Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.
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August 2021
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Glycoproteomics is the tool of choice in glycobiology to decipher the role of protein glycosylation in health and disease in a system-wide context for integration into multi-omics studies. For a hitchhiker's guide to glcoproteomics, see the review by Oliveira and colleagues (pp. 1623–1642). Cover artwork provided by Daniel Kolarich.
Review Article|
July 20 2021
Quantitative neurogenetics: applications in understanding disease
Ali Afrasiabi;
1BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW SYDNEY, Sydney, New South Wales 2052, Australia
Correspondence: Hamid Alinejad-Rokny (h.alinejad@unsw.edu.au) or Ali Afrasiabi (a.afrasiabi@unsw.edu.au)
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Jeremy T. Keane;
Jeremy T. Keane
2Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
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Julian Ik-Tsen Heng;
Julian Ik-Tsen Heng
3Curtin Health Innovation Research Institute, Curtin University, Bentley 6845, Western Australia, Australia
4School of Pharmacy and Biomedical Sciences, Curtin University, Bentley 6845, Western Australia, Australia
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Elizabeth E. Palmer;
Elizabeth E. Palmer
5Sydney Children's Hospital, Randwick, New South Wales 2031, Australia
6School of Women's and Children's Health, University of New South Wales, Randwick, New South Wales 2031, Australia
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Nigel H. Lovell;
Nigel H. Lovell
7The Graduate School of Biomedical Engineering, UNSW SYDNEY, Sydney, New South Wales 2052, Australia
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Hamid Alinejad-Rokny
1BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW SYDNEY, Sydney, New South Wales 2052, Australia
8Health Data Analytics Program Leader, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, Australia
9Core Member of UNSW Data Science Hub, The University of New South Wales (UNSW SYDNEY), Sydney, New South Wales 2052, Australia
Correspondence: Hamid Alinejad-Rokny (h.alinejad@unsw.edu.au) or Ali Afrasiabi (a.afrasiabi@unsw.edu.au)
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Publisher: Portland Press Ltd
Received:
April 20 2021
Revision Received:
June 11 2021
Accepted:
June 21 2021
Online ISSN: 1470-8752
Print ISSN: 0300-5127
© 2021 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society
2021
Biochem Soc Trans (2021) 49 (4): 1621–1631.
Article history
Received:
April 20 2021
Revision Received:
June 11 2021
Accepted:
June 21 2021
Citation
Ali Afrasiabi, Jeremy T. Keane, Julian Ik-Tsen Heng, Elizabeth E. Palmer, Nigel H. Lovell, Hamid Alinejad-Rokny; Quantitative neurogenetics: applications in understanding disease. Biochem Soc Trans 27 August 2021; 49 (4): 1621–1631. doi: https://doi.org/10.1042/BST20200732
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