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Hou B, Wen Z, Bao J, Zhang R, Tong B, Yang S, Wen J, Cui Y, Moore JH, Saykin AJ, Huang H, Thompson PM, Ritchie MD, Davatzikos C, Shen L. Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery. Med Image Anal 2024; 97:103231. [PMID: 38941858 DOI: 10.1016/j.media.2024.103231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 05/04/2024] [Accepted: 06/03/2024] [Indexed: 06/30/2024]
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.
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Affiliation(s)
- Bojian Hou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Richard Zhang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Boning Tong
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Junhao Wen
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90007, USA
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA 90069, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Heng Huang
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA
| | - Paul M Thompson
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90007, USA
| | - Marylyn D Ritchie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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Wu R, He B, Hou B, Saykin AJ, Yan J, Shen L. Cluster Analysis of Cortical Amyloid Burden for Identifying Imaging-driven Subtypes in Mild Cognitive Impairment. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:439-448. [PMID: 38827045 PMCID: PMC11141862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Over the past decade, Alzheimer's disease (AD) has become increasingly severe and gained greater attention. Mild Cognitive Impairment (MCI) serves as an important prodromal stage of AD, highlighting the urgency of early diagnosis for timely treatment and control of the condition. Identifying the subtypes of MCI patients exhibits importance for dissecting the heterogeneity of this complex disorder and facilitating more effective target discovery and therapeutic development. Conventional method uses clinical measurements such as cognitive score and neurophysical assessment to stratify MCI patients into two groups with early MCI (EMCI) and late MCI (LMCI), which shows their progressive stages. However, such clinical method is not designed to de-convolute the heterogeneity of the disorder. This study uses a data-driven approach to divide MCI patients into a novel grouping of two subtypes based on an amyloid dataset of 68 cortical features from positron emission tomography (PET), where each subtype has a homogeneous cortical amyloid burden pattern. Experimental evaluation including visual two-dimensional cluster distribution, Kaplan-Meier plot, genetic association studies, and biomarker distribution analysis demonstrates that the identified subtypes performs better across all metrics than the conventional EMCI and LMCI grouping.
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Affiliation(s)
- Ruiming Wu
- University of Pennsylvania, Philadelphia, PA
| | - Bing He
- Indiana University, Indianapolis, IN
| | - Bojian Hou
- University of Pennsylvania, Philadelphia, PA
| | | | | | - Li Shen
- University of Pennsylvania, Philadelphia, PA
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3
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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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4
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Wang KW, Yuan YX, Zhu B, Zhang Y, Wei YF, Meng FS, Zhang S, Wang JX, Zhou JY. X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study. Front Aging Neurosci 2023; 15:1277731. [PMID: 38035272 PMCID: PMC10682795 DOI: 10.3389/fnagi.2023.1277731] [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] [Received: 08/15/2023] [Accepted: 10/20/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a complex neurodegenerative disease with high heritability. Compared to autosomes, a higher proportion of disorder-associated genes on X chromosome are expressed in the brain. However, only a few studies focused on the identification of the susceptibility loci for AD on X chromosome. Methods Using the data from the Alzheimer's Disease Neuroimaging Initiative Study, we conducted an X chromosome-wide association study between 16 AD quantitative biomarkers and 19,692 single nucleotide polymorphisms (SNPs) based on both the cross-sectional and longitudinal studies. Results We identified 15 SNPs statistically significantly associated with different quantitative biomarkers of the AD. For the cross-sectional study, six SNPs (rs5927116, rs4596772, rs5929538, rs2213488, rs5920524, and rs5945306) are located in or near to six genes DMD, TBX22, LOC101928437, TENM1, SPANXN1, and ZFP92, which have been reported to be associated with schizophrenia or neuropsychiatric diseases in literature. For the longitudinal study, four SNPs (rs4829868, rs5931111, rs6540385, and rs763320) are included in or near to two genes RAC1P4 and AFF2, which have been demonstrated to be associated with brain development or intellectual disability in literature, while the functional annotations of other five novel SNPs (rs12157031, rs428303, rs5953487, rs10284107, and rs5955016) have not been found. Discussion 15 SNPs were found statistically significantly associated with the quantitative biomarkers of the AD. Follow-up study in molecular genetics is needed to verify whether they are indeed related to AD. The findings in this article expand our understanding of the role of the X chromosome in exploring disease susceptibility, introduce new insights into the molecular genetics behind the AD, and may provide a mechanistic clue to further AD-related studies.
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Affiliation(s)
- Kai-Wen Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yu-Xin Yuan
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Bin Zhu
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi-Fang Wei
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Fan-Shuo Meng
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Shun Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jing-Xuan Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ji-Yuan Zhou
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
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5
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Liu Y, Tian J. Neuroprotective factors affect the progression of Alzheimer's disease. Biochem Biophys Res Commun 2023; 681:276-282. [PMID: 37797415 DOI: 10.1016/j.bbrc.2023.09.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/05/2023] [Accepted: 09/18/2023] [Indexed: 10/07/2023]
Abstract
Alzheimer's disease(AD) is a neurodegenerative disease that occurs mostly in the elderly and is characterized by chronic progressive cognitive dysfunction, which seriously threatens the health and life-quality of patients. Alterations at the molecular level, which causes pathological changes of AD brain, have impacted the progression of AD. In this review, we illustrate the recent evidence of the alteration of neuroprotective proteins in AD, such as changes in their contents and variants. Furthermore, we elucidate the single nucleotide polymorphism (SNP) and gene changes. Finally, we highlight the epigenetic changes in AD, which helps to display the characteristics of the disease and provides guidance regarding research hot spots in the field against AD.
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Affiliation(s)
- Yan Liu
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, China
| | - Jinzhou Tian
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, China.
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6
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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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7
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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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8
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Tarzanagh DA, Hou B, Tong B, Long Q, Shen L. Fairness-Aware Class Imbalanced Learning on Multiple Subgroups. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2023; 216:2123-2133. [PMID: 38601022 PMCID: PMC11003754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
We present a novel Bayesian-based optimization framework that addresses the challenge of generalization in overparameterized models when dealing with imbalanced subgroups and limited samples per subgroup. Our proposed tri-level optimization framework utilizes local predictors, which are trained on a small amount of data, as well as a fair and class-balanced predictor at the middle and lower levels. To effectively overcome saddle points for minority classes, our lower-level formulation incorporates sharpness-aware minimization. Meanwhile, at the upper level, the framework dynamically adjusts the loss function based on validation loss, ensuring a close alignment between the global predictor and local predictors. Theoretical analysis demonstrates the framework's ability to enhance classification and fairness generalization, potentially resulting in improvements in the generalization bound. Empirical results validate the superior performance of our tri-level framework compared to existing state-of-the-art approaches. The source code can be found at https://github.com/PennShenLab/FACIMS.
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Veteleanu A, Stevenson-Hoare J, Keat S, Daskoulidou N, Zetterberg H, Heslegrave A, Escott-Price V, Williams J, Sims R, Zelek WM, Carpanini SM, Morgan BP. Alzheimer's disease-associated complement gene variants influence plasma complement protein levels. J Neuroinflammation 2023; 20:169. [PMID: 37480051 PMCID: PMC10362776 DOI: 10.1186/s12974-023-02850-6] [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: 04/17/2023] [Accepted: 07/08/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) has been associated with immune dysregulation in biomarker and genome-wide association studies (GWAS). GWAS hits include the genes encoding complement regulators clusterin (CLU) and complement receptor 1 (CR1), recognised as key players in AD pathology, and complement proteins have been proposed as biomarkers. MAIN BODY To address whether changes in plasma complement protein levels in AD relate to AD-associated complement gene variants we first measured relevant plasma complement proteins (clusterin, C1q, C1s, CR1, factor H) in a large cohort comprising early onset AD (EOAD; n = 912), late onset AD (LOAD; n = 492) and control (n = 504) donors. Clusterin and C1q were significantly increased (p < 0.001) and sCR1 and factor H reduced (p < 0.01) in AD plasma versus controls. ROC analyses were performed to assess utility of the measured complement biomarkers, alone or in combination with amyloid beta, in predicting AD. C1q was the most predictive single complement biomarker (AUC 0.655 LOAD, 0.601 EOAD); combining C1q with other complement or neurodegeneration makers through stepAIC-informed models improved predictive values slightly. Effects of GWS SNPs (rs6656401, rs6691117 in CR1; rs11136000, rs9331888 in CLU; rs3919533 in C1S) on protein concentrations were assessed by comparing protein levels in carriers of the minor vs major allele. To identify new associations between SNPs and changes in plasma protein levels, we performed a GWAS combining genotyping data in the cohort with complement protein levels as endophenotype. SNPs in CR1 (rs6656401), C1S (rs3919533) and CFH (rs6664877) reached significance and influenced plasma levels of the corresponding protein, whereas SNPs in CLU did not influence clusterin levels. CONCLUSION Complement dysregulation is evident in AD and may contribute to pathology. AD-associated SNPs in CR1, C1S and CFH impact plasma levels of the encoded proteins, suggesting a mechanism for impact on disease risk.
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Affiliation(s)
- Aurora Veteleanu
- UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ UK
| | | | - Samuel Keat
- UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ UK
| | - Nikoleta Daskoulidou
- UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ UK
| | - Henrik Zetterberg
- UK Dementia Research Institute at University College London, London, WC1E6BT UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Psychology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1N3BG UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Amanda Heslegrave
- UK Dementia Research Institute at University College London, London, WC1E6BT UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1N3BG UK
| | | | - Julie Williams
- UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ UK
| | - Rebecca Sims
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, CF244HQ UK
| | - Wioleta M. Zelek
- UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ UK
| | - Sarah M. Carpanini
- UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ UK
| | - Bryan Paul Morgan
- UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ UK
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Tong B, Risacher SL, Bao J, Feng Y, Wang X, Ritchie MD, Moore JH, Urbanowicz R, Saykin AJ, Shen L. Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2023; 2023:525-533. [PMID: 37350880 PMCID: PMC10283108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Amyloid imaging has been widely used in Alzheimer's disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.
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Affiliation(s)
- Boning Tong
- University of Pennsylvania, Philadelphia, PA
| | | | | | - Yanbo Feng
- University of Pennsylvania, Philadelphia, PA
| | - Xinkai Wang
- University of Pennsylvania, Philadelphia, PA
| | | | | | | | | | - Li Shen
- University of Pennsylvania, Philadelphia, PA
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11
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Wang X, Feng Y, Tong B, Bao J, Ritchie MD, Saykin AJ, Moore JH, Urbanowicz R, Shen L. Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2023; 2023:544-553. [PMID: 37350896 PMCID: PMC10283099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
STREAMLINE is a simple, transparent, end-to-end automated machine learning (AutoML) pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The initial version is limited to binary classification. In this work, we extend STREAMLINE through implementing multiple regression-based ML models, including linear regression, elastic net, group lasso, and L21 norm. We demonstrate the effectiveness of the regression version of STREAMLINE by applying it to the prediction of Alzheimer's disease (AD) cognitive outcomes using multimodal brain imaging data. Our empirical results demonstrate the feasibility and effectiveness of the newly expanded STREAMLINE as an AutoML pipeline for evaluating AD regression models, and for discovering multimodal imaging biomarkers.
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Affiliation(s)
- Xinkai Wang
- University of Pennsylvania, Philadelphia, PA
| | - Yanbo Feng
- University of Pennsylvania, Philadelphia, PA
| | - Boning Tong
- University of Pennsylvania, Philadelphia, PA
| | | | | | | | | | | | - Li Shen
- University of Pennsylvania, Philadelphia, PA
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12
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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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13
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Bao J, Chang C, Zhang Q, Saykin AJ, Shen L, Long Q. Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis. Brief Bioinform 2023; 24:bbad073. [PMID: 36882008 PMCID: PMC10387302 DOI: 10.1093/bib/bbad073] [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: 10/31/2022] [Revised: 01/14/2023] [Accepted: 02/10/2023] [Indexed: 03/09/2023] Open
Abstract
MOTIVATION With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way. METHOD Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods. RESULTS We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models. AVAILABILITY Code are publicly available at https://github.com/JingxuanBao/SBFA. CONTACT qlong@upenn.edu.
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Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
| | - Changgee Chang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
| | - Qiyiwen Zhang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, 46202, IN, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, PA, USA
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14
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Janakiraman V, Sudhan M, Patil S, Alzahrani KJ, Alzahrani FM, Halawani IF, Ahmed SSSJ. Rheumatoid arthritis treatment with zoledronic acid, a potentialinhibitorofGWAS-derived pharmacogenetics STAT3 and IL2 targets. Gene 2023; 866:147338. [PMID: 36889532 DOI: 10.1016/j.gene.2023.147338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/17/2023] [Accepted: 03/01/2023] [Indexed: 03/08/2023]
Abstract
Rheumatoid arthritis (RA) is an inflammatory condition that primarily affects the joints and progress to affect other vital organs. Variety of drugs are being recommended to control the disease progression that benefits patients to perform day-to-day activities. Few of these RA drugs have noticeable side effects; therefore, it's crucial to choose the appropriate drug for treating RA with an understanding of the disease's pathophysiology. Herein, we investigated the RA genes from GWAS data to construct protein-protein interaction (PPI) network and to define appropriate drug targets for RA. The predicted drug targets were screened with the known RA drugs based on molecular docking. Further, the molecular dynamics simulations were performed to comprehend the conformational changes and stability of the targets upon binding of the selected top ranked RA drug. As a result, our constructed protein network from GWAS data revealed, STAT3 and IL2 could be potential pharmacogenetics targets that interlink most of the RA genes encoding proteins. These interlinked proteins of both the targets showed involvement in cell signaling, immune response, and TNF signaling pathway. Among the 192 RA drugs investigated, zoledronic acid had the lowest binding energy that inhibit both STAT3 (-6.307 kcal/mol) and IL2 (-6.231 kcal/mol). Additionally, STAT3 and IL2 trajectories on zoledronic acid binding exhibit notable differences in MD simulations as compared to a drug-free environment. Also, the in vitro assessment with the zoledronic acid confirms the outcome of our computational study. Overall, our study identify zoledronic acid could be potential inhibitor against these targets, that will benefits patients with RA. Comparative efficiency assessments between the RA drugs through clinical trials are needed to validate our findings in the treatment of RA.
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Affiliation(s)
- V Janakiraman
- Drug Discovery and Multi-omics Laboratory, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education, Chettinad Hospital and Research Institute, Kelambakkam 603103, Tamil Nadu, India
| | - M Sudhan
- Drug Discovery and Multi-omics Laboratory, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education, Chettinad Hospital and Research Institute, Kelambakkam 603103, Tamil Nadu, India
| | - Shankargouda Patil
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA
| | - Khalid J Alzahrani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Fuad M Alzahrani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ibrahim F Halawani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Shiek S S J Ahmed
- Drug Discovery and Multi-omics Laboratory, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education, Chettinad Hospital and Research Institute, Kelambakkam 603103, Tamil Nadu, India.
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15
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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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16
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Spanbauer C, Pan W. Sparse prediction informed by genetic annotations using the logit normal prior for Bayesian regression tree ensembles. Genet Epidemiol 2023; 47:26-44. [PMID: 36349692 PMCID: PMC9892284 DOI: 10.1002/gepi.22505] [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: 06/15/2022] [Revised: 09/08/2022] [Accepted: 09/21/2022] [Indexed: 11/11/2022]
Abstract
Using high-dimensional genetic variants such as single nucleotide polymorphisms (SNP) to predict complex diseases and traits has important applications in basic research and other clinical settings. For example, predicting gene expression is a necessary first step to identify (putative) causal genes in transcriptome-wide association studies. Due to weak signals, high-dimensionality, and linkage disequilibrium (correlation) among SNPs, building such a prediction model is challenging. However, functional annotations at the SNP level (e.g., as epigenomic data across multiple cell- or tissue-types) are available and could be used to inform predictor importance and aid in outcome prediction. Existing approaches to incorporate annotations have been based mainly on (generalized) linear models. Bayesian additive regression trees (BART), in contrast, is a reliable method to obtain high-quality nonlinear out of sample predictions without overfitting. Unfortunately, the default prior from BART may be too inflexible to handle sparse situations where the number of predictors approaches or surpasses the number of observations. Motivated by our real data application, this article proposes an alternative prior based on the logit normal distribution because it provides a framework that is adaptive to sparsity and can model informative functional annotations. It also provides a framework to incorporate prior information about the between SNP correlations. Computational details for carrying out inference are presented along with the results from a simulation study and a genome-wide prediction analysis of the Alzheimer's Disease Neuroimaging Initiative data.
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Affiliation(s)
- Charles Spanbauer
- Division of Biostatistics, University of Minnesota, MN, USA,Corresponding author;
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, MN, USA
| | - The Alzheimer’s Disease Neuroimaging Initiative
- Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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17
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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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18
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He R, Xue H, Pan W. Statistical power of transcriptome-wide association studies. Genet Epidemiol 2022; 46:572-588. [PMID: 35766062 PMCID: PMC9669108 DOI: 10.1002/gepi.22491] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 01/02/2023]
Abstract
Transcriptome-Wide Association Studies (TWASs) have become increasingly popular in identifying genes (or other endophenotypes or exposures) associated with complex traits. In TWAS, one first builds a predictive model for gene expressions using an expression quantitative trait loci (eQTL) data set in stage 1, then tests the association between the predicted gene expression and a trait based on a large, independent genome-wide association study (GWAS) data set in stage 2. However, since the sample size of the eQTL data set is usually small and the coefficient of multiple determination (i.e.,R 2 ${R}^{2}$ ) of the model for many genes is also small, a question of interest is to what extent these factors affect the statistical power of TWAS. In addition, in contrast to a standard (univariate) TWAS (UV-TWAS) considering only a single gene at a time, multivariate TWAS (MV-TWAS) methods have recently emerged to account for the effects of multiple genes, or a gene's nonlinear effects, simultaneously. With the absence of the power analysis for these MV-TWAS methods, it would be of interest to investigate whether one can gain or lose power by using the newly proposed MV-TWAS instead of UV-TWAS. In this paper, we first outline a general method for sample size/power calculations for two-sample TWAS, then use real data-the Alzheimer's Disease Neuroimaging Initiative (ADNI) expression quantitative trait loci (eQTL) data and the Genotype-Tissue Expression (GTEx) eQTL data for stage 1, the International Genomics of Alzheimer's Project Alzheimer's disease (AD) GWAS summary data and UK Biobank (UKB) individual-level data for stage 2-to empirically address these questions. Our most important conclusions are the following. First, a sample size of a few thousands (~8000) would suffice in stage 1, where the power of TWAS would be more determined by cis-heritability of gene expression. Second, as in the general case of simple regression versus multiple regression, the power of MV-TWAS may be higher or lower than that of UV-TWAS, depending on the specific relationships among the GWAS trait and multiple genes (or linear and nonlinear terms of the same gene's expression levels), such as their correlations and effect sizes. Interestingly, several top genes with large power gains in MV-TWAS (over that in UV-TWAS) were known to be (and in our data more significantly) associated with AD. We also reached similar conclusions in an application to the GTEx whole blood gene expression data and UKB GWAS data of high-density lipoprotein cholesterol. The proposed method and the conclusions are expected to be useful in planning and designing future TWAS and other related studies (e.g., Proteome- or Metabolome-Wide Association Studies) when determining the sample sizes for the two stages.
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Affiliation(s)
- Ruoyu He
- School of StatisticsUniversity of MinnesotaMinneapolisMinnesotaUSA
- University of MinnesotaDivision of Biostatistics, School of Public HealthMinneapolisMinnesotaUSA
| | - Haoran Xue
- University of MinnesotaDivision of Biostatistics, School of Public HealthMinneapolisMinnesotaUSA
| | - Wei Pan
- University of MinnesotaDivision of Biostatistics, School of Public HealthMinneapolisMinnesotaUSA
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19
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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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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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Li L, Yu X, Sheng C, Jiang X, Zhang Q, Han Y, Jiang J. A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives. Transl Neurodegener 2022; 11:42. [PMID: 36109823 PMCID: PMC9476275 DOI: 10.1186/s40035-022-00315-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.
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22
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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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23
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Lee B, Yao X, Shen L. Integrative analysis of summary data from GWAS and eQTL studies implicates genes differentially expressed in Alzheimer's disease. BMC Genomics 2022; 23:414. [PMID: 35655140 PMCID: PMC9161451 DOI: 10.1186/s12864-022-08584-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 04/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Although genome-wide association studies (GWAS) have successfully located various genetic variants susceptible to Alzheimer's Disease (AD), it is still unclear how specific variants interact with genes and tissues to elucidate pathologies associated with AD. Summary-data-based Mendelian Randomization (SMR) addresses this problem through an instrumental variable approach that integrates data from independent GWAS and expression quantitative trait locus (eQTL) studies in order to infer a causal effect of gene expression on a trait. RESULTS Our study employed the SMR approach to integrate a set of meta-analytic cis-eQTL information from the Genotype-Tissue Expression (GTEx), CommonMind Consortium (CMC), and Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) consortiums with three sets of meta-analysis AD GWAS results. CONCLUSIONS Our analysis identified twelve total gene probes (associated with twelve distinct genes) with a significant association with AD. Four of these genes survived a test of pleiotropy from linkage (the HEIDI test).Three of these genes - RP11-385F7.1, PRSS36, and AC012146.7 - have not yet been reported differentially expressed in the brain in the context of AD, and thus are the novel findings warranting further investigation.
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Affiliation(s)
- Brian Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
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Horgusluoglu E, Neff R, Song W, Wang M, Wang Q, Arnold M, Krumsiek J, Galindo‐Prieto B, Ming C, Nho K, Kastenmüller G, Han X, Baillie R, Zeng Q, Andrews S, Cheng H, Hao K, Goate A, Bennett DA, Saykin AJ, Kaddurah‐Daouk R, Zhang B. Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease. Alzheimers Dement 2022; 18:1260-1278. [PMID: 34757660 PMCID: PMC9085975 DOI: 10.1002/alz.12468] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 04/14/2021] [Accepted: 04/17/2021] [Indexed: 12/29/2022]
Abstract
Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD-specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co-expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short-chain acylcarnitines/amino acids and medium/long-chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co-expression network analysis of the AMP-AD brain RNA-seq data suggests the CPT1A- and ABCA1-centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short-chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large-scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.
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Affiliation(s)
- Emrin Horgusluoglu
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Ryan Neff
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Won‐Min Song
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Minghui Wang
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Qian Wang
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Matthias Arnold
- Institute of Computational BiologyHelmholtz Zentrum MünchenGerman Research Center for Environmental HealthNeuherbergGermany
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
| | - Jan Krumsiek
- Department of Physiology and BiophysicsWeill Cornell MedicineInstitute for Computational BiomedicineEnglander Institute for Precision MedicineNew YorkNew YorkUSA
| | - Beatriz Galindo‐Prieto
- Department of Physiology and BiophysicsWeill Cornell MedicineInstitute for Computational BiomedicineEnglander Institute for Precision MedicineNew YorkNew YorkUSA
- Helen and Robert Appel Alzheimer's Disease Research InstituteBrain and Mind Research InstituteWeill Cornell MedicineNew YorkNew YorkUSA
| | - Chen Ming
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences; Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Gabi Kastenmüller
- Institute of Computational BiologyHelmholtz Zentrum MünchenGerman Research Center for Environmental HealthNeuherbergGermany
| | - Xianlin Han
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
| | | | - Qi Zeng
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Shea Andrews
- Department of NeuroscienceRonald M. Loeb Center for Alzheimer's DiseaseIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Haoxiang Cheng
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Ke Hao
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Alison Goate
- Department of NeuroscienceRonald M. Loeb Center for Alzheimer's DiseaseIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - David A. Bennett
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences; Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rima Kaddurah‐Daouk
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Duke Institute of Brain SciencesDuke UniversityDurhamNorth CarolinaUSA
- Department of MedicineDuke UniversityDurhamNorth CarolinaUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
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Deep Learning-Based Diagnosis of Alzheimer’s Disease. J Pers Med 2022; 12:jpm12050815. [PMID: 35629237 PMCID: PMC9143671 DOI: 10.3390/jpm12050815] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 12/27/2022] Open
Abstract
Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.
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Zhang W, Wang R, Yuan J, Li B, Zhang L, Wang Y, Zhu R, Zhang J, Huyan T. The TLR4/NF-κB/MAGI-2 signaling pathway mediates postoperative delirium. Aging (Albany NY) 2022; 14:2590-2606. [PMID: 35294925 PMCID: PMC9004557 DOI: 10.18632/aging.203955] [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: 12/28/2021] [Accepted: 02/15/2022] [Indexed: 11/25/2022]
Abstract
Purpose: To evaluate the TLR4/NF-κB/MAGI-2 signaling pathway in postoperative delirium. Methods: Elderly patients aged 65-80 years who received unilateral hip arthroplasty under subarachnoid anesthesia were included. Pre-anesthesia cerebrospinal fluid and perioperative blood samples were collected. After follow-up, patients were divided into two groups according to the occurrence of postoperative delirium (POD) after surgery. The potential differentially expressed proteins in the two groups were determined by proteomics assay and subsequent western blot validation. A POD model of aged mice was established, and the TLR4/NF-κB/MAGI-2 signaling pathway was determined. Main findings: The IL-1β and TNF-α levels in pre-anesthesia cerebrospinal fluid and postoperative blood were higher in patients who developed POD than in those patients who did not. Compared with non-POD patients, MAGI-2 was highly expressed in POD patients, as validated by proteomics assays and western blotting. Higher p-NF-κB-p65, TLR4 and MAGI-2 in POD patients were detected by western blot. The POD model in aged mice was successfully established and verified by three behavioral tests. Postoperative inflammatory cytokines and the TLR4/NF-κB/MAGI-2 signaling pathway were increased in mice with POD. Inhibiting TLR4/NF-κB/MAGI-2 signaling pathway could reduce postoperative delirium. Conclusions: The TLR4/NF-κB/MAGI-2 signaling pathway mediates POD.
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Affiliation(s)
- Wei Zhang
- Department of Anesthesiology and Perioperative Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan Province, China
| | - Ruohan Wang
- Department of Anesthesiology and Perioperative Medicine, Henan University People's Hospital, Henan Provincial People's Hospital, Zhengzhou 450003, Henan Province, China
| | - Jingli Yuan
- Department of Anesthesiology and Perioperative Medicine, Henan University People's Hospital, Henan Provincial People's Hospital, Zhengzhou 450003, Henan Province, China
| | - Bing Li
- Department of Anesthesiology and Perioperative Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan Province, China
| | - Luyao Zhang
- Department of Anesthesiology and Perioperative Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan Province, China
| | - Yangyang Wang
- Department of Anesthesiology and Perioperative Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan Province, China
| | - Ruilou Zhu
- Department of Anesthesiology and Perioperative Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan Province, China
| | - Jiaqiang Zhang
- Department of Anesthesiology and Perioperative Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan Province, China
| | - Ting Huyan
- Key Laboratory for Space Biosciences and Biotechnology, Institute of Special Environment Biophysics, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shaanxi Province, China
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Predictive classification of Alzheimer’s disease using brain imaging and genetic data. Sci Rep 2022; 12:2405. [PMID: 35165327 PMCID: PMC8844076 DOI: 10.1038/s41598-022-06444-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 01/24/2022] [Indexed: 02/06/2023] Open
Abstract
For now, Alzheimer’s disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the existing research methods use single or multi-modal imaging features for prediction, relatively few studies combine brain imaging with genetic features for disease diagnosis. In order to accurately identify AD, healthy control (HC) and the two stages of mild cognitive impairment (MCI: early MCI, late MCI) combined with brain imaging and genetic characteristics, we proposed an integrated Fisher score and multi-modal multi-task feature selection research method. We learned first genetic features with Fisher score to perform dimensionality reduction in order to solve the problem of the large difference between the feature scales of genetic and brain imaging. Then we learned the potential related features of brain imaging and genetic data, and multiplied the selected features with the learned weight coefficients. Through the feature selection program, five imaging and five genetic features were selected to achieve an average classification accuracy of 98% for HC and AD, 82% for HC and EMCI, 86% for HC and LMCI, 80% for EMCI and LMCI, 88% for EMCI and AD, and 72% for LMCI and AD. Compared with only using imaging features, the classification accuracy has been improved to a certain extent, and a set of interrelated features of brain imaging phenotypes and genetic factors were selected.
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28
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Zhang Y, Li X, Hu Y, Yuan H, Wu X, Yang Y, Zhao T, Hu K, Wang Z, Wang G, Zhang K, Liu H. Evaluation of mild cognitive impairment genetic susceptibility risks in a Chinese population. BMC Psychiatry 2022; 22:93. [PMID: 35135506 PMCID: PMC8822756 DOI: 10.1186/s12888-022-03756-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 02/02/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a kind of non-functional cognitive decline between normal aging and dementia. With the increase of individual age, the quality of cognitive function has become a more and more important topic. The study of gene loci in patients with MCI is essential for the prevention of dementia. In this study, we evaluate the gene polymorphism in Chinese Han patients with MCI by propensity score matching (PSM) and comparing them to healthy control (HC) subjects. METHODS Four hundred seventeen patients with mild cognitive impairment and 508 healthy people were included. The two groups were matched by applying one-to-one PSM, and the matching tolerance was set to 0.002. The matching covariates included gender,age,occupation,marital status,living mode. Then, a case-control associated analysis was conducted to analyze the genotype and allele frequencies of single nucleotide polymorphisms (SNPs) in the MCI group and the control group. RESULTS Three hundred eleven cases were successfully matched in each group, and there was no statistical difference on all the matching variables, gender, age, occupation, marital status, living mode between two groups after the match (P > 0.05). The allele frequency of bridging integrator 1(BIN1) rs7561528 showed minimal association with MCI in the Han Chinese population (P = 0.01). Compared with the healthy control (HC) group, A allele frequency of MCI group patients was significantly decreased. The genotype frequency of BIN1 rs6733839 showed minimal association with MCI in the recessive model (P = 0.03). The genotype frequency of rs7561528 showed minimal association with MCI in the codominant, dominant, overdominant, and log-additive model (P < 0.05). The genotype frequencies of StAR-related lipid transfer domain 6 (STARD6) rs10164112 showed nominal association with MCI in the codominant, dominant, and log-additive model (P < 0.05). Unfortunately, the significant differences did not survive Benjamini-Hochberg false discovery rate correction (adjusted P > 0.05). The patients with SPI1 rs1057233 may be the protective factor of MCI (OR = 0.733, 95%CI 0.625-0.859, P < 0.001), and patients with APOE rs10164112 may be a risk factor for MCI (OR = 1.323, 95%CI 1.023-1.711, P = 0.033). CONCLUSIONS The polymorphisms of rs7561528, rs6733839 loci in the BIN1 gene, and rs1057233 loci in the SPI1 gene may be associated with the MCI in Chinese Han population. APOE gene was the risk factor of MCI, but further verification in a large sample population is still needed.
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Affiliation(s)
- Yelei Zhang
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China ,grid.268099.c0000 0001 0348 3990The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325007 China
| | - Xiaoyue Li
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Yu Hu
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Hongwei Yuan
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, 214151 China
| | - Xiaodong Wu
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Yating Yang
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Tongtong Zhao
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000 China ,grid.186775.a0000 0000 9490 772XAnhui Psychiatric Center, Anhui Medical University, Hefei, 238000 China
| | - Ke Hu
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, 214151 China
| | - Zhiqiang Wang
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, 214151 China
| | - Guoqiang Wang
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, 214151 China
| | - Kai Zhang
- Department of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000, China. .,Anhui Psychiatric Center, Anhui Medical University, Hefei, 238000, China.
| | - Huanzhong Liu
- Department of Psychiatry, Chaohu Hospital, Anhui Medical University, 64 North Chaohu Road, Hefei, 238000, China. .,Anhui Psychiatric Center, Anhui Medical University, Hefei, 238000, China.
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Lee B, Yao X, Shen L. Genome-Wide association study of quantitative biomarkers identifies a novel locus for alzheimer's disease at 12p12.1. BMC Genomics 2022; 23:85. [PMID: 35086473 PMCID: PMC8796646 DOI: 10.1186/s12864-021-08269-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 12/14/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genetic study of quantitative biomarkers in Alzheimer's Disease (AD) is a promising method to identify novel genetic factors and relevant endophenotypes, which provides valuable information to deconvolute mechanistic complexity and better understand disease subtypes. RESULTS Using the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we performed a genome-wide association study (GWAS) between 565,373 single nucleotide polymorphisms (SNPs) and 16 key AD biomarkers from 1,576 subjects at four visits. We identified a novel locus rs5011804 at 12p12.1 significantly associated with several AD biomarkers, including three cognitive traits (CDRSB, FAQ, ADAS13) and one imaging trait (fusiform volume). Additional mediation and interaction analyses investigated the relationships among this SNP, relevant biomarkers, and clinical diagnosis, confirming and further elaborating the genetic effects seen in the GWAS. CONCLUSION Our GWAS not only affirms key AD genes but also suggests the promising role of the SNP rs5011804 due to its associations with several AD cognitive and imaging outcomes. The SNP rs5011804 has a reported association with adult asthma and slightly affects intracranial volume but has not been associated with AD before. Our novel findings contribute to a more comprehensive view of the molecular mechanism behind AD.
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Affiliation(s)
- Brian Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
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Wang W, Kong W, Wang S, Wei K. Detecting Biomarkers of Alzheimer's Disease Based on Multi-constrained Uncertainty-Aware Adaptive Sparse Multi-view Canonical Correlation Analysis. J Mol Neurosci 2022; 72:841-865. [PMID: 35080765 DOI: 10.1007/s12031-021-01963-y] [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: 08/31/2021] [Accepted: 12/29/2021] [Indexed: 12/01/2022]
Abstract
Image genetics mainly explores the pathogenesis of Alzheimer's disease (AD) by studying the relationship between genetic data (such as SNP, gene expression data, and DNA methylation) and imaging data (such as structural MRI (sMRI), fMRI, and PET). Most of the existing research on brain imaging genomics uses two-way or three-way bi-multivariate methods to explore the correlation analysis between genes and brain imaging. However, many of these methods are still affected by the gradient domination or cannot take into account the effect of feature redundancy on the results, so that the typical correlation coefficient and program running speed are not significantly improved. In order to solve the above problems, this paper proposes a multi-constrained uncertainty-aware adaptive sparse multi-view canonical correlation analysis method (MC-unAdaSMCCA) to explore associations among SNPs, gene expression data, and sMRI; that is, based on traditional unAdaSMCCA, orthogonal constraints are imposed on the weights of the three data features through linear programming, which can reduce the redundancy of feature weights to improve the correlation between the data and reduce the complexity of the algorithm to significantly speed up the running speed of the program. Three adaptive sparse multi-view canonical correlation analysis methods are used as benchmarks to evaluate the difference between real neuroimaging data and synthetic data. Compared with the other three methods, our proposed method has obtained better or comparable typical correlation coefficients and typical weights. Moreover, the following experimental results show that the MC-unAdaSMCCA method cannot only identify biomarkers related to AD and mild cognitive impairment (MCI), but also has a strong ability to resist noise and process high-dimensional data. Therefore, our proposed method provides a reliable approach to multi-modal imaging genetic researches.
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Affiliation(s)
- Wenbo Wang
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China
| | - Wei Kong
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China.
| | - Shuaiqun Wang
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China
| | - Kai Wei
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China
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Lin Z, Xue H, Malakhov MM, Knutson KA, Pan W. Accounting for nonlinear effects of gene expression identifies additional associated genes in transcriptome-wide association studies. Hum Mol Genet 2022; 31:2462-2470. [PMID: 35043938 PMCID: PMC9307319 DOI: 10.1093/hmg/ddac015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/08/2022] [Accepted: 01/10/2022] [Indexed: 01/21/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) integrate genome-wide association study (GWAS) data with gene expression (GE) data to identify (putative) causal genes for complex traits. There are two stages in TWAS: in Stage 1, a model is built to impute gene expression from genotypes, and in Stage 2, gene-trait association is tested using imputed gene expression. Despite many successes with TWAS, in the current practice, one only assumes a linear relationship between GE and the trait, which however may not hold, leading to loss of power. In this study, we extend the standard TWAS by considering a quadratic effect of GE, in addition to the usual linear effect. We train imputation models for both linear and quadratic gene expression levels in Stage 1, then include both the imputed linear and quadratic expression levels in Stage 2. We applied both the standard TWAS and our approach first to the ADNI gene expression data and the IGAP Alzheimer's disease GWAS summary data, then to the GTEx (V8) gene expression data and the UK Biobank individual-level GWAS data for lipids, followed by validation with different GWAS data, suitable model checking and more robust TWAS methods. In all these applications, the new TWAS approach was able to identify additional genes associated with Alzheimer's disease, LDL and HDL cholesterol levels, suggesting its likely power gains and thus the need to account for potentially nonlinear effects of gene expression on complex traits.
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Affiliation(s)
- Zhaotong Lin
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Haoran Xue
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mykhaylo M Malakhov
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Katherine A Knutson
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- To whom correspondence should be addressed at: A460 Mayo Building, 420 Delaware St SE, Minneapolis, MN 55455, USA. Tel: (612)626-2705; Fax: (612)626-0660;
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Bao J, Wen Z, Kim M, Saykin AJ, Thompson PM, Zhao Y, Shen L. Identifying imaging genetic associations via regional morphometricity estimation. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022; 27:97-108. [PMID: 34890140 PMCID: PMC8730533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain imaging genetics is an emerging research field aiming to reveal the genetic basis of brain traits captured by imaging data. Inspired by heritability analysis, the concept of morphometricity was recently introduced to assess trait association with whole brain morphology. In this study, we extend the concept of morphometricity from its original definition at the whole brain level to a more focal level based on a region of interest (ROI). We propose a novel framework to identify the SNP-ROI association via regional morphometricity estimation of each studied single nucleotide polymorphism (SNP). We perform an empirical study on the structural MRI and genotyping data from a landmark Alzheimer's disease (AD) biobank; and yield promising results. Our findings indicate that the AD-related SNPs have higher overall regional morphometricity estimates than the SNPs not yet related to AD. This observation suggests that the variance of AD SNPs can be explained more by regional morphometric features than non-AD SNPs, supporting the value of imaging traits as targets in studying AD genetics. Also, we identified 11 ROIs, where the AD/non-AD SNPs and significant/insignificant morphometricity estimation of the corresponding SNPs in these ROIs show strong dependency. Supplementary motor area (SMA) and dorsolateral prefrontal cortex (DPC) are enriched by these ROIs. Our results also demonstrate that using all the detailed voxel-level measures within the ROI to incorporate morphometric information outperforms using only a single average ROI measure, and thus provides improved power to detect imaging genetic associations.
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Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Mansu Kim
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Andrew J. Saykin
- Indiana Alzheimer Disease Center, Department of Radiology and Imaging Sciences Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics University of Southern California School of Medicine, Marina del Rey, CA 90292, USA
| | - Yize Zhao
- Department of Biostatistics Yale University School of Public Health, New Haven, CT 06511, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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Bao J, Wen Z, Kim M, Zhao X, Lee BN, Jung SH, Davatzikos C, Saykin AJ, Thompson PM, Kim D, Zhao Y, Shen L. Identifying highly heritable brain amyloid phenotypes through mining Alzheimer's imaging and sequencing biobank data. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022; 27:109-120. [PMID: 34890141 PMCID: PMC8730532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Brain imaging genetics, an emerging and rapidly growing research field, studies the relationship between genetic variations and brain imaging quantitative traits (QTs) to gain new insights into the phenotypic characteristics and genetic mechanisms of the brain. Heritability is an important measurement to quantify the proportion of the observed variance in an imaging QT that is explained by genetic factors, and can often be used to prioritize brain QTs for subsequent imaging genetic association studies. Most existing studies define regional imaging QTs using predefined brain parcellation schemes such as the automated anatomical labeling (AAL) atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion could be negatively affected by heterogeneity within the regions in the partition. To bridge this gap, we propose a novel method to define highly heritable brain regions. Based on voxelwise heritability estimates, we extract brain regions containing spatially connected voxels with high heritability. We perform an empirical study on the amyloid imaging and whole genome sequencing data from a landmark Alzheimer's disease biobank; and demonstrate the regions defined by our method have much higher estimated heritabilities than the regions defined by the AAL atlas. Our proposed method refines the imaging endophenotype constructions in light of their genetic dissection, and yields more powerful imaging QTs for subsequent detection of genetic risk factors along with better interpretability.
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Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Mansu Kim
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Xiwen Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06511, USA
| | - Brian N. Lee
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics University of Southern California School of Medicine, Marina del Rey, CA 90292, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06511, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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Concas MP, Minelli A, Aere S, Morgan A, Tesolin P, Gasparini P, Gennarelli M, Girotto G. Genetic Dissection of Temperament Personality Traits in Italian Isolates. Genes (Basel) 2021; 13:genes13010004. [PMID: 35052345 PMCID: PMC8774962 DOI: 10.3390/genes13010004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 11/16/2022] Open
Abstract
Human personality (i.e., temperament and character) is a complex trait related to mental health, influenced by genetic and environmental factors. Despite the efforts performed during the past decades, its genetic background is only just beginning to be identified. With the aim of dissecting the genetic basis of temperament, we performed a Genome-Wide Association Study (GWAS) on Cloninger’s Temperament and Character Inventory in 587 individuals belonging to different Italian genetic isolates. Data analysis led to the identification of four new genes associated with different temperament scales, such as Novelty Seeking (NS), Harm Avoidance (HA), and Reward Dependence (RD). In detail, we identified suggestive and significant associations between: MAGI2 (highest p-value = 9.14 × 10−8), a gene already associated with schizophrenia and depressive disorder, and the NS–Extravagance scale; CALCB (highest p-value = 4.34 × 10−6), a gene likely involved in the behavioral evolution from wild wolf to domestic dog, and the NS–Disorderliness scale; BTBD3 (highest p-value = 2.152 × 10−8), a gene already linked to obsessive–compulsive disorder, and the HA–Fatigability scale; PRKN (highest p-value = 8.27 × 10−9), a gene described for early onset Parkinson’s disease, and the RD scale. Our work provides new relevant insights into the genetics of temperament, helping to elucidate the molecular basis of psychiatric disorders.
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Affiliation(s)
- Maria Pina Concas
- Institute for Maternal and Child Health-IRCCS “Burlo Garofolo”, 34127 Trieste, Italy; (A.M.); (P.G.); (G.G.)
- Correspondence:
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy; (A.M.); (M.G.)
- Genetics Unit, IRCCS Istituto Centro S. Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy
| | - Susanna Aere
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34139 Trieste, Italy; (S.A.); (P.T.)
| | - Anna Morgan
- Institute for Maternal and Child Health-IRCCS “Burlo Garofolo”, 34127 Trieste, Italy; (A.M.); (P.G.); (G.G.)
| | - Paola Tesolin
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34139 Trieste, Italy; (S.A.); (P.T.)
| | - Paolo Gasparini
- Institute for Maternal and Child Health-IRCCS “Burlo Garofolo”, 34127 Trieste, Italy; (A.M.); (P.G.); (G.G.)
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34139 Trieste, Italy; (S.A.); (P.T.)
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy; (A.M.); (M.G.)
- Genetics Unit, IRCCS Istituto Centro S. Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy
| | - Giorgia Girotto
- Institute for Maternal and Child Health-IRCCS “Burlo Garofolo”, 34127 Trieste, Italy; (A.M.); (P.G.); (G.G.)
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34139 Trieste, Italy; (S.A.); (P.T.)
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Ueki M, Tamiya G. Smooth-threshold multivariate genetic prediction incorporating gene–environment interactions. G3 GENES|GENOMES|GENETICS 2021; 11:6343458. [PMID: 34849749 PMCID: PMC8664495 DOI: 10.1093/g3journal/jkab278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 07/12/2021] [Indexed: 11/17/2022]
Abstract
We propose a genetic prediction modeling approach for genome-wide association study (GWAS) data that can include not only marginal gene effects but also gene–environment (GxE) interaction effects—i.e., multiplicative effects of environmental factors with genes rather than merely additive effects of each. The proposed approach is a straightforward extension of our previous multiple regression-based method, STMGP (smooth-threshold multivariate genetic prediction), with the new feature being that genome-wide test statistics from a GxE interaction analysis are used to weight the corresponding variants. We develop a simple univariate regression approximation to the GxE interaction effect that allows a direct fit of the STMGP framework without modification. The sparse nature of our model automatically removes irrelevant predictors (including variants and GxE combinations), and the model is able to simultaneously incorporate multiple environmental variables. Simulation studies to evaluate the proposed method in comparison with other modeling approaches demonstrate its superior performance under the presence of GxE interaction effects. We illustrate the usefulness of our prediction model through application to real GWAS data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
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Affiliation(s)
- Masao Ueki
- School of Information and Data Sciences, Nagasaki University, Nagasaki 852-8521, Japan
| | - Gen Tamiya
- Tohoku University Graduate School of Medicine, Sendai, Miyagi 980-8575, Japan
- Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo 103-0027, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi 980-8573, Japan
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Zhao Y, Zhao X, Kim M, Bao J, Shen L. A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12905:678-687. [PMID: 35299630 PMCID: PMC8922551 DOI: 10.1007/978-3-030-87240-3_65] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Heritability analysis is an important research topic in brain imaging genetics. Its primary motivation is to identify highly heritable imaging quantitative traits (QTs) for subsequent in-depth imaging genetic analyses. Most existing studies perform heritability analyses on regional imaging QTs using predefined brain parcellation schemes such as the AAL atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion is largely deteriorate with inner partition noise and signal dilution. To bridge the gap, we propose a new semi-parametric Bayesian heritability estimation model to construct highly heritable imaging QTs. Our method leverages the aggregate of genetic signals to imaging QT construction by developing a new brain parcellation driven by voxel-level heritability. To ensure biological plausibility and clinical interpretability of the resulting brain heritability parcellations, hierarchical sparsity and smoothness, coupled with structural connectivity of the brain, are properly imposed on genetic effects to induce spatial contiguity of heritable imaging QTs. Using the ADNI imaging genetic data, we demonstrate the strength of our proposed method, in comparison with the standard GCTA method, in identifying highly heritable and biologically meaningful new imaging QTs.
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Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, NJ, USA
| | - Xiwen Zhao
- Department of Biostatistics, Yale University School of Public Health, NJ, USA
| | - Mansu Kim
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
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Deng Y, Pan W. Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies. PLoS Comput Biol 2021; 17:e1009266. [PMID: 34339418 PMCID: PMC8360571 DOI: 10.1371/journal.pcbi.1009266] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 08/12/2021] [Accepted: 07/12/2021] [Indexed: 11/25/2022] Open
Abstract
It is of great interest and potential to discover causal relationships between pairs of exposures and outcomes using genetic variants as instrumental variables (IVs) to deal with hidden confounding in observational studies. Two most popular approaches are Mendelian randomization (MR), which usually use independent genetic variants/SNPs across the genome, and transcriptome-wide association studies (TWAS) (or their generalizations) using cis-SNPs local to a gene (or some genome-wide and likely dependent SNPs), as IVs. In spite of their many promising applications, both approaches face a major challenge: the validity of their causal conclusions depends on three critical assumptions on valid IVs, and more generally on other modeling assumptions, which however may not hold in practice. The most likely as well as challenging situation is due to the wide-spread horizontal pleiotropy, leading to two of the three IV assumptions being violated and thus to biased statistical inference. More generally, we'd like to conduct a goodness-of-fit (GOF) test to check the model being used. Although some methods have been proposed as being robust to various degrees to the violation of some modeling assumptions, they often give different and even conflicting results due to their own modeling assumptions and possibly lower statistical efficiency, imposing difficulties to the practitioner in choosing and interpreting varying results across different methods. Hence, it would help to directly test whether any assumption is violated or not. In particular, there is a lack of such tests for TWAS. We propose a new and general GOF test, called TEDE (TEsting Direct Effects), applicable to both correlated and independent SNPs/IVs (as commonly used in TWAS and MR respectively). Through simulation studies and real data examples, we demonstrate high statistical power and advantages of our new method, while confirming the frequent violation of modeling (including valid IV) assumptions in practice and thus the importance of model checking by applying such a test in MR/TWAS analysis.
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Affiliation(s)
- Yangqing Deng
- Department of Mathematics, University of North Texas, Denton, Texas, United States of America
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
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Rush A, Sutherland GT. The future of brain banking in Australia: an integrated brain and body biolibrary. Med J Aust 2021; 214:447-449.e1. [PMID: 33993514 DOI: 10.5694/mja2.51049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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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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Wu Y, Yin J, Yang B, Tang L, Feng W, Liu X, Zhao X, Cheng Z. Association Analysis of Polymorphisms in BIN1, MC1R, STARD6 and PVRL2 with Mild Cognitive Impairment in Elderly Carrying APOE ε4 Allele. Neuropsychiatr Dis Treat 2021; 17:1125-1133. [PMID: 33907405 PMCID: PMC8071212 DOI: 10.2147/ndt.s296144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 03/22/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Apolipoprotein (APOE) ε4 is recognized as an independent risk factor for mild cognitive impairment (MCI). However, not everyone with the ε4 allele develops MCI, suggesting that other susceptibility genes exist. This study aimed to identify MCI susceptibility genes, including BIN1, MC1R, STARD6, and PVRL2, in elderly Han Chinese and to verify their association with APOE ε4 allele in MCI onset. METHODS To determine whether polymorphisms in BIN1 (rs6733839, rs7561528), MC1R (rs2228479), STARD6 (rs10164112), and PVRL2 (rs6859) occurred in elderly MCI patients carrying APOE ε4 allele, we carried out a case-control study including 285 MCI patients and 326 healthy controls. RESULTS Statistically significant differences in the proportion of APOE ε4 carriers, and BESCI, ADAS-cog, and CNT scores existed between the NC and MCI groups (all P < 0.01). Frequencies of the rs10164112 T and rs6859 A alleles were significantly higher in the latter than in the former (P = 0.01; 0.029). However, no significant differences in allele and genotype distribution of BIN1 (rs6733839, rs7561528) and MC1R (rs2228479) existed between samples in our two groups (all P > 0.05). When stratified by APOE ε4 status (carriers/non-carriers), genotype frequencies of BIN1 rs7561528, STARD6 rs10164112, and PVRL2 rs6859 among the four groups (NCε4+, NCε4-, MCIε4+, MCIε4-) were significantly different. Additionally, our results suggest a significant association between MCI and BIN1 rs7561528, STARD6 rs10164112, and PVRL2 rs6859 (all P<0.05) in elderly carriers. CONCLUSION This suggests that among the Han Chinese, MCI in elderly APOE ε4 carriers may be related to the BIN1 (rs7561528), STARD6 (rs10164112) and PVRL2 (rs6859). Genotype AA of rs7561528 and TT of rs10164112 might be protective factors against MCI in elderly APOE ε4 carriers.
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Affiliation(s)
- Yue Wu
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center with Nanjing Medical University, Wuxi, Jiangsu Province, People’s Republic of China
| | - Jiajun Yin
- Brain Science Basic Laboratory, The Affiliated Wuxi Mental Health Center with Nanjing Medical University, Wuxi, Jiangsu Province, People’s Republic of China
| | - Bixiu Yang
- Department of Clinical Psychology, The Affiliated Wuxi Mental Health Center with Nanjing Medical University, Wuxi, Jiangsu Province, People’s Republic of China
| | - Li Tang
- Department of General Psychiatry, The Affiliated Wuxi Mental Health Center with Nanjing Medical University, Wuxi, Jiangsu Province, People’s Republic of China
| | - Wei Feng
- Department of Social Prevention and Control, The Affiliated Wuxi Mental Health Center with Nanjing Medical University, Wuxi, Jiangsu Province, People’s Republic of China
| | - Xiaowei Liu
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center with Nanjing Medical University, Wuxi, Jiangsu Province, People’s Republic of China
| | - Xingfu Zhao
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center with Nanjing Medical University, Wuxi, Jiangsu Province, People’s Republic of China
| | - Zaohuo Cheng
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center with Nanjing Medical University, Wuxi, Jiangsu Province, People’s Republic of China
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Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer's disease stage. Sci Rep 2021; 11:3254. [PMID: 33547343 PMCID: PMC7864942 DOI: 10.1038/s41598-020-74399-w] [Citation(s) in RCA: 106] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 01/22/2020] [Indexed: 02/06/2023] Open
Abstract
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data. We also develop a novel data interpretation method to identify top-performing features learned by the deep-models with clustering and perturbation analysis. Using Alzheimer's disease neuroimaging initiative (ADNI) dataset, we demonstrate that deep models outperform shallow models, including support vector machines, decision trees, random forests, and k-nearest neighbors. In addition, we demonstrate that integrating multi-modality data outperforms single modality models in terms of accuracy, precision, recall, and meanF1 scores. Our models have identified hippocampus, amygdala brain areas, and the Rey Auditory Verbal Learning Test (RAVLT) as top distinguished features, which are consistent with the known AD literature.
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Affiliation(s)
- Janani Venugopalan
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Li Tong
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hamid Reza Hassanzadeh
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - May D Wang
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
- Winship Cancer Institute, Parker H. Petit Institute for Bioengineering and Biosciences, Institute of People and Technology, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
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Yingxuan E, Yao X, Liu K, Risacher SL, Saykin AJ, Long Q, Zhao Y, Shen L. Polygenic mediation analysis of Alzheimer's disease implicated intermediate amyloid imaging phenotypes. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:422-431. [PMID: 33936415 PMCID: PMC8075527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Mediation models have been employed in the study of brain disorders to detect the underlying mechanisms between genetic variants and diagnostic outcomes implicitly mediated by intermediate imaging biomarkers. However, the statistical power is influenced by the modest effects of individual genetic variants on both diagnostic and imaging phenotypes and the limited sample sizes ofimaging genetic cohorts. In this study, we propose a polygenic mediation analysis that comprises a polygenic risk score (PRS) to aggregate genetic effects ofa set ofcandidate variants and then explore the implicit effect ofimaging phenotypes between the PRS and disease status. We applied our proposed method to an amyloid imaging genetic study of Alzheimer's disease (AD), identified multiple imaging mediators linking PRS with AD, and further demonstrated the promise of the PRS on mediator detection over individual variants alone.
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Affiliation(s)
- Eng Yingxuan
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xiaohui Yao
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kefei Liu
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | | | - Qi Long
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yize Zhao
- Yale University, New Haven, CT 06511, USA
| | - Li Shen
- University of Pennsylvania, Philadelphia, PA 19104, USA
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44
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Du L, Liu K, Yao X, Risacher SL, Han J, Saykin AJ, Guo L, Shen L. Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:227-239. [PMID: 31634139 PMCID: PMC7156329 DOI: 10.1109/tcbb.2019.2947428] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Brain imaging genetics studies the genetic basis of brain structures and functionalities via integrating genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analysis. MTL methods generally incorporate a few of QTs and could not select features from multiple QTs; while SCCA methods typically employ one modality of QTs to study its association with SNPs. Both MTL and SCCA are computational expensive as the number of SNPs increases. In this paper, we propose a novel multi-task SCCA (MTSCCA) method to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. MTSCCA enforces sparsity at the group level via the G2,1-norm, and jointly selects features across multiple tasks for SNPs and QTs via the l2,1-norm. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains better correlation coefficients and canonical weights patterns. In addition, MTSCCA runs very fast and easy-to-implement, indicating its potential power in genome-wide brain-wide imaging genetics.
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Meng X, Li J, Zhang Q, Chen F, Bian C, Yao X, Yan J, Xu Z, Risacher SL, Saykin AJ, Liang H, Shen L. Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease. BMC Genomics 2020; 21:896. [PMID: 33372590 PMCID: PMC7771059 DOI: 10.1186/s12864-020-07282-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 11/25/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Jin Li
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin, 132012, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Chenyuan Bian
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA
| | - Zhe Xu
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, 150001, China.
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
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Knutson KA, Deng Y, Pan W. Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data. Neuroimage 2020; 223:117347. [PMID: 32898681 PMCID: PMC7778364 DOI: 10.1016/j.neuroimage.2020.117347] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 08/24/2020] [Accepted: 08/28/2020] [Indexed: 02/06/2023] Open
Abstract
Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer's Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV-IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV-IWAS to incorporate variant-specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.
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Affiliation(s)
- Katherine A Knutson
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota United States
| | - Yangqing Deng
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota United States
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota United States.
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47
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Du L, Liu F, Liu K, Yao X, Risacher SL, Han J, Saykin AJ, Shen L. Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3416-3428. [PMID: 32746095 PMCID: PMC7705646 DOI: 10.1109/tmi.2020.2995510] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternative method in multi-modal brain imaging genetics.
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Affiliation(s)
- Lei Du
- School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
| | - Fang Liu
- School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
| | - Kefei Liu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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48
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Yao X, Cong S, Yan J, Risacher SL, Saykin AJ, Moore JH, Shen L. Regional imaging genetic enrichment analysis. Bioinformatics 2020; 36:2554-2560. [PMID: 31860065 DOI: 10.1093/bioinformatics/btz948] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 11/19/2019] [Accepted: 12/18/2019] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful interpretation and modest dimensionality compared with voxelwise approaches. Typical ROI-level measures used in these studies are summary statistics from voxelwise measures in the region, without making full use of individual voxel signals. RESULTS In this article, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxelwise enrichment analysis, which embraces the collective effect of weak voxel-level signals and integrates brain anatomical annotation information. Our proposed method achieves three goals at the same time: (i) increase the statistical power by substantially reducing the burden of multiple comparison correction; (ii) employ brain annotation information to enable biologically meaningful interpretation and (iii) make full use of fine-grained voxelwise signals. We demonstrate our method on an imaging genetic analysis using data from the Alzheimer's Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxelwise FDG-positron emission tomography measures between 116 ROIs and 565 373 single-nucleotide polymorphisms. Compared with traditional ROI-wise and voxelwise approaches, our method identified 2946 novel imaging genetic associations in addition to 33 ones overlapping with the two benchmark methods. In particular, two newly reported variants were further supported by transcriptome evidences from region-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain. AVAILABILITY AND IMPLEMENTATION The R code and sample data are freely available at https://github.com/lshen/RIGEA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shan Cong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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49
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Li J, Chen F, Zhang Q, Meng X, Yao X, Risacher SL, Yan J, Saykin AJ, Liang H, Shen L. Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease. Curr Alzheimer Res 2020; 16:1163-1174. [PMID: 31755389 DOI: 10.2174/1567205016666191121142558] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 11/19/2019] [Accepted: 11/21/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. OBJECTIVE The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. METHODS First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. RESULTS We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. CONCLUSION The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.
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Affiliation(s)
- Jin Li
- College of Automation, Harbin Engineering University, Harbin, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, China
| | - Qiushi Zhang
- College of Information Engineering, Northeast Dianli University, Jilin, China
| | - Xianglian Meng
- College of Automation, Harbin Engineering University, Harbin, China
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, China
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
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50
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Kronenberg NM, Tilston-Lunel A, Thompson FE, Chen D, Yu W, Dholakia K, Gather MC, Gunn-Moore FJ. Willin/FRMD6 Influences Mechanical Phenotype and Neuronal Differentiation in Mammalian Cells by Regulating ERK1/2 Activity. Front Cell Neurosci 2020; 14:552213. [PMID: 33088261 PMCID: PMC7498650 DOI: 10.3389/fncel.2020.552213] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/17/2020] [Indexed: 12/31/2022] Open
Abstract
Willin/FRMD6 is part of a family of proteins with a 4.1 ezrin-radixin-moesin (FERM) domain. It has been identified as an upstream activator of the Hippo pathway and, when aberrant in its expression, is associated with human diseases and disorders. Even though Willin/FRMD6 was originally discovered in the rat sciatic nerve, most studies have focused on its functional roles in cells outside of the nervous system, where Willin/FRMD6 is involved in the formation of apical junctional cell-cell complexes and in regulating cell migration. Here, we investigate the biochemical and biophysical role of Willin/FRMD6 in neuronal cells, employing the commonly used SH-SY5Y neuronal model cell system and combining biochemical measurements with Elastic Resonator Interference Stress Micropscopy (ERISM). We present the first direct evidence that Willin/FRMD6 expression influences both the cell mechanical phenotype and neuronal differentiation. By investigating cells with increased and decreased Willin/FRMD6 expression levels, we show that Willin/FRMD6 not only affects proliferation and migration capacity of cells but also leads to changes in cell morphology and an enhanced formation of neurite-like membrane extensions. These changes were accompanied by alterations of biophysical parameters such as cell force, the organization of actin stress fibers and the formation of focal adhesions. At the biochemical level, changes in Willin/FRMD6 expression inversely affected the activity of the extracellular signal-regulated kinases (ERK) pathway and downstream transcriptional factor NeuroD1, which seems to prime SH-SY5Y cells for retinoic acid (RA)-induced neuronal differentiation.
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Affiliation(s)
- Nils M Kronenberg
- Centre of Biophotonics and SUPA, School of Physics and Astronomy, University of St Andrews, St Andrews, United Kingdom.,Centre for Nanobiophotonics, Department of Chemistry, University of Cologne, Cologne, Germany
| | - Andrew Tilston-Lunel
- Centre of Biophotonics, School of Biology, University of St Andrews, St Andrews, United Kingdom.,Department of Biochemistry, School of Medicine, Boston University, Boston, MA, United States
| | - Frances E Thompson
- Centre of Biophotonics and SUPA, School of Physics and Astronomy, University of St Andrews, St Andrews, United Kingdom
| | - Doris Chen
- Centre of Biophotonics, School of Biology, University of St Andrews, St Andrews, United Kingdom
| | - Wanjia Yu
- Centre of Biophotonics, School of Biology, University of St Andrews, St Andrews, United Kingdom
| | - Kishan Dholakia
- Centre of Biophotonics and SUPA, School of Physics and Astronomy, University of St Andrews, St Andrews, United Kingdom.,Department of Physics, College of Science, Yonsei University, Seoul, South Korea
| | - Malte C Gather
- Centre of Biophotonics and SUPA, School of Physics and Astronomy, University of St Andrews, St Andrews, United Kingdom.,Centre for Nanobiophotonics, Department of Chemistry, University of Cologne, Cologne, Germany
| | - Frank J Gunn-Moore
- Centre of Biophotonics, School of Biology, University of St Andrews, St Andrews, United Kingdom
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