Williams, John A. 
ORCID: 0000-0002-0357-5454
(2021).
Omics-based predictive and causative modeling of neurobehavioral traits.
University of Birmingham.
Ph.D.
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Williams2021PhD.pdf
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Abstract
Neurobehavioral disorders can be phenotypically and genetically complex, and often diagnosed through observational study or subjective assessment alone. Certain neurobehavioral phenotypes, such as those caused by circadian rhythm related behavior, are biochemically well characterized, others, though, do not have yet a well understood genetic aetiology. Furthermore, circadian biology and psychological disorders are often intertwined. To advance our understanding of neurobehavioral trait/gene relationships, I first built a machine learning model that encompasses mouse transcriptomics to predict genes involved in circadian rhythms. Next, I used genome wide association studies to model the causal influence of genetic exposure in humans to an evening chronotype on several mental health and social support traits, from depression to group religious participation. To more accurately model how neurobehaviors relate to one another, I mined psychological assessment instruments
to build a species-agnostic psychological neurobehavior ontology encompassing autism and schizophrenia phenotypes. I, then, tested the utility of this ontology in clustering children on the autism spectrum based on phenotypic profiles. Lastly, I annotated genes to behaviors identified among subgroups through genome wide association studies applied to phenotype profiles. This allowed for the gene prioritization of circadian related experimentation results and the discovery of new, potentially, casual relationships between chronotype and neurobehavioral traits. Finally, the semantic representation of schizophrenia endophenotypes in a consistent, ontology framework catered its application for the identification of novel gene-trait associations in humans. These contributions provide new knowledge to the scientific community of the potential novel circadian functions for known genes, of the likely causal influence of chronotype on social and mental health, provide novel robust ways of modeling the complex phenotype of autism and schizophrenia patients, while annotating neurologically active genes to new behavioral traits for the first time.
| Type of Work: | Thesis (Doctorates > Ph.D.) | |||||||||||||||
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| Award Type: | Doctorates > Ph.D. | |||||||||||||||
| Supervisor(s): |
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| Licence: | All rights reserved | |||||||||||||||
| College/Faculty: | Colleges (2008 onwards) > College of Medical & Dental Sciences | |||||||||||||||
| School or Department: | Institute of Cancer and Genomic Sciences | |||||||||||||||
| Funders: | Medical Research Council | |||||||||||||||
| Subjects: | Q Science > QH Natural history > QH301 Biology Q Science > QH Natural history > QH426 Genetics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
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| URI: | http://etheses.bham.ac.uk/id/eprint/11279 |
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