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Zhuang X, Xu G, Amei A, Cordes D, Wang Z, Oh EC. Detecting time-varying genetic effects in Alzheimer's disease using a longitudinal genome-wide association studies model. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12597. [PMID: 38855650 PMCID: PMC11157162 DOI: 10.1002/dad2.12597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/19/2024] [Accepted: 04/17/2024] [Indexed: 06/11/2024]
Abstract
INTRODUCTION The development and progression of Alzheimer's disease (AD) is a complex process, during which genetic influences on phenotypes may also change. Incorporating longitudinal phenotypes in genome-wide association studies (GWAS) could unmask these genetic loci. METHODS We conducted a longitudinal GWAS using a varying coefficient test to identify age-dependent single nucleotide polymorphisms (SNPs) in AD. Data from 1877 Alzheimer's Neuroimaging Data Initiative participants, including impairment status and amyloid positron emission tomography (PET) scan standardized uptake value ratio (SUVR) scores, were analyzed using a retrospective varying coefficient mixed model association test (RVMMAT). RESULTS RVMMAT identified 244 SNPs with significant time-varying effects on AD impairment status, with 12 SNPs on chromosome 19 validated using National Alzheimer's Coordinating Center data. Age-stratified analyses showed these SNPs' effects peaked between 70 and 80 years. Additionally, 73 SNPs were linked to longitudinal amyloid accumulation changes. Pathway analyses implicated immune and neuroinflammation-related disruptions. DISCUSSION Our findings demonstrate that longitudinal GWAS models can uncover time-varying genetic signals in AD. Highlights Identify time-varying genetic effects using a longitudinal GWAS model in AD.Illustrate age-dependent genetic effects on both diagnoses and amyloid accumulation.Replicate time-varying effect of APOE in a second dataset.
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Affiliation(s)
- Xiaowei Zhuang
- Interdisciplinary Neuroscience PhD ProgramUniversity of Nevada Las VegasLas VegasNevadaUSA
- Laboratory of Neurogenetics and Precision MedicineUniversity of Nevada, Las VegasLas VegasNevadaUSA
- Lou Ruvo Center for Brain HealthCleveland ClinicLas VegasNevadaUSA
| | - Gang Xu
- Department of BiostatisticsYale School of Public HealthNew HavenConnecticutUSA
- Department of Mathematical SciencesUniversity of Nevada, Las VegasLas VegasNevadaUSA
| | - Amei Amei
- Department of Mathematical SciencesUniversity of Nevada, Las VegasLas VegasNevadaUSA
| | - Dietmar Cordes
- Lou Ruvo Center for Brain HealthCleveland ClinicLas VegasNevadaUSA
- Institute of Cognitive ScienceUniversity of Colorado BoulderBoulderColoradoUSA
| | - Zuoheng Wang
- Department of BiostatisticsYale School of Public HealthNew HavenConnecticutUSA
| | - Edwin C. Oh
- Interdisciplinary Neuroscience PhD ProgramUniversity of Nevada Las VegasLas VegasNevadaUSA
- Laboratory of Neurogenetics and Precision MedicineUniversity of Nevada, Las VegasLas VegasNevadaUSA
- School of MedicineDepartment of Internal MedicineUniversity of Nevada, Las VegasLas VegasNevadaUSA
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Zhuang X, Xu G, Amei A, Cordes D, Wang Z, Oh EC. Detecting time-varying genetic effects in Alzheimer's disease using a longitudinal GWAS model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.17.562756. [PMID: 37905044 PMCID: PMC10614870 DOI: 10.1101/2023.10.17.562756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Background The development and progression of Alzheimer's disease (AD) is a complex process that can change over time, during which genetic influences on phenotypes may also fluctuate. Incorporating longitudinal phenotypes in genome wide association studies (GWAS) could help unmask genetic loci with time-varying effects. In this study, we incorporated a varying coefficient test in a longitudinal GWAS model to identify single nucleotide polymorphisms (SNPs) that may have time- or age-dependent effects in AD. Methods Genotype data from 1,877 participants in the Alzheimer's Neuroimaging Data Initiative (ADNI) were imputed using the Haplotype Reference Consortium (HRC) panel, resulting in 9,573,130 SNPs. Subjects' longitudinal impairment status at each visit was considered as a binary and clinical phenotype. Participants' composite standardized uptake value ratio (SUVR) derived from each longitudinal amyloid PET scan was considered as a continuous and biological phenotype. The retrospective varying coefficient mixed model association test (RVMMAT) was used in longitudinal GWAS to detect time-varying genetic effects on the impairment status and SUVR measures. Post-hoc analyses were performed on genome-wide significant SNPs, including 1) pathway analyses; 2) age-stratified genotypic comparisons and regression analyses; and 3) replication analyses using data from the National Alzheimer's Coordinating Center (NACC). Results Our model identified 244 genome-wide significant SNPs that revealed time-varying genetic effects on the clinical impairment status in AD; among which, 12 SNPs on chromosome 19 were successfully replicated using data from NACC. Post-hoc age-stratified analyses indicated that for most of these 244 SNPs, the maximum genotypic effect on impairment status occurred between 70 to 80 years old, and then declined with age. Our model further identified 73 genome-wide significant SNPs associated with the temporal variation of amyloid accumulation. For these SNPs, an increasing genotypic effect on PET-SUVR was observed as participants' age increased. Functional pathway analyses on significant SNPs for both phenotypes highlighted the involvement and disruption of immune responses- and neuroinflammation-related pathways in AD. Conclusion We demonstrate that longitudinal GWAS models with time-varying coefficients can boost the statistical power in AD-GWAS. In addition, our analyses uncovered potential time-varying genetic variants on repeated measurements of clinical and biological phenotypes in AD.
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Li Y, Li P, Zhang W, Zheng X, Gu Q. New Wine in Old Bottle: Caenorhabditis Elegans in Food Science. FOOD REVIEWS INTERNATIONAL 2023. [DOI: 10.1080/87559129.2023.2172429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Yonglu Li
- School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, People’s Republic of China
| | - Ping Li
- School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, People’s Republic of China
| | - Weixi Zhang
- Department of Food Science and Nutrition; Zhejiang Key Laboratory for Agro-food Processing; Fuli Institute of Food Science; National Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang University, Hangzhou, People’s Republic of China
| | - Xiaodong Zheng
- Department of Food Science and Nutrition; Zhejiang Key Laboratory for Agro-food Processing; Fuli Institute of Food Science; National Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang University, Hangzhou, People’s Republic of China
| | - Qing Gu
- School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, People’s Republic of China
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Nsengimana B, Khan FA, Awan UA, Wang D, Fang N, Wei W, Zhang W, Ji S. Pseudogenes and Liquid Phase Separation in Epigenetic Expression. Front Oncol 2022; 12:912282. [PMID: 35875144 PMCID: PMC9305658 DOI: 10.3389/fonc.2022.912282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/13/2022] [Indexed: 11/24/2022] Open
Abstract
Pseudogenes have been considered as non-functional genes. However, peptides and long non-coding RNAs produced by pseudogenes are expressed in different tumors. Moreover, the dysregulation of pseudogenes is associated with cancer, and their expressions are higher in tumors compared to normal tissues. Recent studies show that pseudogenes can influence the liquid phase condensates formation. Liquid phase separation involves regulating different epigenetic stages, including transcription, chromatin organization, 3D DNA structure, splicing, and post-transcription modifications like m6A. Several membrane-less organelles, formed through the liquid phase separate, are also involved in the epigenetic regulation, and their defects are associated with cancer development. However, the association between pseudogenes and liquid phase separation remains unrevealed. The current study sought to investigate the relationship between pseudogenes and liquid phase separation in cancer development, as well as their therapeutic implications.
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Affiliation(s)
- Bernard Nsengimana
- Laboratory of Cell Signal Transduction, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Faiz Ali Khan
- Laboratory of Cell Signal Transduction, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Henan University, Kaifeng, China
- School of Life Sciences, Henan University, Kaifeng, China
- Department of Basic Sciences Research, Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH&RC), Lahore, Pakistan
| | - Usman Ayub Awan
- Department of Medical Laboratory Technology, The University of Haripur, Haripur, Pakistan
| | - Dandan Wang
- Laboratory of Cell Signal Transduction, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Na Fang
- Laboratory of Cell Signal Transduction, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Wenqiang Wei
- Laboratory of Cell Signal Transduction, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Henan University, Kaifeng, China
- *Correspondence: Wenqiang Wei, ; Weijuan Zhang, ; Shaoping Ji,
| | - Weijuan Zhang
- Laboratory of Cell Signal Transduction, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Henan University, Kaifeng, China
- *Correspondence: Wenqiang Wei, ; Weijuan Zhang, ; Shaoping Ji,
| | - Shaoping Ji
- Laboratory of Cell Signal Transduction, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Henan University, Kaifeng, China
- *Correspondence: Wenqiang Wei, ; Weijuan Zhang, ; Shaoping Ji,
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