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Wu Q, Zhang Y, Huang X, Ma T, Hong LE, Kochunov P, Chen S. A multivariate to multivariate approach for voxel-wise genome-wide association analysis. Stat Med 2024; 43:3862-3880. [PMID: 38922949 DOI: 10.1002/sim.10101] [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: 07/03/2023] [Revised: 03/02/2024] [Accepted: 04/24/2024] [Indexed: 06/28/2024]
Abstract
The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (ie, a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and an inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project. The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum.
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Affiliation(s)
- Qiong Wu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yuan Zhang
- Department of Statistics, Ohio State University, Columbus, Ohio, USA
| | - Xiaoqi Huang
- Department of Mathematics, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, USA
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - L Elliot Hong
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Peter Kochunov
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland, Baltimore, Maryland, USA
- The University of Maryland Institute for Health Computing, University of Maryland, North Bethesda, USA
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2
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Dai Y, Itai T, Pei G, Yan F, Chu Y, Jiang X, Weinberg SM, Mukhopadhyay N, Marazita ML, Simon LM, Jia P, Zhao Z. DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development. HGG ADVANCES 2024; 5:100312. [PMID: 38796699 PMCID: PMC11193024 DOI: 10.1016/j.xhgg.2024.100312] [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: 02/08/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 05/28/2024] Open
Abstract
Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between the predicted and actual values for 12 epigenetic features achieved a median range of 0.50-0.83. Specifically, our model revealed that SNPs significantly associated with OFCs tended to exhibit higher SAD scores across various variant categories compared to less related groups, indicating a context-specific impact of OFC-related SNPs. Notably, we identified six SNPs with a significant linear relationship to SAD scores throughout developmental progression, suggesting that these SNPs could play a temporal regulatory role. Furthermore, our cell-type specificity analysis pinpointed the trophoblast cell as having the highest enrichment of risk signals associated with OFCs. Overall, DeepFace can harness distal regulatory signals from extensive epigenomic assays, offering new perspectives for prioritizing OFC variants using contextualized functional genomic features. We expect DeepFace to be instrumental in accessing and predicting the regulatory roles of variants associated with OFCs, and the model can be extended to study other complex diseases or traits.
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Affiliation(s)
- Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Toshiyuki Itai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Guangsheng Pei
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Fangfang Yan
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yan Chu
- Center for Secure Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Seth M Weinberg
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Nandita Mukhopadhyay
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Mary L Marazita
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA; Clinical and Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Lukas M Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
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3
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Pan W, Shan Y, Li C, Huang S, Li T, Li Y, Zhu H. FPLS-DC: functional partial least squares through distance covariance for imaging genetics. Bioinformatics 2024; 40:btae173. [PMID: 38552322 PMCID: PMC11034987 DOI: 10.1093/bioinformatics/btae173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 02/28/2024] [Accepted: 03/27/2024] [Indexed: 04/24/2024] Open
Abstract
MOTIVATION Imaging genetics integrates imaging and genetic techniques to examine how genetic variations influence the function and structure of organs like the brain or heart, providing insights into their impact on behavior and disease phenotypes. The use of organ-wide imaging endophenotypes has increasingly been used to identify potential genes associated with complex disorders. However, analyzing organ-wide imaging data alongside genetic data presents two significant challenges: high dimensionality and complex relationships. To address these challenges, we propose a novel, nonlinear inference framework designed to partially mitigate these issues. RESULTS We propose a functional partial least squares through distance covariance (FPLS-DC) framework for efficient genome wide analyses of imaging phenotypes. It consists of two components. The first component utilizes the FPLS-derived base functions to reduce image dimensionality while screening genetic markers. The second component maximizes the distance correlation between genetic markers and projected imaging data, which is a linear combination of the FPLS-basis functions, using simulated annealing algorithm. In addition, we proposed an iterative FPLS-DC method based on FPLS-DC framework, which effectively overcomes the influence of inter-gene correlation on inference analysis. We efficiently approximate the null distribution of test statistics using a gamma approximation. Compared to existing methods, FPLS-DC offers computational and statistical efficiency for handling large-scale imaging genetics. In real-world applications, our method successfully detected genetic variants associated with the hippocampus, demonstrating its value as a statistical toolbox for imaging genetic studies. AVAILABILITY AND IMPLEMENTATION The FPLS-DC method we propose opens up new research avenues and offers valuable insights for analyzing functional and high-dimensional data. In addition, it serves as a useful tool for scientific analysis in practical applications within the field of imaging genetics research. The R package FPLS-DC is available in Github: https://github.com/BIG-S2/FPLSDC.
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Affiliation(s)
- Wenliang Pan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Yue Shan
- Departments of Biostatistics, Statistics, Genetics, and Computer Science and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Chuang Li
- Department of Statistical Science, School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Shuai Huang
- Departments of Biostatistics, Statistics, Genetics, and Computer Science and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Tengfei Li
- Departments of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Yun Li
- Departments of Biostatistics, Statistics, Genetics, and Computer Science and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Departments of Biostatistics, Statistics, Genetics, and Computer Science and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
- Departments of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599, USA
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4
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Shan Y, Huang C, Li Y, Zhu H. Merging or ensembling: integrative analysis in multiple neuroimaging studies. Biometrics 2024; 80:ujae003. [PMID: 38465984 PMCID: PMC10926268 DOI: 10.1093/biomtc/ujae003] [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: 12/02/2022] [Revised: 11/27/2023] [Accepted: 01/10/2024] [Indexed: 03/12/2024]
Abstract
The aim of this paper is to systematically investigate merging and ensembling methods for spatially varying coefficient mixed effects models (SVCMEM) in order to carry out integrative learning of neuroimaging data obtained from multiple biomedical studies. The "merged" approach involves training a single learning model using a comprehensive dataset that encompasses information from all the studies. Conversely, the "ensemble" approach involves creating a weighted average of distinct learning models, each developed from an individual study. We systematically investigate the prediction accuracy of the merged and ensemble learners under the presence of different degrees of interstudy heterogeneity. Additionally, we establish asymptotic guidelines for making strategic decisions about when to employ either of these models in different scenarios, along with deriving optimal weights for the ensemble learner. To validate our theoretical results, we perform extensive simulation studies. The proposed methodology is also applied to 3 large-scale neuroimaging studies.
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Affiliation(s)
- Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Chao Huang
- Department of Statistics, Florida State University, Tallahassee, FL 32306, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
- Department of Statistics, Florida State University, Tallahassee, FL 32306, United States
- Department of Statistics & Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
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5
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Huang C, Liu R, Zhao B, Kong L. Editorial: Modern statistical learning strategies in imaging genetics, volume II. Front Neurosci 2023; 17:1337411. [PMID: 38125405 PMCID: PMC10731351 DOI: 10.3389/fnins.2023.1337411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Affiliation(s)
- Chao Huang
- Department of Statistics, Florida State University, Tallahassee, FL, United States
| | - Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, United States
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
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Jin Z, Kang J, Yu T. Bayesian nonparametric method for genetic dissection of brain activation region. Front Neurosci 2023; 17:1235321. [PMID: 37920300 PMCID: PMC10618557 DOI: 10.3389/fnins.2023.1235321] [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: 06/06/2023] [Accepted: 09/26/2023] [Indexed: 11/04/2023] Open
Abstract
Biological evidence indicewates that the brain atrophy can be involved at the onset of neuropathological pathways of Alzheimer's disease. However, there is lack of formal statistical methods to perform genetic dissection of brain activation phenotypes such as shape and intensity. To this end, we propose a Bayesian hierarchical model which consists of two levels of hierarchy. At level 1, we develop a Bayesian nonparametric level set (BNLS) model for studying the brain activation region shape. At level 2, we construct a regression model to select genetic variants that are strongly associated with the brain activation intensity, where a spike-and-slab prior and a Gaussian prior are chosen for feature selection. We develop efficient posterior computation algorithms based on the Markov chain Monte Carlo (MCMC) method. We demonstrate the advantages of the proposed method via extensive simulation studies and analyses of imaging genetics data in the Alzheimer's disease neuroimaging initiative (ADNI) study.
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Affiliation(s)
- Zhuxuan Jin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Tianwei Yu
- School of Data Science, Chinese University of Hong Kong - Shenzhen, Shenzhen, China
- Guangdong Provincial Key Laboratory of Big Data Computing, Shenzhen, China
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7
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Zhu H, Li T, Zhao B. Statistical Learning Methods for Neuroimaging Data Analysis with Applications. Annu Rev Biomed Data Sci 2023; 6:73-104. [PMID: 37127052 DOI: 10.1146/annurev-biodatasci-020722-100353] [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] [Indexed: 05/03/2023]
Abstract
The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
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Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, Department of Statistics, Department of Genetics, and Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA;
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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8
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Diaz-Torres S, Ogonowski N, García-Marín LM, Bonham LW, Duran-Aniotz C, Yokoyama JS, Rentería ME. Genetic overlap between cortical brain morphometry and frontotemporal dementia risk. Cereb Cortex 2023; 33:7428-7435. [PMID: 36813468 PMCID: PMC10267623 DOI: 10.1093/cercor/bhad049] [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: 12/17/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 02/24/2023] Open
Abstract
Frontotemporal dementia (FTD) has a complex genetic etiology, where the precise mechanisms underlying the selective vulnerability of brain regions remain unknown. We leveraged summary-based data from genome-wide association studies (GWAS) and performed LD score regression to estimate pairwise genetic correlations between FTD risk and cortical brain imaging. Then, we isolated specific genomic loci with a shared etiology between FTD and brain structure. We also performed functional annotation, summary-data-based Mendelian randomization for eQTL using human peripheral blood and brain tissue data, and evaluated the gene expression in mice targeted brain regions to better understand the dynamics of the FTD candidate genes. Pairwise genetic correlation estimates between FTD and brain morphology measures were high but not statistically significant. We identified 5 brain regions with a strong genetic correlation (rg > 0.45) with FTD risk. Functional annotation identified 8 protein-coding genes. Building upon these findings, we show in a mouse model of FTD that cortical N-ethylmaleimide sensitive factor (NSF) expression decreases with age. Our results highlight the molecular and genetic overlap between brain morphology and higher risk for FTD, specifically for the right inferior parietal surface area and right medial orbitofrontal cortical thickness. In addition, our findings implicate NSF gene expression in the etiology of FTD.
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Affiliation(s)
- Santiago Diaz-Torres
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Natalia Ogonowski
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Centro de Neurociencias Cognitivas (CNC), Universidad de San Andrés, Buenos Aires, Argentina
| | - Luis M García-Marín
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Luke W Bonham
- Memory and Aging Center, University of California, San Francisco, CA, United States
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States
| | - Claudia Duran-Aniotz
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- School of Psychology, Center for Social and Cognitive Neuroscience (CSCN), Universidad Adolfo Ibanez, Santiago, Chile
| | - Jennifer S Yokoyama
- Memory and Aging Center, University of California, San Francisco, CA, United States
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States
- Department of Neurology, Weill Institute of Neurosciences, University of California, San Francisco, CA, United States
| | - Miguel E Rentería
- Mental Health & Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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9
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Diaz-Torres S, He W, Thorp J, Seddighi S, Mullany S, Hammond CJ, Hysi PG, Pasquale LR, Khawaja AP, Hewitt AW, Craig JE, Mackey DA, Wiggs JL, van Duijn C, Lupton MK, Ong JS, MacGregor S, Gharahkhani P. Disentangling the genetic overlap and causal relationships between primary open-angle glaucoma, brain morphology and four major neurodegenerative disorders. EBioMedicine 2023; 92:104615. [PMID: 37201334 PMCID: PMC10206164 DOI: 10.1016/j.ebiom.2023.104615] [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: 12/21/2022] [Revised: 04/21/2023] [Accepted: 04/28/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Primary open-angle glaucoma (POAG) is an optic neuropathy characterized by progressive degeneration of the optic nerve that leads to irreversible visual impairment. Multiple epidemiological studies suggest an association between POAG and major neurodegenerative disorders (Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and Parkinson's disease). However, the nature of the overlap between neurodegenerative disorders, brain morphology and glaucoma remains inconclusive. METHOD In this study, we performed a comprehensive assessment of the genetic and causal relationship between POAG and neurodegenerative disorders, leveraging genome-wide association data from studies of magnetic resonance imaging of the brain, POAG, and four major neurodegenerative disorders. FINDINGS This study found a genetic overlap and causal relationship between POAG and its related phenotypes (i.e., intraocular pressure and optic nerve morphology traits) and brain morphology in 19 regions. We also identified 11 loci with a significant local genetic correlation and a high probability of sharing the same causal variant between neurodegenerative disorders and POAG or its related phenotypes. Of interest, a region on chromosome 17 corresponding to MAPT, a well-known risk locus for Alzheimer's and Parkinson's disease, was shared between POAG, optic nerve degeneration traits, and Alzheimer's and Parkinson's diseases. Despite these local genetic overlaps, we did not identify strong evidence of a causal association between these neurodegenerative disorders and glaucoma. INTERPRETATION Our findings indicate a distinctive and likely independent neurodegenerative process for POAG involving several brain regions although several POAG or optic nerve degeneration risk loci are shared with neurodegenerative disorders, consistent with a pleiotropic effect rather than a causal relationship between these traits. FUNDING PG was supported by an NHMRC Investigator Grant (#1173390), SM by an NHMRC Senior Research Fellowship and an NHMRC Program Grant (APP1150144), DM by an NHMRC Fellowship, LP is funded by the NEIEY015473 and EY032559 grants, SS is supported by an NIH-Oxford Cambridge Fellowship and NIH T32 grant (GM136577), APK is supported by a UK Research and Innovation Future Leaders Fellowship, an Alcon Research Institute Young Investigator Award and a Lister Institute for Preventive Medicine Award.
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Affiliation(s)
- Santiago Diaz-Torres
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland (UQ), Brisbane, QLD, Australia.
| | - Weixiong He
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jackson Thorp
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sahba Seddighi
- Nuffield Department of Population Health, Oxford University, Oxford, UK; Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sean Mullany
- Department of Ophthalmology, Flinders University, Flinders Medical Centre, Bedford Park, Australia
| | - Christopher J Hammond
- Departments of Ophthalmology & Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Pirro G Hysi
- Departments of Ophthalmology & Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Anthony P Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Alex W Hewitt
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Jamie E Craig
- Department of Ophthalmology, Flinders University, Flinders Medical Centre, Bedford Park, Australia
| | - David A Mackey
- Centre for Ophthalmology and Visual Science, University of Western Australia, Lions Eye Institute, Australia
| | - Janey L Wiggs
- Department of Ophthalmology, Harvard Medical School, Boston, 02114, MA, USA
| | | | - Michelle K Lupton
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jue-Sheng Ong
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Stuart MacGregor
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland (UQ), Brisbane, QLD, Australia
| | - Puya Gharahkhani
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland (UQ), Brisbane, QLD, Australia; School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, Australia.
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10
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Zhu W, Xu S, Liu C, Li Y. Minimax Powerful Functional Analysis of Covariance Tests: with Application to Longitudinal Genome-Wide Association Studies. Scand Stat Theory Appl 2023; 50:266-295. [PMID: 39076352 PMCID: PMC11286231 DOI: 10.1111/sjos.12583] [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: 01/07/2021] [Accepted: 01/07/2022] [Indexed: 11/27/2022]
Abstract
We model the Alzheimer's Disease-related phenotype response variables observed on irregular time points in longitudinal Genome-Wide Association Studies as sparse functional data and propose nonparametric test procedures to detect functional genotype effects while controlling the confounding effects of environmental covariates. Our new functional analysis of covariance tests are based on a seemingly unrelated kernel smoother, which takes into account the within-subject temporal correlations, and thus enjoy improved power over existing functional tests. We show that the proposed test combined with a uniformly consistent nonparametric covariance function estimator enjoys the Wilks phenomenon and is minimax most powerful. Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, where an application of the proposed test lead to the discovery of new genes that may be related to Alzheimer's Disease.
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Affiliation(s)
| | - Sheng Xu
- Global Statistics and Data Science, BeiGene Co., Ltd., China
| | - Catherine Liu
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Yehua Li
- Department of Statistics, University of California, Riverside, CA, 92521, USA
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11
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Lista S, Vergallo A, Teipel SJ, Lemercier P, Giorgi FS, Gabelle A, Garaci F, Mercuri NB, Babiloni C, Gaire BP, Koronyo Y, Koronyo-Hamaoui M, Hampel H, Nisticò R. Determinants of approved acetylcholinesterase inhibitor response outcomes in Alzheimer's disease: relevance for precision medicine in neurodegenerative diseases. Ageing Res Rev 2023; 84:101819. [PMID: 36526257 DOI: 10.1016/j.arr.2022.101819] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 11/11/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
Acetylcholinesterase inhibitors (ChEI) are the global standard of care for the symptomatic treatment of Alzheimer's disease (AD) and show significant positive effects in neurodegenerative diseases with cognitive and behavioral symptoms. Although experimental and large-scale clinical evidence indicates the potential long-term efficacy of ChEI, primary outcomes are generally heterogeneous across outpatient clinics and regional healthcare systems. Sub-optimal dosing or slow tapering, heterogeneous guidelines about the timing for therapy initiation (prodromal versus dementia stages), healthcare providers' ambivalence to treatment, lack of disease awareness, delayed medical consultation, prescription of ChEI in non-AD cognitive disorders, contribute to the negative outcomes. We present an evidence-based overview of determinants, spanning genetic, molecular, and large-scale networks, involved in the response to ChEI in patients with AD and other neurodegenerative diseases. A comprehensive understanding of cerebral and retinal cholinergic system dysfunctions along with ChEI response predictors in AD is crucial since disease-modifying therapies will frequently be prescribed in combination with ChEI. Therapeutic algorithms tailored to genetic, biological, clinical (endo)phenotypes, and disease stages will help leverage inter-drug synergy and attain optimal combined response outcomes, in line with the precision medicine model.
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Affiliation(s)
- Simone Lista
- Memory Resources and Research Center (CMRR), Neurology Department, Gui de Chauliac University Hospital, Montpellier, France; School of Pharmacy, University of Rome "Tor Vergata", Rome, Italy.
| | - Andrea Vergallo
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE) Rostock/Greifswald, Rostock, Germany; Department of Psychosomatic Medicine and Psychotherapy, University Medicine Rostock, Rostock, Germany
| | - Pablo Lemercier
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Filippo Sean Giorgi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Audrey Gabelle
- Memory Resources and Research Center (CMRR), Neurology Department, Gui de Chauliac University Hospital, Montpellier, France
| | - Francesco Garaci
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy; Casa di Cura "San Raffaele Cassino", Cassino, Italy
| | - Nicola B Mercuri
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy; IRCCS Santa Lucia Foundation, Rome, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, Italy
| | - Bhakta Prasad Gaire
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences, Division of Applied Cell Biology and Physiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harald Hampel
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Robert Nisticò
- School of Pharmacy, University of Rome "Tor Vergata", Rome, Italy; Laboratory of Pharmacology of Synaptic Plasticity, EBRI Rita Levi-Montalcini Foundation, Rome, Italy.
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12
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Craig SJ, Kenney AM, Lin J, Paul IM, Birch LL, Savage JS, Marini ME, Chiaromonte F, Reimherr ML, Makova KD. Constructing a polygenic risk score for childhood obesity using functional data analysis. ECONOMETRICS AND STATISTICS 2023; 25:66-86. [PMID: 36620476 PMCID: PMC9813976 DOI: 10.1016/j.ecosta.2021.10.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Obesity is a highly heritable condition that affects increasing numbers of adults and, concerningly, of children. However, only a small fraction of its heritability has been attributed to specific genetic variants. These variants are traditionally ascertained from genome-wide association studies (GWAS), which utilize samples with tens or hundreds of thousands of individuals for whom a single summary measurement (e.g., BMI) is collected. An alternative approach is to focus on a smaller, more deeply characterized sample in conjunction with advanced statistical models that leverage longitudinal phenotypes. Novel functional data analysis (FDA) techniques are used to capitalize on longitudinal growth information from a cohort of children between birth and three years of age. In an ultra-high dimensional setting, hundreds of thousands of single nucleotide polymorphisms (SNPs) are screened, and selected SNPs are used to construct two polygenic risk scores (PRS) for childhood obesity using a weighting approach that incorporates the dynamic and joint nature of SNP effects. These scores are significantly higher in children with (vs. without) rapid infant weight gain-a predictor of obesity later in life. Using two independent cohorts, it is shown that the genetic variants identified in very young children are also informative in older children and in adults, consistent with early childhood obesity being predictive of obesity later in life. In contrast, PRSs based on SNPs identified by adult obesity GWAS are not predictive of weight gain in the cohort of young children. This provides an example of a successful application of FDA to GWAS. This application is complemented with simulations establishing that a deeply characterized sample can be just as, if not more, effective than a comparable study with a cross-sectional response. Overall, it is demonstrated that a deep, statistically sophisticated characterization of a longitudinal phenotype can provide increased statistical power to studies with relatively small sample sizes; and shows how FDA approaches can be used as an alternative to the traditional GWAS.
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Affiliation(s)
- Sarah J.C. Craig
- Department of Biology, Penn State University, University Park
- Center for Medical Genomics, Penn State University, University Park, PA
| | - Ana M. Kenney
- Department of Statistics, Penn State University, University Park, PA
| | - Junli Lin
- Department of Statistics, Penn State University, University Park, PA
| | - Ian M. Paul
- Center for Medical Genomics, Penn State University, University Park, PA
- Department of Pediatrics, Penn State College of Medicine, Hershey, PA
| | - Leann L. Birch
- Department of Foods and Nutrition, University of Georgia, Athens, GA
| | - Jennifer S. Savage
- Department of Nutritional Sciences, Penn State University, University Park, PA
- Center for Childhood Obesity Research, Penn State University, University Park, PA
| | - Michele E. Marini
- Center for Childhood Obesity Research, Penn State University, University Park, PA
| | - Francesca Chiaromonte
- Center for Medical Genomics, Penn State University, University Park, PA
- Department of Statistics, Penn State University, University Park, PA
- EMbeDS, Sant’Anna School of Advanced Studies, Piazza Martiri della Libertà, Pisa, Italy
| | - Matthew L. Reimherr
- Center for Medical Genomics, Penn State University, University Park, PA
- Department of Statistics, Penn State University, University Park, PA
| | - Kateryna D. Makova
- Department of Biology, Penn State University, University Park
- Center for Medical Genomics, Penn State University, University Park, PA
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13
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Detection of Association Features Based on Gene Eigenvalues and MRI Imaging Using Genetic Weighted Random Forest. Genes (Basel) 2022; 13:genes13122344. [PMID: 36553611 PMCID: PMC9777775 DOI: 10.3390/genes13122344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
In the studies of Alzheimer's disease (AD), jointly analyzing imaging data and genetic data provides an effective method to explore the potential biomarkers of AD. AD can be separated into healthy controls (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD. In the meantime, identifying the important biomarkers of AD progression, and analyzing these biomarkers in AD provide valuable insights into understanding the mechanism of AD. In this paper, we present a novel data fusion method and a genetic weighted random forest method to mine important features. Specifically, we amplify the difference among AD, LMCI, EMCI and HC by introducing eigenvalues calculated from the gene p-value matrix for feature fusion. Furthermore, we construct the genetic weighted random forest using the resulting fused features. Genetic evolution is used to increase the diversity among decision trees and the decision trees generated are weighted by weights. After training, the genetic weighted random forest is analyzed further to detect the significant fused features. The validation experiments highlight the performance and generalization of our proposed model. We analyze the biological significance of the results and identify some significant genes (CSMD1, CDH13, PTPRD, MACROD2 and WWOX). Furthermore, the calcium signaling pathway, arrhythmogenic right ventricular cardiomyopathy and the glutamatergic synapse pathway were identified. The investigational findings demonstrate that our proposed model presents an accurate and efficient approach to identifying significant biomarkers in AD.
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14
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Meng X, Wei Q, Meng L, Liu J, Wu Y, Liu W. Feature Fusion and Detection in Alzheimer's Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data. Genes (Basel) 2022; 13:837. [PMID: 35627222 PMCID: PMC9140721 DOI: 10.3390/genes13050837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 01/27/2023] Open
Abstract
Voxel-based morphometry provides an opportunity to study Alzheimer's disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10-6) and cell adhesion molecules (corrected p-value = 5.44 × 10-4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
| | - Qingpeng Wei
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
| | - Li Meng
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Junlong Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
| | - Yue Wu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China; (X.M.); (Q.W.); (J.L.); (Y.W.)
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15
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He S, Dou L, Li X, Zhang Y. Review of bioinformatics in Azheimer's Disease Research. Comput Biol Med 2022; 143:105269. [PMID: 35158118 DOI: 10.1016/j.compbiomed.2022.105269] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 01/21/2022] [Accepted: 01/23/2022] [Indexed: 01/05/2023]
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disease with slow course of onset and deterioration with time. With the speedup of global aging, AD has become a disease that seriously threatens the physical health of the elderly; therefore, the effective prevention and treatments of AD is an extremely important area of study for researchers and clinicians. Rapid technological developments have promoted the analysis of various kinds of complex data sets using machine learning methods. The common machine learning algorithms, such as Lasso, SVM and Random Forest, are very important in AD research. To help accelerate AD-related research, we review some recent research progress on Alzheimer's disease, including database, image analysis, gene expression, etc., which can provide AD researchers with more comprehensive research methods.
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Affiliation(s)
- Shida He
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China; Department of Computer Science, University of Tsukuba, Japan
| | - Lijun Dou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China; School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Xuehong Li
- Beidahuang Industry Group General Hospital, Harbin, China.
| | - Ying Zhang
- Department of Anesthesiology, Hospital (T.C.M) Affiliated To Southwest Medical University, Luzhou, China.
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16
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Wen C, Ba H, Pan W, Huang M. Co-sparse reduced-rank regression for association analysis between imaging phenotypes and genetic variants. Bioinformatics 2021; 36:5214-5222. [PMID: 32683450 DOI: 10.1093/bioinformatics/btaa650] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 05/22/2020] [Accepted: 07/14/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The association analysis between genetic variants and imaging phenotypes must be carried out to understand the inherited neuropsychiatric disorders via imaging genetic studies. Given the high dimensionality in imaging and genetic data, traditional methods based on massive univariate regression entail large computational cost and disregard many-to-many correlations between phenotypes and genetic variants. Several multivariate imaging genetic methods have been proposed to alleviate the above problems. However, most of these methods are based on the l1 penalty, which might cause the over-selection of variables and thus mislead scientists in analyzing data from the field of neuroimaging genetics. RESULTS To address these challenges in both statistics and computation, we propose a novel co-sparse reduced-rank regression model that identifies complex correlations in a dimensional reduction manner. We developed an iterative algorithm based on a group primal dual-active set formulation to detect simultaneously important genetic variants and imaging phenotypes efficiently and precisely via non-convex penalty. The simulation studies showed that our method achieved accurate and stable performance in parameter estimation and variable selection. In real application, the proposed approach successfully detected several novel Alzheimer's disease-related genetic variants and regions of interest, which indicate that our method may be a valuable statistical toolbox for imaging genetic studies. AVAILABILITY AND IMPLEMENTATION The R package csrrr, and the code for experiments in this article is available in Github: https://github.com/hailongba/csrrr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Canhong Wen
- International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Hailong Ba
- International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Wenliang Pan
- Department of Statistical Science, School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Meiyan Huang
- School of Biomedical Engineering, Guangzhou 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
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17
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Huang M, Lai H, Yu Y, Chen X, Wang T, Feng Q. Deep-gated recurrent unit and diet network-based genome-wide association analysis for detecting the biomarkers of Alzheimer's disease. Med Image Anal 2021; 73:102189. [PMID: 34343841 DOI: 10.1016/j.media.2021.102189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/30/2021] [Accepted: 07/16/2021] [Indexed: 01/01/2023]
Abstract
Genome-wide association analysis (GWAS) is a commonly used method to detect the potential biomarkers of Alzheimer's disease (AD). Most existing GWAS methods entail a high computational cost, disregard correlations among imaging data and correlations among genetic data, and ignore various associations between longitudinal imaging and genetic data. A novel GWAS method was proposed to identify potential AD biomarkers and address these problems. A network based on a gated recurrent unit was applied without imputing incomplete longitudinal imaging data to integrate the longitudinal data of variable lengths and extract an image representation. In this study, a modified diet network that can considerably reduce the number of parameters in the genetic network was proposed to perform GWAS between image representation and genetic data. Genetic representation can be extracted in this way. A link between genetic representation and AD was established to detect potential AD biomarkers. The proposed method was tested on a set of simulated data and a real AD dataset. Results of the simulated data showed that the proposed method can accurately detect relevant biomarkers. Moreover, the results of real AD dataset showed that the proposed method can detect some new risk-related genes of AD. Based on previous reports, no research has incorporated a deep-learning model into a GWAS framework to investigate the potential information on super-high-dimensional genetic data and longitudinal imaging data and create a link between imaging genetics and AD for detecting potential AD biomarkers. Therefore, the proposed method may provide new insights into the underlying pathological mechanism of AD.
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Affiliation(s)
- Meiyan Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
| | - Haoran Lai
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Yuwei Yu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Xiumei Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Tao Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
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18
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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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19
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Song Y, Ge S, Cao J, Wang L, Nathoo FS. A Bayesian spatial model for imaging genetics. Biometrics 2021; 78:742-753. [PMID: 33765325 DOI: 10.1111/biom.13460] [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: 02/05/2019] [Revised: 02/08/2021] [Accepted: 02/24/2021] [Indexed: 11/29/2022]
Abstract
We develop a Bayesian bivariate spatial model for multivariate regression analysis applicable to studies examining the influence of genetic variation on brain structure. Our model is motivated by an imaging genetics study of the Alzheimer's Disease Neuroimaging Initiative (ADNI), where the objective is to examine the association between images of volumetric and cortical thickness values summarizing the structure of the brain as measured by magnetic resonance imaging (MRI) and a set of 486 single nucleotide polymorphism (SNPs) from 33 Alzheimer's disease (AD) candidate genes obtained from 632 subjects. A bivariate spatial process model is developed to accommodate the correlation structures typically seen in structural brain imaging data. First, we allow for spatial correlation on a graph structure in the imaging phenotypes obtained from a neighborhood matrix for measures on the same hemisphere of the brain. Second, we allow for correlation in the same measures obtained from different hemispheres (left/right) of the brain. We develop a mean-field variational Bayes algorithm and a Gibbs sampling algorithm to fit the model. We also incorporate Bayesian false discovery rate (FDR) procedures to select SNPs. We implement the methodology in a new release of the R package bgsmtr. We show that the new spatial model demonstrates superior performance over a standard model in our application. Data used in the preparation of this article were obtained from the ADNI database (https://adni.loni.usc.edu).
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Affiliation(s)
- Yin Song
- Department of Mathematics and Statistics, University of Victoria, British Columbia, Canada
| | - Shufei Ge
- Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China
| | - Jiguo Cao
- Statistics and Actuarial Science, Simon Fraser University, British Columbia, Canada
| | - Liangliang Wang
- Statistics and Actuarial Science, Simon Fraser University, British Columbia, Canada
| | - Farouk S Nathoo
- Department of Mathematics and Statistics, University of Victoria, British Columbia, Canada
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20
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Affiliation(s)
- Rongjie Liu
- Department of Statistics Florida State University Tallahassee FL U.S.A
| | - Hongtu Zhu
- Department of Biostatistics University of North Carolina at Chapel Hill Chapel Hill NC U.S.A
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21
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Pei G, Hu R, Dai Y, Manuel AM, Zhao Z, Jia P. Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations. Nucleic Acids Res 2021; 49:53-66. [PMID: 33300042 PMCID: PMC7797043 DOI: 10.1093/nar/gkaa1137] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/22/2020] [Accepted: 12/08/2020] [Indexed: 02/06/2023] Open
Abstract
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.
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Affiliation(s)
- Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ruifeng Hu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Astrid Marilyn Manuel
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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22
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Zhong L, Li T, Shu H, Huang C, Michael Johnson J, Schomer DF, Liu HL, Feng Q, Yang W, Zhu H. (TS) 2WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients. Neuroimage 2020; 223:117368. [PMID: 32931941 PMCID: PMC7688588 DOI: 10.1016/j.neuroimage.2020.117368] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 08/23/2020] [Accepted: 09/08/2020] [Indexed: 11/24/2022] Open
Abstract
Glioblastoma (GBM) brain tumor is the most aggressive white matter (WM) invasive cerebral primary neoplasm. Due to its inherently heterogeneous appearance and shape, previous studies pursued either the segmentation precision of the tumors or qualitative analysis of the impact of brain tumors on WM integrity with manual delineation of tumors. This paper aims to develop a comprehensive analytical pipeline, called (TS)2WM, to integrate both the superior performance of brain tumor segmentation and the impact of GBM tumors on the WM integrity via tumor segmentation and tract statistics using the diffusion tensor imaging (DTI) technique. The (TS)2WM consists of three components: (i) A dilated densely connected convolutional network (D2C2N) for automatically segment GBM tumors. (ii) A modified structural connectome processing pipeline to characterize the connectivity pattern of WM bundles. (iii) A multivariate analysis to delineate the local and global associations between different DTI-related measurements and clinical variables on both brain tumors and language-related regions of interest. Among those, the proposed D2C2N model achieves competitive tumor segmentation accuracy compared with many state-of-the-art tumor segmentation methods. Significant differences in various DTI-related measurements at the streamline, weighted network, and binary network levels (e.g., diffusion properties along major fiber bundles) were found in tumor-related, language-related, and hand motor-related brain regions in 62 GBM patients as compared to healthy subjects from the Human Connectome Project.
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Affiliation(s)
- Liming Zhong
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Tengfei Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States; Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Hai Shu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States; Department of Biostatistics, School of Global Public Health, New York University, New York, United States
| | - Chao Huang
- Department of Statistics, Florida State University, Tallahassee, FL, United States
| | - Jason Michael Johnson
- Department of Diagnostic Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Donald F Schomer
- Department of Diagnostic Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Ho-Ling Liu
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
| | - Qianjin Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States; Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States.
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23
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Wesolowska-Andersen A, Zhuo Yu G, Nylander V, Abaitua F, Thurner M, Torres JM, Mahajan A, Gloyn AL, McCarthy MI. Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals. eLife 2020; 9:e51503. [PMID: 31985400 PMCID: PMC7007221 DOI: 10.7554/elife.51503] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 01/27/2020] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue - pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization - genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.
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Affiliation(s)
| | - Grace Zhuo Yu
- Oxford Centre for Diabetes, Endocrinology and MetabolismUniversity of OxfordOxfordUnited Kingdom
| | - Vibe Nylander
- Oxford Centre for Diabetes, Endocrinology and MetabolismUniversity of OxfordOxfordUnited Kingdom
| | | | - Matthias Thurner
- Wellcome Centre for Human GeneticsOxfordUnited Kingdom
- Oxford Centre for Diabetes, Endocrinology and MetabolismUniversity of OxfordOxfordUnited Kingdom
| | | | | | - Anna L Gloyn
- Wellcome Centre for Human GeneticsOxfordUnited Kingdom
- Oxford Centre for Diabetes, Endocrinology and MetabolismUniversity of OxfordOxfordUnited Kingdom
- Oxford NIHR Biomedical CentreChurchill HospitalOxfordUnited Kingdom
| | - Mark I McCarthy
- Wellcome Centre for Human GeneticsOxfordUnited Kingdom
- Oxford Centre for Diabetes, Endocrinology and MetabolismUniversity of OxfordOxfordUnited Kingdom
- Oxford NIHR Biomedical CentreChurchill HospitalOxfordUnited Kingdom
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24
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Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:125-162. [PMID: 31902950 PMCID: PMC6941751 DOI: 10.1109/jproc.2019.2947272] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
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Affiliation(s)
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90232, USA
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25
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Huang M, Yu Y, Yang W, Feng Q. Incorporating spatial-anatomical similarity into the VGWAS framework for AD biomarker detection. Bioinformatics 2019; 35:5271-5280. [PMID: 31095298 PMCID: PMC6954655 DOI: 10.1093/bioinformatics/btz401] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 04/03/2019] [Accepted: 05/07/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The detection of potential biomarkers of Alzheimer's disease (AD) is crucial for its early prediction, diagnosis and treatment. Voxel-wise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues. RESULTS We introduce a novel method to incorporate spatial correlations into a VGWAS framework for the detection of potential AD biomarkers. To consider the characteristics of AD, we first present a modification of a simple linear iterative clustering method for spatial grouping in an anatomically meaningful manner. Second, we propose a spatial-anatomical similarity matrix to incorporate correlations among voxels. Finally, we detect the potential AD biomarkers from imaging and genetic data by using a fast VGWAS method and test our method on 708 subjects obtained from an Alzheimer's Disease Neuroimaging Initiative dataset. Results show that our method can successfully detect some new risk genes and clusters of AD. The detected imaging and genetic biomarkers are used as predictors to classify AD/normal control subjects, and a high accuracy of AD/normal control classification is achieved. To the best of our knowledge, the association between imaging and genetic data has yet to be systematically investigated while building statistical models for classifying AD subjects to create a link between imaging genetics and AD. Therefore, our method may provide a new way to gain insights into the underlying pathological mechanism of AD. AVAILABILITY AND IMPLEMENTATION https://github.com/Meiyan88/SASM-VGWAS.
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Affiliation(s)
- Meiyan Huang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Yuwei Yu
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Qianjin Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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26
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Zhang J, Ibrahim J, Li T, Zhu H. A Powerful Global Test Statistic for Functional Statistical Inference. ACTA ACUST UNITED AC 2019; 33:5765-5772. [PMID: 31485381 DOI: 10.1609/aaai.v33i01.33015765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
We consider the problem of performing an association test between functional data and scalar variables in a varying coefficient model setting. We propose a functional projection regression model and an associated global test statistic to aggregate relatively weak signals across the domain of functional data, while reducing the dimension. An optimal functional projection direction is selected to maximize signal-to-noise ratio with ridge penalty. Theoretically, we systematically study the asymptotic distribution of the global test statistic and provide a strategy to adaptively select the optimal tuning parameter. We use simulations to show that the proposed test outperforms all existing state-of-the-art methods in functional statistical inference. Finally, we apply the proposed testing method to the genome-wide association analysis of imaging genetic data in UK Biobank dataset.
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Affiliation(s)
- Jingwen Zhang
- Department of Biostatistics, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Joseph Ibrahim
- Department of Biostatistics, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Tengfei Li
- Biomedical Research Imaging Center, UNC School of Medicine, University of North Carolina at Chapel Hill
| | - Hongtu Zhu
- Department of Biostatistics, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill
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27
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Shoukri B, Prieto J, Ruellas A, Yatabe M, Sugai J, Styner M, Zhu H, Huang C, Paniagua B, Aronovich S, Ashman L, Benavides E, de Dumast P, Ribera N, Mirabel C, Michoud L, Allohaibi Z, Ioshida M, Bittencourt L, Fattori L, Gomes L, Cevidanes L. Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis. J Dent Res 2019; 98:1103-1111. [PMID: 31340134 PMCID: PMC6704428 DOI: 10.1177/0022034519865187] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
This study's objectives were to test correlations among groups of biomarkers that are associated with condylar morphology and to apply artificial intelligence to test shape analysis features in a neural network (NN) to stage condylar morphology in temporomandibular joint osteoarthritis (TMJOA). Seventeen TMJOA patients (39.9 ± 11.7 y) experiencing signs and symptoms of the disease for less than 10 y and 17 age- and sex-matched control subjects (39.4 ± 15.2 y) completed a questionnaire, had a temporomandibular joint clinical exam, had blood and saliva samples drawn, and had high-resolution cone beam computed tomography scans taken. Serum and salivary levels of 17 inflammatory biomarkers were quantified using protein microarrays. A NN was trained with 259 other condyles to detect and classify the stage of TMJOA and then compared to repeated clinical experts' classifications. Levels of the salivary biomarkers MMP-3, VE-cadherin, 6Ckine, and PAI-1 were correlated to each other in TMJOA patients and were significantly correlated with condylar morphological variability on the posterior surface of the condyle. In serum, VE-cadherin and VEGF were correlated with one another and with significant morphological variability on the anterior surface of the condyle, while MMP-3 and CXCL16 presented statistically significant associations with variability on the anterior surface, lateral pole, and superior-posterior surface of the condyle. The range of mouth opening variables were the clinical markers with the most significant associations with morphological variability at the medial and lateral condylar poles. The repeated clinician consensus classification had 97.8% agreement on degree of degeneration within 1 group difference. Predictive analytics of the NN's staging of TMJOA compared to the repeated clinicians' consensus revealed 73.5% and 91.2% accuracy. This study demonstrated significant correlations among variations in protein expression levels, clinical symptoms, and condylar surface morphology. The results suggest that 3-dimensional variability in TMJOA condylar morphology can be comprehensively phenotyped by the NN.
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Affiliation(s)
- B. Shoukri
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - J.C. Prieto
- Department of Psychiatry, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - A. Ruellas
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - M. Yatabe
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - J. Sugai
- Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - M. Styner
- Department of Psychiatry, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - H. Zhu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - C. Huang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | | | - S. Aronovich
- Department Oral and Maxillofacial Surgery and Hospital Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - L. Ashman
- Department Oral and Maxillofacial Surgery and Hospital Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - E. Benavides
- Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - P. de Dumast
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - N.T. Ribera
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - C. Mirabel
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - L. Michoud
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Z. Allohaibi
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - M. Ioshida
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - L. Bittencourt
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - L. Fattori
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - L.R. Gomes
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - L. Cevidanes
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
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28
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Zhao Y, Zhu H, Lu Z, Knickmeyer RC, Zou F. Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection. Genetics 2019; 212:397-415. [PMID: 31010934 PMCID: PMC6553832 DOI: 10.1534/genetics.119.301906] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 04/08/2019] [Indexed: 02/04/2023] Open
Abstract
It becomes increasingly important in using genome-wide association studies (GWAS) to select important genetic information associated with qualitative or quantitative traits. Currently, the discovery of biological association among SNPs motivates various strategies to construct SNP-sets along the genome and to incorporate such set information into selection procedure for a higher selection power, while facilitating more biologically meaningful results. The aim of this paper is to propose a novel Bayesian framework for hierarchical variable selection at both SNP-set (group) level and SNP (within group) level. We overcome a key limitation of existing posterior updating scheme in most Bayesian variable selection methods by proposing a novel sampling scheme to explicitly accommodate the ultrahigh-dimensionality of genetic data. Specifically, by constructing an auxiliary variable selection model under SNP-set level, the new procedure utilizes the posterior samples of the auxiliary model to subsequently guide the posterior inference for the targeted hierarchical selection model. We apply the proposed method to a variety of simulation studies and show that our method is computationally efficient and achieves substantially better performance than competing approaches in both SNP-set and SNP selection. Applying the method to the Alzheimers Disease Neuroimaging Initiative (ADNI) data, we identify biologically meaningful genetic factors under several neuroimaging volumetric phenotypes. Our method is general and readily to be applied to a wide range of biomedical studies.
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Affiliation(s)
- Yize Zhao
- Department of Healthcare Policy and Research, Cornell University Weill Cornell, New York, New York 10065
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Zhaohua Lu
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105
| | - Rebecca C Knickmeyer
- Department of Pediatrics and Human Development, Michigan State University, East Lansing, Michigan 48824
| | - Fei Zou
- Department of Biostatistics, University of Florida, Gainesville, Florida 32611
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29
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Michoud L, Huang C, Yatabe M, Ruellas A, Ioshida M, Paniagua B, Styner M, Gonçalves JR, Bianchi J, Cevidanes L, Prieto JC. A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 10953. [PMID: 31057201 DOI: 10.1117/12.2506032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts.
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Affiliation(s)
- Loic Michoud
- Dept. of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI, USA 48109
| | - Chao Huang
- Dept. of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, Hanes Hall, Campus Box 3260, NC, USA 27599
| | - Marilia Yatabe
- Dept. of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI, USA 48109
| | - Antonio Ruellas
- Dept. of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI, USA 48109
| | - Marcos Ioshida
- Dept. of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI, USA 48109
| | | | - Martin Styner
- Dept. of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, Hanes Hall, Campus Box 3260, NC, USA 27599
| | - João Roberto Gonçalves
- Dept. of Pediatric Dentistry, Sao Paulo State University (Unesp), School of Dentistry 1680 Humaita St, Araraquara, SP, Brazil 14801-385
| | - Jonas Bianchi
- Dept. of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI, USA 48109.,Dept. of Pediatric Dentistry, Sao Paulo State University (Unesp), School of Dentistry 1680 Humaita St, Araraquara, SP, Brazil 14801-385
| | - Lucia Cevidanes
- Dept. of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 N University Ave, Ann Arbor, MI, USA 48109
| | - Juan-Carlos Prieto
- Dept. of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, Hanes Hall, Campus Box 3260, NC, USA 27599
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30
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Nathoo FS, Kong L, Zhu H. A Review of Statistical Methods in Imaging Genetics. CAN J STAT 2019; 47:108-131. [PMID: 31274952 PMCID: PMC6605768 DOI: 10.1002/cjs.11487] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 10/08/2018] [Indexed: 12/24/2022]
Abstract
With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function, and brain-related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention, and treatment of numerous complex brain-related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high-dimensional imaging phenotypes and high-dimensional genetic data, a big data squared (BD2) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high-dimensional nature of BD2, various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel-wise approaches, reduced rank regression, mixture models, and group sparse multi-task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research.
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Affiliation(s)
- Farouk S Nathoo
- Department of Mathematics and Statistics, University of Victoria
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta
| | - Hongtu Zhu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
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31
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Cremona MA, Xu H, Makova KD, Reimherr M, Chiaromonte F, Madrigal P. Functional data analysis for computational biology. Bioinformatics 2019; 35:3211-3213. [PMID: 30668667 DOI: 10.1093/bioinformatics/btz045] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 01/01/2019] [Accepted: 01/17/2019] [Indexed: 12/25/2022] Open
Abstract
SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marzia A Cremona
- Department of Statistics, The Pennsylvania State University, University Park, PA, USA
| | - Hongyan Xu
- Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Kateryna D Makova
- Department of Biology, The Pennsylvania State University, University Park, PA, USA.,Center for Medical Genomics, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Matthew Reimherr
- Department of Statistics, The Pennsylvania State University, University Park, PA, USA
| | - Francesca Chiaromonte
- Department of Statistics, The Pennsylvania State University, University Park, PA, USA.,Institute of Economics, Sant'Anna School of Advanced Studies, EMbeDS Economics and Management in the era of Data Science, Pisa, Italy
| | - Pedro Madrigal
- Wellcome Trust - MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.,Department of Haematology, University of Cambridge, Cambridge, UK
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32
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Huang M, Deng C, Yu Y, Lian T, Yang W, Feng Q. Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD. Neuroimage Clin 2018; 21:101642. [PMID: 30584014 PMCID: PMC6413305 DOI: 10.1016/j.nicl.2018.101642] [Citation(s) in RCA: 5] [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: 09/06/2018] [Revised: 11/19/2018] [Accepted: 12/10/2018] [Indexed: 02/05/2023]
Abstract
Potential biomarker detection is a crucial area of study for the prediction, diagnosis, and monitoring of Alzheimer's disease (AD). The voxelwise genome-wide association study (vGWAS) is widely used in imaging genomics studies that is usually applied to the detection of AD biomarkers in both imaging and genetic data. However, performing vGWAS remains a challenge because of the computational complexity of the technique and our ignorance of the spatial correlations within the imaging data. In this paper, we propose a novel method based on the exploitation of spatial correlations that may help to detect potential AD biomarkers using a fast vGWAS. To incorporate spatial correlations, we applied a nonlocal method that supposed that a given voxel could be represented by weighting the sum of the other voxels. Three commonly used weighting methods were adopted to calculate the weights among different voxels in this study. Then, a fast vGWAS approach was used to assess the association between the image and the genetic data. The proposed method was estimated using both simulated and real data. In the simulation studies, we designed a set of experiments to evaluate the effectiveness of the nonlocal method for incorporating spatial correlations in vGWAS. The experiments showed that incorporating spatial correlations by the nonlocal method could improve the detecting accuracy of AD biomarkers. For real data, we successfully identified three genes, namely, ANK3, MEIS2, and TLR4, which have significant associations with mental retardation, learning disabilities and age according to previous research. These genes have profound impacts on AD or other neurodegenerative diseases. Our results indicated that our method might be an effective and valuable tool for detecting potential biomarkers of AD.
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Affiliation(s)
- Meiyan Huang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Chunyan Deng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yuwei Yu
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Tao Lian
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Qianjin Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
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33
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Gomulkiewicz R, Kingsolver JG, Carter PA, Heckman N. Variation and Evolution of Function-Valued Traits. ANNUAL REVIEW OF ECOLOGY EVOLUTION AND SYSTEMATICS 2018. [DOI: 10.1146/annurev-ecolsys-110316-022830] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Function-valued traits—phenotypes whose expression depends on a continuous index (such as age, temperature, or space)—occur throughout biology and, like any trait, it is important to understand how they vary and evolve. Although methods for analyzing variation and evolution of function-valued traits are well developed, they have been underutilized by evolutionists, especially those who study natural populations. We seek to summarize advances in the study of function-valued traits and to make their analyses more approachable and accessible to biologists who could benefit greatly from their use. To that end, we explain how curve thinking benefits conceptual understanding and statistical analysis of functional data. We provide a detailed guide to the most flexible and statistically powerful methods and include worked examples (with R code) as supplemental material. We review ways to characterize variation in function-valued traits and analyze consequences for evolution, including constraint. We also discuss how selection on function-valued traits can be estimated and combined with estimates of heritable variation to project evolutionary dynamics.
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Affiliation(s)
- Richard Gomulkiewicz
- School of Biological Sciences, Washington State University, Pullman, Washington 99164, USA
| | - Joel G. Kingsolver
- Department of Biology, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Patrick A. Carter
- School of Biological Sciences, Washington State University, Pullman, Washington 99164, USA
| | - Nancy Heckman
- Department of Statistics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
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34
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Zhu X, Zhang W, Fan Y. A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis. Neuroinformatics 2018; 16:351-361. [PMID: 29907892 PMCID: PMC6092232 DOI: 10.1007/s12021-018-9382-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
To characterize associations between genetic and neuroimaging data, a variety of analytic methods have been proposed in neuroimaging genetic studies. These methods have achieved promising performance by taking into account inherent correlation in either the neuroimaging data or the genetic data alone. In this study, we propose a novel robust reduced rank graph regression based method in a linear regression framework by considering correlations inherent in neuroimaging data and genetic data jointly. Particularly, we model the association analysis problem in a reduced rank regression framework with the genetic data as a feature matrix and the neuroimaging data as a response matrix by jointly considering correlations among the neuroimaging data as well as correlations between the genetic data and the neuroimaging data. A new graph representation of genetic data is adopted to exploit their inherent correlations, in addition to robust loss functions for both the regression and the data representation tasks, and a square-root-operator applied to the robust loss functions for achieving adaptive sample weighting. The resulting optimization problem is solved using an iterative optimization method whose convergence has been theoretically proved. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our method could achieve competitive performance in terms of regression performance between brain structural measures and the Single Nucleotide Polymorphisms (SNPs), compared with state-of-the-art alternative methods.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Weihong Zhang
- Peking Union Medical College Hospital, Beijing, 100730, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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de Dumast P, Mirabel C, Cevidanes L, Ruellas A, Yatabe M, Ioshida M, Ribera NT, Michoud L, Gomes L, Huang C, Zhu H, Muniz L, Shoukri B, Paniagua B, Styner M, Pieper S, Budin F, Vimort JB, Pascal L, Prieto JC. A web-based system for neural network based classification in temporomandibular joint osteoarthritis. Comput Med Imaging Graph 2018; 67:45-54. [PMID: 29753964 DOI: 10.1016/j.compmedimag.2018.04.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 03/20/2018] [Accepted: 04/24/2018] [Indexed: 01/04/2023]
Abstract
OBJECTIVE The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). METHODS This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. RESULTS The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. CONCLUSIONS The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis.
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Affiliation(s)
- Priscille de Dumast
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Clément Mirabel
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Lucia Cevidanes
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA.
| | - Antonio Ruellas
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Marilia Yatabe
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Marcos Ioshida
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Nina Tubau Ribera
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Loic Michoud
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Liliane Gomes
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Chao Huang
- University of North Carolina, Chapel Hill, NC, USA
| | - Hongtu Zhu
- MD Anderson Cancer Center, University of Texas, Houston, TX, USA
| | - Luciana Muniz
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Brandon Shoukri
- Department for Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, USA
| | | | - Martin Styner
- Departments of Psychiatry and Computer Sciences, University of North Carolina, Chapel Hill, NC, USA
| | | | | | | | | | - Juan Carlos Prieto
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
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Guillaume B, Wang C, Poh J, Shen MJ, Ong ML, Tan PF, Karnani N, Meaney M, Qiu A. Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: Application to Epigenome-Wide Association Studies. Neuroimage 2018; 173:57-71. [PMID: 29448075 DOI: 10.1016/j.neuroimage.2018.01.073] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/03/2018] [Accepted: 01/28/2018] [Indexed: 10/18/2022] Open
Abstract
Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to modify this selection adequately. The use of standard diagnostics, such as residual plotting, is clearly not practical for neuroimaging data. However, the selection of covariates is crucial for linear regression to ensure valid statistical inference. In particular, the mean model of regression needs to be reasonably well specified. Unfortunately, this issue is often overlooked in the field of neuroimaging. This study aims to adopt the existing Confounder Adjusted Testing and Estimation (CATE) approach and to extend it for use with neuroimaging data. We propose a modification of CATE that can yield valid statistical inferences using Principal Component Analysis (PCA) estimators instead of Maximum Likelihood (ML) estimators. We then propose a non-parametric hypothesis testing procedure that can improve upon parametric testing. Monte Carlo simulations show that the modification of CATE allows for more accurate modelling of neuroimaging data and can in turn yield a better control of False Positive Rate (FPR) and Family-Wise Error Rate (FWER). We demonstrate its application to an Epigenome-Wide Association Study (EWAS) on neonatal brain imaging and umbilical cord DNA methylation data obtained as part of a longitudinal cohort study. Software for this CATE study is freely available at http://www.bioeng.nus.edu.sg/cfa/Imaging_Genetics2.html.
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Affiliation(s)
- Bryan Guillaume
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Changqing Wang
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Joann Poh
- Department of Biomedical Engineering, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Mo Jun Shen
- Department of Biomedical Engineering, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Mei Lyn Ong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Pei Fang Tan
- Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Neerja Karnani
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Michael Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Canada; Sackler Program for Epigenetics and Psychobiology at McGill University, Canada; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore; Clinical Imaging Research Centre, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore.
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