Literature DB >> 34282824

Quantitative neurogenetics: applications in understanding disease.

Ali Afrasiabi1, Jeremy T Keane2, Julian Ik-Tsen Heng3,4, Elizabeth E Palmer5,6, Nigel H Lovell7, Hamid Alinejad-Rokny1,8,9.   

Abstract

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.
© 2021 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

Entities:  

Keywords:  artificial intelligence; functional analysis; genomic variants; health data analytics; neurogenetics disorders; tissue-specific gene expression

Mesh:

Year:  2021        PMID: 34282824     DOI: 10.1042/BST20200732

Source DB:  PubMed          Journal:  Biochem Soc Trans        ISSN: 0300-5127            Impact factor:   5.407


  3 in total

1.  MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments.

Authors:  Hamid Alinejad-Rokny; Rassa Ghavami Modegh; Hamid R Rabiee; Ehsan Ramezani Sarbandi; Narges Rezaie; Kin Tung Tam; Alistair R R Forrest
Journal:  PLoS Comput Biol       Date:  2022-06-24       Impact factor: 4.779

2.  Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer.

Authors:  Narges Rezaie; Masroor Bayati; Mehrab Hamidi; Maedeh Sadat Tahaei; Sadegh Khorasani; Nigel H Lovell; James Breen; Hamid R Rabiee; Hamid Alinejad-Rokny
Journal:  Commun Biol       Date:  2022-06-07

3.  PeakCNV: A multi-feature ranking algorithm-based tool for genome-wide copy number variation-association study.

Authors:  Mahdieh Labani; Ali Afrasiabi; Amin Beheshti; Nigel H Lovell; Hamid Alinejad-Rokny
Journal:  Comput Struct Biotechnol J       Date:  2022-09-07       Impact factor: 6.155

  3 in total

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