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Wang KW, Yuan YX, Zhu B, Zhang Y, Wei YF, Meng FS, Zhang S, Wang JX, Zhou JY. X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study. Front Aging Neurosci 2023; 15:1277731. [PMID: 38035272 PMCID: PMC10682795 DOI: 10.3389/fnagi.2023.1277731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 10/20/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a complex neurodegenerative disease with high heritability. Compared to autosomes, a higher proportion of disorder-associated genes on X chromosome are expressed in the brain. However, only a few studies focused on the identification of the susceptibility loci for AD on X chromosome. Methods Using the data from the Alzheimer's Disease Neuroimaging Initiative Study, we conducted an X chromosome-wide association study between 16 AD quantitative biomarkers and 19,692 single nucleotide polymorphisms (SNPs) based on both the cross-sectional and longitudinal studies. Results We identified 15 SNPs statistically significantly associated with different quantitative biomarkers of the AD. For the cross-sectional study, six SNPs (rs5927116, rs4596772, rs5929538, rs2213488, rs5920524, and rs5945306) are located in or near to six genes DMD, TBX22, LOC101928437, TENM1, SPANXN1, and ZFP92, which have been reported to be associated with schizophrenia or neuropsychiatric diseases in literature. For the longitudinal study, four SNPs (rs4829868, rs5931111, rs6540385, and rs763320) are included in or near to two genes RAC1P4 and AFF2, which have been demonstrated to be associated with brain development or intellectual disability in literature, while the functional annotations of other five novel SNPs (rs12157031, rs428303, rs5953487, rs10284107, and rs5955016) have not been found. Discussion 15 SNPs were found statistically significantly associated with the quantitative biomarkers of the AD. Follow-up study in molecular genetics is needed to verify whether they are indeed related to AD. The findings in this article expand our understanding of the role of the X chromosome in exploring disease susceptibility, introduce new insights into the molecular genetics behind the AD, and may provide a mechanistic clue to further AD-related studies.
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Affiliation(s)
- Kai-Wen Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yu-Xin Yuan
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Bin Zhu
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi-Fang Wei
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Fan-Shuo Meng
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Shun Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jing-Xuan Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ji-Yuan Zhou
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
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Chaari N, Akdağ HC, Rekik I. Comparative survey of multigraph integration methods for holistic brain connectivity mapping. Med Image Anal 2023; 85:102741. [PMID: 36638747 DOI: 10.1016/j.media.2023.102741] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 12/27/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.
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Affiliation(s)
- Nada Chaari
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Faculty of Management, Istanbul Technical University, Istanbul, Turkey
| | | | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Computing, Imperial-X Translation and Innovation Hub, Imperial College London, London, UK.
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Nam J, Gwon Y. Neuronal biomolecular condensates and their implications in neurodegenerative diseases. Front Aging Neurosci 2023; 15:1145420. [PMID: 37065458 PMCID: PMC10102667 DOI: 10.3389/fnagi.2023.1145420] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/01/2023] [Indexed: 04/03/2023] Open
Abstract
Biomolecular condensates are subcellular organizations where functionally related proteins and nucleic acids are assembled through liquid-liquid phase separation, allowing them to develop on a larger scale without a membrane. However, biomolecular condensates are highly vulnerable to disruptions from genetic risks and various factors inside and outside the cell and are strongly implicated in the pathogenesis of many neurodegenerative diseases. In addition to the classical view of the nucleation-polymerization process that triggers the protein aggregation from the misfolded seed, the pathologic transition of biomolecular condensates can also promote the aggregation of proteins found in the deposits of neurodegenerative diseases. Furthermore, it has been suggested that several protein or protein-RNA complexes located in the synapse and along the neuronal process are neuron-specific condensates displaying liquid-like properties. As their compositional and functional modifications play a crucial role in the context of neurodegeneration, further research is needed to fully understand the role of neuronal biomolecular condensates. In this article, we will discuss recent findings that explore the pivotal role of biomolecular condensates in the development of neuronal defects and neurodegeneration.
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Affiliation(s)
| | - Youngdae Gwon
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
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Moon SW. Neuroimaging Genetics and Network Analysis in Alzheimer's Disease. Curr Alzheimer Res 2023; 20:526-538. [PMID: 37957920 DOI: 10.2174/0115672050265188231107072215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/22/2023] [Accepted: 08/13/2023] [Indexed: 11/15/2023]
Abstract
The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG's contribution to expanding our knowledge about the heterogeneity within Alzheimer's disease as well as the benefits of different network analyses.
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Affiliation(s)
- Seok Woo Moon
- Department of Psychiatry, Institute of Medical Science, Konkuk University School of Medicine, Chungju, Republic of Korea
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Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives. Transl Psychiatry 2022; 12:447. [PMID: 36241627 PMCID: PMC9568576 DOI: 10.1038/s41398-022-02193-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 09/06/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022] Open
Abstract
Cortical morphology is a key determinant of cognitive ability and mental health. Its development is a highly intricate process spanning decades, involving the coordinated, localized expression of thousands of genes. We are now beginning to unravel the genetic architecture of cortical morphology, thanks to the recent availability of large-scale neuroimaging and genomic data and the development of powerful biostatistical tools. Here, we review the progress made in this field, providing an overview of the lessons learned from genetic studies of cortical volume, thickness, surface area, and folding as captured by neuroimaging. It is now clear that morphology is shaped by thousands of genetic variants, with effects that are region- and time-dependent, thereby challenging conventional study approaches. The most recent genome-wide association studies have started discovering common genetic variants influencing cortical thickness and surface area, yet together these explain only a fraction of the high heritability of these measures. Further, the impact of rare variants and non-additive effects remains elusive. There are indications that the quickly increasing availability of data from whole-genome sequencing and large, deeply phenotyped population cohorts across the lifespan will enable us to uncover much of the missing heritability in the upcoming years. Novel approaches leveraging shared information across measures will accelerate this process by providing substantial increases in statistical power, together with more accurate mapping of genetic relationships. Important challenges remain, including better representation of understudied demographic groups, integration of other 'omics data, and mapping of effects from gene to brain to behavior across the lifespan.
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Ruffini N, Klingenberg S, Heese R, Schweiger S, Gerber S. The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach. Front Aging Neurosci 2022; 14:866886. [PMID: 35832065 PMCID: PMC9271745 DOI: 10.3389/fnagi.2022.866886] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/13/2022] [Indexed: 12/12/2022] Open
Abstract
The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.
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Affiliation(s)
- Nicolas Ruffini
- Institute of Human Genetics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Leibniz Institute for Resilience Research, Leibniz Association, Mainz, Germany
| | - Susanne Klingenberg
- Institute of Human Genetics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Raoul Heese
- Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany
| | - Susann Schweiger
- Institute of Human Genetics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Susanne Gerber
- Institute of Human Genetics, University Medical Center, Johannes Gutenberg University, Mainz, Germany
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Molecular Signatures of Mitochondrial Complexes Involved in Alzheimer’s Disease via Oxidative Phosphorylation and Retrograde Endocannabinoid Signaling Pathways. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:9565545. [PMID: 35432724 PMCID: PMC9006080 DOI: 10.1155/2022/9565545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/22/2022] [Indexed: 11/17/2022]
Abstract
Objective The inability to intervene in Alzheimer's disease (AD) forces the search for promising gene-targeted therapies. This study was aimed at exploring molecular signatures and mechanistic pathways to improve the diagnosis and treatment of AD. Methods Microarray datasets were collected to filter differentially expressed genes (DEGs) between AD and nondementia controls. Weight gene correlation network analysis (WGCNA) was employed to analyze the correlation of coexpression modules with AD phenotype. A global regulatory network was established and then visualized using Cytoscape software to determine hub genes and their mechanistic pathways. Receiver operating characteristic (ROC) analysis was conducted to estimate the diagnostic performance of hub genes in AD prediction. Results A total of 2,163 DEGs from 13,049 background genes were screened in AD relative to nondementia controls. Among the six coexpression modules constructed by WGCNA, DEGs of the key modules with the strongest correlation with AD were extracted to build a global regulatory network. According to the Maximal Clique Centrality (MCC) method, five hub genes associated with mitochondrial complexes were chosen. Further pathway enrichment analysis of hub genes, such as oxidative phosphorylation and retrograde endocannabinoid signaling, was identified. According to the area under the curve (AUC) of about 70%, each hub gene exhibited a good diagnostic performance in predicting AD. Conclusions Our findings highlight the perturbation of mitochondrial complexes underlying AD onset, which is mediated by molecular signatures involved in oxidative phosphorylation (COX5A, NDUFAB1, SDHB, UQCRC2, and UQCRFS1) and retrograde endocannabinoid signaling (NDUFAB1) pathways.
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Lee B, Yao X, Shen L. Genome-Wide association study of quantitative biomarkers identifies a novel locus for alzheimer's disease at 12p12.1. BMC Genomics 2022; 23:85. [PMID: 35086473 PMCID: PMC8796646 DOI: 10.1186/s12864-021-08269-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 12/14/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genetic study of quantitative biomarkers in Alzheimer's Disease (AD) is a promising method to identify novel genetic factors and relevant endophenotypes, which provides valuable information to deconvolute mechanistic complexity and better understand disease subtypes. RESULTS Using the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we performed a genome-wide association study (GWAS) between 565,373 single nucleotide polymorphisms (SNPs) and 16 key AD biomarkers from 1,576 subjects at four visits. We identified a novel locus rs5011804 at 12p12.1 significantly associated with several AD biomarkers, including three cognitive traits (CDRSB, FAQ, ADAS13) and one imaging trait (fusiform volume). Additional mediation and interaction analyses investigated the relationships among this SNP, relevant biomarkers, and clinical diagnosis, confirming and further elaborating the genetic effects seen in the GWAS. CONCLUSION Our GWAS not only affirms key AD genes but also suggests the promising role of the SNP rs5011804 due to its associations with several AD cognitive and imaging outcomes. The SNP rs5011804 has a reported association with adult asthma and slightly affects intracranial volume but has not been associated with AD before. Our novel findings contribute to a more comprehensive view of the molecular mechanism behind AD.
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Affiliation(s)
- Brian Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
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Minami Y, Yuan Y, Ueda HR. Towards organism-level systems biology by next-generation genetics and whole-organ cell profiling. Biophys Rev 2021; 13:1113-1126. [PMID: 35059031 PMCID: PMC8724464 DOI: 10.1007/s12551-021-00859-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/18/2021] [Indexed: 02/06/2023] Open
Abstract
The system-level identification and analysis of molecular and cellular networks in mammals can be accelerated by "next-generation" genetics, which is defined as genetics that can achieve desired genetic makeup in a single generation without any animal crossing. We recently established a highly efficient procedure for producing knock-out (KO) mice using the "Triple-CRISPR" method, which targets a single gene by triple gRNAs in the CRISPR/Cas9 system. This procedure achieved an almost perfect KO efficiency (96-100%). We also established a highly efficient procedure, the "ES-mouse" method, for producing knock-in (KI) mice within a single generation. In this method, ES cells were treated with three inhibitors to keep their potency and then injected into 8-cell-stage embryos. These procedures dramatically shortened the time required to produce KO or KI mice from years down to about 3 months. The produced KO and KI mice can also be systematically profiled at a single-cell resolution by the "whole-organ cell profiling," which was realized by tissue-clearing methods, such as CUBIC, and an advanced light-sheet microscopy. The review describes the establishment and application of these technologies above in analyzing the three states (NREM sleep, REM sleep, and awake) of mammalian brains. It also discusses the role of calcium and muscarinic receptors in these states as well as the current challenges and future opportunities in the next-generation mammalian genetics and whole-organ cell profiling for organism-level systems biology.
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Affiliation(s)
- Yoichi Minami
- Department of Systems Pharmacology, Graduate School of Medicine, the University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033 Japan
| | - Yufei Yuan
- Department of Systems Pharmacology, Graduate School of Medicine, the University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033 Japan
| | - Hiroki R. Ueda
- Department of Systems Pharmacology, Graduate School of Medicine, the University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033 Japan
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, 1-3 Yamadaoka, Suita, Osaka 565-0871 Japan
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Li L, Huang Y, Han Y, Jiang J. Use of deep learning genomics to discriminate Alzheimer's disease and healthy controls. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5788-5791. [PMID: 34892435 DOI: 10.1109/embc46164.2021.9629983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Because gene is an important clinical risk factor resulting in AD, genomic studies, such as genome-wide association studies (GWAS), have widely been applied into AD studies. However, main shortcomings of GWAS method were that hereditary deletions were evident in the GWAS studies, which resulted in low classification or prediction abilities by using GWAS analysis. Therefore, this paper proposed a novel deep learning genomics approach and applied it to discriminate AD patients and healthy control (HC) subjects. In this study, we selected genotype data of 988 subjects enrolled in the ADNI, including 622 AD patients and 366 HC subjects. The proposed deep learning genomics (DLG) approach was composed of three steps: quality control, SNP genotype coding, and classification. The Resnet framework was used as the DLG model in this study. In the comparative GWAS analysis, APOE ε4 status and the normalized theta-value of the significant SNP loci were seen as predictors to classify genetically using Support Vector Machine (SVM). All data were divided into one training & validation group and one test group. 5-fold cross-validation was used in 500 times. Finally, we compared the classification results between DLG model and traditional GWAS analysis. As a result, the accuracy, sensitivity, and specificity of classification for traditional GWAS analysis was 71.38%±0.63%, 63.13%±2.87% and 85.59%±6.66% in the test group; while the accuracy, sensitivity, and specificity of classification for DLG model was 92.65%±4.80%, 85.00%±16.25% and 97.10%±4.38% in the test group. Hence, the DLG model can achieve higher accuracy and sensitivity when applied to AD. More importantly, we discovered several novel genetic biomarkers of AD, including rs6311 and rs6313 in HTR2A, and rs690705 in RFC3. The roles of these novel loci in AD should be explored future.
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Cuttler K, Hassan M, Carr J, Cloete R, Bardien S. Emerging evidence implicating a role for neurexins in neurodegenerative and neuropsychiatric disorders. Open Biol 2021; 11:210091. [PMID: 34610269 PMCID: PMC8492176 DOI: 10.1098/rsob.210091] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Synaptopathies are brain disorders characterized by dysfunctional synapses, which are specialized junctions between neurons that are essential for the transmission of information. Synaptic dysfunction can occur due to mutations that alter the structure and function of synaptic components or abnormal expression levels of a synaptic protein. One class of synaptic proteins that are essential to their biology are cell adhesion proteins that connect the pre- and post-synaptic compartments. Neurexins are one type of synaptic cell adhesion molecule that have, recently, gained more pathological interest. Variants in both neurexins and their common binding partners, neuroligins, have been associated with several neuropsychiatric disorders. In this review, we summarize some of the key physiological functions of the neurexin protein family and the protein networks they are involved in. Furthermore, examination of published literature has implicated neurexins in both neuropsychiatric and neurodegenerative disorders. There is a clear link between neurexins and neuropsychiatric disorders, such as autism spectrum disorder and schizophrenia. However, multiple expression studies have also shown changes in neurexin expression in several neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease. Therefore, this review highlights the potential importance of neurexins in brain disorders and the importance of doing more targeted studies on these genes and proteins.
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Affiliation(s)
- Katelyn Cuttler
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Maryam Hassan
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Jonathan Carr
- Division of Neurology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa,South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit, Cape Town, South Africa
| | - Ruben Cloete
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Soraya Bardien
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa,South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit, Cape Town, South Africa
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Li Y, Nan B, Zhu J. A Structured Brain-wide and Genome-wide Association Study Using ADNI PET Images. CAN J STAT 2021; 49:182-202. [PMID: 34566241 DOI: 10.1002/cjs.11605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A multi-stage variable selection method is introduced for detecting association signals in structured brain-wide and genome-wide association studies (brain-GWAS). Compared to conventional single-voxel-to-single-SNP approaches, our approach is more efficient and powerful in selecting the important signals by integrating anatomic and gene grouping structures in the brain and the genome, respectively. It avoids large number of multiple comparisons while effectively controls the false discoveries. Validity of the proposed approach is demonstrated by both theoretical investigation and numerical simulations. We apply the proposed method to a brain-GWAS using ADNI PET imaging and genomic data. We confirm previously reported association signals and also find several novel SNPs and genes that either are associated with brain glucose metabolism or have their association significantly modified by Alzheimer's disease status.
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Affiliation(s)
- Yanming Li
- Department of Biotatistics & Data Science, University of Kansas Medical Center Kansas City, KS 66160
| | - Bin Nan
- Department of Statistics, University of California at Irvine Irvine, CA 92697
| | - Ji Zhu
- Department of Statistics, University of Michigan Ann Arbor, MI 48109
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Nobile M, Maggioni E, Mauri M, Garzitto M, Piccin S, Bonivento C, Giorda R, Girometti R, Tomasino B, Molteni M, Fabbro F, Brambilla P. Brain Anatomical Mediators of GRIN2B Gene Association with Attention/Hyperactivity Problems: An Integrated Genetic-Neuroimaging Study. Genes (Basel) 2021; 12:genes12081193. [PMID: 34440367 PMCID: PMC8394308 DOI: 10.3390/genes12081193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/19/2021] [Accepted: 07/28/2021] [Indexed: 12/02/2022] Open
Abstract
This study aims to investigate the genetic and neural determinants of attention and hyperactivity problems. Using a proof-of-concept imaging genetics mediation design, we explore the relationship between the glutamatergic GRIN2B gene variants and inattention/hyperactivity with neuroanatomical measures as intermediates. Fifty-eight children and adolescents were evaluated for behavioral problems at three time points over approximately 7 years. The final assessment included blood drawing for genetic analyses and 3T magnetic resonance imaging. Attention/hyperactivity problems based on the Child Behavior Checklist/6-18, six GRIN2B polymorphisms and regional cortical thickness, and surface area and volume were estimated. Using general linear model (GLM) and mediation analyses, we tested whether GRIN2B exerted an influence on stable inattention/hyperactivity over development, and to what extent this effect was mediated by brain morphology. GLM results enlightened the relation between GRIN2B rs5796555-/A, volume in the left cingulate isthmus and inferior parietal cortices and inattention/hyperactivity. The mediation results showed that rs5796555-/A effect on inattention/hyperactivity was partially mediated by volume in the left isthmus of the cingulate cortex, suggesting a key role of this region in translating glutamatergic GRIN2B variations to attention/hyperactivity problems. This evidence can have important implications in the management of neurodevelopmental and psychiatric disorders.
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Affiliation(s)
- Maria Nobile
- Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, 22040 Bosisio Parini, Italy; (M.N.); (M.M.); (M.M.)
| | - Eleonora Maggioni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Maddalena Mauri
- Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, 22040 Bosisio Parini, Italy; (M.N.); (M.M.); (M.M.)
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy
| | - Marco Garzitto
- Scientific Institute, IRCCS E. Medea, San Vito al Tagliamento, 33170 Pordenone, Italy; (M.G.); (C.B.); (B.T.); (F.F.)
| | - Sara Piccin
- Unit of Psychiatry, Department of Medicine (DAME), University of Udine, 33100 Udine, Italy;
| | - Carolina Bonivento
- Scientific Institute, IRCCS E. Medea, San Vito al Tagliamento, 33170 Pordenone, Italy; (M.G.); (C.B.); (B.T.); (F.F.)
| | - Roberto Giorda
- Molecular Biology Laboratory, Scientific Institute, IRCCS Eugenio Medea, 22040 Bosisio Parini, Italy;
| | - Rossano Girometti
- Institute of Radiology, Department of Medicine, University of Udine, 33100 Udine, Italy;
| | - Barbara Tomasino
- Scientific Institute, IRCCS E. Medea, San Vito al Tagliamento, 33170 Pordenone, Italy; (M.G.); (C.B.); (B.T.); (F.F.)
| | - Massimo Molteni
- Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, 22040 Bosisio Parini, Italy; (M.N.); (M.M.); (M.M.)
| | - Franco Fabbro
- Scientific Institute, IRCCS E. Medea, San Vito al Tagliamento, 33170 Pordenone, Italy; (M.G.); (C.B.); (B.T.); (F.F.)
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
- Department of Pathophysiology and Transplantation, University of Milan, 20122 Milan, Italy
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Correspondence: ; Tel.: +39-02-5503-2717
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14
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Moody JN, Valerio KE, Hasselbach AN, Prieto S, Logue MW, Hayes SM, Hayes JP. Body Mass Index and Polygenic Risk for Alzheimer's Disease Predict Conversion to Alzheimer's Disease. J Gerontol A Biol Sci Med Sci 2021; 76:1415-1422. [PMID: 33880516 PMCID: PMC8277084 DOI: 10.1093/gerona/glab117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Indexed: 12/25/2022] Open
Abstract
Body mass index (BMI) is a risk factor for Alzheimer's disease (AD) although the relationship is complex. Obesity in midlife is associated with increased risk for AD, whereas evidence supports both higher and lower BMI increasing risk for AD in late life. This study examined the influence of individual differences in genetic risk for AD to further clarify the relationship between late-life BMI and conversion to AD. Participants included 52 individuals diagnosed as having mild cognitive impairment (MCI) at baseline who converted to AD within 24 months and 52 matched MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. BMI was measured at baseline. Genetic risk for AD was assessed via genome-wide polygenic risk scores. Conditional logistic regression models were run to determine if BMI and polygenic risk predicted conversion to AD. Results showed an interaction between BMI and genetic risk, such that individuals with lower BMI and higher polygenic risk were more likely to convert to AD relative to individuals with higher BMI. These results remained significant after adjusting for cerebrospinal fluid biomarkers of AD. Exploratory sex-stratified analyses revealed this relationship only remained significant in males. These results show that higher genetic risk in the context of lower BMI predicts conversion to AD in the next 24 months, particularly among males. These findings suggest that genetic risk for AD in the context of lower BMI may serve as a prodromal risk factor for future conversion to AD.
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Affiliation(s)
- Jena N Moody
- Department of Psychology, The Ohio State University, Columbus, USA
| | - Kate E Valerio
- Department of Psychology, The Ohio State University, Columbus, USA
| | | | - Sarah Prieto
- Department of Psychology, The Ohio State University, Columbus, USA
| | - Mark W Logue
- National Center for PTSD, VA Boston Healthcare System, Massachusetts, USA.,Psychiatry and Biomedical Genetics, Boston University School of Medicine, Massachusetts, USA
| | - Scott M Hayes
- Department of Psychology, The Ohio State University, Columbus, USA.,Chronic Brain Injury Initiative, The Ohio State University, Columbus, USA
| | - Jasmeet P Hayes
- Department of Psychology, The Ohio State University, Columbus, USA.,Chronic Brain Injury Initiative, The Ohio State University, Columbus, USA
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15
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Vos de Wael R, Royer J, Tavakol S, Wang Y, Paquola C, Benkarim O, Eichert N, Larivière S, Xu T, Misic B, Smallwood J, Valk SL, Bernhardt BC. Structural Connectivity Gradients of the Temporal Lobe Serve as Multiscale Axes of Brain Organization and Cortical Evolution. Cereb Cortex 2021; 31:5151-5164. [PMID: 34148082 PMCID: PMC8491677 DOI: 10.1093/cercor/bhab149] [Citation(s) in RCA: 7] [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] [Indexed: 12/13/2022] Open
Abstract
The temporal lobe is implicated in higher cognitive processes and is one of the regions that underwent substantial reorganization during primate evolution. Its functions are instantiated, in part, by the complex layout of its structural connections. Here, we identified low-dimensional representations of structural connectivity variations in human temporal cortex and explored their microstructural underpinnings and associations to macroscale function. We identified three eigenmodes which described gradients in structural connectivity. These gradients reflected inter-regional variations in cortical microstructure derived from quantitative magnetic resonance imaging and postmortem histology. Gradient-informed models accurately predicted macroscale measures of temporal lobe function. Furthermore, the identified gradients aligned closely with established measures of functional reconfiguration and areal expansion between macaques and humans, highlighting their potential role in shaping temporal lobe function throughout primate evolution. Findings were replicated in several datasets. Our results provide robust evidence for three axes of structural connectivity in human temporal cortex with consistent microstructural underpinnings and contributions to large-scale brain network function.
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Affiliation(s)
- Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Shahin Tavakol
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Yezhou Wang
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Nicole Eichert
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, UK
| | - Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, NY 10022, USA
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | | | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103, Germany
| | - Boris C Bernhardt
- Address correspondence to Boris C. Bernhardt, McConnell Brain Imaging Centre, Montreal Neurological Institute (NW-256), McGill University, 3801 Rue University, Montréal, QC H3A2B4, Canada.
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16
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Vogrinc D, Goričar K, Dolžan V. Genetic Variability in Molecular Pathways Implicated in Alzheimer's Disease: A Comprehensive Review. Front Aging Neurosci 2021; 13:646901. [PMID: 33815092 PMCID: PMC8012500 DOI: 10.3389/fnagi.2021.646901] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/16/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting a significant part of the population. The majority of AD cases occur in the elderly with a typical age of onset of the disease above 65 years. AD presents a major burden for the healthcare system and since population is rapidly aging, the burden of the disease will increase in the future. However, no effective drug treatment for a full-blown disease has been developed to date. The genetic background of AD is extensively studied; numerous genome-wide association studies (GWAS) identified significant genes associated with increased risk of AD development. This review summarizes more than 100 risk loci. Many of them may serve as biomarkers of AD progression, even in the preclinical stage of the disease. Furthermore, we used GWAS data to identify key pathways of AD pathogenesis: cellular processes, metabolic processes, biological regulation, localization, transport, regulation of cellular processes, and neurological system processes. Gene clustering into molecular pathways can provide background for identification of novel molecular targets and may support the development of tailored and personalized treatment of AD.
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Affiliation(s)
| | | | - Vita Dolžan
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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17
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Ruffini N, Klingenberg S, Schweiger S, Gerber S. Common Factors in Neurodegeneration: A Meta-Study Revealing Shared Patterns on a Multi-Omics Scale. Cells 2020; 9:E2642. [PMID: 33302607 PMCID: PMC7764447 DOI: 10.3390/cells9122642] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 11/24/2020] [Accepted: 12/04/2020] [Indexed: 02/06/2023] Open
Abstract
Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) are heterogeneous, progressive diseases with frequently overlapping symptoms characterized by a loss of neurons. Studies have suggested relations between neurodegenerative diseases for many years (e.g., regarding the aggregation of toxic proteins or triggering endogenous cell death pathways). We gathered publicly available genomic, transcriptomic, and proteomic data from 177 studies and more than one million patients to detect shared genetic patterns between the neurodegenerative diseases on three analyzed omics-layers. The results show a remarkably high number of shared differentially expressed genes between the transcriptomic and proteomic levels for all conditions, while showing a significant relation between genomic and proteomic data between AD and PD and AD and ALS. We identified a set of 139 genes being differentially expressed in several transcriptomic experiments of all four diseases. These 139 genes showed overrepresented gene ontology (GO) Terms involved in the development of neurodegeneration, such as response to heat and hypoxia, positive regulation of cytokines and angiogenesis, and RNA catabolic process. Furthermore, the four analyzed neurodegenerative diseases (NDDs) were clustered by their mean direction of regulation throughout all transcriptomic studies for this set of 139 genes, with the closest relation regarding this common gene set seen between AD and HD. GO-Term and pathway analysis of the proteomic overlap led to biological processes (BPs), related to protein folding and humoral immune response. Taken together, we could confirm the existence of many relations between Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis on transcriptomic and proteomic levels by analyzing the pathways and GO-Terms arising in these intersections. The significance of the connection and the striking relation of the results to processes leading to neurodegeneration between the transcriptomic and proteomic data for all four analyzed neurodegenerative diseases showed that exploring many studies simultaneously, including multiple omics-layers of different neurodegenerative diseases simultaneously, holds new relevant insights that do not emerge from analyzing these data separately. Furthermore, the results shed light on processes like the humoral immune response that have previously been described only for certain diseases. Our data therefore suggest human patients with neurodegenerative diseases should be addressed as complex biological systems by integrating multiple underlying data sources.
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Affiliation(s)
- Nicolas Ruffini
- Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; (N.R.); (S.K.); (S.S.)
- Leibniz Institute for Resilience Research, Leibniz Association, Wallstraße 7, 55122 Mainz, Germany
| | - Susanne Klingenberg
- Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; (N.R.); (S.K.); (S.S.)
| | - Susann Schweiger
- Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; (N.R.); (S.K.); (S.S.)
| | - Susanne Gerber
- Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany; (N.R.); (S.K.); (S.S.)
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18
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Neuner SM, Tcw J, Goate AM. Genetic architecture of Alzheimer's disease. Neurobiol Dis 2020; 143:104976. [PMID: 32565066 PMCID: PMC7409822 DOI: 10.1016/j.nbd.2020.104976] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 05/30/2020] [Accepted: 06/13/2020] [Indexed: 02/06/2023] Open
Abstract
Advances in genetic and genomic technologies over the last thirty years have greatly enhanced our knowledge concerning the genetic architecture of Alzheimer's disease (AD). Several genes including APP, PSEN1, PSEN2, and APOE have been shown to exhibit large effects on disease susceptibility, with the remaining risk loci having much smaller effects on AD risk. Notably, common genetic variants impacting AD are not randomly distributed across the genome. Instead, these variants are enriched within regulatory elements active in human myeloid cells, and to a lesser extent liver cells, implicating these cell and tissue types as critical to disease etiology. Integrative approaches are emerging as highly effective for identifying the specific target genes through which AD risk variants act and will likely yield important insights related to potential therapeutic targets in the coming years. In the future, additional consideration of sex- and ethnicity-specific contributions to risk as well as the contribution of complex gene-gene and gene-environment interactions will likely be necessary to further improve our understanding of AD genetic architecture.
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Affiliation(s)
- Sarah M Neuner
- Nash Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Julia Tcw
- Nash Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alison M Goate
- Nash Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.
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19
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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: 69] [Impact Index Per Article: 17.3] [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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20
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Abstract
Radiogenomics, defined as the integrated analysis of radiologic imaging and genetic data, is a well-established tool shown to augment neuroimaging in the clinical diagnosis, prognostication, and scientific study of late-onset Alzheimer disease (LOAD). Early work using candidate single nucleotide polymorphisms (SNPs) identified genetic variation in APOE, BIN1, CLU, and CR1 as key modifiers of brain structure and function using magnetic resonance imaging (MRI). More recently, polygenic risk scores used in conjunction with MRI and positron emission tomography have shown great promise as a risk-stratification tool for clinical trials and care-management decisions. In addition, recent work using multimodal MRI and positron emission tomography as proxies of LOAD progression has identified novel risk variants that are enhancing our understanding of LOAD pathophysiology and progression. Herein, we highlight key studies and trends in the radiogenomics of LOAD over the past two decades and their implications for clinical practice and scientific research.
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21
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Yuan S, Li H, Xie J, Sun X. Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease. Int J Mol Sci 2019; 20:E5912. [PMID: 31775305 PMCID: PMC6928939 DOI: 10.3390/ijms20235912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/21/2019] [Accepted: 11/22/2019] [Indexed: 01/02/2023] Open
Abstract
The pathological features of Alzheimer's Disease (AD) first appear in the medial temporal lobe and then in other brain structures with the development of the disease. In this work, we investigated the association between genetic loci and subcortical structure volumes of AD on 393 samples in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Brain subcortical structures were clustered into modules using Pearson's correlation coefficient of volumes across all samples. Module volumes were used as quantitative traits to identify not only the main effect loci but also the interactive effect loci for each module. Thirty-five subcortical structures were clustered into five modules, each corresponding to a particular brain structure/area, including the limbic system (module I), the corpus callosum (module II), thalamus-cerebellum-brainstem-pallidum (module III), the basal ganglia neostriatum (module IV), and the ventricular system (module V). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results indicate that the gene annotations of the five modules were distinct, with few overlaps between different modules. We identified several main effect loci and interactive effect loci for each module. All these loci are related to the function of module structures and basic biological processes such as material transport and signal transduction.
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Affiliation(s)
| | | | | | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China; (S.Y.)
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22
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Kochunov P, Patel B, Ganjgahi H, Donohue B, Ryan M, Hong EL, Chen X, Adhikari B, Jahanshad N, Thompson PM, Van't Ent D, den Braber A, de Geus EJC, Brouwer RM, Boomsma DI, Hulshoff Pol HE, de Zubicaray GI, McMahon KL, Martin NG, Wright MJ, Nichols TE. Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data. Front Neuroinform 2019; 13:16. [PMID: 30914942 PMCID: PMC6422938 DOI: 10.3389/fninf.2019.00016] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 02/25/2019] [Indexed: 11/30/2022] Open
Abstract
Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse; www.solar-eclipse-genetics.org) to improve the convergence of heritability estimates across different methods. The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Binish Patel
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Habib Ganjgahi
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Brian Donohue
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Meghann Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Elliot L Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Xu Chen
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Bhim Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, United States
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, United States
| | - Dennis Van't Ent
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Anouk den Braber
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Rachel M Brouwer
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Hilleke E Hulshoff Pol
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Greig I de Zubicaray
- Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Katie L McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | | | - Margaret J Wright
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Thomas E Nichols
- Big Data Institute, University of Oxford, Oxford, United Kingdom
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23
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Panayides AS, Pattichis MS, Leandrou S, Pitris C, Constantinidou A, Pattichis CS. Radiogenomics for Precision Medicine With a Big Data Analytics Perspective. IEEE J Biomed Health Inform 2018; 23:2063-2079. [PMID: 30596591 DOI: 10.1109/jbhi.2018.2879381] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.
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24
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Bi XA, Xu Q, Luo X, Sun Q, Wang Z. Analysis of Progression Toward Alzheimer's Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster. Front Neurosci 2018; 12:716. [PMID: 30349454 PMCID: PMC6186825 DOI: 10.3389/fnins.2018.00716] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 09/19/2018] [Indexed: 12/02/2022] Open
Abstract
Alzheimer’s disease (AD) could be described into following four stages: healthy control (HC), early mild cognitive impairment (EMCI), late MCI (LMCI) and AD dementia. The discriminations between different stages of AD are considerably important issues for future pre-dementia treatment. However, it is still challenging to identify LMCI from EMCI because of the subtle changes in imaging which are not noticeable. In addition, there were relatively few studies to make inferences about the brain dynamic changes in the cognitive progression from EMCI to LMCI to AD. Inspired by the above problems, we proposed an advanced approach of evolutionary weighted random support vector machine cluster (EWRSVMC). Where the predictions of numerous weighted SVM classifiers are aggregated for improving the generalization performance. We validated our method in multiple binary classifications using Alzheimer’s Disease Neuroimaging Initiative dataset. As a result, the encouraging accuracy of 90% for EMCI/LMCI and 88.89% for LMCI/AD were achieved respectively, demonstrating the excellent discriminating ability. Furthermore, disease-related brain regions underlying the AD progression could be found out on the basis of the amount of discriminative information. The findings of this study provide considerable insight into the neurophysiological mechanisms in AD development.
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Affiliation(s)
- Xia-An Bi
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qian Xu
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xianhao Luo
- College of Mathematics and Statistics, Hunan Normal University, Changsha, China
| | - Qi Sun
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zhigang Wang
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
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25
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Ganjgahi H, Winkler AM, Glahn DC, Blangero J, Donohue B, Kochunov P, Nichols TE. Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes. Nat Commun 2018; 9:3254. [PMID: 30108209 PMCID: PMC6092439 DOI: 10.1038/s41467-018-05444-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Accepted: 07/09/2018] [Indexed: 01/05/2023] Open
Abstract
Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors, such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present a method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.
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Affiliation(s)
- Habib Ganjgahi
- Department of Statistics, University of Oxford, Oxford, UK
- Medical Research Council Harwell Institute, Harwell, UK
| | - Anderson M Winkler
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Big Data Analytics Group, Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Brian Donohue
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Department of Statistics, University of Warwick, Coventry, UK.
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Medial temporal lobe atrophy relates more strongly to sleep-wake rhythm fragmentation than to age or any other known risk. Neurobiol Learn Mem 2018; 160:132-138. [PMID: 29864525 DOI: 10.1016/j.nlm.2018.05.017] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 05/19/2018] [Accepted: 05/24/2018] [Indexed: 11/21/2022]
Abstract
Atrophy of the medial temporal lobe of the brain is key to memory function and memory complaints in old age. While age and some morbidities are major risk factors for medial temporal lobe atrophy, individual differences remain, and mechanisms are insufficiently known. The largest combined neuroimaging and whole genome study to date indicates that medial temporal lobe volume is most associated with common polymorphisms in the GRIN2B gene that encodes for the 2B subunit (NR2B) of the NMDA receptor. Because sleep disruption induces a selective loss of NR2B from hippocampal synaptic membranes in rodents, and because of several other reports on medial temporal lobe sensitivity to sleep disruption, we hypothesized a contribution of the typical age-related increase in sleep-wake rhythm fragmentation to medial temporal lobe atrophy. Magnetic resonance imaging and actigraphy in 138 aged individuals showed that individual differences in sleep-wake rhythm fragmentation accounted for more (19%) of the variance in medial temporal lobe atrophy than age did (15%), or any of a list of health and brain structural indicators. The findings suggest a role of sleep-wake rhythm fragmentation in age-related medial temporal lobe atrophy, that might in part be prevented or reversible.
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27
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Jiang Y, Lin MK, Jicha GA, Ding X, McIlwrath SL, Fardo DW, Broster LS, Schmitt FA, Kryscio R, Lipsky RH. Functional human GRIN2B promoter polymorphism and variation of mental processing speed in older adults. Aging (Albany NY) 2018; 9:1293-1306. [PMID: 28439047 PMCID: PMC5425128 DOI: 10.18632/aging.101228] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 04/17/2017] [Indexed: 02/07/2023]
Abstract
We investigated the role of a single nucleotide polymorphism rs3764030 (G>A) within the human GRIN2B promoter in mental processing speed in healthy, cognitively intact, older adults. In vitro DNA-binding and reporter gene assays of different allele combinations in transfected cells showed that the A allele was a gain-of-function variant associated with increasing GRIN2B mRNA levels. We tested the hypothesis that individuals with A allele will have better memory performance (i.e. faster reaction times) in older age. Twenty-eight older adults (ages 65-86) from a well-characterized longitudinal cohort were recruited and performed a modified delayed match-to-sample task. The rs3764030 polymorphism was genotyped and participants were grouped based on the presence of the A allele into GG and AA/AG. Carriers of the A allele maintained their speed of memory retrieval over age compared to GG carriers (p = 0.026 slope of the regression line between AA and AG versus GG groups). To validate the results, 12 older adults from the same cohort participated in a different version of the short-term memory task. Reaction times were significantly slower with age in older adults with G allele (p < 0.001). These findings support a role for rs3764030 in maintaining faster mental processing speed over aging.
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Affiliation(s)
- Yang Jiang
- Department of Behavioral Science, University of Kentucky, Lexington, KY 40536, USA.,Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA
| | - Ming Kuan Lin
- Department of Molecular Neuroscience, the Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Gregory A Jicha
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA.,Department of Neurology, University of Kentucky, Lexington, KY 40536, USA
| | - Xiuhua Ding
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA.,Departments of Statistics and Biostatistics, University of Kentucky, Lexington, KY 40536, USA
| | - Sabrina L McIlwrath
- Department of Behavioral Science, University of Kentucky, Lexington, KY 40536, USA
| | - David W Fardo
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA.,Departments of Statistics and Biostatistics, University of Kentucky, Lexington, KY 40536, USA
| | - Lucas S Broster
- Department of Behavioral Science, University of Kentucky, Lexington, KY 40536, USA
| | - Frederick A Schmitt
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA.,Department of Neurology, University of Kentucky, Lexington, KY 40536, USA
| | - Richard Kryscio
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA.,Departments of Statistics and Biostatistics, University of Kentucky, Lexington, KY 40536, USA
| | - Robert H Lipsky
- Department of Molecular Neuroscience, the Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA.,Department of Neurosciences, Inova Neuroscience Institute, Inova Health System, Falls Church, VA 22042, USA
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The C677T variant in MTHFR modulates associations between blood-based and cerebrospinal fluid biomarkers of neurodegeneration. Neuroreport 2018; 27:948-51. [PMID: 27380243 PMCID: PMC4937804 DOI: 10.1097/wnr.0000000000000636] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The C677T functional variant in the methylene-tetrahydrofolate reductase (MTHFR) gene results in reduced enzymatic activity and elevated blood levels of homocysteine. Plasma levels of apolipoprotein E (ApoE) are negatively correlated with cerebral amyloid burden, but plasma homocysteine concentrations are associated with increased amyloid-β (Aβ) deposition in the brain. Here, we sought to determine whether associations between low plasma ApoE levels and elevated in-vivo amyloid burden were modulated by carrying the C677T variant. We tested this hypothesis in a large sample of elderly participants from the Alzheimer's Disease Neuroimaging Initiative. We used general linear models to examine associations between plasma homocysteine concentrations, circulating ApoE levels, cerebrospinal fluid concentrations of Aβ, and their modulation by MTHFR and ApoE genotype. Age, sex, and dementia status were included as covariates in all analyses. Higher circulating levels of ApoE predicted increased cerebrospinal fluid concentrations of Aβ, indicating lower in-vivo burden, in C-allele carriers, but not in homozygotes at the C677T variant, who showed significant elevations in plasma homocysteine levels. This modulation by the MTHFR genotype did not remain significant after controlling for ApoE genotype. In T-homozygotes who do not carry the ApoE-ε4 allele, the relationship between low plasma ApoE levels and an increased risk of dementia is likely obscured by the presence of elevated plasma homocysteine. This report suggests the value of genotyping patients at the C677T functional variant when using plasma ApoE levels as a preclinical biomarker for Alzheimer's disease.
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29
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Li Q, Wineinger NE, Fu DJ, Libiger O, Alphs L, Savitz A, Gopal S, Cohen N, Schork NJ. Genome-wide association study of paliperidone efficacy. Pharmacogenet Genomics 2017; 27:7-18. [PMID: 27846195 PMCID: PMC5152628 DOI: 10.1097/fpc.0000000000000250] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Supplemental Digital Content is available in the text. Objective Clinical response to the atypical antipsychotic paliperidone is known to vary among schizophrenic patients. We carried out a genome-wide association study to identify common genetic variants predictive of paliperidone efficacy. Methods We leveraged a collection of 1390 samples from individuals of European ancestry enrolled in 12 clinical studies investigating the efficacy of the extended-release tablet paliperidone ER (n1=490) and the once-monthly injection paliperidone palmitate (n2=550 and n3=350). We carried out a genome-wide association study using a general linear model (GLM) analysis on three separate cohorts, followed by meta-analysis and using a mixed linear model analysis on all samples. The variations in response explained by each single nucleotide polymorphism (h2SNP) were estimated. Results No SNP passed genome-wide significance in the GLM-based analyses with suggestive signals from rs56240334 [P=7.97×10−8 for change in the Clinical Global Impression Scale-Severity (CGI-S); P=8.72×10−7 for change in the total Positive and Negative Syndrome Scale (PANSS)] in the intron of ADCK1. The mixed linear model-based association P-values for rs56240334 were consistent with the results from GLM-based analyses and the association with change in CGI-S (P=4.26×10−8) reached genome-wide significance (i.e. P<5×10−8). We also found suggestive evidence for a polygenic contribution toward paliperidone treatment response with estimates of heritability, h2SNP, ranging from 0.31 to 0.43 for change in the total PANSS score, the PANSS positive Marder factor score, and CGI-S. Conclusion Genetic variations in the ADCK1 gene may differentially predict paliperidone efficacy in schizophrenic patients. However, this finding should be replicated in additional samples.
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Affiliation(s)
- Qingqin Li
- aNeuroscience, Janssen Research & Development, LLC bJanssen Scientific Affairs, LLC, Titusville cJanssen Research & Development, LLC, Raritan dBlue Note Biosciences, LLC, Princeton, New Jersey eBiostatistics and Bioinformatics, The Scripps Translational Science Institute fDepartment of Molecular and Experimental Medicine, The Scripps Research Institute gScripps Health hHuman Biology, J. Craig Venter Institute, La Jolla iMD Revolution, San Diego, California, USA
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Alzheimer's brains show inter-related changes in RNA and lipid metabolism. Neurobiol Dis 2017; 106:1-13. [PMID: 28630030 PMCID: PMC5560656 DOI: 10.1016/j.nbd.2017.06.008] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 05/17/2017] [Accepted: 06/12/2017] [Indexed: 12/18/2022] Open
Abstract
Alzheimer's disease (AD) involves changes in both lipid and RNA metabolism, but it remained unknown if these differences associate with AD's cognition and/or post-mortem neuropathology indices. Here, we report RNA-sequencing evidence of inter-related associations between lipid processing, cognition level, and AD neuropathology. In two unrelated cohorts, we identified pathway-enriched facilitation of lipid processing and alternative splicing genes, including the neuronal-enriched NOVA1 and hnRNPA1. Specifically, this association emerged in temporal lobe tissue samples from donors where postmortem evidence demonstrated AD neuropathology, but who presented normal cognition proximate to death. The observed changes further associated with modified ATP synthesis and mitochondrial transcripts, indicating metabolic relevance; accordingly, mass-spectrometry-derived lipidomic profiles distinguished between individuals with and without cognitive impairment prior to death. In spite of the limited group sizes, tissues from persons with both cognitive impairment and AD pathology showed elevation in several drug-targeted genes of other brain, vascular and autoimmune disorders, accompanied by pathology-related increases in distinct lipid processing transcripts, and in the RNA metabolism genes hnRNPH2, TARDBP, CLP1 and EWSR1. To further detect 3'-polyadenylation variants, we employed multiple cDNA primer pairs. This identified variants that showed limited differences in scope and length between the tested cohorts, yet enabled superior clustering of demented and non-demented AD brains versus controls compared to total mRNA expression values. Our findings indicate inter-related cognition-associated differences in AD's lipid processing, alternative splicing and 3'-polyadenylation, calling for pursuing the underlying psychological and therapeutics implications.
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Rohde PD, Gaertner B, Ward K, Sørensen P, Mackay TFC. Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior. Genetics 2017; 206:1969-1984. [PMID: 28550016 PMCID: PMC5560801 DOI: 10.1534/genetics.117.200642] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 05/22/2017] [Indexed: 02/06/2023] Open
Abstract
Human psychiatric disorders such as schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder often include adverse behaviors including increased aggressiveness. Individuals with psychiatric disorders often exhibit social withdrawal, which can further increase the probability of conducting a violent act. Here, we used the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP) to investigate the genetic basis of variation in male aggressive behavior for flies reared in a socialized and socially isolated environment. We identified genetic variation for aggressive behavior, as well as significant genotype-by-social environmental interaction (GSEI); i.e., variation among DGRP genotypes in the degree to which social isolation affected aggression. We performed genome-wide association (GWA) analyses to identify genetic variants associated with aggression within each environment. We used genomic prediction to partition genetic variants into gene ontology (GO) terms and constituent genes, and identified GO terms and genes with high prediction accuracies in both social environments and for GSEI. The top predictive GO terms significantly increased the proportion of variance explained, compared to prediction models based on all segregating variants. We performed genomic prediction across environments, and identified genes in common between the social environments that turned out to be enriched for genome-wide associated variants. A large proportion of the associated genes have previously been associated with aggressive behavior in Drosophila and mice. Further, many of these genes have human orthologs that have been associated with neurological disorders, indicating partially shared genetic mechanisms underlying aggression in animal models and human psychiatric disorders.
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Affiliation(s)
- Palle Duun Rohde
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, 8000 Aarhus, Denmark
- ISEQ, Center for Integrative Sequencing, Aarhus University, 8000 Aarhus, Denmark
| | - Bryn Gaertner
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Kirsty Ward
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Trudy F C Mackay
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
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32
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Mizoguchi T, Minakuchi H, Ishisaka M, Tsuruma K, Shimazawa M, Hara H. Behavioral abnormalities with disruption of brain structure in mice overexpressing VGF. Sci Rep 2017; 7:4691. [PMID: 28680036 PMCID: PMC5498671 DOI: 10.1038/s41598-017-04132-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 05/10/2017] [Indexed: 01/01/2023] Open
Abstract
VGF nerve growth factor inducible (VGF) is a neuropeptide induced by nerve growth factor and brain-derived neurotrophic factor. This peptide is involved in synaptic plasticity, neurogenesis, and neurite growth in the brain. Patients with depression and bipolar disorder have lower-than-normal levels of VGF, whereas patients with schizophrenia and other cohorts of patients with depression have higher-than-normal levels. VGF knockout mice display behavioral abnormalities such as higher depressive behavior and memory dysfunction. However, it is unclear whether upregulation of VGF affects brain function. In the present study, we generated mice that overexpress VGF and investigated several behavioral phenotypes and the brain structure. These adult VGF-overexpressing mice showed (a) hyperactivity, working memory impairment, a higher depressive state, and lower sociality compared with wild-type mice; (b) lower brain weight without a change in body weight; (c) increased lateral ventricle volume compared with wild-type mice; and (d) striatal morphological defects. These results suggest that VGF may modulate a variety of behaviors and brain development. This transgenic mouse line may provide a useful model for research on mental illnesses.
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Affiliation(s)
- Takahiro Mizoguchi
- Molecular Pharmacology, Department of Biofunctional Evaluation, Gifu Pharmaceutical University, Gifu, Japan
| | - Hiroko Minakuchi
- Molecular Pharmacology, Department of Biofunctional Evaluation, Gifu Pharmaceutical University, Gifu, Japan
| | - Mitsue Ishisaka
- Molecular Pharmacology, Department of Biofunctional Evaluation, Gifu Pharmaceutical University, Gifu, Japan
| | - Kazuhiro Tsuruma
- Molecular Pharmacology, Department of Biofunctional Evaluation, Gifu Pharmaceutical University, Gifu, Japan
| | - Masamitsu Shimazawa
- Molecular Pharmacology, Department of Biofunctional Evaluation, Gifu Pharmaceutical University, Gifu, Japan
| | - Hideaki Hara
- Molecular Pharmacology, Department of Biofunctional Evaluation, Gifu Pharmaceutical University, Gifu, Japan.
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Roussotte FF, Hua X, Narr KL, Small GW, Thompson PM. The C677T variant in MTHFR modulates associations between brain integrity, mood, and cognitive functioning in old age. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:280-288. [PMID: 28435933 DOI: 10.1016/j.bpsc.2016.09.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
INTRODUCTION The C677T functional variant in the methylene-tetrahydrofolate reductase (MTHFR) gene leads to reduced enzymatic activity and elevated blood levels of homocysteine. Hyperhomocysteinemia has been linked with higher rates of cardiovascular diseases, cognitive decline, and late-life depression. METHODS AND MATERIALS Here, 3D magnetic resonance imaging data was analyzed from 738 individuals (age: 75.5 ± 6.8 years; 438 men/300 women) including 173 Alzheimer's patients, 359 subjects with mild cognitive impairment, and 206 healthy older adults, scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS We found that this variant associates with localized brain atrophy, after controlling for age, sex, and dementia status, in brain regions implicated in both intellectual and emotional functioning, notably the medial orbitofrontal cortices. The medial orbitofrontal cortex is involved in the cognitive modulation of emotional processes, and localized atrophy in this region was previously linked with both cognitive impairment and depressive symptoms. Here, we report that increased plasma homocysteine mediates the association between MTHFR genotype and lower medial orbitofrontal volumes, and that these volumes mediate the association between cognitive decline and depressed mood in this elderly cohort. We additionally show that vitamin B12 deficiency interacts with the C677T variant in the etiology of hyperhomocysteinemia. CONCLUSION This study sheds light on important relationships between vascular risk factors, age-related cognitive decline, and late-life depression, and represents a significant advance in our understanding of clinically relevant associations relating to MTHFR genotype.
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Affiliation(s)
- Florence F Roussotte
- Department of Neurology, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, USA.,Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
| | - Xue Hua
- Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA.,Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
| | - Katherine L Narr
- Department of Neurology, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, USA
| | - Gary W Small
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, USA
| | - Paul M Thompson
- Department of Neurology, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, USA.,Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, USA.,Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA.,Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA.,Departments of Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
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ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide. Neuroimage 2017; 145:389-408. [PMID: 26658930 PMCID: PMC4893347 DOI: 10.1016/j.neuroimage.2015.11.057] [Citation(s) in RCA: 123] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Revised: 10/16/2015] [Accepted: 11/23/2015] [Indexed: 11/22/2022] Open
Abstract
In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.
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Horgusluoglu E, Nudelman K, Nho K, Saykin AJ. Adult neurogenesis and neurodegenerative diseases: A systems biology perspective. Am J Med Genet B Neuropsychiatr Genet 2017; 174:93-112. [PMID: 26879907 PMCID: PMC4987273 DOI: 10.1002/ajmg.b.32429] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Accepted: 01/29/2016] [Indexed: 12/21/2022]
Abstract
New neurons are generated throughout adulthood in two regions of the brain, the olfactory bulb and dentate gyrus of the hippocampus, and are incorporated into the hippocampal network circuitry; disruption of this process has been postulated to contribute to neurodegenerative diseases including Alzheimer's disease and Parkinson's disease. Known modulators of adult neurogenesis include signal transduction pathways, the vascular and immune systems, metabolic factors, and epigenetic regulation. Multiple intrinsic and extrinsic factors such as neurotrophic factors, transcription factors, and cell cycle regulators control neural stem cell proliferation, maintenance in the adult neurogenic niche, and differentiation into mature neurons; these factors act in networks of signaling molecules that influence each other during construction and maintenance of neural circuits, and in turn contribute to learning and memory. The immune system and vascular system are necessary for neuronal formation and neural stem cell fate determination. Inflammatory cytokines regulate adult neurogenesis in response to immune system activation, whereas the vasculature regulates the neural stem cell niche. Vasculature, immune/support cell populations (microglia/astrocytes), adhesion molecules, growth factors, and the extracellular matrix also provide a homing environment for neural stem cells. Epigenetic changes during hippocampal neurogenesis also impact memory and learning. Some genetic variations in neurogenesis related genes may play important roles in the alteration of neural stem cells differentiation into new born neurons during adult neurogenesis, with important therapeutic implications. In this review, we discuss mechanisms of and interactions between these modulators of adult neurogenesis, as well as implications for neurodegenerative disease and current therapeutic research. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Emrin Horgusluoglu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana
| | - Kelly Nudelman
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Andrew J. Saykin
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, Indiana
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Preciados M, Yoo C, Roy D. Estrogenic Endocrine Disrupting Chemicals Influencing NRF1 Regulated Gene Networks in the Development of Complex Human Brain Diseases. Int J Mol Sci 2016; 17:E2086. [PMID: 27983596 PMCID: PMC5187886 DOI: 10.3390/ijms17122086] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Revised: 11/21/2016] [Accepted: 11/29/2016] [Indexed: 12/13/2022] Open
Abstract
During the development of an individual from a single cell to prenatal stages to adolescence to adulthood and through the complete life span, humans are exposed to countless environmental and stochastic factors, including estrogenic endocrine disrupting chemicals. Brain cells and neural circuits are likely to be influenced by estrogenic endocrine disruptors (EEDs) because they strongly dependent on estrogens. In this review, we discuss both environmental, epidemiological, and experimental evidence on brain health with exposure to oral contraceptives, hormonal therapy, and EEDs such as bisphenol-A (BPA), polychlorinated biphenyls (PCBs), phthalates, and metalloestrogens, such as, arsenic, cadmium, and manganese. Also we discuss the brain health effects associated from exposure to EEDs including the promotion of neurodegeneration, protection against neurodegeneration, and involvement in various neurological deficits; changes in rearing behavior, locomotion, anxiety, learning difficulties, memory issues, and neuronal abnormalities. The effects of EEDs on the brain are varied during the entire life span and far-reaching with many different mechanisms. To understand endocrine disrupting chemicals mechanisms, we use bioinformatics, molecular, and epidemiologic approaches. Through those approaches, we learn how the effects of EEDs on the brain go beyond known mechanism to disrupt the circulatory and neural estrogen function and estrogen-mediated signaling. Effects on EEDs-modified estrogen and nuclear respiratory factor 1 (NRF1) signaling genes with exposure to natural estrogen, pharmacological estrogen-ethinyl estradiol, PCBs, phthalates, BPA, and metalloestrogens are presented here. Bioinformatics analysis of gene-EEDs interactions and brain disease associations identified hundreds of genes that were altered by exposure to estrogen, phthalate, PCBs, BPA or metalloestrogens. Many genes modified by EEDs are common targets of both 17 β-estradiol (E2) and NRF1. Some of these genes are involved with brain diseases, such as Alzheimer's Disease (AD), Parkinson's Disease, Huntington's Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorder, and Brain Neoplasms. For example, the search of enriched pathways showed that top ten E2 interacting genes in AD-APOE, APP, ATP5A1, CALM1, CASP3, GSK3B, IL1B, MAPT, PSEN2 and TNF-underlie the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) AD pathway. With AD, the six E2-responsive genes are NRF1 target genes: APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1. These genes are also responsive to the following EEDs: ethinyl estradiol (APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1), BPA (APBB2, EIF2S1, ENO1, MAPT, and PAXIP1), dibutyl phthalate (DPYSL2, EIF2S1, and ENO1), diethylhexyl phthalate (DPYSL2 and MAPT). To validate findings from Comparative Toxicogenomics Database (CTD) curated data, we used Bayesian network (BN) analysis on microarray data of AD patients. We observed that both gender and NRF1 were associated with AD. The female NRF1 gene network is completely different from male human AD patients. AD-associated NRF1 target genes-APLP1, APP, GRIN1, GRIN2B, MAPT, PSEN2, PEN2, and IDE-are also regulated by E2. NRF1 regulates targets genes with diverse functions, including cell growth, apoptosis/autophagy, mitochondrial biogenesis, genomic instability, neurogenesis, neuroplasticity, synaptogenesis, and senescence. By activating or repressing the genes involved in cell proliferation, growth suppression, DNA damage/repair, apoptosis/autophagy, angiogenesis, estrogen signaling, neurogenesis, synaptogenesis, and senescence, and inducing a wide range of DNA damage, genomic instability and DNA methylation and transcriptional repression, NRF1 may act as a major regulator of EEDs-induced brain health deficits. In summary, estrogenic endocrine disrupting chemicals-modified genes in brain health deficits are part of both estrogen and NRF1 signaling pathways. Our findings suggest that in addition to estrogen signaling, EEDs influencing NRF1 regulated communities of genes across genomic and epigenomic multiple networks may contribute in the development of complex chronic human brain health disorders.
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Affiliation(s)
- Mark Preciados
- Department of Environmental & Occupational Health, Florida International University, Miami, FL 33199, USA.
| | - Changwon Yoo
- Department of Biostatistics, Florida International University, Miami, FL 33199, USA.
| | - Deodutta Roy
- Department of Environmental & Occupational Health, Florida International University, Miami, FL 33199, USA.
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Tao C, Nichols TE, Hua X, Ching CRK, Rolls ET, Thompson PM, Feng J. Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications. Neuroimage 2016; 144:35-57. [PMID: 27666385 DOI: 10.1016/j.neuroimage.2016.08.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2015] [Revised: 08/01/2016] [Accepted: 08/14/2016] [Indexed: 11/18/2022] Open
Abstract
We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches.
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Affiliation(s)
- Chenyang Tao
- Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, PR China; Department of Computer Science, Warwick University, Coventry, UK
| | | | - Xue Hua
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Interdepartmental Neuroscience Graduate Program, UCLA School of Medicine, Los Angeles, CA, USA
| | - Edmund T Rolls
- Department of Computer Science, Warwick University, Coventry, UK; Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, USC, Los Angeles, CA, USA
| | - Jianfeng Feng
- Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, PR China; Department of Computer Science, Warwick University, Coventry, UK; School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai 200433, PR China.
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Jack CR, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, Dale AM, Carmichael O, Ching C, DeCarli C, Desikan RS, Fennema-Notestine C, Fjell AM, Fletcher E, Fox NC, Gunter J, Gutman BA, Holland D, Hua X, Insel P, Kantarci K, Killiany RJ, Krueger G, Leung KK, Mackin S, Maillard P, Malone IB, Mattsson N, McEvoy L, Modat M, Mueller S, Nosheny R, Ourselin S, Schuff N, Senjem ML, Simonson A, Thompson PM, Rettmann D, Vemuri P, Walhovd K, Zhao Y, Zuk S, Weiner M. Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2. Alzheimers Dement 2016; 11:740-56. [PMID: 26194310 DOI: 10.1016/j.jalz.2015.05.002] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 04/28/2015] [Accepted: 05/05/2015] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. METHODS We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. RESULTS Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. DISCUSSION Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.
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Affiliation(s)
| | - Josephine Barnes
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | | | | | - James Brewer
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Shona Clegg
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anders M Dale
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Owen Carmichael
- Department of Neurology, University of California at Davis, Davis, CA, USA
| | - Christopher Ching
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Charles DeCarli
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Rahul S Desikan
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California at San Diego, La Jolla, CA, USA
| | - Anders M Fjell
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Evan Fletcher
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Nick C Fox
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jeff Gunter
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Boris A Gutman
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dominic Holland
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Xue Hua
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Philip Insel
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Ron J Killiany
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | | | - Kelvin K Leung
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Scott Mackin
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
| | - Pauline Maillard
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Ian B Malone
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Niklas Mattsson
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
| | - Linda McEvoy
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Marc Modat
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Susanne Mueller
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Rachel Nosheny
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Sebastien Ourselin
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Norbert Schuff
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | | | - Alix Simonson
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dan Rettmann
- MR Applications and Workflow, GE Healthcare, Rochester, MN, USA
| | | | | | | | - Samantha Zuk
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Weiner
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA; Department of Medicine, University of California at San Francisco, San Francisco, CA, USA; Department of Neurology, University of California at San Francisco, San Francisco, CA, USA
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Pergola G, Di Carlo P, Andriola I, Gelao B, Torretta S, Attrotto MT, Fazio L, Raio A, Albergo D, Masellis R, Rampino A, Blasi G, Bertolino A. Combined effect of genetic variants in the GluN2B coding gene (GRIN2B) on prefrontal function during working memory performance. Psychol Med 2016; 46:1135-1150. [PMID: 26690829 DOI: 10.1017/s0033291715002639] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND The GluN2B subunit of N-methyl-d-aspartate receptors is crucially involved in the physiology of the prefrontal cortex during working memory (WM). Consistently, genetic variants in the GluN2B coding gene (GRIN2B) have been associated with cognitive phenotypes. However, it is unclear how GRIN2B genetic variation affects gene expression and prefrontal cognitive processing. Using a composite score, we tested the combined effect of GRIN2B variants on prefrontal activity during WM performance in healthy subjects. METHOD We computed a composite score to combine the effects of single nucleotide polymorphisms on post-mortem prefrontal GRIN2B mRNA expression. We then computed the composite score in independent samples of healthy participants in a peripheral blood expression study (n = 46), in a WM behavioural study (n = 116) and in a WM functional magnetic resonance imaging study (n = 122). RESULTS Five polymorphisms were associated with GRIN2B expression: rs2160517, rs219931, rs11055792, rs17833967 and rs12814951 (all corrected p < 0.05). The score computed to account for their combined effect reliably indexed gene expression. GRIN2B composite score correlated negatively with intelligence quotient, WM behavioural efficiency and dorsolateral prefrontal cortex activity. Moreover, there was a non-linear association between GRIN2B genetic score and prefrontal activity, i.e. both high and low putative genetic score levels were associated with high blood oxygen level-dependent signals in the prefrontal cortex. CONCLUSIONS Multiple genetic variants in GRIN2B are jointly associated with gene expression, prefrontal function and behaviour during WM. These results support the role of GRIN2B genetic variants in WM prefrontal activity in human adults.
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Affiliation(s)
- G Pergola
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - P Di Carlo
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - I Andriola
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - B Gelao
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - S Torretta
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - M T Attrotto
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - L Fazio
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - A Raio
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - D Albergo
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - R Masellis
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - A Rampino
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - G Blasi
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - A Bertolino
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
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Tong Y, Chen Q, Nichols TE, Rasetti R, Callicott JH, Berman KF, Weinberger DR, Mattay VS. Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics. PLoS One 2016; 11:e0151391. [PMID: 26974435 PMCID: PMC4790904 DOI: 10.1371/journal.pone.0151391] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 02/27/2016] [Indexed: 11/21/2022] Open
Abstract
A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others.
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Affiliation(s)
- Yunxia Tong
- Clinical and Translational Neuroscience Branch, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail:
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America
| | - Thomas E. Nichols
- Department of Statistics, University of Warwick, Warwick, United Kingdom
| | - Roberta Rasetti
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Joseph H. Callicott
- Clinical and Translational Neuroscience Branch, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Karen F. Berman
- Clinical and Translational Neuroscience Branch, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America
- Departments of Psychiatry, Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Venkata S. Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America
- Departments of Neurology and Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
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41
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ. 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement 2016; 11:e1-120. [PMID: 26073027 DOI: 10.1016/j.jalz.2014.11.001] [Citation(s) in RCA: 203] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/18/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Johan Luthman
- Neuroscience Clinical Development, Neuroscience & General Medicine Product Creation Unit, Eisai Inc., Philadelphia, PA, USA
| | - John C Morris
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam Schwarz
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Roussotte FF, Jahanshad N, Hibar DP, Thompson PM. Altered regional brain volumes in elderly carriers of a risk variant for drug abuse in the dopamine D2 receptor gene (DRD2). Brain Imaging Behav 2016; 9:213-22. [PMID: 24634060 DOI: 10.1007/s11682-014-9298-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Dopamine D2 receptors mediate the rewarding effects of many drugs of abuse. In humans, several polymorphisms in DRD2, the gene encoding these receptors, increase our genetic risk for developing addictive disorders. Here, we examined one of the most frequently studied candidate variant for addiction in DRD2 for association with brain structure. We tested whether this variant showed associations with regional brain volumes across two independent elderly cohorts, totaling 1,032 subjects. We first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI1). We hypothesized that this addiction-related polymorphism would be associated with structural brain differences in regions previously implicated in familial vulnerability for drug dependence. Then, we assessed the generalizability of our findings by testing this polymorphism in a non-overlapping replication sample of 294 elderly subjects from a continuation of the first ADNI project (ADNI2) to minimize the risk of reporting false positive results. In both cohorts, the minor allele-previously linked with increased risk for addiction-was associated with larger volumes in various brain regions implicated in reward processing. These findings suggest that neuroanatomical phenotypes associated with familial vulnerability for drug dependence may be partially mediated by DRD2 genotype.
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Affiliation(s)
- Florence F Roussotte
- Imaging Genetics Center, Institute for Neuroimaging and Informatics Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
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Zhang Z, Zheng Y, Zhang X, Liu C, Joyce BT, Kibbe WA, Hou L, Zhang W. Linking short tandem repeat polymorphisms with cytosine modifications in human lymphoblastoid cell lines. Hum Genet 2015; 135:223-32. [PMID: 26714498 DOI: 10.1007/s00439-015-1628-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 12/17/2015] [Indexed: 01/26/2023]
Abstract
Inter-individual variation in cytosine modifications has been linked to complex traits in humans. Cytosine modification variation is partially controlled by single nucleotide polymorphisms (SNPs), known as modified cytosine quantitative trait loci (mQTL). However, little is known about the role of short tandem repeat polymorphisms (STRPs), a class of structural genetic variants, in regulating cytosine modifications. Utilizing the published data on the International HapMap Project lymphoblastoid cell lines (LCLs), we assessed the relationships between 721 STRPs and the modification levels of 283,540 autosomal CpG sites. Our findings suggest that, in contrast to the predominant cis-acting mode for SNP-based mQTL, STRPs are associated with cytosine modification levels in both cis-acting (local) and trans-acting (distant) modes. In local scans within the ±1 Mb windows of target CpGs, 21, 9, and 21 cis-acting STRP-based mQTL were detected in CEU (Caucasian residents from Utah, USA), YRI (Yoruba people from Ibadan, Nigeria), and the combined samples, respectively. In contrast, 139,420, 76,817, and 121,866 trans-acting STRP-based mQTL were identified in CEU, YRI, and the combined samples, respectively. A substantial proportion of CpG sites detected with local STRP-based mQTL were not associated with SNP-based mQTL, suggesting that STRPs represent an independent class of mQTL. Functionally, genetic variants neighboring CpG-associated STRPs are enriched with genome-wide association study (GWAS) loci for a variety of complex traits and diseases, including cancers, based on the National Human Genome Research Institute (NHGRI) GWAS Catalog. Therefore, elucidating these STRP-based mQTL in addition to SNP-based mQTL can provide novel insights into the genetic architectures of complex traits.
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Affiliation(s)
- Zhou Zhang
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA.,Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Xu Zhang
- Section of Hematology/Oncology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Cong Liu
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Brian Thomas Joyce
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA.,Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Warren A Kibbe
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA.,The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Wei Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA. .,The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. .,Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
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44
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Hashimoto R, Ohi K, Yamamori H, Yasuda Y, Fujimoto M, Umeda-Yano S, Watanabe Y, Fukunaga M, Takeda M. Imaging genetics and psychiatric disorders. Curr Mol Med 2015; 15:168-75. [PMID: 25732148 PMCID: PMC4460286 DOI: 10.2174/1566524015666150303104159] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2014] [Revised: 12/20/2014] [Accepted: 01/18/2015] [Indexed: 02/01/2023]
Abstract
Imaging genetics is an integrated research method that uses neuroimaging and genetics to assess the impact of genetic variation on brain function and structure. Imaging genetics is both a tool for the discovery of risk genes for psychiatric disorders and a strategy for characterizing the neural systems affected by risk gene variants to elucidate quantitative and mechanistic aspects of brain function implicated in psychiatric disease. Early studies of imaging genetics included association analyses between brain morphology and single nucleotide polymorphisms whose function is well known, such as catechol-Omethyltransferase (COMT) and brain-derived neurotrophic factor (BDNF). GWAS of psychiatric disorders have identified genes with unknown functions, such as ZNF804A, and imaging genetics has been used to investigate clues of the biological function of these genes. The difficulty in replicating the findings of studies with small sample sizes has motivated the creation of largescale collaborative consortiums, such as ENIGMA, CHARGE and IMAGEN, to collect thousands of images. In a genome-wide association study, the ENIGMA consortium successfully identified common variants in the genome associated with hippocampal volume at 12q24, and the CHARGE consortium replicated this finding. The new era of imaging genetics has just begun, and the next challenge we face is the discovery of small effect size signals from large data sets obtained from genetics and neuroimaging. New methods and technologies for data reduction with appropriate statistical thresholds, such as polygenic analysis and parallel independent component analysis (ICA), are warranted. Future advances in imaging genetics will aid in the discovery of genes and provide mechanistic insight into psychiatric disorders.
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Affiliation(s)
| | | | | | | | | | | | | | | | - M Takeda
- Molecular Research Center for Children's Mental Development, United Graduate School of Child Development, Osaka University, Osaka, Japan.
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45
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Kim J, Pan W. A cautionary note on using secondary phenotypes in neuroimaging genetic studies. Neuroimage 2015; 121:136-45. [PMID: 26220747 PMCID: PMC4604049 DOI: 10.1016/j.neuroimage.2015.07.058] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 06/12/2015] [Accepted: 07/20/2015] [Indexed: 11/18/2022] Open
Abstract
Almost all genome-wide association studies (GWASs), including Alzheimer's Disease Neuroimaging Initiative (ADNI), are based on the case-control study design, implying that the resulting case-control data are likely a biased, not random, sample of the target population. Although association analysis of the disease (e.g. Alzheimer's disease in the ADNI) can be conducted using a standard logistic regression by ignoring the biased case-control sampling, a standard linear regression analysis on a secondary phenotype (e.g. any neuroimaging phenotype in the ADNI) may in general lead to biased inference, including biased parameter estimates, inflated Type I errors and reduced power for association testing. Despite of this well known result in genetic epidemiology, to our surprise, all the published studies on secondary phenotypes with the ADNI data have ignored this potential problem. Here we aim to answer whether such a standard analysis of a secondary phenotype is valid or problematic with the ADNI data. Through both real data analyses and simulation studies, we found that, strikingly, such an analysis was generally valid (with only small biases or slightly inflated Type I errors) for the ADNI data, though cautions must be taken when analyzing other data. We also illustrate applications and possible problems of two methods specifically developed for valid analysis of secondary phenotypes.
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Affiliation(s)
- Junghi Kim
- Division of Biostatistics, University of Minnesota, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, USA.
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46
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Hua WY, Ghosh D. Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies. Biometrics 2015; 71:812-20. [PMID: 25939365 DOI: 10.1111/biom.12314] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 02/01/2015] [Accepted: 03/01/2015] [Indexed: 12/19/2022]
Abstract
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates covariates. Our contributions are 3-fold: (1) establishing the equivalence between KMR and KDC; (2) showing that the principles of KMR can be applied to the interpretation of KDC; (3) the development of a broader class of KDC statistics, where the class members are statistics corresponding to different kernel combinations. Finally, we perform simulation studies and an analysis of real data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The ADNI study suggest that SNPs of FLJ16124 exhibit pairwise interaction effects that are strongly correlated to the changes of brain region volumes.
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Affiliation(s)
- Wen-Yu Hua
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, New York, U.S.A
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, U.S.A
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47
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Strike LT, Couvy-Duchesne B, Hansell NK, Cuellar-Partida G, Medland SE, Wright MJ. Genetics and Brain Morphology. Neuropsychol Rev 2015; 25:63-96. [DOI: 10.1007/s11065-015-9281-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 02/08/2015] [Indexed: 12/17/2022]
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48
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Hibar DP, Stein JL, Jahanshad N, Kohannim O, Hua X, Toga AW, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Weiner MW, Thompson PM. Genome-wide interaction analysis reveals replicated epistatic effects on brain structure. Neurobiol Aging 2015; 36 Suppl 1:S151-8. [PMID: 25264344 PMCID: PMC4332874 DOI: 10.1016/j.neurobiolaging.2014.02.033] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 02/10/2014] [Accepted: 02/16/2014] [Indexed: 11/24/2022]
Abstract
The discovery of several genes that affect the risk for Alzheimer's disease ignited a worldwide search for single-nucleotide polymorphisms (SNPs), common genetic variants that affect the brain. Genome-wide search of all possible SNP-SNP interactions is challenging and rarely attempted because of the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, for example, iterative sure independence screening, make it possible to analyze data sets with vastly more predictors than observations. Using an implementation of the sure independence screening algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on magnetic resonance imaging and mapped using tensor-based morphometry. We identified a significant SNP-SNP interaction between rs1345203 and rs1213205 that explains 1.9% of the variance in temporal lobe volume. We mapped the whole brain, voxelwise effects of the interaction in the Alzheimer's Disease Neuroimaging Initiative data set and separately in an independent replication data set of healthy twins (Queensland Twin Imaging). Each additional loading in the interaction effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both Alzheimer's Disease Neuroimaging Initiative and Queensland Twin Imaging samples.
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Affiliation(s)
- Derrek P Hibar
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Jason L Stein
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Omid Kohannim
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Xue Hua
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Katie L McMahon
- Centre for Magnetic Resonance, School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Greig I de Zubicaray
- Functional Magnetic Resonance Imaging Laboratory, School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Margaret J Wright
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Michael W Weiner
- Department of Radiology, UC San Francisco, San Francisco, CA, USA; Department of Medicine, UC San Francisco, San Francisco, CA, USA; Department of Psychiatry, UC San Francisco, San Francisco, CA, USA; Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
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49
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Zhan X, Ghosh D. Incorporating auxiliary information for improved prediction using combination of kernel machines. ACTA ACUST UNITED AC 2015; 22:47-57. [PMID: 25419198 DOI: 10.1016/j.stamet.2014.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
With evolving genomic technologies, it is possible to get different measures of the same underlying biological phenomenon using different technologies. The goal of this paper is to build a prediction model for an outcome variable Y from covariates X. Besides X, we have surrogate covariates W which are related to X. We want to utilize the information in W to boost the prediction for Y using X. In this paper, we propose a kernel machine-based method to improve prediction of Y by X by incorporating auxiliary information W. By combining single kernel machines, we also propose a hybrid kernel machine predictor, which can yield a smaller prediction error than its constituents. The prediction error of our kernel machine predictors is evaluated using simulations. We also apply our method to a lung cancer dataset and an Alzheimer's disease dataset.
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Affiliation(s)
- Xiang Zhan
- Department of Statistics, Pennsylvania State University, University Park, PA 16802, U.S.A
| | - Debashis Ghosh
- Department of Statistics, Pennsylvania State University, University Park, PA 16802, U.S.A. ; Department of Public Health Sciences, Pennsylvania State University, University Park, PA 16802, U.S.A
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50
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Hua WY, Nichols TE, Ghosh D. Multiple comparison procedures for neuroimaging genomewide association studies. Biostatistics 2015; 16:17-30. [PMID: 24963012 PMCID: PMC4263222 DOI: 10.1093/biostatistics/kxu026] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2013] [Revised: 03/22/2014] [Accepted: 05/14/2014] [Indexed: 01/18/2023] Open
Abstract
Recent research in neuroimaging has focused on assessing associations between genetic variants that are measured on a genomewide scale and brain imaging phenotypes. A large number of works in the area apply massively univariate analyses on a genomewide basis to find single nucleotide polymorphisms that influence brain structure. In this paper, we propose using various dimensionality reduction methods on both brain structural MRI scans and genomic data, motivated by the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We also consider a new multiple testing adjustment method and compare it with two existing false discovery rate (FDR) adjustment methods. The simulation results suggest an increase in power for the proposed method. The real-data analysis suggests that the proposed procedure is able to find associations between genetic variants and brain volume differences that offer potentially new biological insights.
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Affiliation(s)
- Wen-Yu Hua
- Department of Statistics, Penn State University, State College, PA 16802, USA
| | - Thomas E Nichols
- Department of Statistics, University of Warwick, Conventry, CV4 7AL, UK
| | - Debashis Ghosh
- Department of Statistics, Penn State University, State College, PA 16802, USA
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