1
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Angelucci F, Ai AR, Piendel L, Cerman J, Hort J. Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials. Curr Opin Struct Biol 2024; 87:102857. [PMID: 38838385 DOI: 10.1016/j.sbi.2024.102857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/15/2024] [Accepted: 05/12/2024] [Indexed: 06/07/2024]
Abstract
The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.
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Affiliation(s)
- Francesco Angelucci
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
| | - Alice Ruixue Ai
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway
| | - Lydia Piendel
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic; Augusta University/University of Georgia Medical Partnership, Medical College of Georgia, Athens, GA, USA
| | - Jiri Cerman
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Jakub Hort
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic; INDRC, International Neurodegenerative Disorders Research Center, Prague, Czech Republic
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2
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Tuo S, Jiang J. A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search. Interdiscip Sci 2024:10.1007/s12539-024-00621-2. [PMID: 38954231 DOI: 10.1007/s12539-024-00621-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 02/06/2024] [Accepted: 02/17/2024] [Indexed: 07/04/2024]
Abstract
To elucidate the genetic basis of complex diseases, it is crucial to discover the single-nucleotide polymorphisms (SNPs) contributing to disease susceptibility. This is particularly challenging for high-order SNP epistatic interactions (HEIs), which exhibit small individual effects but potentially large joint effects. These interactions are difficult to detect due to the vast search space, encompassing billions of possible combinations, and the computational complexity of evaluating them. This study proposes a novel explicit-encoding-based multitasking harmony search algorithm (MTHS-EE-DHEI) specifically designed to address this challenge. The algorithm operates in three stages. First, a harmony search algorithm is employed, utilizing four lightweight evaluation functions, such as Bayesian network and entropy, to efficiently explore potential SNP combinations related to disease status. Second, a G-test statistical method is applied to filter out insignificant SNP combinations. Finally, two machine learning-based methods, multifactor dimensionality reduction (MDR) as well as random forest (RF), are employed to validate the classification performance of the remaining significant SNP combinations. This research aims to demonstrate the effectiveness of MTHS-EE-DHEI in identifying HEIs compared to existing methods, potentially providing valuable insights into the genetic architecture of complex diseases. The performance of MTHS-EE-DHEI was evaluated on twenty simulated disease datasets and three real-world datasets encompassing age-related macular degeneration (AMD), rheumatoid arthritis (RA), and breast cancer (BC). The results demonstrably indicate that MTHS-EE-DHEI outperforms four state-of-the-art algorithms in terms of both detection power and computational efficiency. The source code is available at https://github.com/shouhengtuo/MTHS-EE-DHEI.git .
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Affiliation(s)
- Shouheng Tuo
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China.
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, China.
- Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an, 710121, China.
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China
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3
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Aranda S, Jiménez E, Canales-Rodríguez EJ, Verdolini N, Alonso S, Sepúlveda E, Julià A, Marsal S, Bobes J, Sáiz PA, García-Portilla P, Menchón JM, Crespo JM, González-Pinto A, Pérez V, Arango C, Sierra P, Sanjuán J, Pomarol-Clotet E, Vieta E, Vilella E. Processing speed mediates the relationship between DDR1 and psychosocial functioning in euthymic patients with bipolar disorder presenting psychotic symptoms. Mol Psychiatry 2024:10.1038/s41380-024-02480-1. [PMID: 38374360 DOI: 10.1038/s41380-024-02480-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/01/2024] [Accepted: 02/05/2024] [Indexed: 02/21/2024]
Abstract
The DDR1 locus is associated with the diagnosis of schizophrenia and with processing speed in patients with schizophrenia and first-episode psychosis. Here, we investigated whether DDR1 variants are associated with bipolar disorder (BD) features. First, we performed a case‒control association study comparing DDR1 variants between patients with BD and healthy controls. Second, we performed linear regression analyses to assess the associations of DDR1 variants with neurocognitive domains and psychosocial functioning. Third, we conducted a mediation analysis to explore whether neurocognitive impairment mediated the association between DDR1 variants and psychosocial functioning in patients with BD. Finally, we studied the association between DDR1 variants and white matter microstructure. We did not find any statistically significant associations in the case‒control association study; however, we found that the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse neurocognitive performance in patients with BD with psychotic symptoms. In addition, the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse psychosocial functioning through processing speed. We did not find correlations between white matter microstructure abnormalities and the neurocognitive domains associated with the combined genotypes rs1264323AA-rs2267641AC/CC. Overall, the results suggest that DDR1 may be a marker of worse neurocognitive performance and psychosocial functioning in patients with BD, specifically those with psychotic symptoms.
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Affiliation(s)
- Selena Aranda
- Institut d'Investigació Sanitària Pere Virgili-CERCA, Reus, Spain
- Hospital Universitari Institut Pere Mata, Reus, Spain
- Universitat Rovira i Virgili, Reus, Spain
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Esther Jiménez
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
- Department of Psychiatry, University of the Basque Country (UPV-EHU), Vitoria-Gasteiz, Spain
| | - Erick J Canales-Rodríguez
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, Spain
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Norma Verdolini
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, Spain
| | - Silvia Alonso
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - Esteban Sepúlveda
- Institut d'Investigació Sanitària Pere Virgili-CERCA, Reus, Spain
- Hospital Universitari Institut Pere Mata, Reus, Spain
- Universitat Rovira i Virgili, Reus, Spain
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain
| | - Julio Bobes
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, Universidad de Oviedo, Oviedo, Spain
- nstituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Instituto Universitario de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain
- Servicio de Salud del Principado de Asturias (SESPA) Oviedo, Oviedo, Spain
| | - Pilar A Sáiz
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, Universidad de Oviedo, Oviedo, Spain
- nstituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Instituto Universitario de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain
- Servicio de Salud del Principado de Asturias (SESPA) Oviedo, Oviedo, Spain
| | - Paz García-Portilla
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, Universidad de Oviedo, Oviedo, Spain
- nstituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Instituto Universitario de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain
- Servicio de Salud del Principado de Asturias (SESPA) Oviedo, Oviedo, Spain
| | - Jose M Menchón
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - José M Crespo
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - Ana González-Pinto
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, University of the Basque Country (UPV-EHU), Vitoria-Gasteiz, Spain
- Araba University Hospital, Bioaraba Research Institute, UPV/EHU, Vitoria-Gasteiz, Spain
| | - Víctor Pérez
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Hospital de Mar. Mental Health Institute, Barcelona, Spain
- Neurosciences Research Unit, Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain
| | - Celso Arango
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Institute of Psychiatry and Mental Health, Madrid, Spain
- Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Universidad Complutense, Madrid, Spain
| | - Pilar Sierra
- La Fe University and Polytechnic Hospital, Valencia, Spain
- Department of Psychiatry, School of Medicine, University of Valencia, Valencia, Spain
| | - Julio Sanjuán
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, School of Medicine, University of Valencia, Valencia, Spain
| | - Edith Pomarol-Clotet
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, Spain
| | - Eduard Vieta
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - Elisabet Vilella
- Institut d'Investigació Sanitària Pere Virgili-CERCA, Reus, Spain.
- Hospital Universitari Institut Pere Mata, Reus, Spain.
- Universitat Rovira i Virgili, Reus, Spain.
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain.
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Hermes S, Cady J, Armentrout S, O’Connor J, Holdaway SC, Cruchaga C, Wingo T, Greytak EM. Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores. J Alzheimers Dis 2024; 99:1425-1440. [PMID: 38788065 PMCID: PMC11284654 DOI: 10.3233/jad-230236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Background Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail to capture much of the heritability. Additionally, PRS models are highly dependent on the population structure of the data on which effect sizes are assessed and have poor generalizability to new data. Objective The goal of this study is to construct a paragenic risk score that, in addition to single genetic marker data used in PRS, incorporates epistatic interaction features and machine learning methods to predict risk for LOAD. Methods We construct a new state-of-the-art genetic model for risk of Alzheimer's disease. Our approach innovates over PRS models in two ways: First, by directly incorporating epistatic interactions between SNP loci using an evolutionary algorithm guided by shared pathway information; and second, by estimating risk via an ensemble of non-linear machine learning models rather than a single linear model. We compare the paragenic model to several PRS models from the literature trained on the same dataset. Results The paragenic model is significantly more accurate than the PRS models under 10-fold cross-validation, obtaining an AUC of 83% and near-clinically significant matched sensitivity/specificity of 75%. It remains significantly more accurate when evaluated on an independent holdout dataset and maintains accuracy within APOE genotype strata. Conclusions Paragenic models show potential for improving disease risk prediction for complex heritable diseases such as LOAD over PRS models.
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Affiliation(s)
| | | | | | | | | | - Carlos Cruchaga
- Department of Psychiatry, Washington University, St. Louis, MO, USA
- Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University, St. Louis, MO, USA
| | - Thomas Wingo
- Goizueta Alzheimer’s Disease Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
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Alatrany AS, Khan W, Hussain A, Al-Jumeily D. Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies. PLoS One 2023; 18:e0283712. [PMID: 37126509 PMCID: PMC10150974 DOI: 10.1371/journal.pone.0283712] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/15/2023] [Indexed: 05/02/2023] Open
Abstract
The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction of AD might be useful to mitigate or at-least slow down its progression for which, identification of the factors affecting the AD and its accurate diagnoses, are vital. In this study, we use Genome-Wide Association Studies (GWAS) dataset which comprises significant genetic markers of complex diseases. The original dataset contains large number of attributes (620901) for which we propose a hybrid feature selection approach based on association test, principal component analysis, and the Boruta algorithm, to identify the most promising predictors of AD. The selected features are then forwarded to a wide and deep neural network models to classify the AD cases and healthy controls. The experimental outcomes indicate that our approach outperformed the existing methods when evaluated on standard dataset, producing an accuracy and f1-score of 99%. The outcomes from this study are impactful particularly, the identified features comprising AD-associated genes and a reliable classification model that might be useful for other chronic diseases.
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Affiliation(s)
- Abbas Saad Alatrany
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom
- University of Information Technology and Communications, Baghdad, Iraq
- Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
| | - Wasiq Khan
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom
| | - Abir Hussain
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom
- Department of Electrical Engineering, University of Sharjah, Sharjah, UAE
| | - Dhiya Al-Jumeily
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom
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Hermes S, Cady J, Armentrout S, O’Connor J, Carlson S, Cruchaga C, Wingo T, Greytak EM. Epistatic Features and Machine Learning Improve Alzheimer's Risk Prediction Over Polygenic Risk Scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.10.23285766. [PMID: 36798198 PMCID: PMC9934790 DOI: 10.1101/2023.02.10.23285766] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Background Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late onset Alzheimer's disease (LOAD), PRS models fail to capture much of the heritability. Additionally, PRS models are highly dependent on the population structure of data on which effect sizes are assessed, and have poor generalizability to new data. Objective The goal of this study is to construct a paragenic risk score that, in addition to single genetic marker data used in PRS, incorporates epistatic interaction features and machine learning methods to predict lifetime risk for LOAD. Methods We construct a new state-of-the-art genetic model for lifetime risk of Alzheimer's disease. Our approach innovates over PRS models in two ways: First, by directly incorporating epistatic interactions between SNP loci using an evolutionary algorithm guided by shared pathway information; and second, by estimating risk via an ensemble of machine learning models (gradient boosting machines and deep learning) instead of simple logistic regression. We compare the paragenic model to a PRS model from the literature trained on the same dataset. Results The paragenic model is significantly more accurate than the PRS model under 10-fold cross-validation, obtaining an AUC of 83% and near-clinically significant matched sensitivity/specificity of 75%, and remains significantly more accurate when evaluated on an independent holdout dataset. Additionally, the paragenic model maintains accuracy within APOE genotypes. Conclusion Paragenic models show potential for improving lifetime disease risk prediction for complex heritable diseases such as LOAD over PRS models.
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Affiliation(s)
| | - Janet Cady
- Parabon NanoLabs, Inc., Reston, Virginia, USA
| | | | | | | | - Carlos Cruchaga
- Department of Psychiatry, Washington University, St. Louis, MO, USA
- Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University St. Louis, MO, USA
| | - Thomas Wingo
- Goizueta Alzheimer’s Disease Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
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Tuo S, Li C, Liu F, Li A, He L, Geem ZW, Shang J, Liu H, Zhu Y, Feng Z, Chen T. MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00813-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractGenome-wide association studies have succeeded in identifying genetic variants associated with complex diseases, but the findings have not been well interpreted biologically. Although it is widely accepted that epistatic interactions of high-order single nucleotide polymorphisms (SNPs) [(1) Single nucleotide polymorphisms (SNP) are mainly deoxyribonucleic acid (DNA) sequence polymorphisms caused by variants at a single nucleotide at the genome level. They are the most common type of heritable variation in humans.] are important causes of complex diseases, the combinatorial explosion of millions of SNPs and multiple tests impose a large computational burden. Moreover, it is extremely challenging to correctly distinguish high-order SNP epistatic interactions from other high-order SNP combinations due to small sample sizes. In this study, a multitasking harmony search algorithm (MTHSA-DHEI) is proposed for detecting high-order epistatic interactions [(2) In classical genetics, if genes X1 and X2 are mutated and each mutation by itself produces a unique disease status (phenotype) but the mutations together cause the same disease status as the gene X1 mutation, gene X1 is epistatic and gene X2 is hypostatic, and gene X1 has an epistatic effect (main effect) on disease status. In this work, a high-order epistatic interaction occurs when two or more SNP loci have a joint influence on disease status.], with the goal of simultaneously detecting multiple types of high-order (k1-order, k2-order, …, kn-order) SNP epistatic interactions. Unified coding is adopted for multiple tasks, and four complementary association evaluation functions are employed to improve the capability of discriminating the high-order SNP epistatic interactions. We compare the proposed MTHSA-DHEI method with four excellent methods for detecting high-order SNP interactions for 8 high-orderepistatic interaction models with no marginal effect (EINMEs) and 12 epistatic interaction models with marginal effects (EIMEs) (*) and implement the MTHSA-DHEI algorithm with a real dataset: age-related macular degeneration (AMD). The experimental results indicate that MTHSA-DHEI has power and an F1-score exceeding 90% for all EIMEs and five EINMEs and reduces the computational time by more than 90%. It can efficiently perform multiple high-order detection tasks for high-order epistatic interactions and improve the discrimination ability for diverse epistasis models.
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Mishra R, Li B. The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease. Aging Dis 2020; 11:1567-1584. [PMID: 33269107 PMCID: PMC7673858 DOI: 10.14336/ad.2020.0312] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/12/2020] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.
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Affiliation(s)
- Rohan Mishra
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
| | - Bin Li
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
- Georgetown University Medical Center, Washington D.C. 20057, USA
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Wen J, Ford CT, Janies D, Shi X. A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models. Bioinformatics 2020; 36:3803-3810. [PMID: 32227194 DOI: 10.1093/bioinformatics/btaa216] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 03/05/2020] [Accepted: 03/26/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Epistasis reflects the distortion on a particular trait or phenotype resulting from the combinatorial effect of two or more genes or genetic variants. Epistasis is an important genetic foundation underlying quantitative traits in many organisms as well as in complex human diseases. However, there are two major barriers in identifying epistasis using large genomic datasets. One is that epistasis analysis will induce over-fitting of an over-saturated model with the high-dimensionality of a genomic dataset. Therefore, the problem of identifying epistasis demands efficient statistical methods. The second barrier comes from the intensive computing time for epistasis analysis, even when the appropriate model and data are specified. RESULTS In this study, we combine statistical techniques and computational techniques to scale up epistasis analysis using Empirical Bayesian Elastic Net (EBEN) models. Specifically, we first apply a matrix manipulation strategy for pre-computing the correlation matrix and pre-filter to narrow down the search space for epistasis analysis. We then develop a parallelized approach to further accelerate the modeling process. Our experiments on synthetic and empirical genomic data demonstrate that our parallelized methods offer tens of fold speed up in comparison with the classical EBEN method which runs in a sequential manner. We applied our parallelized approach to a yeast dataset, and we were able to identify both main and epistatic effects of genetic variants associated with traits such as fitness. AVAILABILITY AND IMPLEMENTATION The software is available at github.com/shilab/parEBEN.
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Affiliation(s)
- Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Colby T Ford
- Department of Bioinformatics and Genomics, College of Computing and Informatics.,School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Daniel Janies
- Department of Bioinformatics and Genomics, College of Computing and Informatics
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
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O’Reilly GD, Jabot F, Gunn MR, Sherwin WB. Predicting Shannon’s information for genes in finite populations: new uses for old equations. CONSERV GENET RESOUR 2018. [DOI: 10.1007/s12686-018-1079-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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11
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Gonda X, Petschner P, Eszlari N, Baksa D, Edes A, Antal P, Juhasz G, Bagdy G. Genetic variants in major depressive disorder: From pathophysiology to therapy. Pharmacol Ther 2018; 194:22-43. [PMID: 30189291 DOI: 10.1016/j.pharmthera.2018.09.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In spite of promising preclinical results there is a decreasing number of new registered medications in major depression. The main reason behind this fact is the lack of confirmation in clinical studies for the assumed, and in animals confirmed, therapeutic results. This suggests low predictive value of animal studies for central nervous system disorders. One solution for identifying new possible targets is the application of genetics and genomics, which may pinpoint new targets based on the effect of genetic variants in humans. The present review summarizes such research focusing on depression and its therapy. The inconsistency between most genetic studies in depression suggests, first of all, a significant role of environmental stress. Furthermore, effect of individual genes and polymorphisms is weak, therefore gene x gene interactions or complete biochemical pathways should be analyzed. Even genes encoding target proteins of currently used antidepressants remain non-significant in genome-wide case control investigations suggesting no main effect in depression, but rather an interaction with stress. The few significant genes in GWASs are related to neurogenesis, neuronal synapse, cell contact and DNA transcription and as being nonspecific for depression are difficult to harvest pharmacologically. Most candidate genes in replicable gene x environment interactions, on the other hand, are connected to the regulation of stress and the HPA axis and thus could serve as drug targets for depression subgroups characterized by stress-sensitivity and anxiety while other risk polymorphisms such as those related to prominent cognitive symptoms in depression may help to identify additional subgroups and their distinct treatment. Until these new targets find their way into therapy, the optimization of current medications can be approached by pharmacogenomics, where metabolizing enzyme polymorphisms remain prominent determinants of therapeutic success.
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Affiliation(s)
- Xenia Gonda
- Department of Psychiatry and Psychotherapy, Kutvolgyi Clinical Centre, Semmelweis University, Budapest, Hungary; NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary.
| | - Peter Petschner
- MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
| | - Nora Eszlari
- NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
| | - Daniel Baksa
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Andrea Edes
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Peter Antal
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Gabriella Juhasz
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; Neuroscience and Psychiatry Unit, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Gyorgy Bagdy
- NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary.
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Influence of SNPs in Genes that Modulate Lung Disease Severity in a Group of Mexican Patients with Cystic Fibrosis. Arch Med Res 2018; 49:18-26. [PMID: 29703608 DOI: 10.1016/j.arcmed.2018.04.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Accepted: 04/11/2018] [Indexed: 11/20/2022]
Abstract
BACKGROUND The variation in cystic fibrosis (CF) lung disease not always is explained by the CFTR genotype, so it has become apparent that modifier genes must play a considerable role in the phenotypic heterogeneity of CF, so we investigated the association of allelic variants in modifier genes that modulate the severity of lung function in a group of Mexican patients diagnosed with CF. METHODS We included 140 CF patients classified according to lung phenotype and analyzed 17 single nucleotide polymorphisms (SNPs) by TaqMan® allelic discrimination. RESULTS We demonstrated that patients with GG or GC genotype of the allelic variant rs11003125 (MBL2-550) of the MBL2 gene exhibit most of the lung manifestations at an earlier age; and the rs1042713 allelic variant of ADRB2 gene, showed statistical difference only with the age of first spirometry. When we used the dominant model, the MBL2 allele rs11003125 (MBL2-550; p = 0.022, Odds Ratio (OR) 2.87, 95% CI 1.14-7.27) was significantly associated with CF patients as risk factor, and the ADRB2 allele rs1042713 (p.Arg16Gly; p = 0.005, Odds Ratio (OR) 0.37, 95% CI 0.19-0.75) was significantly associated with CF patients as protect factor. CONCLUSIONS Our findings suggest that the MBL2 and ADRB2 genes exerts an important genetic influence on the lung disease in our patients. Taking into account our results, we insist on not leaving aside this type of studies, since having techniques such as GWAS or WES will be able to advance in achieving a better quality of life for CF patients with severe lung disease.
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Sherwin WB, Chao A, Jost L, Smouse PE. Information Theory Broadens the Spectrum of Molecular Ecology and Evolution. Trends Ecol Evol 2017; 32:948-963. [PMID: 29126564 DOI: 10.1016/j.tree.2017.09.012] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 09/22/2017] [Accepted: 09/26/2017] [Indexed: 01/18/2023]
Abstract
Information or entropy analysis of diversity is used extensively in community ecology, and has recently been exploited for prediction and analysis in molecular ecology and evolution. Information measures belong to a spectrum (or q profile) of measures whose contrasting properties provide a rich summary of diversity, including allelic richness (q=0), Shannon information (q=1), and heterozygosity (q=2). We present the merits of information measures for describing and forecasting molecular variation within and among groups, comparing forecasts with data, and evaluating underlying processes such as dispersal. Importantly, information measures directly link causal processes and divergence outcomes, have straightforward relationship to allele frequency differences (including monotonicity that q=2 lacks), and show additivity across hierarchical layers such as ecology, behaviour, cellular processes, and nongenetic inheritance.
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Affiliation(s)
- W B Sherwin
- Evolution and Ecology Research Centre, School of Biological Earth and Environmental Science, University of New South Wales, Sydney, NSW 2052, Australia; Murdoch University Cetacean Research Unit, Murdoch University, South Road, Murdoch, WA 6150, Australia.
| | - A Chao
- Institute of Statistics, National Tsing Hua University, Hsin-Chu 30043, Taiwan
| | - L Jost
- EcoMinga Foundation, Via a Runtun, Baños, Tungurahua, Ecuador
| | - P E Smouse
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ 08901-8551, USA
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