Quantitative neurogenetics: applications in understanding disease

Ali Afrasiabi*, Jeremy T. Keane, Julian Ik-Tsen Heng, Elizabeth E. Palmer, Nigel H. Lovell, Hamid Alinejad-Rokny*

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    8 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)1621-1631
    Number of pages11
    JournalBiochemical Society Transactions
    Volume49
    Issue number4
    Early online date20 Jul 2021
    DOIs
    Publication statusPublished - Aug 2021

    Keywords

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

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