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Xi D, Cui D, Zhang M, Zhang J, Shang M, Guo L, Han J, Du L. Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics. Comput Struct Biotechnol J 2024; 23:3288-3299. [PMID: 39296810 PMCID: PMC11409045 DOI: 10.1016/j.csbj.2024.08.027] [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: 06/30/2024] [Revised: 08/29/2024] [Accepted: 08/29/2024] [Indexed: 09/21/2024] Open
Abstract
Brain imaging genetics is an evolving neuroscience topic aiming to identify genetic variations related to neuroimaging measurements of interest. Traditional linear regression methods have shown success, but their reliance on individual-level imaging and genetic data limits their applicability. Herein, we proposed S-GsMTLR, a group sparse multi-task linear regression method designed to harness summary statistics from genome-wide association studies (GWAS) of neuroimaging quantitative traits. S-GsMTLR directly employs GWAS summary statistics, bypassing the requirement for raw imaging genetic data, and applies multivariate multi-task sparse learning to these univariate GWAS results. It amalgamates the strengths of conventional sparse learning methods, including sophisticated modeling techniques and efficient feature selection. Additionally, we implemented a rapid optimization strategy to alleviate computational burdens by identifying genetic variants associated with phenotypes of interest across the entire chromosome. We first evaluated S-GsMTLR using summary statistics derived from the Alzheimer's Disease Neuroimaging Initiative. The results were remarkably encouraging, demonstrating its comparability to conventional methods in modeling and identification of risk loci. Furthermore, our method was evaluated with two additional GWAS summary statistics datasets: One focused on white matter microstructures and the other on whole brain imaging phenotypes, where the original individual-level data was unavailable. The results not only highlighted S-GsMTLR's ability to pinpoint significant loci but also revealed intriguing structures within genetic variations and loci that went unnoticed by GWAS. These findings suggest that S-GsMTLR is a promising multivariate sparse learning method in brain imaging genetics. It eliminates the need for original individual-level imaging and genetic data while demonstrating commendable modeling and feature selection capabilities.
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Affiliation(s)
- Duo Xi
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Dingnan Cui
- Northwestern Polytechnical University, Xi'an, 710072, China
| | | | - Jin Zhang
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Muheng Shang
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Lei Guo
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Junwei Han
- Northwestern Polytechnical University, Xi'an, 710072, China
| | - Lei Du
- Northwestern Polytechnical University, Xi'an, 710072, China
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Yang R, Han Z, Zhou W, Li X, Zhang X, Zhu L, Wang J, Li X, Zhang CL, Han Y, Li L, Liu S. Population structure and selective signature of Kirghiz sheep by Illumina Ovine SNP50 BeadChip. PeerJ 2024; 12:e17980. [PMID: 39308831 PMCID: PMC11416764 DOI: 10.7717/peerj.17980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 08/06/2024] [Indexed: 09/25/2024] Open
Abstract
Objective By assessing the genetic diversity and associated selective traits of Kirghiz sheep (KIR), we aim to uncover the mechanisms that contribute to sheep's adaptability to the Pamir Plateau environment. Methods This study utilized Illumina Ovine SNP50 BeadChip data from KIR residing in the Pamir Plateau, Qira Black sheep (QBS) inhabiting the Taklamakan Desert, and commonly introduced breeds including Dorper sheep (DOR), Suffolk sheep (SUF), and Hu sheep (HU). The data was analyzed using principal component analysis, phylogenetic analysis, population admixture analysis, kinship matrix analysis, linkage disequilibrium analysis, and selective signature analysis. We employed four methods for selective signature analysis: fixation index (Fst), cross-population extended homozygosity (XP-EHH), integrated haplotype score (iHS), and nucleotide diversity (Pi). These methods aim to uncover the genetic mechanisms underlying the germplasm resources of Kirghiz sheep, enhance their production traits, and explore their adaptation to challenging environmental conditions. Results The test results unveiled potential selective signals associated with adaptive traits and growth characteristics in sheep under harsh environmental conditions, and annotated the corresponding genes accordingly. These genes encompass various functionalities such as adaptations associated with plateau, cold, and arid environment (ETAA1, UBE3D, TLE4, NXPH1, MAT2B, PPARGC1A, VEGFA, TBX15 and PLXNA4), wool traits (LMO3, TRPS1, EPHA5), body size traits (PLXNA2, EFNA5), reproductive traits (PPP3CA, PDHA2, NTRK2), and immunity (GATA3). Conclusion Our study identified candidate genes associated with the production traits and adaptation to the harsh environment of the Pamir Plateau in Kirghiz sheep. These findings provide valuable resources for local sheep breeding programs. The objective of this study is to offer valuable insights for the sustainable development of the Kirghiz sheep industry.
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Affiliation(s)
- Ruizhi Yang
- College of Life Science and Technology, Tarim University, Alar, Xinjiang, China
| | - Zhipeng Han
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
- Xinjiang Production and Construction Corps, Key Laboratory of Tarim Animal Husbandry Science and Technology, Alar, Xinjiang, China
| | - Wen Zhou
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
- Xinjiang Production and Construction Corps, Key Laboratory of Tarim Animal Husbandry Science and Technology, Alar, Xinjiang, China
| | - Xuejiao Li
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
| | - Xuechen Zhang
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
- Xinjiang Production and Construction Corps, Key Laboratory of Tarim Animal Husbandry Science and Technology, Alar, Xinjiang, China
| | - Lijun Zhu
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
- Xinjiang Production and Construction Corps, Key Laboratory of Tarim Animal Husbandry Science and Technology, Alar, Xinjiang, China
| | - Jieru Wang
- College of Life Science and Technology, Tarim University, Alar, Xinjiang, China
| | - Xiaopeng Li
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
| | - Cheng-long Zhang
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
| | - Yahui Han
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
| | - Lianrui Li
- College of Life Science and Technology, Tarim University, Alar, Xinjiang, China
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
- Xinjiang Production and Construction Corps, Key Laboratory of Tarim Animal Husbandry Science and Technology, Alar, Xinjiang, China
- Xinjiang Production and Construction Corps, Engineering Laboratory of Tarim Animal Diseases Diagnosis and Control, Alar, Xinjiang, China
| | - Shudong Liu
- College of Animal Science and Technology, Tarim University, Alar, Xinjiang, China
- Xinjiang Production and Construction Corps, Key Laboratory of Tarim Animal Husbandry Science and Technology, Alar, Xinjiang, China
- Xinjiang Production and Construction Corps, Engineering Laboratory of Tarim Animal Diseases Diagnosis and Control, Alar, Xinjiang, China
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Hsi RS, Zhang S, Triozzi JL, Hung AM, Xu Y, Bejan CA. Evaluation of Genetic Associations with Clinical Phenotypes of Kidney Stone Disease. EUR UROL SUPPL 2024; 67:38-44. [PMID: 39156495 PMCID: PMC11327546 DOI: 10.1016/j.euros.2024.07.109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/10/2024] [Indexed: 08/20/2024] Open
Abstract
Background and objective Previous studies have reported a strong genetic contribution to kidney stone risk. This study aims to identify genetic associations of kidney stone disease within a large-scale electronic health record system. Methods We performed genome-wide association studies (GWASs) for nephrolithiasis from genotyped samples of 5571 cases and 83 692 controls. This analysis included a primary GWAS focused on nephrolithiasis and subsequent subgroup GWASs stratified by stone composition types. For significant risk variants, we performed association analyses with stone composition and first-time 24-h urine parameters. To assess disease severity, we investigated the associations with age at first stone diagnosis, age at first stone-related procedure, and time between first and second stone-related procedures. Key findings and limitations The primary GWAS analysis identified ten significant loci, all located on chromosome 16 within coding regions of the UMOD gene. The strongest signal was rs28544423 (odds ratio 1.17, 95% confidence interval 1.11-1.23, p = 2.7 × 10-9). In subgroup GWASs stratified by six kidney stone composition subtypes, 19 significant loci were identified including two loci in coding regions (brushite; NXPH1, rs79970906 and rs4725104). The UMOD single nucleotide polymorphism rs28544423 was associated with differences in 24-h excretion of urinary analytes, and the minor allele was positively associated with calcium oxalate dihydrate stone composition (p < 0.05). No associations were found between UMOD variants and disease severity. Limitations include an omitted variable bias and a misclassification bias. Conclusions and clinical implications We replicated germline variants associated with kidney stone disease risk at UMOD and reported novel variants associated with stone composition. Genetic variants of UMOD are associated with differences in 24-h urine parameters and stone composition, but not disease severity. Patient summary We identify genetic variants linked to kidney stone disease within an electronic health record (EHR) system. These findings suggest a role for the EHR to enable a precision-medicine approach for stone disease.
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Affiliation(s)
- Ryan S. Hsi
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Siwei Zhang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jefferson L. Triozzi
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adriana M. Hung
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- VA Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA
| | - Cosmin A. Bejan
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA
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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Linchangco GV, Hui Q, Wilson P, Ho YL, Cho K, Arumäe K, Wittemans LBL, Nellåker C, Vainik U, Sun YV, Holmes C, Lindgren CM, Nicholson G. Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records. Nat Commun 2024; 15:5801. [PMID: 38987242 PMCID: PMC11237142 DOI: 10.1038/s41467-024-49998-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/25/2024] [Indexed: 07/12/2024] Open
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.
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Affiliation(s)
- Samvida S Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Habib Ganjgahi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Gregorio V Linchangco
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Peter Wilson
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kadri Arumäe
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Christoffer Nellåker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Uku Vainik
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, University of McGill, Montreal, Canada
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Chris Holmes
- Department of Statistics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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Mu S, Bao J, Xu H, Shivakumar M, Yang S, Ning X, Kim D, Davatzikos C, Shou H, Shen L. Multivariate mediation analysis with voxel-based morphometry revealed the neurodegeneration pathways from genetic variants to Alzheimer's Disease. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:344-353. [PMID: 38827096 PMCID: PMC11141831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Neurodegenerative processes are increasingly recognized as potential causative factors in Alzheimer's disease (AD) pathogenesis. While many studies have leveraged mediation analysis models to elucidate the underlying mechanisms linking genetic variants to AD diagnostic outcomes, the majority have predominantly focused on regional brain measure as a mediator, thereby compromising the granularity of the imaging data. In our investigation, using the imaging genetics data from a landmark AD cohort, we contrasted both region-based and voxel-based brain measurements as imaging endophenotypes, and examined their roles in mediating genetic effects on AD outcomes. Our findings underscored that using voxel-based morphometry offers enhanced statistical power. Moreover, we delineated specific mediation pathways between SNP, brain volume, and AD outcomes, shedding light on the intricate relationship among these variables.
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Affiliation(s)
- Shizhuo Mu
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jingxuan Bao
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hanxiang Xu
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Shu Yang
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xia Ning
- The Ohio State University, Columbus, OH 43210, USA
| | - Dokyoon Kim
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Haochang Shou
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li Shen
- University of Pennsylvania, Philadelphia, PA 19104, USA
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Seo H, Brand L, Wang H. Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline. BMC Med Inform Decis Mak 2024; 24:61. [PMID: 38807132 PMCID: PMC11134626 DOI: 10.1186/s12911-024-02455-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 02/05/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Alzheimer's Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Given that there is currently no cure, it is critical to detect AD in its early stage during the disease progression. Recently, many statistical learning methods have been presented to identify cognitive decline with temporal data, but few of these methods integrate heterogeneous phenotype and genetic information together to improve the accuracy of prediction. In addition, many of these models are often unable to handle incomplete temporal data; this often manifests itself in the removal of records to ensure consistency in the number of records across participants. RESULTS To address these issues, in this work we propose a novel approach to integrate the genetic data and the longitudinal phenotype data to learn a fixed-length "enriched" biomarker representation derived from the temporal heterogeneous neuroimaging records. Armed with this enriched representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD. CONCLUSION The proposed method shows improved prediction performance when applied to data derived from Alzheimer's Disease Neruoimaging Initiative cohort. In addition, our approach can be easily interpreted to allow for the identification and validation of biomarkers associated with cognitive decline.
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Affiliation(s)
- Hoon Seo
- Department of Computer Science, Colorado School of Mines, Golden, Colorado, 80401, USA
| | - Lodewijk Brand
- Department of Computer Science, Colorado School of Mines, Golden, Colorado, 80401, USA
| | - Hua Wang
- Department of Computer Science, Colorado School of Mines, Golden, Colorado, 80401, USA.
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Jiao CN, Shang J, Li F, Cui X, Wang YL, Gao YL, Liu JX. Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases. IEEE J Biomed Health Inform 2024; 28:3029-3041. [PMID: 38427553 DOI: 10.1109/jbhi.2024.3372294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject. Specifically, the upper triangle elements of the functional connectivity matrix are extracted as connectivity features. The clustering coefficient and the average weighted node degree are developed to assess the significance of every brain area. Since the constructed brain network and genetic data are characterized by non-linearity, high-dimensionality, and few subjects, the deep subspace clustering algorithm is proposed to reconstruct the original data. Our multilayer neural network helps capture the non-linear manifolds, and subspace clustering learns pairwise affinities between samples. Moreover, most approaches in neuroimaging genetics are unsupervised learning, neglecting the diagnostic information related to diseases. We presented a label constraint with diagnostic status to instruct the imaging genetics correlation analysis. To this end, a diagnosis-guided deep subspace clustering association (DDSCA) method is developed to discover brain connectome and risk genetic factors by integrating genotypes with functional network phenotypes. Extensive experiments prove that DDSCA achieves superior performance to most association methods and effectively selects disease-relevant genetic markers and brain connectome at the coarse-grained and fine-grained levels.
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Hsi RS, Zhang S, Triozzi JL, Hung AM, Xu Y, Bejan CA. Evaluation of genetic associations with clinical phenotypes of kidney stone disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.18.24301501. [PMID: 38343797 PMCID: PMC10854345 DOI: 10.1101/2024.01.18.24301501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Introduction and Objective We sought to replicate and discover genetic associations of kidney stone disease within a large-scale electronic health record (EHR) system. Methods We performed genome-wide association studies (GWASs) for nephrolithiasis from genotyped samples of 5,571 cases and 83,692 controls. Among the significant risk variants, we performed association analyses of stone composition and first-time 24-hour urine parameters. To assess disease severity, we investigated the associations of risk variants with age at first stone diagnosis, age at first procedure, and time from first to second procedure. Results The main GWAS analysis identified 10 significant loci, each located on chromosome 16 within coding regions of the UMOD gene, which codes for uromodulin, a urine protein with inhibitory activity for calcium crystallization. The strongest signal was from SNP 16:20359633-C-T (odds ratio [OR] 1.17, 95% CI 1.11-1.23), with the remaining significant SNPs having similar effect sizes. In subgroup GWASs by stone composition, 19 significant loci were identified, of which two loci were located in coding regions (brushite; NXPH1 , rs79970906 and rs4725104). The UMOD SNP 16:20359633-C-T was associated with differences in 24-hour excretion of urinary calcium, uric acid, phosphorus, sulfate; and the minor allele was positively associated with calcium oxalate dihydrate stone composition (p<0.05). No associations were found between UMOD variants and disease severity. Conclusions We replicated germline variants associated with kidney stone disease risk at UMOD and reported novel variants associated with stone composition. Genetic variants of UMOD are associated with differences in 24-hour urine parameters and stone composition, but not disease severity.
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Lee BN, Wang J, Hall MA, Kim D, Stites SD, Shen L. Sex modifies effects of imaging and CSF biomarkers on cognitive and functional outcomes: a study of Alzheimer's disease. Neurobiol Aging 2024; 133:67-77. [PMID: 37913627 PMCID: PMC10841593 DOI: 10.1016/j.neurobiolaging.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/03/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory and functional impairments. Two of 3 patients with AD are biologically female; therefore, the biological underpinnings of this diagnosis disparity may inform interventions slowing the AD progression. To bridge this gap, we conducted analyses of 1078 male and female participants from the Alzheimer's Disease Neuroimaging Initiative to examine associations between levels of cerebral spinal fluid (CSF)/neuroimaging biomarkers and cognitive/functional outcomes. The Chow test was used to quantify sex differences by determining if biological sex affects relationships between the studied biomarkers and outcomes. Multiple magnetic resonance imaging (whole brain, entorhinal cortex, middle temporal gyrus, fusiform gyrus, hippocampus), position emission tomography (AV45), and CSF (P-TAU, TAU) biomarkers were differentially associated with cognitive and functional outcomes. Post-hoc bootstrapped and association analyses confirmed these differential effects and emphasized the necessity of using separate, sex-stratified models. The studied imaging/CSF biomarkers may account for some of the sex-based variation in AD pathophysiology. The identified sex-varying relationships between CSF/imaging biomarkers and cognitive/functional outcomes warrant future biological investigation in independent cohorts.
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Affiliation(s)
- Brian N Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA
| | - Junwen Wang
- Department of Health Sciences Research, Mayo Clinic Alix School of Medicine, Phoenix, AZ, USA
| | - Molly A Hall
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA
| | - Shana D Stites
- Department of Psychiatry, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.
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Sha J, Bao J, Liu K, Yang S, Wen Z, Wen J, Cui Y, Tong B, Moore JH, Saykin AJ, Davatzikos C, Long Q, Shen L. Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease. Methods 2023; 218:27-38. [PMID: 37507059 PMCID: PMC10528049 DOI: 10.1016/j.ymeth.2023.07.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/26/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.
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Affiliation(s)
- Jiahang Sha
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Kefei Liu
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215000, China.
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA; Stevens Neuroimaging and Informatics Institute, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA.
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Boning Tong
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA.
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
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Yao Y, Charkraborty D, Zhang L, Shen X, Pan W. Deep causal feature extraction and inference with neuroimaging genetic data. Stat Med 2023; 42:3665-3684. [PMID: 37336556 PMCID: PMC11193942 DOI: 10.1002/sim.9824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 04/04/2023] [Accepted: 05/29/2023] [Indexed: 06/21/2023]
Abstract
Alzheimer's disease (AD) is a severe public health issue in the world. Magnetic Resonance Imaging (MRI) offers a way to study brain differences between AD patients and healthy individuals through feature extraction and comparison. However, in most previous works, the extracted features were not aimed to be causal, hindering biological understanding and interpretation. In order to extract causal features, we propose using instrumental variable (IV) regression with genetic variants as IVs. Specifically, we propose Deep Feature Extraction via Instrumental Variable Regression (DeepFEIVR), which uses a nonlinear neural network to extract causal features from three-dimensional neuroimages to predict an outcome (eg, AD status in our application) while maintaining a linear relationship between the extracted features and IVs. DeepFEIVR not only can handle high dimensional individual-level data for model building, but also is applicable to GWAS summary data to test associations of the extracted features with the outcome in subsequent analysis. In addition, we propose an extension of DeepFEIVR, called DeepFEIVR-CA, for covariate adjustment (CA). We apply DeepFEIVR and DeepFEIVR-CA to the Alzheimer's Disease Neuroimaging Initiative (ADNI) individual-level data as training data for model building, then apply to the UK Biobank neuroimaging and the International Genomics of Alzheimer's Project (IGAP) AD GWAS summary data, showcasing how the extracted causal features are related to AD and various brain endophenotypes.
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Affiliation(s)
- Yuchen Yao
- School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Dipnil Charkraborty
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Lin Zhang
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
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12
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Li M, Larsen PA. Single-cell sequencing of entorhinal cortex reveals widespread disruption of neuropeptide networks in Alzheimer's disease. Alzheimers Dement 2023; 19:3575-3592. [PMID: 36825405 DOI: 10.1002/alz.12979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/12/2023] [Accepted: 01/15/2023] [Indexed: 02/25/2023]
Abstract
INTRODUCTION Abnormalities of neuropeptides (NPs) that play important roles in modulating neuronal activities are commonly observed in Alzheimer's disease (AD). We hypothesize that NP network disruption is widespread in AD brains. METHODS Single-cell transcriptomic data from the entorhinal cortex (EC) were used to investigate the NP network disruption in AD. Bulk RNA-sequencing data generated from the temporal cortex by independent groups and machine learning were employed to identify key NPs involved in AD. The relationship between aging and AD-associated NP (ADNP) expression was studied using GTEx data. RESULTS The proportion of cells expressing NPs but not their receptors decreased significantly in AD. Neurons expressing higher level and greater diversity of NPs were disproportionately absent in AD. Increased age coincides with decreased ADNP expression in the hippocampus. DISCUSSION NP network disruption is widespread in AD EC. Neurons expressing more NPs may be selectively vulnerable to AD. Decreased expression of NPs participates in early AD pathogenesis. We predict that the NP network can be harnessed for treatment and/or early diagnosis of AD.
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Affiliation(s)
- Manci Li
- Department of Veterinary and Biomedical Sciences, University of Minnesota, St. Paul, Minnesota, USA
- Minnesota Center for Prion Research and Outreach, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
| | - Peter A Larsen
- Department of Veterinary and Biomedical Sciences, University of Minnesota, St. Paul, Minnesota, USA
- Minnesota Center for Prion Research and Outreach, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
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13
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Wu S, Venkataraman A, Ghosal S. GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083359 PMCID: PMC11005466 DOI: 10.1109/embc40787.2023.10341000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
We introduce an explainable deep neural architecture that combines brain structure with genetic influence to improve disease severity prediction in Alzheimer's disease. Our framework consists of an encoder, a decoder, and a rank-consistent ordinal regression module. The encoder projects neural imaging and genetics data into a low-dimensional latent space regularized by the decoder. The ordinal regression module guides the feature embedding process to find discriminative patterns representative of disease severity. We also add a learnable dropout layer that learns feature importance and extracts explainable biomarkers from the data. We evaluate our model using structural MRI (sMRI) and Single Nucleotide Polymorphism (SNP) data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In 2-class severity classification comparison, our model has a median F-score of 0.86 (baseline median F-score range: 0.57-0.81). In 3-class classification comparison, our model's median F-score is 0.50 (baseline range: 0.17 - 0.41). In 4-class classification comparison, our model's median F-score is 0.40 (baseline range: 0.14 - 0.39). We demonstrate that our model provides improved disease diagnosis alongside sparse and clinically relevant biomarkers.Clinical relevance-This study provides a deep-learning model that can predict Alzheimer's disease severity levels while identifying consistent and clinically relevant biomarkers.
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Affiliation(s)
- Sarah Wu
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Sayan Ghosal
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
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14
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Beaulac C, Wu S, Gibson E, Miranda MF, Cao J, Rocha L, Beg MF, Nathoo FS. Neuroimaging feature extraction using a neural network classifier for imaging genetics. BMC Bioinformatics 2023; 24:271. [PMID: 37391692 DOI: 10.1186/s12859-023-05394-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 06/21/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. RESULTS We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. CONCLUSIONS The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.
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Affiliation(s)
- Cédric Beaulac
- School of Engineering Science, Simon Fraser University, Burnaby, Canada.
- Department of Mathematics and Statistics, University of Victoria, Victoria, Canada.
| | - Sidi Wu
- Department of Statistics and Actuarial Sciences, Simon Fraser University, Burnaby, Canada
| | - Erin Gibson
- School of Engineering Science, Simon Fraser University, Burnaby, Canada
| | - Michelle F Miranda
- Department of Mathematics and Statistics, University of Victoria, Victoria, Canada
| | - Jiguo Cao
- Department of Statistics and Actuarial Sciences, Simon Fraser University, Burnaby, Canada
| | - Leno Rocha
- Department of Mathematics and Statistics, University of Victoria, Victoria, Canada
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, Canada
| | - Farouk S Nathoo
- Department of Mathematics and Statistics, University of Victoria, Victoria, Canada
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15
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Ali M, Archer DB, Gorijala P, Western D, Timsina J, Fernández MV, Wang TC, Satizabal CL, Yang Q, Beiser AS, Wang R, Chen G, Gordon B, Benzinger TLS, Xiong C, Morris JC, Bateman RJ, Karch CM, McDade E, Goate A, Seshadri S, Mayeux RP, Sperling RA, Buckley RF, Johnson KA, Won HH, Jung SH, Kim HR, Seo SW, Kim HJ, Mormino E, Laws SM, Fan KH, Kamboh MI, Vemuri P, Ramanan VK, Yang HS, Wenzel A, Rajula HSR, Mishra A, Dufouil C, Debette S, Lopez OL, DeKosky ST, Tao F, Nagle MW, Hohman TJ, Sung YJ, Dumitrescu L, Cruchaga C. Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease. Acta Neuropathol Commun 2023; 11:68. [PMID: 37101235 PMCID: PMC10134547 DOI: 10.1186/s40478-023-01563-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.
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Affiliation(s)
- Muhammad Ali
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Daniel Western
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Maria V Fernández
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Ting-Chen Wang
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health, San Antonio, TX, 78229, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | | | - Gengsheng Chen
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | - Brian Gordon
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | - Chengjie Xiong
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Department of Neurology, Washington University, St Louis, MO, USA
| | - Randall J Bateman
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Department of Neurology, Washington University, St Louis, MO, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Celeste M Karch
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
| | - Eric McDade
- Department of Neurology, Washington University, St Louis, MO, USA
| | - Alison Goate
- Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Richard P Mayeux
- The Department of Neurology, Columbia University, New York, NY, USA
| | - Reisa A Sperling
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital and Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel F Buckley
- Brigham and Women's Hospital and Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Keith A Johnson
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hong-Hee Won
- Department of Digital Health, Samsung Medical Center, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Digital Health, Samsung Medical Center, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hang-Rai Kim
- Department of Neurology, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Digital Health, Samsung Medical Center, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Elizabeth Mormino
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, 270 Joondalup Dr, Joondalup, WA, 6027, Australia
| | - Kang-Hsien Fan
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Vijay K Ramanan
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Hyun-Sik Yang
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, USA
| | - Allen Wenzel
- Wisconsin Alzheimer's Institute, Madison, WI, USA
| | - Hema Sekhar Reddy Rajula
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
| | - Aniket Mishra
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
| | - Carole Dufouil
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
| | - Stephanie Debette
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
- Department of Neurology, Boston University School of Medicine, Boston, MA, 2115, USA
- Department of Neurology, CHU de Bordeaux, 33000, Bordeaux, France
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Steven T DeKosky
- Department of Neurology and McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Feifei Tao
- Neurogenomics, Genetics-Guided Dementia Discovery, Eisai, Inc, Cambridge, MA, USA
| | - Michael W Nagle
- Neurogenomics, Genetics-Guided Dementia Discovery, Eisai, Inc, Cambridge, MA, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA.
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA.
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA.
- Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, 63110, USA.
- Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA.
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16
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Moon SW, Zhao L, Matloff W, Hobel S, Berger R, Kwon D, Kim J, Toga AW, Dinov ID. Brain structure and allelic associations in Alzheimer's disease. CNS Neurosci Ther 2023; 29:1034-1048. [PMID: 36575854 PMCID: PMC10018103 DOI: 10.1111/cns.14073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 12/06/2022] [Accepted: 12/11/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic. AIMS This paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations. MATERIALS AND METHODS The participants, ages 65-85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD-associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample-major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta-GWAS study by Jansen and colleagues. RESULTS We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models. DISCUSSION In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM-NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC. CONCLUSION This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.
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Affiliation(s)
- Seok Woo Moon
- Department of Neuropsychiatry, Research Institute of Medical ScienceKonkuk University School of MedicineSeoulKorea
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Lu Zhao
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - William Matloff
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Sam Hobel
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Ryan Berger
- Microbiology & ImmunologyUniversity of MichiganAnn ArborMichiganUSA
| | - Daehong Kwon
- Department of Biomedical Science and EngineeringKonkuk UniversitySeoulKorea
| | - Jaebum Kim
- Department of Biomedical Science and EngineeringKonkuk UniversitySeoulKorea
| | - Arthur W. Toga
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
| | - Ivo D. Dinov
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCCaliforniaLos AngelesUSA
- Department of Health Behavior and Biological Sciences, Statistics Online Computational Resource (SOCR), Michigan Institute for Data Science (MIDAS)University of MichiganAnn ArborMichiganUSA
- Department of StatisticsUniversity of CaliforniaLos AngelesCaliforniaUSA
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17
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Du L, Zhang J, Zhao Y, Shang M, Guo L, Han J. inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:396-413. [PMID: 37442417 PMCID: PMC10634656 DOI: 10.1016/j.gpb.2023.03.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 01/29/2023] [Accepted: 03/14/2023] [Indexed: 07/15/2023]
Abstract
Identifying genetic risk factors for Alzheimer's disease (AD) is an important research topic. To date, different endophenotypes, such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes, have shown the great value in uncovering risk genes compared to case-control studies. Biologically, a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis. However, existing methods mainly focus on the effect of endophenotypes alone; the effect of cross-endophenotype (CEP) associations remains largely unexploited. In this study, we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors, and proposed two integrated multi-task sparse canonical correlation analysis (inMTSCCA) methods, i.e., pairwise endophenotype correlation-guided MTSCCA (pcMTSCCA) and high-order endophenotype correlation-guided MTSCCA (hocMTSCCA). pcMTSCCA employed pairwise correlations between magnetic resonance imaging (MRI)-derived, plasma-derived, and cerebrospinal fluid (CSF)-derived endophenotypes as an additional penalty. hocMTSCCA used high-order correlations among these multi-omic data for regularization. To figure out genetic risk factors at individual and group levels, as well as altered endophenotypic markers, we introduced sparsity-inducing penalties for both models. We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real (consisting of neuroimaging data, proteomic analytes, and genetic data) datasets. The results showed that our methods obtained better or comparable canonical correlation coefficients (CCCs) and better feature subsets than benchmarks. Most importantly, the identified genetic loci and heterogeneous endophenotypic markers showed high relevance. Therefore, jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors. The source code and manual of inMTSCCA are available at https://ngdc.cncb.ac.cn/biocode/tools/BT007330.
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Affiliation(s)
- Lei Du
- Department of Intelligent Science and Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Jin Zhang
- Department of Intelligent Science and Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ying Zhao
- Department of Intelligent Science and Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Muheng Shang
- Department of Intelligent Science and Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Lei Guo
- Department of Intelligent Science and Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Junwei Han
- Department of Intelligent Science and Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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18
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Zhang X, Hao Y, Zhang J, Ji Y, Zou S, Zhao S, Xie S, Du L. A multi-task SCCA method for brain imaging genetics and its application in neurodegenerative diseases. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 232:107450. [PMID: 36905750 DOI: 10.1016/j.cmpb.2023.107450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 02/24/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES In brain imaging genetics, multi-task sparse canonical correlation analysis (MTSCCA) is effective to study the bi-multivariate associations between genetic variations such as single nucleotide polymorphisms (SNPs) and multi-modal imaging quantitative traits (QTs). However, most existing MTSCCA methods are neither supervised nor capable of distinguishing the shared patterns of multi-modal imaging QTs from the specific patterns. METHODS A new diagnosis-guided MTSCCA (DDG-MTSCCA) with parameter decomposition and graph-guided pairwise group lasso penalty was proposed. Specifically, the multi-tasking modeling paradigm enables us to comprehensively identify risk genetic loci by jointly incorporating multi-modal imaging QTs. The regression sub-task was raised to guide the selection of diagnosis-related imaging QTs. To reveal the diverse genetic mechanisms, the parameter decomposition and different constraints were utilized to facilitate the identification of modality-consistent and -specific genotypic variations. Besides, a network constraint was added to find out meaningful brain networks. The proposed method was applied to synthetic data and two real neuroimaging data sets respectively from Alzheimer's disease neuroimaging initiative (ADNI) and Parkinson's progression marker initiative (PPMI) databases. RESULTS Compared with the competitive methods, the proposed method exhibited higher or comparable canonical correlation coefficients (CCCs) and better feature selection results. In particular, in the simulation study, DDG-MTSCCA showed the best anti-noise ability and achieved the highest average hit rate, about 25% higher than MTSCCA. On the real data of Alzheimer's disease (AD) and Parkinson's disease (PD), our method obtained the highest average testing CCCs, about 40% ∼ 50% higher than MTSCCA. Especially, our method could select more comprehensive feature subsets, and the top five SNPs and imaging QTs were all disease-related. The ablation experimental results also demonstrated the significance of each component in the model, i.e., the diagnosis guidance, parameter decomposition, and network constraint. CONCLUSIONS These results on simulated data, ADNI and PPMI cohorts suggested the effectiveness and generalizability of our method in identifying meaningful disease-related markers. DDG-MTSCCA could be a powerful tool in brain imaging genetics, worthy of in-depth study.
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Affiliation(s)
- Xin Zhang
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Yipeng Hao
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Jin Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Yanuo Ji
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Shihong Zou
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Songyun Xie
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Lei Du
- School of Automation, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China.
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19
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Chakraborty D, Zhuang Z, Xue H, Fiecas MB, Shen X, Pan W. Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer's Disease. Genes (Basel) 2023; 14:626. [PMID: 36980898 PMCID: PMC10047952 DOI: 10.3390/genes14030626] [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: 02/09/2023] [Revised: 02/27/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia.
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Affiliation(s)
- Dipnil Chakraborty
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Zhong Zhuang
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Haoran Xue
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mark B. Fiecas
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Xiatong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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20
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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Wittemans LBL, Nellaker C, Holmes C, Lindgren CM, Nicholson G. The genetic architecture of changes in adiposity during adulthood. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.09.23284364. [PMID: 36711652 PMCID: PMC9882550 DOI: 10.1101/2023.01.09.23284364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.
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Affiliation(s)
- Samvida S. Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | | | - Duncan S. Palmer
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, UK
| | - Laura B. L. Wittemans
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Christoffer Nellaker
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Chris Holmes
- Department of Statistics, University of Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M. Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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21
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Huang Z. A Function of Amyloid-β in Mediating Activity-Dependent Axon/Synapse Competition May Unify Its Roles in Brain Physiology and Pathology. J Alzheimers Dis 2023; 92:29-57. [PMID: 36710681 PMCID: PMC10023438 DOI: 10.3233/jad-221042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Amyloid-β protein precursor (AβPP) gives rise to amyloid-β (Aβ), a peptide at the center of Alzheimer's disease (AD). AβPP, however, is also an ancient molecule dating back in evolution to some of the earliest forms of metazoans. This suggests a possible ancestral function that may have been obscured by those that evolve later. Based on literature from the functions of Aβ/AβPP in nervous system development, plasticity, and disease, to those of anti-microbial peptides (AMPs) in bacterial competition as well as mechanisms of cell competition uncovered first by Drosophila genetics, I propose that Aβ/AβPP may be part of an ancient mechanism employed in cell competition, which is subsequently co-opted during evolution for the regulation of activity-dependent neural circuit development and plasticity. This hypothesis is supported by foremost the high similarities of Aβ to AMPs, both of which possess unique, opposite (i.e., trophic versus toxic) activities as monomers and oligomers. A large body of data further suggests that the different Aβ oligomeric isoforms may serve as the protective and punishment signals long predicted to mediate activity-dependent axonal/synaptic competition in the developing nervous system and that the imbalance in their opposite regulation of innate immune and glial cells in the brain may ultimately underpin AD pathogenesis. This hypothesis can not only explain the diverse roles observed of Aβ and AβPP family molecules, but also provide a conceptual framework that can unify current hypotheses on AD. Furthermore, it may explain major clinical observations not accounted for and identify approaches for overcoming shortfalls in AD animal modeling.
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Affiliation(s)
- Zhen Huang
- Departments of Neuroscience and Neurology, University of Wisconsin-Madison, Madison, WI, USA
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22
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Huang P, Zhang M. Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. Neurosci Bull 2023; 39:99-112. [PMID: 35771383 PMCID: PMC9849544 DOI: 10.1007/s12264-022-00905-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/07/2022] [Indexed: 01/22/2023] Open
Abstract
Neurodegenerative diseases (NDs) have become a significant threat to an aging human society. Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers. Magnetic resonance imaging (MRI) is a powerful tool for investigating structural and functional brain alterations in NDs. With the advantages of being non-invasive and non-radioactive, it has been frequently used in both animal research and large-scale clinical investigations. MRI may serve as a bridge connecting micro- and macro-level analysis and promoting bench-to-bed translational research. Nevertheless, due to the abundance and complexity of MRI techniques, exploiting their potential is not always straightforward. This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
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23
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Moon SW. Neuroimaging Genetics and Network Analysis in Alzheimer's Disease. Curr Alzheimer Res 2023; 20:526-538. [PMID: 37957920 DOI: 10.2174/0115672050265188231107072215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/22/2023] [Accepted: 08/13/2023] [Indexed: 11/15/2023]
Abstract
The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG's contribution to expanding our knowledge about the heterogeneity within Alzheimer's disease as well as the benefits of different network analyses.
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Affiliation(s)
- Seok Woo Moon
- Department of Psychiatry, Institute of Medical Science, Konkuk University School of Medicine, Chungju, Republic of Korea
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24
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Vishal K, Bhuiyan P, Qi J, Chen Y, Zhang J, Yang F, Li J. Unraveling the Mechanism of Immunity and Inflammation Related to Molecular Signatures Crosstalk Among Obesity, T2D, and AD: Insights From Bioinformatics Approaches. Bioinform Biol Insights 2023; 17:11779322231167977. [PMID: 37124128 PMCID: PMC10134115 DOI: 10.1177/11779322231167977] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/17/2023] [Indexed: 05/02/2023] Open
Abstract
Individuals with type 2 diabetes (T2D) and obesity have a higher risk of developing Alzheimer disease (AD), and increasing evidence indicates a link between impaired immune signaling pathways and the development of AD. However, the shared cellular mechanisms and molecular signatures among these 3 diseases remain unknown. The purpose of this study was to uncover similar molecular markers and pathways involved in obesity, T2D, and AD using bioinformatics and a network biology approach. First, we investigated the 3 RNA sequencing (RNA-seq) gene expression data sets and determined 224 commonly shared differentially expressed genes (DEGs) from obesity, T2D, and AD diseases. Gene ontology and pathway enrichment analyses revealed that mutual DEGs were mainly enriched with immune and inflammatory signaling pathways. In addition, we constructed a protein-protein interactions network for finding hub genes, which have not previously been identified as playing a critical role in these 3 diseases. Furthermore, the transcriptional factors and protein kinases regulating commonly shared DEGs among obesity, T2D, and AD were also identified. Finally, we suggested potential drug candidates as possible therapeutic interventions for 3 diseases. The results of this bioinformatics analysis provided a new understanding of the potential links between obesity, T2D, and AD pathologies.
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Affiliation(s)
- Kumar Vishal
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China
- Department of Biochemistry & Molecular Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China
| | - Piplu Bhuiyan
- Department of Anesthesiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Junxia Qi
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China
- Department of Biochemistry & Molecular Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China
| | - Yang Chen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China
- Department of Biochemistry & Molecular Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China
| | - Jubiao Zhang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China
- Department of Biochemistry & Molecular Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China
| | - Fen Yang
- Department of Biochemistry & Molecular Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China
| | - Juxue Li
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China
- Department of Biochemistry & Molecular Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Fen Yang, Department of Biochemistry & Molecular Biology, School of Basic Medical Sciences, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing 211166, Jiangsu, China.
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25
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Chen J, Shi B, Li Y, Feng Y, Ni J, Shi J, Luo C, Wang J, Tian J. An AS-qPCR-based method for the detection of Alzheimer's disease-related SNPs. J Cell Biochem 2023; 124:118-126. [PMID: 36436137 DOI: 10.1002/jcb.30350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 11/28/2022]
Abstract
Alzheimer's disease (AD) is one of the most serious neurodegenerative diseases in the world and has a strong genetic predisposition. At present, there is still no effective method for the early diagnosis and prevention of AD. Accumulating evidence shows the association of several loci with AD risk, such as apolipoprotein E (APOE) and translocase of outer mitochondrial membrane 40 (TOMM40). However, for routine disease diagnosis in clinics, genotype detection methods based on gene sequencing technology are time-consuming and excessively costly. Thus, in this study, we developed a high-sensitivity, low-cost, and convenient single nucleotide polymorphism (SNP) detection assay method based on allele-specific quantitative polymerase chain reaction (AS-qPCR) technology, which can be used to determine the SNP genotype in APOE and TOMM40. A total of 40 patients were recruited from the outpatient department of the memory clinic of Dongzhimen Hospital, Beijing University of Chinese Medicine. The SNP detection assay method includes three steps. First, positive plasmids with different genotypes (TT/CC/TC) in APOE rs429358, rs7412, and TOMM40 rs11556505 were prepared. Second, 3'-T/3'-C primers were designed to amplify these positive plasmids for each SNP site. Finally, we calculated the log10 of the copy number ratio for each positive plasmid, and the genotype interpretation interval was established. Based on this method, we investigated whether the SNPs in 40 patients could be accurately calculated using AS-qPCR technology. The accuracy of SNP detection was verified by PCR-Pooling sequencing. The results showed that SNP genotypes assessed by AS-qPCR technology corresponded perfectly to the results obtained by conventional DNA sequencing. We have developed a genotype detection method for AD based on AS-qPCR, which can be performed easily, rapidly, accurately, and at low cost. The method will contribute to the early diagnosis of patients with late-onset Alzheimer's and the detection of large clinical samples in the future.
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Affiliation(s)
- Jing Chen
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
| | - Bingjie Shi
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
| | - Yihao Li
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
| | - Yaru Feng
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
| | - Jingnian Ni
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Jing Shi
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Chenyi Luo
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.,Shenzhen Research Institute of Beijing University of Chinese Medicine, Shenzhen, China
| | - Jianxun Wang
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.,Shenzhen Research Institute of Beijing University of Chinese Medicine, Shenzhen, China
| | - Jinzhou Tian
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
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26
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Sha J, Bao J, Liu K, Yang S, Wen Z, Cui Y, Wen J, Davatzikos C, Moore JH, Saykin AJ, Long Q, Shen L. Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer's Disease. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2022; 2022:541-548. [PMID: 36845995 PMCID: PMC9944667 DOI: 10.1109/bibm55620.2022.9995342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.
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Affiliation(s)
- Jiahang Sha
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Kefei Liu
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Jason H. Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
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27
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Pala D, Lee B, Ning X, Kim D, Shen L. Mediation Analysis and Mixed-Effects Models for the Identification of Stage-specific Imaging Genetics Patterns in Alzheimer's Disease. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2022; 2022:2667-2673. [PMID: 36824222 PMCID: PMC9942815 DOI: 10.1109/bibm55620.2022.9995405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Alzheimer's disease (AD) is one of the most common and severe forms of Senile Dementia. Genome-wide association studies (GWAS) have identified dozens of AD susceptible loci. To better understand potential mechanism-of-action for AD, quantitative brain imaging features have been studied as mediators linking genetic variants to AD outcomes. In this study, Mediation analysis, Chow test and Mixed-effects Models are used to investigate the biological pathways by which genetic variants affect both brain structures/functions and disease diagnosis. We analyzed the imaging and genetics data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project, including a Polygenic Hazard Score (PHS) and 13 imaging quantitative traits (QTs) extracted from the AV45 PET scans quantifying the amyloid deposition in different brain regions of subjects from four separate diagnostic groups. Mediation analysis assessed the mediating effects of image QTs between PHS and diagnosis, whereas Chow test and Linear Mixed-Effects models were used to characterize intra-group differences in the associations between genetic scores and imaging QTs for different disease stages. Results show that promising stage-specific imaging QTs that mediate the genetic effect of the studied PHS on disease status have been identified, providing novel insights into the predictive power of the PHS and the mediating power of amyloid imaging QTs with respect to multiple stages over the AD progression.
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Affiliation(s)
- Daniele Pala
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Brian Lee
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Xia Ning
- Department of Biomedical Informatics, The Ohio State University, Columbus, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
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28
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Ganguly D, Thomas JA, Ali A, Kumar R. Mechanistic and therapeutic implications of EphA-4 receptor tyrosine kinase in the pathogenesis of Alzheimer's disease. Eur J Neurosci 2022; 56:5532-5546. [PMID: 34989046 DOI: 10.1111/ejn.15591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/14/2021] [Accepted: 12/28/2021] [Indexed: 12/14/2022]
Abstract
Erythropoietin-producing hepatoma (Eph) receptors belong to a family of tyrosine kinase receptors that plays a pivotal role in the development of the brain. Eph can be divided broadly into two groups, namely, EphA and EphB, comprising nine and five members, respectively. In recent years, the role of EphA-4 has become increasingly apparent in the onset of Alzheimer's disease (AD). Emerging evidence suggests that EphA-4 results in synaptic dysfunction, which in turn promotes the progression of AD. Moreover, pharmacological or genetic ablation of EphA-4 in the murine model of AD can alleviate the symptoms. The current review summarizes different pathways by which EphA-4 can influence pathogenesis. Since, majority of the studies had reported the protective effect of EphA-4 inhibition during AD, designing therapeutics based on decreasing its enzymatic activity might be necessary for introducing the novel interventions. Therefore, the review described peptide and nanobodies inhibitors of EphA-4 that exhibit the potential to modulate EphA-4 and could be used as lead molecules for the targeted therapy of AD.
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Affiliation(s)
- Devargya Ganguly
- Department of Biotechnology, GITAM Institute of Sciences, GITAM (Deemed to be) University, Vishakhapatnam, India
| | - Joshua Abby Thomas
- Department of Biotechnology, GITAM Institute of Sciences, GITAM (Deemed to be) University, Vishakhapatnam, India
| | - Abid Ali
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Rahul Kumar
- Department of Biotechnology, GITAM Institute of Sciences, GITAM (Deemed to be) University, Vishakhapatnam, India
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29
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Wu R, Bao J, Kim M, Saykin AJ, Moore JH, Shen L. Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns. Genes (Basel) 2022; 13:1520. [PMID: 36140686 PMCID: PMC9498881 DOI: 10.3390/genes13091520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/12/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022] Open
Abstract
Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer's disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP-SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.
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Affiliation(s)
- Ruiming Wu
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jingxuan Bao
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mansu Kim
- The Catholic University of Korea, Seoul 06591, Korea
| | | | | | - Li Shen
- University of Pennsylvania, Philadelphia, PA 19104, USA
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GC-CNNnet: Diagnosis of Alzheimer’s Disease with PET Images Using Genetic and Convolutional Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7413081. [PMID: 35983158 PMCID: PMC9381254 DOI: 10.1155/2022/7413081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 06/01/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022]
Abstract
There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene ε 4 is considered a genetic risk factor for Alzheimer's disease. The purpose of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) have a causal relationship with quantitative PET imaging traits. Additionally, the classification of AD is based on the frequency of brain tissue variations in PET images using a combination of k-nearest-neighbor (KNN), support vector machine (SVM), linear discrimination analysis (LDA), and convolutional neural network (CNN) techniques. According to the results, the suggested SNPs appear to be associated with quantitative traits more strongly than the SNPs in the ApoE genes. Regarding the classification result, the highest accuracy is obtained by the CNN with 91.1%. These results indicate that the KNN and CNN methods are beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with a lower level of accuracy.
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Kim M, Wu R, Yao X, Saykin AJ, Moore JH, Shen L. Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer's disease. BMC Med Genomics 2022; 15:168. [PMID: 35915443 PMCID: PMC9344647 DOI: 10.1186/s12920-022-01323-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 07/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80[Formula: see text]. The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome. RESULTS We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer's Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method. CONCLUSION We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes.
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Affiliation(s)
- Mansu Kim
- Department of Artificial intelligence, Catholic University of Korea, Bucheon, Republic of Korea
| | - Ruiming Wu
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, USA
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Andrew J. Saykin
- Indiana Alzheimer Disease Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, USA
| | - Jason H. Moore
- Department of Computational Biomedicine, Cedars Sinai Medical Center, West Hollywood, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Department of Artificial intelligence, Catholic University of Korea, Bucheon, Republic of Korea
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
- Indiana Alzheimer Disease Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, USA
- Department of Computational Biomedicine, Cedars Sinai Medical Center, West Hollywood, USA
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Feng L, Bi X, Zhang H. Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation. J Am Stat Assoc 2021; 116:144-158. [PMID: 34955572 DOI: 10.1080/01621459.2020.1766468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has been proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of individuals' general intellectual abilities, and develop a class of high-order imaging regression models to identify brain subregions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain subregions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called Internal Variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for Total Variation (TV), which has been considered in the literature to deal with similar problems but is problematic in high order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Then, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including re-orienting, bias-field correcting, extracting, normalizing and registering the magnetic resonance images from 978 individuals. Our analysis identified a subregion across the cingulate cortex and the corpus callosum as being associated with individuals' verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechansims for verbal reasoning.
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Affiliation(s)
- Long Feng
- Department of Biostatistics, Yale University
| | - Xuan Bi
- Information and Decision Sciences, Carlson School of Management, University of Minnesota
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Guan H, Wang C, Tao D. MRI-based Alzheimer's disease prediction via distilling the knowledge in multi-modal data. Neuroimage 2021; 244:118586. [PMID: 34563678 DOI: 10.1016/j.neuroimage.2021.118586] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 09/09/2021] [Accepted: 09/16/2021] [Indexed: 12/14/2022] Open
Abstract
Mild cognitive impairment (MCI) conversion prediction, i.e., identifying MCI patients of high risks converting to Alzheimer's disease (AD), is essential for preventing or slowing the progression of AD. Although previous studies have shown that the fusion of multi-modal data can effectively improve the prediction accuracy, their applications are largely restricted by the limited availability or high cost of multi-modal data. Building an effective prediction model using only magnetic resonance imaging (MRI) remains a challenging research topic. In this work, we propose a multi-modal multi-instance distillation scheme, which aims to distill the knowledge learned from multi-modal data to an MRI-based network for MCI conversion prediction. In contrast to existing distillation algorithms, the proposed multi-instance probabilities demonstrate a superior capability of representing the complicated atrophy distributions, and can guide the MRI-based network to better explore the input MRI. To our best knowledge, this is the first study that attempts to improve an MRI-based prediction model by leveraging extra supervision distilled from multi-modal information. Experiments demonstrate the advantage of our framework, suggesting its potentials in the data-limited clinical settings.
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Affiliation(s)
- Hao Guan
- School of Computer Science, The University of Sydney, Australia
| | - Chaoyue Wang
- School of Computer Science, The University of Sydney, Australia.
| | - Dacheng Tao
- School of Computer Science, The University of Sydney, Australia; JD Explore Academy, China.
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Fu WY, Hung KW, Lau SF, Butt B, Yuen VWH, Fu G, Chan IC, Ip FCF, Fu AKY, Ip NY. Rhynchophylline Administration Ameliorates Amyloid-β Pathology and Inflammation in an Alzheimer's Disease Transgenic Mouse Model. ACS Chem Neurosci 2021; 12:4249-4256. [PMID: 34738783 DOI: 10.1021/acschemneuro.1c00600] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD), the most common neurodegenerative disease, has limited treatment options. As such, extensive studies have been conducted to identify novel therapeutic approaches. We previously reported that rhynchophylline (Rhy), a small molecule EphA4 inhibitor, rescues impaired hippocampal synaptic plasticity and cognitive dysfunctions in APP/PS1 mice, an AD transgenic mouse model. To assess whether Rhy can be developed as an alternative treatment for AD, it is important to examine its pharmacokinetics and effects on other disease-associated pathologies. Here, we show that Rhy ameliorates amyloid plaque burden and reduces inflammation in APP/PS1 mice. Transcriptome analysis revealed that Rhy regulates various molecular pathways in APP/PS1 mouse brains associated with amyloid metabolism and inflammation, specifically the ubiquitin proteasome system, angiogenesis, and microglial functional states. These results show that Rhy, which is blood-brain barrier permeable, is beneficial to amyloid pathology and regulates multiple molecular pathways.
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Affiliation(s)
- Wing-Yu Fu
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong 999077China
| | - Kwok-Wang Hung
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
| | - Shun-Fat Lau
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong 999077China
| | - Busma Butt
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
| | - Vincent Wai-Hin Yuen
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
| | - Guangmiao Fu
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
| | - Ivy C. Chan
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
| | - Fanny C. F. Ip
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong 999077China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen−Hong Kong Institute of Brain Science, Shenzhen, Guangdong 518057, China
| | - Amy K. Y. Fu
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong 999077China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen−Hong Kong Institute of Brain Science, Shenzhen, Guangdong 518057, China
| | - Nancy Y. Ip
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong 999077China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen−Hong Kong Institute of Brain Science, Shenzhen, Guangdong 518057, China
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Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models. Neurobiol Dis 2021; 161:105558. [PMID: 34767943 PMCID: PMC10112395 DOI: 10.1016/j.nbd.2021.105558] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.
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Li L, Huang Y, Han Y, Jiang J. Use of deep learning genomics to discriminate Alzheimer's disease and healthy controls. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5788-5791. [PMID: 34892435 DOI: 10.1109/embc46164.2021.9629983] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Because gene is an important clinical risk factor resulting in AD, genomic studies, such as genome-wide association studies (GWAS), have widely been applied into AD studies. However, main shortcomings of GWAS method were that hereditary deletions were evident in the GWAS studies, which resulted in low classification or prediction abilities by using GWAS analysis. Therefore, this paper proposed a novel deep learning genomics approach and applied it to discriminate AD patients and healthy control (HC) subjects. In this study, we selected genotype data of 988 subjects enrolled in the ADNI, including 622 AD patients and 366 HC subjects. The proposed deep learning genomics (DLG) approach was composed of three steps: quality control, SNP genotype coding, and classification. The Resnet framework was used as the DLG model in this study. In the comparative GWAS analysis, APOE ε4 status and the normalized theta-value of the significant SNP loci were seen as predictors to classify genetically using Support Vector Machine (SVM). All data were divided into one training & validation group and one test group. 5-fold cross-validation was used in 500 times. Finally, we compared the classification results between DLG model and traditional GWAS analysis. As a result, the accuracy, sensitivity, and specificity of classification for traditional GWAS analysis was 71.38%±0.63%, 63.13%±2.87% and 85.59%±6.66% in the test group; while the accuracy, sensitivity, and specificity of classification for DLG model was 92.65%±4.80%, 85.00%±16.25% and 97.10%±4.38% in the test group. Hence, the DLG model can achieve higher accuracy and sensitivity when applied to AD. More importantly, we discovered several novel genetic biomarkers of AD, including rs6311 and rs6313 in HTR2A, and rs690705 in RFC3. The roles of these novel loci in AD should be explored future.
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Vilor-Tejedor N, Garrido-Martín D, Rodriguez-Fernandez B, Lamballais S, Guigó R, Gispert JD. Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO! Comput Struct Biotechnol J 2021; 19:5800-5810. [PMID: 34765095 PMCID: PMC8567328 DOI: 10.1016/j.csbj.2021.10.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/08/2021] [Accepted: 10/12/2021] [Indexed: 12/01/2022] Open
Abstract
Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.
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Affiliation(s)
- Natalia Vilor-Tejedor
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Diego Garrido-Martín
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
| | | | - Sander Lamballais
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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Hu Y, Tan H, Li C, Zhang H. Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method. Genet Epidemiol 2021; 45:710-720. [PMID: 34184773 PMCID: PMC8434958 DOI: 10.1002/gepi.22423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 06/07/2021] [Accepted: 06/17/2021] [Indexed: 02/05/2023]
Abstract
Regional human brain volumes including total area, average thickness, and total volume are heritable and associated with neurological disorders. However, the genetic architecture of brain structure and function is still largely unknown and worthy of exploring. The Pediatric Imaging, Neurocognition, and Genetics (PING) data set provides an excellent resource with genome-wide genetic data and related neuroimaging data. In this study, we perform genome-wide association studies (GWAS) of 315 brain volumetric phenotypes from the PING data set including 1036 samples with 539,865 single-nucleotide polymorphisms (SNPs). We introduce a nonparametric test based on K-sample Ball Divergence (KBD) to identify genetic risk variants that influence regional brain volumes. We carry out simulations to demonstrate that KBD is a powerful test for identifying significant SNPs associated with multivariate phenotypes although controlling the type I error rate. We successfully identify nine SNPs below a significance level of 5 × 10-5 for the PING data. Among the nine identified genetic variants, two SNPs rs486179 and rs562110 are located in the ADRA1A gene that is a well-known risk factor of mental illness, such as schizophrenia and attention deficit hyperactivity disorder. Our study suggests that the nonparametric test KBD is an effective method for identifying genetic variants associated with complex diseases in large-scale GWAS of multiple phenotypes.
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Affiliation(s)
- Yue Hu
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, 06511
| | - Haizhu Tan
- Department of Preventive Medicine, Shantou University Medical College, Xinling Road 22, Shantou, Guangdong, P. R. China
| | - Cai Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, 06511
| | - Heping Zhang
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT, 06511
- Correspondence Author: Heping Zhang, 300 George Street, Ste 523, New Haven, CT, 06511. . Phone: 203-785-5185
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Sheng J, Wang L, Cheng H, Zhang Q, Zhou R, Shi Y. Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease. Neurosci Lett 2021; 762:136147. [PMID: 34332030 DOI: 10.1016/j.neulet.2021.136147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 03/27/2021] [Accepted: 07/26/2021] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.
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Affiliation(s)
- Jinhua Sheng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China.
| | - Luyun Wang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China; College of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou, Zhejiang 310018, China
| | - Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | | | - Rougang Zhou
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China; School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Mstar Technologies Inc., Hangzhou, Zhejiang 310018, China
| | - Yuchen Shi
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China
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Li X, Lin Y, Meng X, Qiu Y, Hu B. An L 0 Regularization Method for Imaging Genetics and Whole Genome Association Analysis on Alzheimer's Disease. IEEE J Biomed Health Inform 2021; 25:3677-3684. [PMID: 34181562 DOI: 10.1109/jbhi.2021.3093027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Although the neuroimaging measures build a bridge between genetic variants and disease phenotypes, an assessment of single nucleotide variants changes in brain structure and their clinically influence on the progression of Alzheimer's disease remain largely preliminary. Note that each variant has very weak correlation signal to neuroimaging measures or Alzheimer's disease phenotypes. Therefore, traditional sparse regression-based image genetics approaches confront with unresolvable features, relative high regression error or inapplicability of high-dimensional data. Adopting an [Formula: see text] regularization method, we significantly elevate the regression accuracy of imaging genetics compared with group-sparse multitask regression method. With further analysis on the simulation results, we conclude that multiple regression tasks model may be unsuitable for image genetics. In addition, we carried out a whole genome association analysis between genetic variants (about 388 million loci) and phenotypes (cognition normal, mild cognitive impairment and Alzheimer's disease) with using the [Formula: see text] regularization method. After annotating the effect of all variants by Ensembl Variant Effect Predictor (VEP), our method locates 33 missense variants which can explain 40% phenotype variance. Then, we mapped each missense variant to the nearest gene and carried out pathway enrichment analysis. The Notch signaling pathway and Apoptosis pathway have been reported to be related to the formation of Alzheimer's disease.
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Li L, Yang Y, Zhang Q, Wang J, Jiang J, Neuroimaging Initiative AD. Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer's Disease or Mild Cognitive Impairment. Behav Neurol 2021; 2021:3359103. [PMID: 34336000 PMCID: PMC8298161 DOI: 10.1155/2021/3359103] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 06/11/2021] [Indexed: 11/26/2022] Open
Abstract
OBJECTIVES Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. METHODS In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. RESULTS We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50%, 98.39% ± 2.50%, and 99.44% ± 1.11% using the DLG model, while 71.38% ± 0.63%, 63.13% ± 2.87%, and 85.59% ± 6.66% using traditional GWAS. Similar results were obtained from the other two intergroup classifications. CONCLUSION The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.
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Affiliation(s)
- Lanlan Li
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Yeying Yang
- LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
| | - Qi Zhang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Jiao Wang
- School of Life Science, Shanghai University, Shanghai 200444, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
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Wang M, Shao W, Hao X, Shen L, Zhang D. Identify Consistent Cross-Modality Imaging Genetic Patterns via Discriminant Sparse Canonical Correlation Analysis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1549-1561. [PMID: 31581090 DOI: 10.1109/tcbb.2019.2944825] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. However, the traditional SCCA algorithm has been designed to seek a linear correlation between the SNP genotype and brain imaging phenotype, ignoring the discriminant similarity information between within-class subjects in brain imaging genetics association analysis. In addition, multi-modality brain imaging phenotypes are extracted from different perspectives and imaging markers from the same region consistently showing up in multimodalities may provide more insights for the mechanistic understanding of diseases. In this paper, a novel multi-modality discriminant SCCA algorithm (MD-SCCA) is proposed to overcome these limitations as well as to improve learning results by incorporating valuable discriminant similarity information into the SCCA algorithm. Specifically, we first extract the discriminant similarity information between within-class subjects by the sparse representation. Second, the discriminant similarity information is enforced within SCCA to construct a discriminant SCCA algorithm (D-SCCA). At last, the MD-SCCA algorithm is adopted to fully explore the relationships among different modalities of different subjects. In experiments, both synthetic dataset and real data from the Alzheimer's Disease Neuroimaging Initiative database are used to test the performance of our algorithm. The empirical results have demonstrated that the proposed algorithm not only produces improved cross-validation performances but also identifies consistent cross-modality imaging genetic biomarkers.
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Zhao L, Batta I, Matloff W, O'Driscoll C, Hobel S, Toga AW. Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies. Neuroinformatics 2021; 19:285-303. [PMID: 32822005 DOI: 10.1007/s12021-020-09486-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In this work, we introduce Neuroimaging PheWAS (phenome-wide association study): a web-based system for searching over a wide variety of brain-wide imaging phenotypes to discover true system-level gene-brain relationships using a unified genotype-to-phenotype strategy. This design features a user-friendly graphical user interface (GUI) for anonymous data uploading, study definition and management, and interactive result visualizations as well as a cloud-based computational infrastructure and multiple state-of-art methods for statistical association analysis and multiple comparison correction. We demonstrated the potential of Neuroimaging PheWAS with a case study analyzing the influences of the apolipoprotein E (APOE) gene on various brain morphological properties across the brain in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Benchmark tests were performed to evaluate the system's performance using data from UK Biobank. The Neuroimaging PheWAS system is freely available. It simplifies the execution of PheWAS on neuroimaging data and provides an opportunity for imaging genetics studies to elucidate routes at play for specific genetic variants on diseases in the context of detailed imaging phenotypic data.
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Affiliation(s)
- Lu Zhao
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Ishaan Batta
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - William Matloff
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Caroline O'Driscoll
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Samuel Hobel
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
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Brain-Specific Gene Expression and Quantitative Traits Association Analysis for Mild Cognitive Impairment. Biomedicines 2021; 9:biomedicines9060658. [PMID: 34201204 PMCID: PMC8229744 DOI: 10.3390/biomedicines9060658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/03/2021] [Accepted: 06/04/2021] [Indexed: 11/30/2022] Open
Abstract
Transcriptome–wide association studies (TWAS) have identified several genes that are associated with qualitative traits. In this work, we performed TWAS using quantitative traits and predicted gene expressions in six brain subcortical structures in 286 mild cognitive impairment (MCI) samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. The six brain subcortical structures were in the limbic region, basal ganglia region, and cerebellum region. We identified 9, 15, and 6 genes that were stably correlated longitudinally with quantitative traits in these three regions, of which 3, 8, and 6 genes have not been reported in previous Alzheimer’s disease (AD) or MCI studies. These genes are potential drug targets for the treatment of early–stage AD. Single–Nucleotide Polymorphism (SNP) analysis results indicated that cis–expression Quantitative Trait Loci (cis–eQTL) SNPs with gene expression predictive abilities may affect the expression of their corresponding genes by specific binding to transcription factors or by modulating promoter and enhancer activities. Further, baseline structure volumes and cis–eQTL SNPs from correlated genes in each region were used to predict the conversion risk of MCI patients. Our results showed that limbic volumes and cis–eQTL SNPs of correlated genes in the limbic region have effective predictive abilities.
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Wang M, Shao W, Hao X, Zhang D. Identify Complex Imaging Genetic Patterns via Fusion Self-Expressive Network Analysis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1673-1686. [PMID: 33661732 DOI: 10.1109/tmi.2021.3063785] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In the brain imaging genetic studies, it is a challenging task to estimate the association between quantitative traits (QTs) extracted from neuroimaging data and genetic markers such as single-nucleotide polymorphisms (SNPs). Most of the existing association studies are based on the extensions of sparse canonical correlation analysis (SCCA) for the identification of complex bi-multivariate associations, which can take the specific structure and group information into consideration. However, they often take the original data as input without considering its underlying complex multi-subspace structure, which will deteriorate the performance of the following integrative analysis. Accordingly, in this paper, the self-expressive property is exploited for the reconstruction of the original data before the association analysis, which can well describe the similarity structure. Specifically, we first apply the within-class similarity information to construct self-expressive networks by sparse representation. Then, we use the fusion method to iteratively fuse the self-expressive networks from multi-modality brain phenotypes into one network. Finally, we calculate the imaging genetic association based on the fused self-expressive network. We conduct the experiments on both single-modality and multi-modality phenotype data. Related experimental results validate that our method can not only better estimate the potential association between genetic markers and quantitative traits but also identify consistent multi-modality imaging genetic biomarkers to guide the interpretation of Alzheimer's disease.
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Li Y, Haber A, Preuss C, John C, Uyar A, Yang HS, Logsdon BA, Philip V, Karuturi RKM, Carter GW. Transfer learning-trained convolutional neural networks identify novel MRI biomarkers of Alzheimer's disease progression. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12140. [PMID: 34027015 PMCID: PMC8120261 DOI: 10.1002/dad2.12140] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 11/09/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Genome-wide association studies (GWAS) for late onset Alzheimer's disease (AD) may miss genetic variants relevant for delineating disease stages when using clinically defined case/control as a phenotype due to its loose definition and heterogeneity. METHODS We use a transfer learning technique to train three-dimensional convolutional neural network (CNN) models based on structural magnetic resonance imaging (MRI) from the screening stage in the Alzheimer's Disease Neuroimaging Initiative consortium to derive image features that reflect AD progression. RESULTS CNN-derived image phenotypes are significantly associated with fasting metabolites related to early lipid metabolic changes as well as insulin resistance and with genetic variants mapped to candidate genes enriched for amyloid beta degradation, tau phosphorylation, calcium ion binding-dependent synaptic loss, APP-regulated inflammation response, and insulin resistance. DISCUSSION This is the first attempt to show that non-invasive MRI biomarkers are linked to AD progression characteristics, reinforcing their use in early AD diagnosis and monitoring.
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Affiliation(s)
- Yi Li
- The Jackson LaboratoryFarmingtonConnecticutUSA
| | - Annat Haber
- The Jackson LaboratoryFarmingtonConnecticutUSA
| | | | - Cai John
- The Jackson LaboratoryFarmingtonConnecticutUSA
| | - Asli Uyar
- The Jackson LaboratoryFarmingtonConnecticutUSA
| | | | | | | | | | - Gregory W. Carter
- The Jackson LaboratoryFarmingtonConnecticutUSA
- The Jackson LaboratoryBar HarborMaineUSA
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Li J, Liu W, Li H, Chen F, Luo H, Bao P, Li Y, Jiang H, Gao Y, Liang H, Fang S. Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage. BMC Bioinformatics 2021; 22:223. [PMID: 33931008 PMCID: PMC8086096 DOI: 10.1186/s12859-021-04145-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 04/21/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Brain image genetics provides enormous opportunities for examining the effects of genetic variations on the brain. Many studies have shown that the structure, function, and abnormality (e.g., those related to Alzheimer's disease) of the brain are heritable. However, which genetic variations contribute to these phenotypic changes is not completely clear. Advances in neuroimaging and genetics have led us to obtain detailed brain anatomy and genome-wide information. These data offer us new opportunities to identify genetic variations such as single nucleotide polymorphisms (SNPs) that affect brain structure. In this paper, we perform a genome-wide variant-based study, and aim to identify top SNPs or SNP sets which have genetic effects with the largest neuroanotomic coverage at both voxel and region-of-interest (ROI) levels. Based on the voxelwise genome-wide association study (GWAS) results, we used the exhaustive search to find the top SNPs or SNP sets that have the largest voxel-based or ROI-based neuroanatomic coverage. For SNP sets with >2 SNPs, we proposed an efficient genetic algorithm to identify top SNP sets that can cover all ROIs or a specific ROI. RESULTS We identified an ensemble of top SNPs, SNP-pairs and SNP-sets, whose effects have the largest neuroanatomic coverage. Experimental results on real imaging genetics data show that the proposed genetic algorithm is superior to the exhaustive search in terms of computational time for identifying top SNP-sets. CONCLUSIONS We proposed and applied an informatics strategy to identify top SNPs, SNP-pairs and SNP-sets that have genetic effects with the largest neuroanatomic coverage. The proposed genetic algorithm offers an efficient solution to accomplish the task, especially for identifying top SNP-sets.
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Affiliation(s)
- Jin Li
- College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001 China
| | - Wenjie Liu
- College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001 China
| | - Huang Li
- Computer and Information Science, IUPUI, 723 W Michigan St, Indianapolis, IN 46202 USA
| | - Feng Chen
- College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001 China
| | - Haoran Luo
- College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001 China
| | - Peihua Bao
- College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001 China
| | - Yanzhao Li
- College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001 China
| | - Hailong Jiang
- College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001 China
| | - Yue Gao
- College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001 China
| | - Hong Liang
- College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001 China
| | - Shiaofen Fang
- Computer and Information Science, IUPUI, 723 W Michigan St, Indianapolis, IN 46202 USA
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Vogrinc D, Goričar K, Dolžan V. Genetic Variability in Molecular Pathways Implicated in Alzheimer's Disease: A Comprehensive Review. Front Aging Neurosci 2021; 13:646901. [PMID: 33815092 PMCID: PMC8012500 DOI: 10.3389/fnagi.2021.646901] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/16/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting a significant part of the population. The majority of AD cases occur in the elderly with a typical age of onset of the disease above 65 years. AD presents a major burden for the healthcare system and since population is rapidly aging, the burden of the disease will increase in the future. However, no effective drug treatment for a full-blown disease has been developed to date. The genetic background of AD is extensively studied; numerous genome-wide association studies (GWAS) identified significant genes associated with increased risk of AD development. This review summarizes more than 100 risk loci. Many of them may serve as biomarkers of AD progression, even in the preclinical stage of the disease. Furthermore, we used GWAS data to identify key pathways of AD pathogenesis: cellular processes, metabolic processes, biological regulation, localization, transport, regulation of cellular processes, and neurological system processes. Gene clustering into molecular pathways can provide background for identification of novel molecular targets and may support the development of tailored and personalized treatment of AD.
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Affiliation(s)
| | | | - Vita Dolžan
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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Lu L, Elbeleidy S, Baker LZ, Wang H, Nie F. Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:891-904. [PMID: 33253116 DOI: 10.1109/tmi.2020.3041227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A critical challenge in using longitudinal neuroimaging data to study the progressions of Alzheimer's Disease (AD) is the varied number of missing records of the patients during the course when AD develops. To tackle this problem, in this paper we propose a novel formulation to learn an enriched representation with fixed length for imaging biomarkers, which aims to simultaneously capture the information conveyed by both baseline neuroimaging record and progressive variations characterized by varied counts of available follow-up records over time. Because the learned biomarker representations are a set of fixed-length vectors, they can be readily used by traditional machine learning models to study AD developments. Take into account that the missing brain scans are not aligned in terms of time in a studied cohort, we develop a new objective that maximizes the ratio of the summations of a number of l1 -norm distances for improved robustness, which, though, is difficult to efficiently solve in general. Thus, we derive a new efficient and non-greedy iterative solution algorithm and rigorously prove its convergence. We have performed extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. A clear performance gain has been achieved in predicting ten different cognitive scores when we compare the original baseline biomarker representations against the learned representations with longitudinal enrichments. We further observe that the top selected biomarkers by our new method are in accordance with known knowledge in AD studies. These promising results have demonstrated improved performances of our new method that validate its effectiveness.
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Gui W, Qiu C, Shao Q, Li J. Associations of Vascular Risk Factors, APOE and TOMM40 Polymorphisms With Cognitive Function in Dementia-Free Chinese Older Adults: A Community-Based Study. Front Psychiatry 2021; 12:617773. [PMID: 33790814 PMCID: PMC8005534 DOI: 10.3389/fpsyt.2021.617773] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 02/01/2021] [Indexed: 12/14/2022] Open
Abstract
Objective: The associations of vascular risk factors (VRFs), apolipoprotein E (APOE), and translocase of outer mitochondrial membrane 40 (TOMM40) with cognitive function have been investigated mostly in western societies. In the present study, we sought to examine the associations of VRFs [i.e., current smoking, current drinking, physical inactivity, obesity, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), diabetes, and hypertension] and variants located in APOE (ε2/3/4) and TOMM40 (rs2075650) with global cognitive function in Chinese older adults, with a focus on their potential interactions. Methods: This is a cross-sectional study that included 422 permanent residents (mean age 69.2 years, 54.3% female) living in Beijing, who were free of dementia. Data were collected through interviews, clinical examinations, and laboratory tests. The two genetic polymorphisms were genotyped, and participants were dichotomized as carriers vs. non-carriers of APOE ε4 or TOMM40 G. Global cognitive function was assessed with the Mini-Mental State Examination (MMSE). Data were analyzed with multivariable linear regression models. Results: Physical inactivity and diabetes were independently associated with a lower MMSE score (all p < 0.05). When four putative VRFs (i.e., current smoking, physical inactivity, high LDL-C, and diabetes) were aggregated, an increasing number of having these factors was associated with a decreasing MMSE score in a dose-response manner (p = 0.001). TOMM40 polymorphisms, independent of the APOE ε4 allele, interacted with aggregated VRFs to influence cognitive performance, such that having one or more of these VRFs was particularly detrimental to the cognition of TOMM40 carriers. Further analyses revealed interactions of the TOMM40 polymorphism with (i) physical inactivity and (ii) diabetes, such that having either physical inactivity or diabetes in combination with carrying a TOMM40 G allele, compared to having neither, was significantly associated with a markedly lower MMSE score (all p < 0.05). Conclusion: This study provides some evidence supporting the association of vascular risk factors with poor cognitive performance among dementia-free Chinese older adults and further revealed their interactions with the TOMM40 polymorphism. The results underscore the vulnerability of global cognitive function to VRFs, which could be reinforced by carrying the TOMM40 rs2075650 G allele. These findings have potential implications for developing tailored intervention programs to maintain cognitive function.
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Affiliation(s)
- Wenjun Gui
- CAS Key Laboratory of Mental Health, Center on Aging Psychology, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Chengxuan Qiu
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China.,Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Qi Shao
- CAS Key Laboratory of Mental Health, Center on Aging Psychology, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Juan Li
- CAS Key Laboratory of Mental Health, Center on Aging Psychology, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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