Quantitative neurogenetics: applications in understanding disease.
Ali AfrasiabiJeremy T KeaneJulian Ik-Tsen HengElizabeth E PalmerNigel H LovellHamid Alinejad-RoknyPublished in: Biochemical Society transactions (2022)
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.
Keyphrases
- genome wide
- gene expression
- artificial intelligence
- dna methylation
- white matter
- resting state
- copy number
- machine learning
- emergency department
- deep learning
- cerebral ischemia
- genome wide association
- functional connectivity
- cell proliferation
- big data
- genome wide association study
- depressive symptoms
- cell death
- brain injury
- subarachnoid hemorrhage
- drug induced