Elliott ML, Knodt AR, Cooke M, Kim MJ, Melzer TR, Keenan R, Ireland D, Ramrakha S, Poulton R, Caspi A, Moffitt TE, Hariri AR. General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage 2019; 189:516-532. [PMID: 30708106 PMCID: PMC6462481 DOI: 10.1016/j.neuroimage.2019.01.068] [Citation(s) in RCA: 159] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 01/22/2019] [Accepted: 01/27/2019] [Indexed: 01/15/2023] Open
Abstract
Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA.
| | - Annchen R Knodt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
| | - Megan Cooke
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
| | - M Justin Kim
- Department of Psychology, University of Hawaii at Manoa, Honolulu, HI, 96822, USA
| | - Tracy R Melzer
- New Zealand Brain Research Institute, Christchurch, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Ross Keenan
- New Zealand Brain Research Institute, Christchurch, New Zealand; Christchurch Radiology Group, Christchurch, New Zealand
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA; Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK; Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27708, USA; Center for Genomic and Computational Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - Terrie E Moffitt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA; Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK; Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27708, USA; Center for Genomic and Computational Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - Ahmad R Hariri
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
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13
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Apostolova LG, Risacher SL, Duran T, Stage EC, Goukasian N, West JD, Do TM, Grotts J, Wilhalme H, Nho K, Phillips M, Elashoff D, Saykin AJ. Associations of the Top 20 Alzheimer Disease Risk Variants With Brain Amyloidosis. JAMA Neurol 2018; 75:328-341. [PMID: 29340569 PMCID: PMC5885860 DOI: 10.1001/jamaneurol.2017.4198] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 10/19/2017] [Indexed: 01/28/2023]
Abstract
Importance Late-onset Alzheimer disease (AD) is highly heritable. Genome-wide association studies have identified more than 20 AD risk genes. The precise mechanism through which many of these genes are associated with AD remains unknown. Objective To investigate the association of the top 20 AD risk variants with brain amyloidosis. Design, Setting, and Participants This study analyzed the genetic and florbetapir F 18 data from 322 cognitively normal control individuals, 496 individuals with mild cognitive impairment, and 159 individuals with AD dementia who had genome-wide association studies and 18F-florbetapir positron emission tomographic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a prospective, observational, multisite tertiary center clinical and biomarker study. This ongoing study began in 2005. Main Outcomes and Measures The study tested the association of AD risk allele carrier status (exposure) with florbetapir mean standard uptake value ratio (outcome) using stepwise multivariable linear regression while controlling for age, sex, and apolipoprotein E ε4 genotype. The study also reports on an exploratory 3-dimensional stepwise regression model using an unbiased voxelwise approach in Statistical Parametric Mapping 8 with cluster and significance thresholds at 50 voxels and uncorrected P < .01. Results This study included 977 participants (mean [SD] age, 74 [7.5] years; 535 [54.8%] male and 442 [45.2%] female) from the ADNI-1, ADNI-2, and ADNI-Grand Opportunity. The adenosine triphosphate-binding cassette subfamily A member 7 (ABCA7) gene had the strongest association with amyloid deposition (χ2 = 8.38, false discovery rate-corrected P < .001), after apolioprotein E ε4. Significant associations were found between ABCA7 in the asymptomatic and early symptomatic disease stages, suggesting an association with rapid amyloid accumulation. The fermitin family homolog 2 (FERMT2) gene had a stage-dependent association with brain amyloidosis (FERMT2 × diagnosis χ2 = 3.53, false discovery rate-corrected P = .05), which was most pronounced in the mild cognitive impairment stage. Conclusions and Relevance This study found an association of several AD risk variants with brain amyloidosis. The data also suggest that AD genes might differentially regulate AD pathologic findings across the disease stages.
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Affiliation(s)
- Liana G. Apostolova
- Department of Neurology, School of Medicine, Indiana University, Indianapolis
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, School of Medicine, Indiana University, Indianapolis
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, School of Medicine, Indiana University, Indianapolis
| | - Tugce Duran
- Department of Neurology, School of Medicine, Indiana University, Indianapolis
| | - Eddie C. Stage
- Department of Neurology, School of Medicine, Indiana University, Indianapolis
| | - Naira Goukasian
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - John D. West
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, School of Medicine, Indiana University, Indianapolis
| | - Triet M. Do
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Jonathan Grotts
- Department of Medicine Statistics Core, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Holly Wilhalme
- Department of Medicine Statistics Core, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, School of Medicine, Indiana University, Indianapolis
| | - Meredith Phillips
- Department of Neurology, School of Medicine, Indiana University, Indianapolis
| | - David Elashoff
- Department of Medicine Statistics Core, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, School of Medicine, Indiana University, Indianapolis
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis
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14
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Sarnowski C, Satizabal CL, DeCarli C, Pitsillides AN, Cupples LA, Vasan RS, Wilson JG, Bis JC, Fornage M, Beiser AS, DeStefano AL, Dupuis J, Seshadri S. Whole genome sequence analyses of brain imaging measures in the Framingham Study. Neurology 2017; 90:e188-e196. [PMID: 29282330 PMCID: PMC5772158 DOI: 10.1212/wnl.0000000000004820] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 09/22/2017] [Indexed: 11/15/2022] Open
Abstract
Objective We sought to identify rare variants influencing brain imaging phenotypes in the Framingham Heart Study by performing whole genome sequence association analyses within the Trans-Omics for Precision Medicine Program. Methods We performed association analyses of cerebral and hippocampal volumes and white matter hyperintensity (WMH) in up to 2,180 individuals by testing the association of rank-normalized residuals from mixed-effect linear regression models adjusted for sex, age, and total intracranial volume with individual variants while accounting for familial relatedness. We conducted gene-based tests for rare variants using (1) a sliding-window approach, (2) a selection of functional exonic variants, or (3) all variants. Results We detected new loci in 1p21 for cerebral volume (minor allele frequency [MAF] 0.005, p = 10−8) and in 16q23 for hippocampal volume (MAF 0.05, p = 2.7 × 10−8). Previously identified associations in 12q24 for hippocampal volume (rs7294919, p = 4.4 × 10−4) and in 17q25 for WMH (rs7214628, p = 2.0 × 10−3) were confirmed. Gene-based tests detected associations (p ≤ 2.3 × 10−6) in new loci for cerebral (5q13, 8p12, 9q31, 13q12-q13, 15q24, 17q12, 19q13) and hippocampal volumes (2p12) and WMH (3q13, 4p15) including Alzheimer disease– (UNC5D) and Parkinson disease–associated genes (GBA). Pathway analyses evidenced enrichment of associated genes in immunity, inflammation, and Alzheimer disease and Parkinson disease pathways. Conclusions Whole genome sequence–wide search reveals intriguing new loci associated with brain measures. Replication of novel loci is needed to confirm these findings.
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Affiliation(s)
- Chloé Sarnowski
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston.
| | - Claudia L Satizabal
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - Charles DeCarli
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - Achilleas N Pitsillides
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - L Adrienne Cupples
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - Ramachandran S Vasan
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - James G Wilson
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - Joshua C Bis
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - Myriam Fornage
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - Alexa S Beiser
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - Anita L DeStefano
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - Josée Dupuis
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
| | - Sudha Seshadri
- From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston
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15
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Liu K, Yao X, Yan J, Chasioti D, Risacher S, Nho K, Saykin A, Shen L. Transcriptome-Guided Imaging Genetic Analysis via a Novel Sparse CCA Algorithm. GRAPHS IN BIOMEDICAL IMAGE ANALYSIS, COMPUTATIONAL ANATOMY AND IMAGING GENETICS : FIRST INTERNATIONAL WORKSHOP, GRAIL 2017, 6TH INTERNATIONAL WORKSHOP, MFCA 2017, AND THIRD INTERNATIONAL WORKSHOP, MICGEN 2017, HELD IN CONJUNCTION WITH M... 2017; 10551:220-229. [PMID: 30294724 PMCID: PMC6171533 DOI: 10.1007/978-3-319-67675-3_20] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. In imaging genetics, genes associated with a phenotype should at least expressed in the phenotypical region. We study the association between the genotype and amyloid imaging data and propose a transcriptome-guided SCCA framework that incorporates the gene expression information into the SCCA criterion. An alternating optimization method is used to solve the formulated problem. Although the problem is not biconcave, a closed-form solution has been found for each subproblem. The results on real data show that using the gene expression data to guide the feature selection facilities the detection of genetic markers that are not only associated with the identified QTs, but also highly expressed there.
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Affiliation(s)
- Kefei Liu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Xiaohui Yao
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- School of Informatics and Computing, Indiana University, Indianapolis, IN, USA
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- School of Informatics and Computing, Indiana University, Indianapolis, IN, USA
| | - Danai Chasioti
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- School of Informatics and Computing, Indiana University, Indianapolis, IN, USA
| | - Shannon Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Andrew Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- School of Informatics and Computing, Indiana University, Indianapolis, IN, USA
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16
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Iyappan A, Younesi E, Redolfi A, Vrooman H, Khanna S, Frisoni GB, Hofmann-Apitius M. Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features. J Alzheimers Dis 2017; 59:1153-1169. [PMID: 28731430 PMCID: PMC5611802 DOI: 10.3233/jad-161148] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.
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Affiliation(s)
- Anandhi Iyappan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany
| | - Alberto Redolfi
- Laboratory of Epidemiology and Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Henri Vrooman
- Departments of Radiology and Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC University Medical Center, The Netherlands
| | - Shashank Khanna
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | - Giovanni B Frisoni
- Laboratory of Epidemiology and Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and Laboratoire de Neuroimagerie du Vieillissement (LANVIE), University Hospitals and University of Geneva, Geneva, Switzerland
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
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17
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Foley SF, Tansey KE, Caseras X, Lancaster T, Bracht T, Parker G, Hall J, Williams J, Linden DEJ. Multimodal Brain Imaging Reveals Structural Differences in Alzheimer's Disease Polygenic Risk Carriers: A Study in Healthy Young Adults. Biol Psychiatry 2017; 81:154-161. [PMID: 27157680 PMCID: PMC5177726 DOI: 10.1016/j.biopsych.2016.02.033] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 02/08/2016] [Accepted: 02/29/2016] [Indexed: 01/02/2023]
Abstract
BACKGROUND Recent genome-wide association studies have identified genetic loci that jointly make a considerable contribution to risk of developing Alzheimer's disease (AD). Because neuropathological features of AD can be present several decades before disease onset, we investigated whether effects of polygenic risk are detectable by neuroimaging in young adults. We hypothesized that higher polygenic risk scores (PRSs) for AD would be associated with reduced volume of the hippocampus and other limbic and paralimbic areas. We further hypothesized that AD PRSs would affect the microstructure of fiber tracts connecting the hippocampus with other brain areas. METHODS We analyzed the association between AD PRSs and brain imaging parameters using T1-weighted structural (n = 272) and diffusion-weighted scans (n = 197). RESULTS We found a significant association between AD PRSs and left hippocampal volume, with higher risk associated with lower left hippocampal volume (p = .001). This effect remained when the APOE gene was excluded (p = .031), suggesting that the relationship between hippocampal volume and AD is the result of multiple genetic factors and not exclusively variability in the APOE gene. The diffusion tensor imaging analysis revealed that fractional anisotropy of the right cingulum was inversely correlated with AD PRSs (p = .009). We thus show that polygenic effects of AD risk variants on brain structure can already be detected in young adults. CONCLUSIONS This finding paves the way for further investigation of the effects of AD risk variants and may become useful for efforts to combine genotypic and phenotypic data for risk prediction and to enrich future prevention trials of AD.
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Affiliation(s)
- Sonya F Foley
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom; Central Biotechnology Services, TIME Institute, Wales, United Kingdom.
| | - Katherine E Tansey
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Medical Research Council Integrative Epidemiology Unit, School of Social and Community Medicine, Faculty of Medicine & Dentistry, University of Bristol, Bristol, United Kingdom
| | - Xavier Caseras
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
| | - Thomas Lancaster
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
| | - Tobias Bracht
- Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
| | - Greg Parker
- Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
| | - Jeremy Hall
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Neuroscience and Mental Health Research Institute, Wales, United Kingdom
| | - Julie Williams
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom
| | - David E J Linden
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
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18
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Disrupted global metastability and static and dynamic brain connectivity across individuals in the Alzheimer's disease continuum. Sci Rep 2017; 7:40268. [PMID: 28074926 PMCID: PMC5225495 DOI: 10.1038/srep40268] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 12/05/2016] [Indexed: 11/09/2022] Open
Abstract
As findings on the neuropathological and behavioral components of Alzheimer's disease (AD) continue to accrue, converging evidence suggests that macroscale brain functional disruptions may mediate their association. Recent developments on theoretical neuroscience indicate that instantaneous patterns of brain connectivity and metastability may be a key mechanism in neural communication underlying cognitive performance. However, the potential significance of these patterns across the AD spectrum remains virtually unexplored. We assessed the clinical sensitivity of static and dynamic functional brain disruptions across the AD spectrum using resting-state fMRI in a sample consisting of AD patients (n = 80) and subjects with either mild (n = 44) or subjective (n = 26) cognitive impairment (MCI, SCI). Spatial maps constituting the nodes in the functional brain network and their associated time-series were estimated using spatial group independent component analysis and dual regression, and whole-brain oscillatory activity was analyzed both globally (metastability) and locally (static and dynamic connectivity). Instantaneous phase metrics showed functional coupling alterations in AD compared to MCI and SCI, both static (putamen, dorsal and default-mode) and dynamic (temporal, frontal-superior and default-mode), along with decreased global metastability. The results suggest that brains of AD patients display altered oscillatory patterns, in agreement with theoretical premises on cognitive dynamics.
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19
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Whelan CD, Hibar DP, van Velzen LS, Zannas AS, Carrillo-Roa T, McMahon K, Prasad G, Kelly S, Faskowitz J, deZubiracay G, Iglesias JE, van Erp TGM, Frodl T, Martin NG, Wright MJ, Jahanshad N, Schmaal L, Sämann PG, Thompson PM. Heritability and reliability of automatically segmented human hippocampal formation subregions. Neuroimage 2016; 128:125-137. [PMID: 26747746 PMCID: PMC4883013 DOI: 10.1016/j.neuroimage.2015.12.039] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 11/28/2015] [Accepted: 12/23/2015] [Indexed: 12/01/2022] Open
Abstract
The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium.
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Affiliation(s)
- Christopher D Whelan
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Derrek P Hibar
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Laura S van Velzen
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - Anthony S Zannas
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Tania Carrillo-Roa
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Katie McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Gautam Prasad
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Sinéad Kelly
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Joshua Faskowitz
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Greig deZubiracay
- Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Juan E Iglesias
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, USA
| | - Thomas Frodl
- Department of Psychiatry, Otto-von Guericke-University of Magdeburg, Germany
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Lianne Schmaal
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - Philipp G Sämann
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA.
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20
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Kochunov P, Fu M, Nugent K, Wright SN, Du X, Muellerklein F, Morrissey M, Eskandar G, Shukla DK, Jahanshad N, Thompson PM, Patel B, Postolache TT, Strauss KA, Shuldiner AR, Mitchell BD, Hong LE. Heritability of complex white matter diffusion traits assessed in a population isolate. Hum Brain Mapp 2015; 37:525-35. [PMID: 26538488 DOI: 10.1002/hbm.23047] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 10/07/2015] [Accepted: 10/22/2015] [Indexed: 11/09/2022] Open
Abstract
INTRODUCTION Diffusion weighted imaging (DWI) methods can noninvasively ascertain cerebral microstructure by examining pattern and directions of water diffusion in the brain. We calculated heritability for DWI parameters in cerebral white (WM) and gray matter (GM) to study the genetic contribution to the diffusion signals across tissue boundaries. METHODS Using Old Order Amish (OOA) population isolate with large family pedigrees and high environmental homogeneity, we compared the heritability of measures derived from three representative DWI methods targeting the corpus callosum WM and cingulate gyrus GM: diffusion tensor imaging (DTI), the permeability-diffusivity (PD) model, and the neurite orientation dispersion and density imaging (NODDI) model. These successively more complex models represent the diffusion signal modeling using one, two, and three diffusion compartments, respectively. RESULTS We replicated the high heritability of the DTI-based fractional anisotropy (h(2) = 0.67) and radial diffusivity (h(2) = 0.72) in WM. High heritability in both WM and GM tissues were observed for the permeability-diffusivity index from the PD model (h(2) = 0.64 and 0.84), and the neurite density from the NODDI model (h(2) = 0.70 and 0.55). The orientation dispersion index from the NODDI model was only significantly heritable in GM (h(2) = 0.68). CONCLUSION DWI measures from multicompartmental models were significantly heritable in WM and GM. DWI can offer valuable phenotypes for genetic research; and genes thus identified may reveal mechanisms contributing to mental and neurological disorders in which diffusion imaging anomalies are consistently found. Hum Brain Mapp 37:525-535, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Mao Fu
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Katie Nugent
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Susan N Wright
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Xiaoming Du
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Florian Muellerklein
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Mary Morrissey
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - George Eskandar
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Dinesh K Shukla
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Neda Jahanshad
- Keck School of Medicine of USC, Imaging Genetics Center, Marina Del Rey, California
| | - Paul M Thompson
- Keck School of Medicine of USC, Imaging Genetics Center, Marina Del Rey, California
| | - Binish Patel
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Teodor T Postolache
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | | | - Alan R Shuldiner
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland.,Veterans Affairs Maryland Health Care System, Geriatric Research and Education Clinical Center, Baltimore, Maryland
| | - L Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
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21
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Lazaris A, Hwang KS, Goukasian N, Ramirez LM, Eastman J, Blanken AE, Teng E, Gylys K, Cole G, Saykin AJ, Shaw LM, Trojanowski JQ, Jagust WJ, Weiner MW, Apostolova LG. Alzheimer risk genes modulate the relationship between plasma apoE and cortical PiB binding. NEUROLOGY-GENETICS 2015; 1:e22. [PMID: 27066559 PMCID: PMC4809461 DOI: 10.1212/nxg.0000000000000022] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Accepted: 08/13/2015] [Indexed: 01/28/2023]
Abstract
Objective: We investigated the association between apoE protein plasma levels and brain amyloidosis and the effect of the top 10 Alzheimer disease (AD) risk genes on this association. Methods: Our dataset consisted of 18 AD, 52 mild cognitive impairment, and 3 cognitively normal Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) participants with available [11C]-Pittsburgh compound B (PiB) and peripheral blood protein data. We used cortical pattern matching to study associations between plasma apoE and cortical PiB binding and the effect of carrier status for the top 10 AD risk genes. Results: Low plasma apoE was significantly associated with high PiB SUVR, except in the sensorimotor and entorhinal cortex. For BIN1 rs744373, the association was observed only in minor allele carriers. For CD2AP rs9349407 and CR1 rs3818361, the association was preserved only in minor allele noncarriers. We did not find evidence for modulation by CLU, PICALM, ABCA7, BIN1, and MS4A6A. Conclusions: Our data show that BIN1 rs744373, CD2AP rs9349407, and CR1 rs3818361 genotypes modulate the association between apoE protein plasma levels and brain amyloidosis, implying a potential epigenetic/downstream interaction.
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Affiliation(s)
- Andreas Lazaris
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Kristy S Hwang
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Naira Goukasian
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Leslie M Ramirez
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Jennifer Eastman
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Anna E Blanken
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Edmond Teng
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Karen Gylys
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Greg Cole
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Andrew J Saykin
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Leslie M Shaw
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - John Q Trojanowski
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - William J Jagust
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Michael W Weiner
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
| | - Liana G Apostolova
- University of California Berkeley (A.L.), Berkeley; Oakland University William Beaumont School of Medicine (K.S.H.), Rochester, MI; Department of Neurology (K.S.H., N.G., A.E.B., E.T., G.C., L.G.A.), David Geffen School of Medicine at UCLA, Los Angeles, CA; Drexel University College of Medicine (L.M.R.), Philadelphia, PA; Northwestern University Feinberg School of Medicine (J.E.), Chicago, IL; Veterans Affairs Greater Los Angeles Healthcare System (E.T., G.C.), Los Angeles, CA; School of Nursing (K.G.), UCLA, Los Angeles, CA; Department of Radiology and Imaging Sciences, Center for Neuroimaging (A.J.S., L.G.A.), Department of Neurology (L.G.A.), and Department of Medical and Molecular Genetics (L.G.A.), School of Medicine, Indiana University, Indianapolis; Department of Pathology and Laboratory Medicine (L.M.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Public Health and Neuroscience (W.J.J.), UC Berkeley, CA; and Department of Veterans' Affairs Medical Center (M.W.W.), San Francisco, CA
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22
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Johnson LA, Sohrabi HR, Hall JR, Kevin T, Edwards M, O'Bryant SE, Martins RN. A depressive endophenotype of poorer cognition among cognitively healthy community-dwelling adults: results from the Western Australia memory study. Int J Geriatr Psychiatry 2015; 30:881-6. [PMID: 25394326 DOI: 10.1002/gps.4231] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Revised: 09/25/2014] [Accepted: 10/03/2014] [Indexed: 01/01/2023]
Abstract
OBJECTIVE The objective was to evaluate in a cognitively normal population the utility of an endophenotype of the depression-cognition link previously shown to be related to cognitive functioning in mild cognitive impairment and Alzheimer's disease. METHODS The data of 460 cognitively normal adults aged 32-92 years (M = 63.5, standard deviation = 9.24) from the Western Australian Memory Study with the Cross-national comparisons of the Cambridge Cognitive Examination-revised (CAMCOG-R) scores and 30-item Geriatric Depression Scale (GDS) scores were analyzed to determine the relationship between the five-item depressive endophenotype (DepE) scale drawn from the GDS and level of performance on a measure of cognitive functioning. RESULTS For the entire sample, there was a nonsignificant trend toward a negative relationship between DepE and CAMCOG-R scores. When analyzed for those 65 years and older, there was a significant negative relationship between the two measures (p = 0.001) with DepE scores significantly increasing the risk for performing more poorly on the CAMCOG-R (odds ratio = 1.53). Analysis of data for those 70 years and older showed that DepE was the only predictor significantly related to poorer CAMCOG-R performance (p = 0.001). For the 70 years and older group, DepE scores significantly increased the risk of poorer CAMCOG-R scores (odds ratio = 2.23). Analysis of the entire sample on the basis of ApoEε4 carrier status revealed that DepE scores were significantly negatively related only to ApoEε4 noncarrier regardless of age. CONCLUSIONS Elevated DepE scores are associated with poor neuropsychological performance among cognitively normal older adults. Use of the DepE may allow for the identification of a subset of older adults where depression is a primary factor in cognitive decline and who may benefit from antidepressant therapies.
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Affiliation(s)
- Leigh A Johnson
- Institute for Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, TX, USA.,Department of Internal Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Hamid R Sohrabi
- The School of Medical Sciences, Edith Cowan University, Joondalup, WA, Australia.,The McCusker Alzheimer's Research Foundation, Hollywood Private Hospital, Nedlands, WA, Australia.,The Centre of Excellence for Alzheimer's Disease Research and Care, Edith Cowan University, Joondalup, WA, Australia
| | - James R Hall
- Institute for Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, TX, USA.,Department of Psychiatry, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Taddei Kevin
- The School of Medical Sciences, Edith Cowan University, Joondalup, WA, Australia.,The McCusker Alzheimer's Research Foundation, Hollywood Private Hospital, Nedlands, WA, Australia.,The Centre of Excellence for Alzheimer's Disease Research and Care, Edith Cowan University, Joondalup, WA, Australia
| | - Melissa Edwards
- Department of Internal Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Sid E O'Bryant
- Institute for Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, TX, USA.,Department of Internal Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Ralph N Martins
- The School of Medical Sciences, Edith Cowan University, Joondalup, WA, Australia.,The McCusker Alzheimer's Research Foundation, Hollywood Private Hospital, Nedlands, WA, Australia.,The Centre of Excellence for Alzheimer's Disease Research and Care, Edith Cowan University, Joondalup, WA, Australia
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23
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Hibar DP, Stein JL, Jahanshad N, Kohannim O, Hua X, Toga AW, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Weiner MW, Thompson PM. Genome-wide interaction analysis reveals replicated epistatic effects on brain structure. Neurobiol Aging 2015; 36 Suppl 1:S151-8. [PMID: 25264344 PMCID: PMC4332874 DOI: 10.1016/j.neurobiolaging.2014.02.033] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 02/10/2014] [Accepted: 02/16/2014] [Indexed: 11/24/2022]
Abstract
The discovery of several genes that affect the risk for Alzheimer's disease ignited a worldwide search for single-nucleotide polymorphisms (SNPs), common genetic variants that affect the brain. Genome-wide search of all possible SNP-SNP interactions is challenging and rarely attempted because of the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, for example, iterative sure independence screening, make it possible to analyze data sets with vastly more predictors than observations. Using an implementation of the sure independence screening algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on magnetic resonance imaging and mapped using tensor-based morphometry. We identified a significant SNP-SNP interaction between rs1345203 and rs1213205 that explains 1.9% of the variance in temporal lobe volume. We mapped the whole brain, voxelwise effects of the interaction in the Alzheimer's Disease Neuroimaging Initiative data set and separately in an independent replication data set of healthy twins (Queensland Twin Imaging). Each additional loading in the interaction effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both Alzheimer's Disease Neuroimaging Initiative and Queensland Twin Imaging samples.
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Affiliation(s)
- Derrek P Hibar
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Jason L Stein
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Omid Kohannim
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Xue Hua
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Katie L McMahon
- Centre for Magnetic Resonance, School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Greig I de Zubicaray
- Functional Magnetic Resonance Imaging Laboratory, School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Margaret J Wright
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Michael W Weiner
- Department of Radiology, UC San Francisco, San Francisco, CA, USA; Department of Medicine, UC San Francisco, San Francisco, CA, USA; Department of Psychiatry, UC San Francisco, San Francisco, CA, USA; Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
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24
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Malhotra A, Younesi E, Bagewadi S, Hofmann-Apitius M. Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer's disease. Genome Med 2014; 6:97. [PMID: 25484918 PMCID: PMC4256903 DOI: 10.1186/s13073-014-0097-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Accepted: 10/09/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A number of compelling candidate Alzheimer's biomarkers remain buried within the literature. Indeed, there should be a systematic effort towards gathering this information through approaches that mine publicly available data and substantiate supporting evidence through disease modeling methods. In the presented work, we demonstrate that an integrative gray zone mining approach can be used as a way to tackle this challenge successfully. METHODS The methodology presented in this work combines semantic information retrieval and experimental data through context-specific modeling of molecular interactions underlying stages in Alzheimer's disease (AD). Information about putative, highly speculative AD biomarkers was harvested from the literature using a semantic framework and was put into a functional context through disease- and stage-specific models. Staging models of AD were further validated for their functional relevance and novel biomarker candidates were predicted at the mechanistic level. RESULTS Three interaction models were built representing three stages of AD, namely mild, moderate, and severe stages. Integrated analysis of these models using various arrays of evidence gathered from experimental data and published knowledge resources led to identification of four candidate biomarkers in the mild stage. Mode of action of these candidates was further reasoned in the mechanistic context of models by chains of arguments. Accordingly, we propose that some of these 'emerging' potential biomarker candidates have a reasonable mechanistic explanation and deserve to be investigated in more detail. CONCLUSIONS Systematic exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches. Integrative analysis of this knowledge in the context of disease mechanism is a promising approach towards identification of candidate biomarkers taking into consideration the complex etiology of disease. The added value of this strategy becomes apparent particularly in the area of biomarker discovery for neurodegenerative diseases where predictive biomarkers are desperately needed.
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Affiliation(s)
- Ashutosh Malhotra
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany ; Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, 53113 Bonn, Germany
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany
| | - Shweta Bagewadi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany ; Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, 53113 Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany ; Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, 53113 Bonn, Germany
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25
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Abstract
Neuroimaging is a potentially valuable tool to link individual differences in the human genome to structure and functional variations, narrowing the gaps in the casual chain from a given genetic variation to a brain disorder. Because genes are not usually expressed at the level of mental behavior, but are mediated by their molecular and cellular effects, molecular imaging could play a key role. This article reviews the literature using molecular imaging as an intermediate phenotype and/or biomarker for illness related to certain genetic alterations, focusing on the most common neurodegenerative disorders, Alzheimer's disease (AD) and Parkinson disease.
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Affiliation(s)
- José Leite
- PET/CT, Clínica de Diagnóstico Por Imagem (CDPI), Rio de Janeiro, Rio de Janeiro, Brazil.
| | - Roberta Hespanhol
- PET/CT, Clínica de Diagnóstico Por Imagem (CDPI), Rio de Janeiro, Rio de Janeiro, Brazil
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26
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Kochunov P, Jahanshad N, Sprooten E, Nichols TE, Mandl RC, Almasy L, Booth T, Brouwer RM, Curran JE, de Zubicaray GI, Dimitrova R, Duggirala R, Fox PT, Hong LE, Landman BA, Lemaitre H, Lopez LM, Martin NG, McMahon KL, Mitchell BD, Olvera RL, Peterson CP, Starr JM, Sussmann JE, Toga AW, Wardlaw JM, Wright MJ, Wright SN, Bastin ME, McIntosh AM, Boomsma DI, Kahn RS, den Braber A, de Geus EJC, Deary IJ, Hulshoff Pol HE, Williamson DE, Blangero J, van 't Ent D, Thompson PM, Glahn DC. Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling. Neuroimage 2014; 95:136-50. [PMID: 24657781 PMCID: PMC4043878 DOI: 10.1016/j.neuroimage.2014.03.033] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2013] [Revised: 02/21/2014] [Accepted: 03/04/2014] [Indexed: 01/25/2023] Open
Abstract
Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Neda Jahanshad
- Imaging Genetics Center, Institute of Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Emma Sprooten
- Olin Neuropsychiatry Research Center in the Institute of Living, Yale University School of Medicine, New Haven, CT, USA
| | - Thomas E Nichols
- Department of Statistics & Warwick Manufacturing Group, The University of Warwick, Coventry, UK; Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Oxford University, UK
| | - René C Mandl
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Laura Almasy
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Tom Booth
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Rachel M Brouwer
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joanne E Curran
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | | | - Rali Dimitrova
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Ravi Duggirala
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bennett A Landman
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Hervé Lemaitre
- U1000 Research Unit Neuroimaging and Psychiatry, INSERM-CEA-Faculté de Médecine Paris-Sud, Orsay, France
| | - Lorna M Lopez
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | | | - Katie L McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rene L Olvera
- Department of Psychiatry, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | - Charles P Peterson
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK; Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Jessika E Sussmann
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Arthur W Toga
- Imaging Genetics Center, Institute of Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | | | - Susan N Wright
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - René S Kahn
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anouk den Braber
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Hilleke E Hulshoff Pol
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Douglas E Williamson
- Department of Psychiatry, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Dennis van 't Ent
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Paul M Thompson
- Imaging Genetics Center, Institute of Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Neurology, Pediatrics, Engineering, Psychiatry, Radiology, & Ophthalmology, University of Southern California, Los Angeles, CA, USA
| | - David C Glahn
- Olin Neuropsychiatry Research Center in the Institute of Living, Yale University School of Medicine, New Haven, CT, USA
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Braskie MN, Thompson PM. A focus on structural brain imaging in the Alzheimer's disease neuroimaging initiative. Biol Psychiatry 2014; 75:527-33. [PMID: 24367935 PMCID: PMC4019004 DOI: 10.1016/j.biopsych.2013.11.020] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2013] [Revised: 11/05/2013] [Accepted: 11/06/2013] [Indexed: 01/18/2023]
Abstract
In recent years, numerous laboratories and consortia have used neuroimaging to evaluate the risk for and progression of Alzheimer's disease (AD). The Alzheimer's Disease Neuroimaging Initiative is a longitudinal, multicenter study that is evaluating a range of biomarkers for use in diagnosis of AD, prediction of patient outcomes, and clinical trials. These biomarkers include brain metrics derived from magnetic resonance imaging (MRI) and positron emission tomography scans as well as metrics derived from blood and cerebrospinal fluid. We focus on Alzheimer's Disease Neuroimaging Initiative studies published between 2011 and March 2013 for which structural MRI was a major outcome measure. Our main goal was to review key articles offering insights into progression of AD and the relationships of structural MRI measures to cognition and to other biomarkers in AD. In Supplement 1, we also discuss genetic and environmental risk factors for AD and exciting new analysis tools for the efficient evaluation of large-scale structural MRI data sets such as the Alzheimer's Disease Neuroimaging Initiative data.
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Affiliation(s)
- Meredith N Braskie
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, California; Department of Neurology, University of Southern California, Los Angeles, California
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, California; Department of Neurology, University of Southern California, Los Angeles, California; Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, California; Department of Radiology, University of Southern California, Los Angeles, California; Department of Pediatrics, University of Southern California, Los Angeles, California; Department of Ophthalmology, University of Southern California, Los Angeles, California; Keck School of Medicine, and Viterbi School of Engineering, University of Southern California, Los Angeles, California.
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28
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Jahanshad N, Rajagopalan P, Thompson PM. Neuroimaging, nutrition, and iron-related genes. Cell Mol Life Sci 2013; 70:4449-61. [PMID: 23817740 PMCID: PMC3827893 DOI: 10.1007/s00018-013-1369-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 04/23/2013] [Accepted: 05/13/2013] [Indexed: 02/08/2023]
Abstract
Several dietary factors and their genetic modifiers play a role in neurological disease and affect the human brain. The structural and functional integrity of the living brain can be assessed using neuroimaging, enabling large-scale epidemiological studies to identify factors that help or harm the brain. Iron is one nutritional factor that comes entirely from our diet, and its storage and transport in the body are under strong genetic control. In this review, we discuss how neuroimaging can help to identify associations between brain integrity, genetic variations, and dietary factors such as iron. We also review iron's essential role in cognition, and we note some challenges and confounds involved in interpreting links between diet and brain health. Finally, we outline some recent discoveries regarding the genetics of iron and its effects on the brain, suggesting the promise of neuroimaging in revealing how dietary factors affect the brain.
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Affiliation(s)
- Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769 USA
| | - Priya Rajagopalan
- Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769 USA
| | - Paul M. Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769 USA
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Wang Y, Goh W, Wong L, Montana G. Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes. BMC Bioinformatics 2013; 14 Suppl 16:S6. [PMID: 24564704 PMCID: PMC3853073 DOI: 10.1186/1471-2105-14-s16-s6] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive. RESULTS We have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR (Parallel Random Forest Regression). Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity. AVAILABILITY The Java codes are freely available at http://www2.imperial.ac.uk/~gmontana.
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Ramanan VK, Saykin AJ. Pathways to neurodegeneration: mechanistic insights from GWAS in Alzheimer's disease, Parkinson's disease, and related disorders. AMERICAN JOURNAL OF NEURODEGENERATIVE DISEASE 2013; 2:145-175. [PMID: 24093081 PMCID: PMC3783830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Accepted: 08/25/2013] [Indexed: 06/02/2023]
Abstract
The discovery of causative genetic mutations in affected family members has historically dominated our understanding of neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS). Nevertheless, most cases of neurodegenerative disease are not explained by Mendelian inheritance of known genetic variants, but instead are thought to have a complex etiology with numerous genetic and environmental factors contributing to susceptibility. Although unbiased genome-wide association studies (GWAS) have identified novel associations to neurodegenerative diseases, most of these hits explain only modest fractions of disease heritability. In addition, despite the substantial overlap of clinical and pathologic features among major neurodegenerative diseases, surprisingly few GWAS-implicated variants appear to exhibit cross-disease association. These realities suggest limitations of the focus on individual genetic variants and create challenges for the development of diagnostic and therapeutic strategies, which traditionally target an isolated molecule or mechanistic step. Recently, GWAS of complex diseases and traits have focused less on individual susceptibility variants and instead have emphasized the biological pathways and networks revealed by genetic associations. This new paradigm draws on the hypothesis that fundamental disease processes may be influenced on a personalized basis by a combination of variants - some common and others rare, some protective and others deleterious - in key genes and pathways. Here, we review and synthesize the major pathways implicated in neurodegeneration, focusing on GWAS from the most prevalent neurodegenerative disorders, AD and PD. Using literature mining, we also discover a novel regulatory network that is enriched with AD- and PD-associated genes and centered on the SP1 and AP-1 (Jun/Fos) transcription factors. Overall, this pathway- and network-driven model highlights several potential shared mechanisms in AD and PD that will inform future studies of these and other neurodegenerative disorders. These insights also suggest that biomarker and treatment strategies may require simultaneous targeting of multiple components, including some specific to disease stage, in order to assess and modulate neurodegeneration. Pathways and networks will provide ideal vehicles for integrating relevant findings from GWAS and other modalities to enhance clinical translation.
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Affiliation(s)
- Vijay K Ramanan
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineIndianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of MedicineIndianapolis, IN, USA
- Medical Scientist Training Program, Indiana University School of MedicineIndianapolis, IN, USA
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineIndianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of MedicineIndianapolis, IN, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of MedicineIndianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of MedicineIndianapolis, IN, USA
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Understanding cognitive deficits in Alzheimer's disease based on neuroimaging findings. Trends Cogn Sci 2013; 17:510-6. [PMID: 24029445 DOI: 10.1016/j.tics.2013.08.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2013] [Accepted: 08/07/2013] [Indexed: 01/21/2023]
Abstract
Brain amyloid can be measured using positron emission tomography (PET). There are mixed reports regarding whether amyloid measures are correlated with measures of cognition (in particular memory), depending on the cohorts and cognitive domains assessed. In Alzheimer's disease (AD) patients and those at heightened risk for AD, cognitive performance may be related to the level and extent of classical AD pathology (amyloid plaques and neurofibrillary angles), but it is also influenced by neurodegeneration, neurocognitive reserve, and vascular health. We discuss what recent neuroimaging research has discovered about cognitive deficits in AD and offer suggestions for future research.
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Johnson LA, Hall JR, O'Bryant SE. A depressive endophenotype of mild cognitive impairment and Alzheimer's disease. PLoS One 2013; 8:e68848. [PMID: 23874786 PMCID: PMC3708919 DOI: 10.1371/journal.pone.0068848] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2012] [Accepted: 06/05/2013] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a devastating public health problem that affects over 5.4 million Americans. Depression increases the risk of Mild Cognitive Impairment (MCI) and AD. By understanding the influence of depression on cognition, the potential exists to identify subgroups of depressed elders at greater risk for cognitive decline and AD. The current study sought to: 1) clinically identify a sub group of geriatric patients who suffer from depression related cognitive impairment; 2) cross validate this depressive endophenotype of MCI/AD in an independent cohort. METHODS AND FINDINGS Data was analyzed from 519 participants of Project FRONTIER. Depression was assessed with the GDS30 and cognition was assessed using the EXIT 25 and RBANS. Five GDS items were used to create the Depressive endophenotype of MCI and AD (DepE). DepE was significantly negatively related to RBANS index scores of Immediate Memory (B=-2.22, SE=.37, p<0.001), visuospatial skills (B=-1.11, SE=0.26, p<0.001), Language (B=-1.03, SE=0.21, p<0.001), Attention (B=-2.56, SE=0.49, p<0.001), and Delayed Memory (B=-1.54, SE = 037, p<0.001), and higher DepE scores were related to poorer executive functioning (EXIT25; B=0.65, SE=0.19, p=0.001). DepE scores significantly increased risk for MCI diagnosis (odds ratio [OR] = 2.04; 95% CI=1.54-2.69). Data from 235 participants in the TARCC (Texas Alzheimer's Research & Care Consortium) were analyzed for cross-validation of findings in an independent cohort. The DepE was significantly related to poorer scores on all measures, and a significantly predicted of cognitive change over 12- and 24-months. CONCLUSION The current findings suggest that a depressive endophenotype of MCI and AD exists and can be clinically identified using the GDS-30. Higher scores increased risk for MCI and was cross-validated by predicting AD in the TARCC. A key purpose for the search for distinct subgroups of individuals at risk for AD and MCI is to identify novel treatment and preventative opportunities.
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Affiliation(s)
- Leigh A Johnson
- Department of Internal Medicine, University of North Texas Health Science Center, Fort Worth, Texas, United States of America.
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Kohannim O, Hua X, Rajagopalan P, Hibar DP, Jahanshad N, Grill JD, Apostolova LG, Toga AW, Jack CR, Weiner MW, Thompson PM. Multilocus genetic profiling to empower drug trials and predict brain atrophy. NEUROIMAGE-CLINICAL 2013; 2:827-35. [PMID: 24179834 PMCID: PMC3777716 DOI: 10.1016/j.nicl.2013.05.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Revised: 04/14/2013] [Accepted: 05/11/2013] [Indexed: 12/16/2022]
Abstract
Designers of clinical trials for Alzheimer's disease (AD) and mild cognitive impairment (MCI) are actively considering structural and functional neuroimaging, cerebrospinal fluid and genetic biomarkers to reduce the sample sizes needed to detect therapeutic effects. Genetic pre-selection, however, has been limited to Apolipoprotein E (ApoE). Recently discovered polymorphisms in the CLU, CR1 and PICALM genes are also moderate risk factors for AD; each affects lifetime AD risk by ~ 10–20%. Here, we tested the hypothesis that pre-selecting subjects based on these variants along with ApoE genotype would further boost clinical trial power, relative to considering ApoE alone, using an MRI-derived 2-year atrophy rate as our outcome measure. We ranked subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on their cumulative risk from these four genes. We obtained sample size estimates in cohorts enriched in subjects with greater aggregate genetic risk. Enriching for additional genetic biomarkers reduced the required sample sizes by up to 50%, for MCI trials. Thus, AD drug trial enrichment with multiple genotypes may have potential implications for the timeliness, cost, and power of trials. ApoE genotype status helps enrich MCI trials, using a structural MRI outcome measure. CLU, PICALM and CR1 risk genes boost potential MCI trial power beyond ApoE alone. CLU, PICALM and CR1 show significant, aggregate effects on TBM maps of brain atrophy.
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Affiliation(s)
- Omid Kohannim
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Xue Hua
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Priya Rajagopalan
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Derrek P. Hibar
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Joshua D. Grill
- Mary Easton Center for Alzheimer's Disease Research, UCLA School of Medicine, Los Angeles, CA, USA
| | - Liana G. Apostolova
- Mary Easton Center for Alzheimer's Disease Research, UCLA School of Medicine, Los Angeles, CA, USA
| | - Arthur W. Toga
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | | | - Michael W. Weiner
- Depts. of Radiology, Medicine and Psychiatry, UCSF, San Francisco, CA, USA
- Dept. of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
- Corresponding author at: Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA. Tel.: + 1 310 206 2101; fax: + 1 310 206 5518.
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Braskie MN, Toga AW, Thompson PM. Recent advances in imaging Alzheimer's disease. J Alzheimers Dis 2013; 33 Suppl 1:S313-27. [PMID: 22672880 DOI: 10.3233/jad-2012-129016] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Advances in brain imaging technology in the past five years have contributed greatly to the understanding of Alzheimer's disease (AD). Here, we review recent research related to amyloid imaging, new methods for magnetic resonance imaging analyses, and statistical methods. We also review research that evaluates AD risk factors and brain imaging, in the context of AD prediction and progression. We selected a variety of illustrative studies, describing how they advanced the field and are leading AD research in promising new directions.
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Affiliation(s)
- Meredith N Braskie
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-7334, USA
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Jahanshad N, Kochunov PV, Sprooten E, Mandl RC, Nichols TE, Almasy L, Blangero J, Brouwer RM, Curran JE, de Zubicaray GI, Duggirala R, Fox PT, Hong LE, Landman BA, Martin NG, McMahon KL, Medland SE, Mitchell BD, Olvera RL, Peterson CP, Starr JM, Sussmann JE, Toga AW, Wardlaw JM, Wright MJ, Hulshoff Pol HE, Bastin ME, McIntosh AM, Deary IJ, Thompson PM, Glahn DC. Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group. Neuroimage 2013; 81:455-469. [PMID: 23629049 DOI: 10.1016/j.neuroimage.2013.04.061] [Citation(s) in RCA: 288] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2013] [Revised: 03/28/2013] [Accepted: 04/10/2013] [Indexed: 10/26/2022] Open
Abstract
The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).
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Affiliation(s)
- Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
| | - Peter V Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Emma Sprooten
- Olin Neuropsychiatry Research Center in the Institute of Living, Yale University School of Medicine, New Haven, CT, USA; Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - René C Mandl
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Thomas E Nichols
- Department of Statistics & Warwick Manufacturing Group, The University of Warwick, Coventry, UK; Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Oxford University, UK
| | - Laura Almasy
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Rachel M Brouwer
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joanne E Curran
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | | | - Ravi Duggirala
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA; South Texas Veterans Administration Medical Center, San Antonio, TX, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | - Katie L McMahon
- University of Queensland, Center for Advanced Imaging, Brisbane, Australia
| | - Sarah E Medland
- Queensland Institute of Medical Research, Brisbane, Australia
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rene L Olvera
- Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | - Charles P Peterson
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Jessika E Sussmann
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Arthur W Toga
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Hilleke E Hulshoff Pol
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Paul M Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA.
| | - David C Glahn
- Olin Neuropsychiatry Research Center in the Institute of Living, Yale University School of Medicine, New Haven, CT, USA
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Abstract
The striatum, comprising the caudate nucleus, putamen and nucleus accumbens, occupies a strategic location within cortico-striato-pallido-thalamic-cortical (corticostriatal) re-entrant neural circuits. Striatal neurodevelopment is precisely determined by phylogenetically conserved homeobox genes. Consisting primarily of medium spiny neurons, the striatum is strictly topographically organized based on cortical afferents and efferents. Particular corticostriatal neural circuits are considered to subserve certain domains of cognition, emotion and behaviour. Thus, the striatum may serve as a map of structural change in the cortical afferent pathways owing to deafferentation or neuroplasticity, and conversely, structural change in the striatum per se may structurally disrupt corticostriatal pathways. The morphology of the striatum may be quantified in vivo using advanced magnetic resonance imaging, as may cognitive functioning pertaining to corticostriatal circuits. It is proposed that striatal morphology may be a biomarker in neurodegenerative disease and potentially the basis of an endophenotype.
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Silver M, Janousova E, Hua X, Thompson PM, Montana G. Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression. Neuroimage 2012; 63:1681-94. [PMID: 22982105 PMCID: PMC3549495 DOI: 10.1016/j.neuroimage.2012.08.002] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2012] [Revised: 08/01/2012] [Accepted: 08/03/2012] [Indexed: 02/04/2023] Open
Abstract
We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimer's disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene-gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 99 probable AD patients and 164 healthy elderly controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathway database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24 months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing insulin signalling, vascular smooth muscle contraction and focal adhesion. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection. High ranking genes include a number previously linked in gene expression studies to β-amyloid plaque formation in the AD brain (PIK3R3,PIK3CG,PRKCAandPRKCB), and to AD related changes in hippocampal gene expression (ADCY2, ACTN1, ACACA, and GNAI1). Other high ranking previously validated AD endophenotype-related genes include CR1, TOMM40 and APOE.
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Affiliation(s)
- Matt Silver
- Statistics Section, Department of Mathematics, Imperial College London, UK
| | - Eva Janousova
- Statistics Section, Department of Mathematics, Imperial College London, UK
- Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic
| | - Xue Hua
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Giovanni Montana
- Statistics Section, Department of Mathematics, Imperial College London, UK
- Corresponding author.
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Wang H, Nie F, Huang H, Kim S, Nho K, Risacher SL, Saykin AJ, Shen L. Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics 2012; 28:229-37. [PMID: 22155867 PMCID: PMC3259438 DOI: 10.1093/bioinformatics/btr649] [Citation(s) in RCA: 106] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2011] [Revised: 11/01/2011] [Accepted: 11/17/2011] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Recent advances in high-throughput genotyping and brain imaging techniques enable new approaches to study the influence of genetic variation on brain structures and functions. Traditional association studies typically employ independent and pairwise univariate analysis, which treats single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as isolated units and ignores important underlying interacting relationships between the units. New methods are proposed here to overcome this limitation. RESULTS Taking into account the interlinked structure within and between SNPs and imaging QTs, we propose a novel Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) method to identify quantitative trait loci for multiple disease-relevant QTs and apply it to a study in mild cognitive impairment and Alzheimer's disease. Built upon regression analysis, our model uses a new form of regularization, group ℓ(2,1)-norm (G(2,1)-norm), to incorporate the biological group structures among SNPs induced from their genetic arrangement. The new G(2,1)-norm considers the regression coefficients of all the SNPs in each group with respect to all the QTs together and enforces sparsity at the group level. In addition, an ℓ(2,1)-norm regularization is utilized to couple feature selection across multiple tasks to make use of the shared underlying mechanism among different brain regions. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected SNP predictors relevant to the imaging QTs. AVAILABILITY Software is publicly available at: http://ranger.uta.edu/%7eheng/imaging-genetics/.
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Affiliation(s)
- Hua Wang
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
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Vounou M, Janousova E, Wolz R, Stein JL, Thompson PM, Rueckert D, Montana G. Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease. Neuroimage 2011; 60:700-16. [PMID: 22209813 DOI: 10.1016/j.neuroimage.2011.12.029] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2011] [Revised: 11/18/2011] [Accepted: 12/14/2011] [Indexed: 11/17/2022] Open
Abstract
Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimer's disease (AD). Using a sample from the Alzheimer's Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data re-sampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD.
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Affiliation(s)
- Maria Vounou
- Statistics Section, Department of Mathematics, Imperial College London, UK
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Hibar DP, Kohannim O, Stein JL, Chiang MC, Thompson PM. Multilocus genetic analysis of brain images. Front Genet 2011; 2:73. [PMID: 22303368 PMCID: PMC3268626 DOI: 10.3389/fgene.2011.00073] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2011] [Accepted: 10/03/2011] [Indexed: 01/08/2023] Open
Abstract
The quest to identify genes that influence disease is now being extended to find genes that affect biological markers of disease, or endophenotypes. Brain images, in particular, provide exquisitely detailed measures of anatomy, function, and connectivity in the living brain, and have identified characteristic features for many neurological and psychiatric disorders. The emerging field of imaging genomics is discovering important genetic variants associated with brain structure and function, which in turn influence disease risk and fundamental cognitive processes. Statistical approaches for testing genetic associations are not straightforward to apply to brain images because the data in brain images is spatially complex and generally high dimensional. Neuroimaging phenotypes typically include 3D maps across many points in the brain, fiber tracts, shape-based analyses, and connectivity matrices, or networks. These complex data types require new methods for data reduction and joint consideration of the image and the genome. Image-wide, genome-wide searches are now feasible, but they can be greatly empowered by sparse regression or hierarchical clustering methods that isolate promising features, boosting statistical power. Here we review the evolution of statistical approaches to assess genetic influences on the brain. We outline the current state of multivariate statistics in imaging genomics, and future directions, including meta-analysis. We emphasize the power of novel multivariate approaches to discover reliable genetic influences with small effect sizes.
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Affiliation(s)
- Derrek P. Hibar
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
| | - Omid Kohannim
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
| | - Jason L. Stein
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
| | - Ming-Chang Chiang
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
- Department of Biomedical Engineering, National Yang-Ming UniversityTaipei, Taiwan
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles School of MedicineLos Angeles, CA, USA
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Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects. Neuroimage 2011; 56:1875-91. [PMID: 21497199 DOI: 10.1016/j.neuroimage.2011.03.077] [Citation(s) in RCA: 94] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2010] [Revised: 02/19/2011] [Accepted: 03/28/2011] [Indexed: 12/18/2022] Open
Abstract
Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18,044 genes across 31,662 voxels of the whole brain in 731 elderly subjects (mean age: 75.56±6.82SD years; 430 males) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18,044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.
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