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Xiu Z, Sun L, Cao H, Qu H, Glessner JT, Ding Z, Zheng G, Wang N, Xia Q, Li J, Hakonarson H, Liu W, Li J. Shared molecular mechanisms and transdiagnostic potential of neurodevelopmental disorders and immune disorders. Brain Behav Immun 2024:S0889-1591(24)00375-1. [PMID: 38677625 DOI: 10.1016/j.bbi.2024.04.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/27/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024] Open
Abstract
The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta-analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta-analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory. Response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders.
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Affiliation(s)
- Zhanjie Xiu
- Department of Cell Biology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, China; Department of Bioinformatics, Tianjin Medical University, Tianjin, China
| | - Ling Sun
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China
| | - Haiyan Cao
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China
| | - Huiqi Qu
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Joseph T Glessner
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Zhiyong Ding
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd., Jinan, China
| | - Gang Zheng
- National Supercomputer Center in Tianjin (NSCC-TJ), Tianjin, China
| | - Nan Wang
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd., Jinan, China
| | - Qianghua Xia
- Department of Cell Biology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, China; Department of Bioinformatics, Tianjin Medical University, Tianjin, China
| | - Jie Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, United States; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
| | - Wei Liu
- Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China; Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, China.
| | - Jin Li
- Department of Cell Biology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, China; Department of Rheumatology and Immunology, Tianjin Medical University General Hospital, Tianjin, China.
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Tao H, Li H, Xu K, Hong H, Jiang S, Du G, Wang J, Sun Y, Huang X, Ding Y, Li F, Zheng X, Chen H, Bo X. Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles. Brief Bioinform 2021; 22:6102668. [PMID: 33454752 PMCID: PMC8424394 DOI: 10.1093/bib/bbaa405] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/26/2020] [Accepted: 12/10/2020] [Indexed: 12/14/2022] Open
Abstract
The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.
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Affiliation(s)
- Huan Tao
- Beijing Institute of Radiation Medicine
| | - Hao Li
- Beijing Institute of Radiation Medicine
| | - Kang Xu
- Beijing Institute of Radiation Medicine
| | - Hao Hong
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Shuai Jiang
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Guifang Du
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | | | - Yu Sun
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Xin Huang
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Yang Ding
- Beijing Institute of Radiation Medicine
| | - Fei Li
- Chinese Academy of Sciences, Department of Computer Network Information Center
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