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Zhang Q, Liu J, Liu H, Ao L, Xi Y, Chen D. Genome-wide epistasis analysis reveals gene-gene interaction network on an intermediate endophenotype P-tau/Aβ 42 ratio in ADNI cohort. Sci Rep 2024; 14:3984. [PMID: 38368488 PMCID: PMC10874417 DOI: 10.1038/s41598-024-54541-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 02/14/2024] [Indexed: 02/19/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the "missing heritability" of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP-SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-[Formula: see text] (A[Formula: see text]) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP-SNP interactions, explaining a high-level variance of P-tau/A[Formula: see text] level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene-gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the "missing heritability" of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.
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Affiliation(s)
- Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Junfeng Liu
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China
| | - Lang Ao
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Dandan Chen
- School of Automation Engineering, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China.
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China.
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2
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Kim BH, Lee H, Ham H, Kim HJ, Jang H, Kim JP, Park YH, Kim M, Seo SW. Clinical effects of novel susceptibility genes for beta-amyloid: a gene-based association study in the Korean population. Front Aging Neurosci 2023; 15:1278998. [PMID: 37901794 PMCID: PMC10602697 DOI: 10.3389/fnagi.2023.1278998] [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: 08/17/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Amyloid-beta (Aβ) is a pathological hallmark of Alzheimer's disease (AD). We aimed to identify genes related to Aβ uptake in the Korean population and investigate the effects of these novel genes on clinical outcomes, including neurodegeneration and cognitive impairments. We recruited a total of 759 Korean participants who underwent neuropsychological tests, brain magnetic resonance imaging, 18F-flutemetamol positron emission tomography, and microarray genotyping data. We performed gene-based association analysis, and also performed expression quantitative trait loci and network analysis. In genome-wide association studies, no single nucleotide polymorphism (SNP) passed the genome-wide significance threshold. In gene-based association analysis, six genes (LCMT1, SCRN2, LRRC46, MRPL10, SP6, and OSBPL7) were significantly associated with Aβ standardised uptake value ratio in the brain. The three most significant SNPs (rs4787307, rs9903904, and rs11079797) on these genes are associated with the regulation of the LCMT1, OSBPL7, and SCRN2 genes, respectively. These SNPs are involved in decreasing hippocampal volume and cognitive scores by mediating Aβ uptake. The 19 enriched gene sets identified by pathway analysis included axon and chemokine activity. Our findings suggest novel susceptibility genes associated with the uptake of Aβ, which in turn leads to worse clinical outcomes. Our findings might lead to the discovery of new AD treatment targets.
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Affiliation(s)
- Bo-Hyun Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - HyunWoo Lee
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hongki Ham
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyemin Jang
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yu Hyun Park
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Mansu Kim
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Sang Won Seo
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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3
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Ali M, Archer DB, Gorijala P, Western D, Timsina J, Fernández MV, Wang TC, Satizabal CL, Yang Q, Beiser AS, Wang R, Chen G, Gordon B, Benzinger TLS, Xiong C, Morris JC, Bateman RJ, Karch CM, McDade E, Goate A, Seshadri S, Mayeux RP, Sperling RA, Buckley RF, Johnson KA, Won HH, Jung SH, Kim HR, Seo SW, Kim HJ, Mormino E, Laws SM, Fan KH, Kamboh MI, Vemuri P, Ramanan VK, Yang HS, Wenzel A, Rajula HSR, Mishra A, Dufouil C, Debette S, Lopez OL, DeKosky ST, Tao F, Nagle MW, Hohman TJ, Sung YJ, Dumitrescu L, Cruchaga C. Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease. Acta Neuropathol Commun 2023; 11:68. [PMID: 37101235 PMCID: PMC10134547 DOI: 10.1186/s40478-023-01563-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.
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Affiliation(s)
- Muhammad Ali
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Daniel Western
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Maria V Fernández
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Ting-Chen Wang
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health, San Antonio, TX, 78229, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | | | - Gengsheng Chen
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | - Brian Gordon
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | - Chengjie Xiong
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Department of Neurology, Washington University, St Louis, MO, USA
| | - Randall J Bateman
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Department of Neurology, Washington University, St Louis, MO, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Celeste M Karch
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
| | - Eric McDade
- Department of Neurology, Washington University, St Louis, MO, USA
| | - Alison Goate
- Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Richard P Mayeux
- The Department of Neurology, Columbia University, New York, NY, USA
| | - Reisa A Sperling
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital and Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel F Buckley
- Brigham and Women's Hospital and Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Keith A Johnson
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hong-Hee Won
- Department of Digital Health, Samsung Medical Center, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Digital Health, Samsung Medical Center, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hang-Rai Kim
- Department of Neurology, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Digital Health, Samsung Medical Center, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Elizabeth Mormino
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, 270 Joondalup Dr, Joondalup, WA, 6027, Australia
| | - Kang-Hsien Fan
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Vijay K Ramanan
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Hyun-Sik Yang
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, USA
| | - Allen Wenzel
- Wisconsin Alzheimer's Institute, Madison, WI, USA
| | - Hema Sekhar Reddy Rajula
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
| | - Aniket Mishra
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
| | - Carole Dufouil
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
| | - Stephanie Debette
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
- Department of Neurology, Boston University School of Medicine, Boston, MA, 2115, USA
- Department of Neurology, CHU de Bordeaux, 33000, Bordeaux, France
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Steven T DeKosky
- Department of Neurology and McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Feifei Tao
- Neurogenomics, Genetics-Guided Dementia Discovery, Eisai, Inc, Cambridge, MA, USA
| | - Michael W Nagle
- Neurogenomics, Genetics-Guided Dementia Discovery, Eisai, Inc, Cambridge, MA, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA.
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA.
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA.
- Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, 63110, USA.
- Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA.
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4
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Nho K, Risacher SL, Apostolova L, Bice PJ, Brosch J, Deardorff R, Faber K, Farlow MR, Foroud T, Gao S, Rosewood T, Kim JP, Nudelman K, Yu M, Aisen P, Sperling R, Hooli B, Shcherbinin S, Svaldi D, Jack CR, Jagust WJ, Landau S, Vasanthakumar A, Waring JF, Doré V, Laws SM, Masters CL, Porter T, Rowe CC, Villemagne VL, Dumitrescu L, Hohman TJ, Libby JB, Mormino E, Buckley RF, Johnson K, Yang HS, Petersen RC, Ramanan VK, Vemuri P, Cohen AD, Fan KH, Kamboh MI, Lopez OL, Bennett DA, Ali M, Benzinger T, Cruchaga C, Hobbs D, De Jager PL, Fujita M, Jadhav V, Lamb BT, Tsai AP, Castanho I, Mill J, Weiner MW, Saykin AJ. Novel CYP1B1-RMDN2 Alzheimer's disease locus identified by genome-wide association analysis of cerebral tau deposition on PET. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.27.23286048. [PMID: 36993271 PMCID: PMC10055458 DOI: 10.1101/2023.02.27.23286048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Determining the genetic architecture of Alzheimer's disease (AD) pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we performed a genome-wide association study of cortical tau quantified by positron emission tomography in 3,136 participants from 12 independent studies. The CYP1B1-RMDN2 locus was associated with tau deposition. The most significant signal was at rs2113389, which explained 4.3% of the variation in cortical tau, while APOE4 rs429358 accounted for 3.6%. rs2113389 was associated with higher tau and faster cognitive decline. Additive effects, but no interactions, were observed between rs2113389 and diagnosis, APOE4 , and Aβ positivity. CYP1B1 expression was upregulated in AD. rs2113389 was associated with higher CYP1B1 expression and methylation levels. Mouse model studies provided additional functional evidence for a relationship between CYP1B1 and tau deposition but not Aβ. These results may provide insight into the genetic basis of cerebral tau and novel pathways for therapeutic development in AD.
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Oatman SR, Reddy JS, Quicksall Z, Carrasquillo MM, Wang X, Liu CC, Yamazaki Y, Nguyen TT, Malphrus K, Heckman M, Biswas K, Nho K, Baker M, Martens YA, Zhao N, Kim JP, Risacher SL, Rademakers R, Saykin AJ, DeTure M, Murray ME, Kanekiyo T, Dickson DW, Bu G, Allen M, Ertekin-Taner N. Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins. Mol Neurodegener 2023; 18:2. [PMID: 36609403 PMCID: PMC9825010 DOI: 10.1186/s13024-022-00592-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis. METHODS Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables. RESULTS We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways. CONCLUSIONS Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.
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Affiliation(s)
- Stephanie R. Oatman
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Joseph S. Reddy
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | | | - Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Chia-Chen Liu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yu Yamazaki
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Thuy T. Nguyen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kimberly Malphrus
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Michael Heckman
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Kristi Biswas
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kwangsik Nho
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
| | - Matthew Baker
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yuka A. Martens
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Na Zhao
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Jun Pyo Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Shannon L. Risacher
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
| | - Andrew J. Saykin
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
| | - Michael DeTure
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Takahisa Kanekiyo
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
| | - Dennis W. Dickson
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Guojun Bu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
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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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Bi XA, Zhou W, Li L, Xing Z. Detecting Risk Gene and Pathogenic Brain Region in EMCI Using a Novel GERF Algorithm Based on Brain Imaging and Genetic Data. IEEE J Biomed Health Inform 2021; 25:3019-3028. [PMID: 33750717 DOI: 10.1109/jbhi.2021.3067798] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Fusion analysis of disease-related multi-modal data is becoming increasingly important to illuminate the pathogenesis of complex brain diseases. However, owing to the small amount and high dimension of multi-modal data, current machine learning methods do not fully achieve the high veracity and reliability of fusion feature selection. In this paper, we propose a genetic-evolutionary random forest (GERF) algorithm to discover the risk genes and disease-related brain regions of early mild cognitive impairment (EMCI) based on the genetic data and resting-state functional magnetic resonance imaging (rs-fMRI) data. Classical correlation analysis method is used to explore the association between brain regions and genes, and fusion features are constructed. The genetic-evolutionary idea is introduced to enhance the classification performance, and to extract the optimal features effectively. The proposed GERF algorithm is evaluated by the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results show that the algorithm achieves satisfactory classification accuracy in small sample learning. Moreover, we compare the GERF algorithm with other methods to prove its superiority. Furthermore, we propose the overall framework of detecting pathogenic factors, which can be accurately and efficiently applied to the multi-modal data analysis of EMCI and be able to extend to other diseases. This work provides a novel insight for early diagnosis and clinicopathologic analysis of EMCI, which facilitates clinical medicine to control further deterioration of diseases and is good for the accurate electric shock using transcranial magnetic stimulation.
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Kim HR, Jung SH, Kim J, Jang H, Kang SH, Hwangbo S, Kim JP, Kim SY, Kim B, Kim S, Jeong JH, Yoon SJ, Park KW, Kim EJ, Yoon B, Jang JW, Hong JY, Choi SH, Noh Y, Kim KW, Kim SE, Lee JS, Jung NY, Lee J, Kim BC, Son SJ, Hong CH, Na DL, Seo SW, Won HH, Kim HJ. Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population. Alzheimers Res Ther 2021; 13:117. [PMID: 34154648 PMCID: PMC8215820 DOI: 10.1186/s13195-021-00854-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 06/02/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified a number of genetic variants for Alzheimer's disease (AD). However, most GWAS were conducted in individuals of European ancestry, and non-European populations are still underrepresented in genetic discovery efforts. Here, we performed GWAS to identify single nucleotide polymorphisms (SNPs) associated with amyloid β (Aβ) positivity using a large sample of Korean population. METHODS One thousand four hundred seventy-four participants of Korean ancestry were recruited from multicenters in South Korea. Discovery dataset consisted of 1190 participants (383 with cognitively unimpaired [CU], 330 with amnestic mild cognitive impairment [aMCI], and 477 with AD dementia [ADD]) and replication dataset consisted of 284 participants (46 with CU, 167 with aMCI, and 71 with ADD). GWAS was conducted to identify SNPs associated with Aβ positivity (measured by amyloid positron emission tomography). Aβ prediction models were developed using the identified SNPs. Furthermore, bioinformatics analysis was conducted for the identified SNPs. RESULTS In addition to APOE, we identified nine SNPs on chromosome 7, which were associated with a decreased risk of Aβ positivity at a genome-wide suggestive level. Of these nine SNPs, four novel SNPs (rs73375428, rs2903923, rs3828947, and rs11983537) were associated with a decreased risk of Aβ positivity (p < 0.05) in the replication dataset. In a meta-analysis, two SNPs (rs7337542 and rs2903923) reached a genome-wide significant level (p < 5.0 × 10-8). Prediction performance for Aβ positivity increased when rs73375428 were incorporated (area under curve = 0.75; 95% CI = 0.74-0.76) in addition to clinical factors and APOE genotype. Cis-eQTL analysis demonstrated that the rs73375428 was associated with decreased expression levels of FGL2 in the brain. CONCLUSION The novel genetic variants associated with FGL2 decreased risk of Aβ positivity in the Korean population. This finding may provide a candidate therapeutic target for AD, highlighting the importance of genetic studies in diverse populations.
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Affiliation(s)
- Hang-Rai Kim
- Department of Neurology, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jaeho Kim
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Song Hwangbo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - So Yeon Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Beomsu Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Soyeon Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Jee Hyang Jeong
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University School of Medicine, Seoul, Republic of Korea
| | - Soo Jin Yoon
- Department of Neurology, Eulji University Hospital, Eulji University School of Medicine, Daejeon, Republic of Korea
| | - Kyung Won Park
- Department of Neurology, Dong-A University College of Medicine, Department of Translational Biomedical Sciences, Graduate School of Dong-A University, Busan, Republic of Korea
| | - Eun-Joo Kim
- Department of Neurology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, Busan, Republic of Korea
| | - Bora Yoon
- Department of Neurology, Konyang University College of Medicine, Daejeon, Republic of Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Republic of Korea
| | - Jin Yong Hong
- Department of Neurology, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Republic of Korea
| | - Young Noh
- Department of Neurology, Gachon University College of Medicine, Gil Medical Center, Incheon, Republic of Korea
| | - Ko Woon Kim
- Department of Neurology, School of Medicine, Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Si Eun Kim
- Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Republic of Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Republic of Korea
| | - Na-Yeon Jung
- Department of Neurology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine and Medical Research Institute, Busan, Republic of Korea
| | - Juyoun Lee
- Department of Neurology, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Byeong C Kim
- Departmet of Neurology, Chonnam National University School of Medicine, Gwangju, Republic of Korea
| | - Sang Joon Son
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Chang Hyung Hong
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hong-Hee Won
- Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
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Meng X, Li J, Zhang Q, Chen F, Bian C, Yao X, Yan J, Xu Z, Risacher SL, Saykin AJ, Liang H, Shen L. Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease. BMC Genomics 2020; 21:896. [PMID: 33372590 PMCID: PMC7771059 DOI: 10.1186/s12864-020-07282-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 11/25/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Jin Li
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin, 132012, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Chenyuan Bian
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA
| | - Zhe Xu
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, 150001, China.
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
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Li J, Chen F, Zhang Q, Meng X, Yao X, Risacher SL, Yan J, Saykin AJ, Liang H, Shen L. Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease. Curr Alzheimer Res 2020; 16:1163-1174. [PMID: 31755389 DOI: 10.2174/1567205016666191121142558] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 11/19/2019] [Accepted: 11/21/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. OBJECTIVE The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. METHODS First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. RESULTS We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. CONCLUSION The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.
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Affiliation(s)
- Jin Li
- College of Automation, Harbin Engineering University, Harbin, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, China
| | - Qiushi Zhang
- College of Information Engineering, Northeast Dianli University, Jilin, China
| | - Xianglian Meng
- College of Automation, Harbin Engineering University, Harbin, China
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, China
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
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Zhou J, Qiu Y, Chen S, Liu L, Liao H, Chen H, Lv S, Li X. A Novel Three-Stage Framework for Association Analysis Between SNPs and Brain Regions. Front Genet 2020; 11:572350. [PMID: 33193677 PMCID: PMC7542238 DOI: 10.3389/fgene.2020.572350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 08/17/2020] [Indexed: 12/17/2022] Open
Abstract
Motivation: At present, a number of correlation analysis methods between SNPs and ROIs have been devised to explore the pathogenic mechanism of Alzheimer's disease. However, some of the deficiencies inherent in these methods, including lack of statistical efficacy and biological meaning. This study aims at addressing issues: insufficient correlation by previous methods (relative high regression error) and the lack of biological meaning in association analysis. Results: In this paper, a novel three-stage SNPs and ROIs correlation analysis framework is proposed. Firstly, clustering algorithm is applied to remove the potential linkage unbalanced structure of two SNPs. Then, the group sparse model is used to introduce prior information such as gene structure and linkage unbalanced structure to select feature SNPs. After the above steps, each SNP has a weight vector corresponding to each ROI, and the importance of SNPs can be judged according to the weights in the feature vector, and then the feature SNPs can be selected. Finally, for the selected feature SNPS, a support vector machine regression model is used to implement the prediction of the ROIs phenotype values. The experimental results under multiple performance measures show that the proposed method has better accuracy than other methods.
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Affiliation(s)
- Juan Zhou
- School of Software, East China Jiaotong University, Nanchang, China
| | - Yangping Qiu
- School of Software, East China Jiaotong University, Nanchang, China
| | - Shuo Chen
- School of Software, East China Jiaotong University, Nanchang, China
| | - Liyue Liu
- School of Software, East China Jiaotong University, Nanchang, China
| | - Huifa Liao
- School of Software, East China Jiaotong University, Nanchang, China
| | - Hongli Chen
- School of Software, East China Jiaotong University, Nanchang, China
| | - Shanguo Lv
- School of Software, East China Jiaotong University, Nanchang, China
| | - Xiong Li
- School of Software, East China Jiaotong University, Nanchang, China
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12
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Bi XA, Liu Y, Xie Y, Hu X, Jiang Q. Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment. Bioinformatics 2020; 36:2561-2568. [PMID: 31971559 PMCID: PMC7178433 DOI: 10.1093/bioinformatics/btz967] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 12/12/2019] [Accepted: 01/18/2020] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION The multimodal data fusion analysis becomes another important field for brain disease detection and increasing researches concentrate on using neural network algorithms to solve a range of problems. However, most current neural network optimizing strategies focus on internal nodes or hidden layer numbers, while ignoring the advantages of external optimization. Additionally, in the multimodal data fusion analysis of brain science, the problems of small sample size and high-dimensional data are often encountered due to the difficulty of data collection and the specialization of brain science data, which may result in the lower generalization performance of neural network. RESULTS We propose a genetically evolved random neural network cluster (GERNNC) model. Specifically, the fusion characteristics are first constructed to be taken as the input and the best type of neural network is selected as the base classifier to form the initial random neural network cluster. Second, the cluster is adaptively genetically evolved. Based on the GERNNC model, we further construct a multi-tasking framework for the classification of patients with brain disease and the extraction of significant characteristics. In a study of genetic data and functional magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative, the framework exhibits great classification performance and strong morbigenous factor detection ability. This work demonstrates that how to effectively detect pathogenic components of the brain disease on the high-dimensional medical data and small samples. AVAILABILITY AND IMPLEMENTATION The Matlab code is available at https://github.com/lizi1234560/GERNNC.git.
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Affiliation(s)
- Xia-an Bi
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yingchao Liu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yiming Xie
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xi Hu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
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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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Horgusluoglu-Moloch E, Risacher SL, Crane PK, Hibar D, Thompson PM, Saykin AJ, Nho K. Genome-wide association analysis of hippocampal volume identifies enrichment of neurogenesis-related pathways. Sci Rep 2019; 9:14498. [PMID: 31601890 PMCID: PMC6787090 DOI: 10.1038/s41598-019-50507-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 09/09/2019] [Indexed: 01/04/2023] Open
Abstract
Adult neurogenesis occurs in the dentate gyrus of the hippocampus during adulthood and contributes to sustaining the hippocampal formation. To investigate whether neurogenesis-related pathways are associated with hippocampal volume, we performed gene-set enrichment analysis using summary statistics from a large-scale genome-wide association study (N = 13,163) of hippocampal volume from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium and two year hippocampal volume changes from baseline in cognitively normal individuals from Alzheimer's Disease Neuroimaging Initiative Cohort (ADNI). Gene-set enrichment analysis of hippocampal volume identified 44 significantly enriched biological pathways (FDR corrected p-value < 0.05), of which 38 pathways were related to neurogenesis-related processes including neurogenesis, generation of new neurons, neuronal development, and neuronal migration and differentiation. For genes highly represented in the significantly enriched neurogenesis-related pathways, gene-based association analysis identified TESC, ACVR1, MSRB3, and DPP4 as significantly associated with hippocampal volume. Furthermore, co-expression network-based functional analysis of gene expression data in the hippocampal subfields, CA1 and CA3, from 32 normal controls showed that distinct co-expression modules were mostly enriched in neurogenesis related pathways. Our results suggest that neurogenesis-related pathways may be enriched for hippocampal volume and that hippocampal volume may serve as a potential phenotype for the investigation of human adult neurogenesis.
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Grants
- UL1 TR001108 NCATS NIH HHS
- R01 CA129769 NCI NIH HHS
- R35 CA197289 NCI NIH HHS
- P50 GM115318 NIGMS NIH HHS
- R01 AG019771 NIA NIH HHS
- P30 AG010133 NIA NIH HHS
- R03 AG054936 NIA NIH HHS
- U01 AG024904 NIA NIH HHS
- UL1 TR002369 NCATS NIH HHS
- R01 LM011360 NLM NIH HHS
- U54 EB020403 NIBIB NIH HHS
- K01 AG049050 NIA NIH HHS
- R01 LM012535 NLM NIH HHS
- CIHR
- NLM R01 LM012535, NIA R03 AG054936, NIA R01 AG19771, NIA P30 AG10133, NLM R01 LM011360, NSF IIS-1117335, DOD W81XWH-14-2-0151, NCAA 14132004, NIGMS P50GM115318, NCATS UL1 TR001108, NIA K01 AG049050, the Alzheimer’s Association, the Indiana Clinical and Translational Science Institute, and the IU Health-IU School of Medicine Strategic Neuroscience Research Initiative.
- ENIGMA was supported in part by a Consortium grant (U54EB020403 to PMT) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative, including the NIBIB and NCI.
- Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Additional support for data analysis was provided by NLM R01 LM012535, NIA R03 AG054936, NIA R01 AG19771, NIA P30 AG10133, NLM R01 LM011360, NSF IIS-1117335, DOD W81XWH-14-2-0151, NCAA 14132004, NIGMS P50GM115318, NCATS UL1 TR001108, NIA K01 AG049050, the Alzheimer’s Association, the Indiana Clinical and Translational Science Institute, and the IU Health-IU School of Medicine Strategic Neuroscience Research Initiative.
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Affiliation(s)
- Emrin Horgusluoglu-Moloch
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shannon L Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, School of Medicine, Seattle, WA, USA
| | - Derrek Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Neuroscience Biomarkers, Janssen Research and Development, LLC, San Diego, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andrew J Saykin
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Kwangsik Nho
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
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15
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Zhou T, Liu M, Thung KH, Shen D. Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2411-2422. [PMID: 31021792 PMCID: PMC8034601 DOI: 10.1109/tmi.2019.2913158] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The fusion of complementary information contained in multi-modality data [e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and genetic data] has advanced the progress of automated Alzheimer's disease (AD) diagnosis. However, multi-modality based AD diagnostic models are often hindered by the missing data, i.e., not all the subjects have complete multi-modality data. One simple solution used by many previous studies is to discard samples with missing modalities. However, this significantly reduces the number of training samples, thus leading to a sub-optimal classification model. Furthermore, when building the classification model, most existing methods simply concatenate features from different modalities into a single feature vector without considering their underlying associations. As features from different modalities are often closely related (e.g., MRI and PET features are extracted from the same brain region), utilizing their inter-modality associations may improve the robustness of the diagnostic model. To this end, we propose a novel latent representation learning method for multi-modality based AD diagnosis. Specifically, we use all the available samples (including samples with incomplete modality data) to learn a latent representation space. Within this space, we not only use samples with complete multi-modality data to learn a common latent representation, but also use samples with incomplete multi-modality data to learn independent modality-specific latent representations. We then project the latent representations to the label space for AD diagnosis. We perform experiments using 737 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the experimental results verify the effectiveness of our proposed method.
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Affiliation(s)
- Tao Zhou
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
- Inception Institute of Artificial Intelligence, Abu Dhabi 51133, United Arab Emirates
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Kim-Han Thung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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Abstract
After more than 10 years of accumulated efforts, genome-wide association studies (GWAS) have led to many findings, most of which have been deposited into the GWAS Catalog. Between GWAS's inception and March 2017, the GWAS Catalog has collected 2429 studies, 1818 phenotypes, and 28,462 associated SNPs. We reclassified the psychology-related phenotypes into 217 reclassified phenotypes, which accounted for 514 studies and 7052 SNPs. In total, 1223 of the SNPs reached genome-wide significance. Of these, 147 were replicated for the same psychological trait in different studies. Another 305 SNPs were replicated within one original study. The SNPs rs2075650 and rs4420638 were linked to the most replications within a single reclassified phenotype or very similar reclassified phenotypes; both were associated with Alzheimer's disease (AD). Schizophrenia was associated with 74 within-phenotype SNPs reported in independents studies. Alzheimer's disease and schizophrenia were both linked to some physical phenotypes, including cholesterol and body mass index, through common GWAS signals. Alzheimer's disease also shared risk SNPs with age-related phenotypes such as age-related macular degeneration and longevity. Smoking-related SNPs were linked to lung cancer and respiratory function. Alcohol-related SNPs were associated with cardiovascular and digestive system phenotypes and disorders. Two separate studies also identified a shared risk SNP for bipolar disorder and educational attainment. This review revealed a list of reproducible SNPs worthy of future functional investigation. Additionally, by identifying SNPs associated with multiple phenotypes, we illustrated the importance of studying the relationships among phenotypes to resolve the nature of their causal links. The insights within this review will hopefully pave the way for future evidence-based genetic studies.
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Prendecki M, Florczak-Wyspianska J, Kowalska M, Ilkowski J, Grzelak T, Bialas K, Wiszniewska M, Kozubski W, Dorszewska J. Biothiols and oxidative stress markers and polymorphisms of TOMM40 and APOC1 genes in Alzheimer's disease patients. Oncotarget 2018; 9:35207-35225. [PMID: 30443289 PMCID: PMC6219666 DOI: 10.18632/oncotarget.26184] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 09/01/2018] [Indexed: 12/16/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive disease, with frequently observed improper biothiols turnover, homocysteine (Hcy) and glutathione (GSH). GSH protects cells from oxidative stress and may be determined by 8-oxo-2’-deoxyguanosine (8-oxo2dG) level and its repair enzyme 8-oxoguanine DNA glycosylase (OGG1). The presence of unfavorable alleles, e.g., in APOE cluster, TOMM40 or APOC1 is known to facilitate the dementia onset under oxidative stress. The aim of the study was to analyze rs1052452, rs2075650 TOMM40 polymorphisms, rs4420638 APOC1, and their correlation with Hcy, GSH, 8-oxo2dG, OGG1 levels in plasma of AD patients and controls. We recruited 230 individuals: 88 AD, 80 controls without (UC), 62 controls with (RC) positive family history of AD. The TOMM40 genotype was determined by HRM and capillary electrophoresis, while APOC1 by HRM. The concentrations of OGG1, 8-oxo2dG were determined by ELISA, whereas Hcy, GSH by HPLC/EC. We showed that over 60% of AD patients had increased Hcy levels (p<0.01 vs. UC, p<0.001 vs. RC), while GSH (p<0.01 vs. UC), 8-oxo2dG (p<0.01 vs. UC, p<0.001 vs. RC) were reduced. Minor variants: rs10524523-L, rs4420638-G, rs2075650-G were significantly overrepresented in AD. For rs4420638-G, rs2075650-G variants, the association remained significant in APOE E4 non-carriers. The misbalance of analyzed biothiols, and 8-oxo2dG, OGG1 were more pronounced in carriers of major variants: rs10524523-S/VL, rs4420638-A, rs2075650-A. We showed, for the first time, that APOC1 and TOMM40 rs2075650 polymorphisms may be independent risk factors of developing AD, whose major variants are accompanied by disruption of biothiols metabolism and inefficient removal of DNA oxidation.
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Affiliation(s)
- Michal Prendecki
- Laboratory of Neurobiology, Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
| | | | - Marta Kowalska
- Laboratory of Neurobiology, Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
| | - Jan Ilkowski
- Department of Emergency Medicine, Poznan University of Medical Sciences, Poznan, Poland
| | - Teresa Grzelak
- Division of Biology of Civilization-Linked Diseases, Department of Chemistry and Clinical Biochemistry, Poznan University of Medical Sciences, Poznan, Poland
| | - Katarzyna Bialas
- Laboratory of Neurobiology, Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
| | - Malgorzata Wiszniewska
- Faculty of Health Care, Stanislaw Staszic University of Applied Sciences in Pila, Pila, Poland.,Department of Neurology, Specialistic Hospital in Pila, Pila, Poland
| | - Wojciech Kozubski
- Chair and Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
| | - Jolanta Dorszewska
- Laboratory of Neurobiology, Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
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Identification of genome-wide SNP-SNP interactions associated with important traits in chicken. BMC Genomics 2017; 18:892. [PMID: 29162033 PMCID: PMC5698929 DOI: 10.1186/s12864-017-4252-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 10/31/2017] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND In addition to additive genetic effects, epistatic interactions can play key roles in the control of phenotypic variation of traits of interest. In the current study, 475 male birds from lean and fat chicken lines were utilized as a resource population to detect significant epistatic effects associated with growth and carcass traits. RESULTS A total of 421 significant epistatic effects were associated with testis weight (TeW), from which 11 sub-networks (Sub-network1 to Sub-network11) were constructed. In Sub-network1, which was the biggest network, there was an interaction between GGA21 and GGAZ. Three genes on GGA21 (SDHB, PARK7 and VAMP3) and nine genes (AGTPBP1, CAMK4, CDC14B, FANCC, FBP1, GNAQ, PTCH1, ROR2 and STARD4) on GGAZ that might be potentially important candidate genes for testis growth and development were detected based on the annotated gene function. In Sub-network2, there was a SNP on GGA19 that interacted with 8 SNPs located on GGA10. The SNP (Gga_rs15834332) on GGA19 was located between C-C motif chemokine ligand 5 (CCL5) and MIR142. There were 32 Refgenes on GGA10, including TCF12 which is predicted to be a target gene of miR-142-5p. We hypothesize that miR-142-5p and TCF12 may interact with one another to regulate testis growth and development. Two genes (CDH12 and WNT8A) in the same cadherin signaling pathway were implicated as potentially important genes in the control of metatarsus circumference (MeC). There were no significant epistatic effects identified for the other carcass and growth traits, e.g. heart weight (HW), liver weight (LW), spleen weight (SW), muscular and glandular stomach weight (MGSW), carcass weight (CW), body weight (BW1, BW3, BW5, BW7), chest width (ChWi), metatarsus length (MeL). CONCLUSIONS The results of the current study are helpful to better understand the genetic basis of carcass and growth traits, especially for testis growth and development in broilers.
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Cong W, Meng X, Li J, Zhang Q, Chen F, Liu W, Wang Y, Cheng S, Yao X, Yan J, Kim S, Saykin AJ, Liang H, Shen L. Genome-wide network-based pathway analysis of CSF t-tau/Aβ1-42 ratio in the ADNI cohort. BMC Genomics 2017; 18:421. [PMID: 28558704 PMCID: PMC5450240 DOI: 10.1186/s12864-017-3798-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Accepted: 05/16/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The cerebrospinal fluid (CSF) levels of total tau (t-tau) and Aβ1-42 are potential early diagnostic markers for probable Alzheimer's disease (AD). The influence of genetic variation on these CSF biomarkers has been investigated in candidate or genome-wide association studies (GWAS). However, the investigation of statistically modest associations in GWAS in the context of biological networks is still an under-explored topic in AD studies. The main objective of this study is to gain further biological insights via the integration of statistical gene associations in AD with physical protein interaction networks. RESULTS The CSF and genotyping data of 843 study subjects (199 CN, 85 SMC, 239 EMCI, 207 LMCI, 113 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed. PLINK was used to perform GWAS on the t-tau/Aβ1-42 ratio using quality controlled genotype data, including 563,980 single nucleotide polymorphisms (SNPs), with age, sex and diagnosis as covariates. Gene-level p-values were obtained by VEGAS2. Genes with p-value ≤ 0.05 were mapped on to a protein-protein interaction (PPI) network (9,617 nodes, 39,240 edges, from the HPRD Database). We integrated a consensus model strategy into the iPINBPA network analysis framework, and named it as CM-iPINBPA. Four consensus modules (CMs) were discovered by CM-iPINBPA, and were functionally annotated using the pathway analysis tool Enrichr. The intersection of four CMs forms a common subnetwork of 29 genes, including those related to tau phosphorylation (GSK3B, SUMO1, AKAP5, CALM1 and DLG4), amyloid beta production (CASP8, PIK3R1, PPA1, PARP1, CSNK2A1, NGFR, and RHOA), and AD (BCL3, CFLAR, SMAD1, and HIF1A). CONCLUSIONS This study coupled a consensus module (CM) strategy with the iPINBPA network analysis framework, and applied it to the GWAS of CSF t-tau/Aβ1-42 ratio in an AD study. The genome-wide network analysis yielded 4 enriched CMs that share not only genes related to tau phosphorylation or amyloid beta production but also multiple genes enriching several KEGG pathways such as Alzheimer's disease, colorectal cancer, gliomas, renal cell carcinoma, Huntington's disease, and others. This study demonstrated that integration of gene-level associations with CMs could yield statistically significant findings to offer valuable biological insights (e.g., functional interaction among the protein products of these genes) and suggest high confidence candidates for subsequent analyses.
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Affiliation(s)
- Wang Cong
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Xianglian Meng
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
- Harbin Huade University, No.288 Xue Yuan Rd. Limin Development Zone, Harbin, 150025 China
| | - Jin Li
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Qiushi Zhang
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
- College of Information Engineering, Northeast Dianli University, 169 Changchun Street, Jilin City, Jilin 132012 China
| | - Feng Chen
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Wenjie Liu
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Ying Wang
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Sipu Cheng
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Xiaohui Yao
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- School of Informatics and Computing, Indiana University, 719 Indiana Avenue, Indianapolis, IN 46202 USA
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- School of Informatics and Computing, Indiana University, 719 Indiana Avenue, Indianapolis, IN 46202 USA
- Indiana University Network Science Institute, Bloomington, IN 47405 USA
| | - Sungeun Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- Indiana University Network Science Institute, Bloomington, IN 47405 USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- Indiana University Network Science Institute, Bloomington, IN 47405 USA
| | - Hong Liang
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- School of Informatics and Computing, Indiana University, 719 Indiana Avenue, Indianapolis, IN 46202 USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
- Harbin Huade University, No.288 Xue Yuan Rd. Limin Development Zone, Harbin, 150025 China
- College of Information Engineering, Northeast Dianli University, 169 Changchun Street, Jilin City, Jilin 132012 China
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- School of Informatics and Computing, Indiana University, 719 Indiana Avenue, Indianapolis, IN 46202 USA
- Indiana University Network Science Institute, Bloomington, IN 47405 USA
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20
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Li J, Zhang Q, Chen F, Meng X, Liu W, Chen D, Yan J, Kim S, Wang L, Feng W, Saykin AJ, Liang H, Shen L. Genome-wide association and interaction studies of CSF T-tau/Aβ 42 ratio in ADNI cohort. Neurobiol Aging 2017. [PMID: 28641921 DOI: 10.1016/j.neurobiolaging.2017.05.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The pathogenic relevance in Alzheimer's disease (AD) presents a decrease of cerebrospinal fluid amyloid-ß42 (Aß42) burden and an increase in cerebrospinal fluid total tau (T-tau) levels. In this work, we performed genome-wide association study (GWAS) and genome-wide interaction study of T-tau/Aß42 ratio as an AD imaging quantitative trait on 843 subjects and 563,980 single-nucleotide polymorphisms (SNPs) in ADNI cohort. We aim to identify not only SNPs with significant main effects but also SNPs with interaction effects to help explain "missing heritability". Linear regression method was used to detect SNP-SNP interactions among SNPs with uncorrected p-value ≤0.01 from the GWAS. Age, gender, and diagnosis were considered as covariates in both studies. The GWAS results replicated the previously reported AD-related genes APOE, APOC1, and TOMM40, as well as identified 14 novel genes, which showed genome-wide statistical significance. Genome-wide interaction study revealed 7 pairs of SNPs meeting the cell-size criteria and with bonferroni-corrected p-value ≤0.05. As we expect, these interaction pairs all had marginal main effects but explained a relatively high-level variance of T-tau/Aß42, demonstrating their potential association with AD pathology.
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Affiliation(s)
- Jin Li
- College of Automation, Harbin Engineering University, Harbin, China
| | - Qiushi Zhang
- College of Automation, Harbin Engineering University, Harbin, China; College of Information Engineering, Northeast Dianli University, Jilin, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, China
| | - Xianglian Meng
- College of Automation, Harbin Engineering University, Harbin, China
| | - Wenjie Liu
- College of Automation, Harbin Engineering University, Harbin, China
| | - Dandan Chen
- College of Automation, Harbin Engineering University, Harbin, China; College of Information Engineering, Northeast Dianli University, Jilin, China
| | - Jingwen Yan
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA; Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, USA
| | - Sungeun Kim
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Lei Wang
- College of Automation, Harbin Engineering University, Harbin, China
| | - Weixing Feng
- College of Automation, Harbin Engineering University, Harbin, China
| | - Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, China.
| | - Li Shen
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA; Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, USA.
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21
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Wang Q, Su X, Jiang X, Dong X, Fan Y, Zhang J, Yu C, Gao W, Shi S, Jiang J, Jiang W, Wei T. iTRAQ technology-based identification of human peripheral serum proteins associated with depression. Neuroscience 2016; 330:291-325. [PMID: 27268281 DOI: 10.1016/j.neuroscience.2016.05.055] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2015] [Revised: 05/25/2016] [Accepted: 05/27/2016] [Indexed: 12/26/2022]
Abstract
Clinical depression is one of the most common and debilitating psychiatric disorders and contributes to increased risks of disability and suicide. Differentially expressed serum proteins may serve as biomarkers for diagnosing depression. In this study, samples from depressed patients are aggregated into a pool (22×100μL serum was used) and samples from healthy volunteers are aggregated into the other pool (20×100μL serum was used). Isobaric tag for relative and absolute quantitation (iTRAQ) technology and tandem mass spectrometry were employed to screen for differentially expressed serum protein in two separate pools. We identified 472 proteins in the serum samples, and 154 of these presented differences in abundance between the depression and control groups. Ingenuity pathway analysis (IPA) was employed to identify the highest scoring proteins in signaling pathway networks. Finally, four differentially expressed proteins were validated by enzyme-linked immuno sorbent assay (ELISA). Proteomic studies revealed that levels of c-reaction protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), serum amyloid A1 (SAA1) and angiopoietin-like 3 (ANGPTL3) were substantially increased in depressed patients compared with the healthy control group. Therefore, these differentially expressed proteins may represent potential markers for the clinical diagnosis of depression.
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Affiliation(s)
- Q Wang
- Department of Biopharmaceutical Sciences, College of Pharmacy, Harbin Medical University-Daqing, Daqing, Heilongjiang 163000, PR China
| | - X Su
- Department of Biopharmaceutical Sciences, College of Pharmacy, Harbin Medical University-Daqing, Daqing, Heilongjiang 163000, PR China
| | - X Jiang
- Department of Neurology, The Third People's Hospital of Daqing, Daqing, Heilongjiang 163000, PR China
| | - X Dong
- Department of Biopharmaceutical Sciences, College of Pharmacy, Harbin Medical University-Daqing, Daqing, Heilongjiang 163000, PR China
| | - Y Fan
- Department of Biopharmaceutical Sciences, College of Pharmacy, Harbin Medical University-Daqing, Daqing, Heilongjiang 163000, PR China
| | - J Zhang
- Department of Biopharmaceutical Sciences, College of Pharmacy, Harbin Medical University-Daqing, Daqing, Heilongjiang 163000, PR China
| | - C Yu
- Department of Biopharmaceutical Sciences, College of Pharmacy, Harbin Medical University-Daqing, Daqing, Heilongjiang 163000, PR China
| | - W Gao
- Zhejiang Academy of Medical Sciences, Hangzhou, Zhejiang 310013, PR China
| | - S Shi
- Department of Biopharmaceutical Sciences, College of Pharmacy, Harbin Medical University-Daqing, Daqing, Heilongjiang 163000, PR China
| | - J Jiang
- Department of Biopharmaceutical Sciences, College of Pharmacy, Harbin Medical University-Daqing, Daqing, Heilongjiang 163000, PR China
| | - W Jiang
- Department of Neurology, The Third People's Hospital of Daqing, Daqing, Heilongjiang 163000, PR China
| | - T Wei
- Department of Biopharmaceutical Sciences, College of Pharmacy, Harbin Medical University-Daqing, Daqing, Heilongjiang 163000, PR China.
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