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Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases
Approaches to the identification of genetic variants associated with complex brain diseases have evolved in recent decades. This evolution was supported by advancements in medical imaging and genotyping technologies that result in rich data production in the field of imaging genetics and radiogenomi...
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| Format: | Thesis |
| Language: | English |
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Department of Clinical Laboratory Sciences
2021
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| _version_ | 1867613293596639232 |
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| access_status_str | Open Access |
| author | Elsheikh, Samar Salah Mohamedahmed |
| author2 | Mulder, Nicola J |
| author_browse | Elsheikh, Samar Salah Mohamedahmed Mulder, Nicola J |
| author_facet | Mulder, Nicola J Elsheikh, Samar Salah Mohamedahmed |
| author_sort | Elsheikh, Samar Salah Mohamedahmed |
| collection | Thesis |
| description | Approaches to the identification of genetic variants associated with complex brain diseases have evolved in recent decades. This evolution was supported by advancements in medical imaging and genotyping technologies that result in rich data production in the field of imaging genetics and radiogenomics. Studies in these fields have taken different designs and directions from genomewide associations to studying the complex interplay between genetics and structural connectivity of a wide range of brain-related diseases. Nevertheless, such combinations of heterogeneous, high dimensional and inter-related data has introduced new challenges which cannot be handled with traditional statistical methods. In this thesis, we proposed analysis pipelines and methodologies to study the causal relationship between neuroimaging features, including tumour characteristics and connectomics, genetics and clinical factors in brain-related diseases. In doing so, we adopted two longitudinal study designs and modelled the association between Alzheimer's disease progression and genetic factors, utilising local and global brain connectivity networks. In addition to that, we performed a multi-stage radiogenomic analysis in glioblastoma using non-parametric statistical methods. To address some limitations in the methods, we adopted the Structural Equation Model and developed a mathematical model to examine the inter-correlation between neuroimaging and multi-omic characteristics of brain-related diseases. Our findings have successfully identified risk genes that were previously reported in the literature of Alzheimer's and glioblastoma diseases, and discovered potential risk variants which associate with disease progression. More specifically, we found some loci in the genes CDH18, ANTXR2 and IGF1, located in Chromosomes 5, 4 and 12, to have effect on the brain connectivity over time in Alzheimer's disease. We also found that the expression of APP, HFE, PLAU and BLMH have significant effects on the structural connectivity of local areas in the brain, these are the left Heschl gyrus, right anterior cingulate gyrus, left fusiform gyrus and left Heschl gyrus, respectively. These potential association patterns could be useful for early disease diagnosis, treatment and neurodegeneration prediction. More importantly, we identified gaps in the imaging genetics methodologies, we proposed a mathematical model accounting for these limitations and evaluated the model which produced promising results. Our proposed flexible model, BiGen, addresses the gaps in the existing tools by combining neuroimaging, genetics, environmental, and phenotype information to a single complex analysis, accounting for the heterogeneity, inter-correlation, and non-linearity of the variables. Moreover, BiGen adopts an important assumption which is hardly met in the literature of imaging genetics, and that is, all the four variables are assumed to be latent constructs, that means they can not be observed directly from the data, and are measured through observed indicators. This is an important assumption in both neuroimaging, behavioural and genetic studies, and it is one of the reasons why BiGen is flexible and can easily be extended to include more indicators and latent constructs in the context of brain-related diseases. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/32609 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:49.949Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Department of Clinical Laboratory Sciences |
| publisherStr | Department of Clinical Laboratory Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/32609 Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases Elsheikh, Samar Salah Mohamedahmed Mulder, Nicola J Crimi, Alessandro Chimusa, Emile R Integrative Biomedical Sciences Approaches to the identification of genetic variants associated with complex brain diseases have evolved in recent decades. This evolution was supported by advancements in medical imaging and genotyping technologies that result in rich data production in the field of imaging genetics and radiogenomics. Studies in these fields have taken different designs and directions from genomewide associations to studying the complex interplay between genetics and structural connectivity of a wide range of brain-related diseases. Nevertheless, such combinations of heterogeneous, high dimensional and inter-related data has introduced new challenges which cannot be handled with traditional statistical methods. In this thesis, we proposed analysis pipelines and methodologies to study the causal relationship between neuroimaging features, including tumour characteristics and connectomics, genetics and clinical factors in brain-related diseases. In doing so, we adopted two longitudinal study designs and modelled the association between Alzheimer's disease progression and genetic factors, utilising local and global brain connectivity networks. In addition to that, we performed a multi-stage radiogenomic analysis in glioblastoma using non-parametric statistical methods. To address some limitations in the methods, we adopted the Structural Equation Model and developed a mathematical model to examine the inter-correlation between neuroimaging and multi-omic characteristics of brain-related diseases. Our findings have successfully identified risk genes that were previously reported in the literature of Alzheimer's and glioblastoma diseases, and discovered potential risk variants which associate with disease progression. More specifically, we found some loci in the genes CDH18, ANTXR2 and IGF1, located in Chromosomes 5, 4 and 12, to have effect on the brain connectivity over time in Alzheimer's disease. We also found that the expression of APP, HFE, PLAU and BLMH have significant effects on the structural connectivity of local areas in the brain, these are the left Heschl gyrus, right anterior cingulate gyrus, left fusiform gyrus and left Heschl gyrus, respectively. These potential association patterns could be useful for early disease diagnosis, treatment and neurodegeneration prediction. More importantly, we identified gaps in the imaging genetics methodologies, we proposed a mathematical model accounting for these limitations and evaluated the model which produced promising results. Our proposed flexible model, BiGen, addresses the gaps in the existing tools by combining neuroimaging, genetics, environmental, and phenotype information to a single complex analysis, accounting for the heterogeneity, inter-correlation, and non-linearity of the variables. Moreover, BiGen adopts an important assumption which is hardly met in the literature of imaging genetics, and that is, all the four variables are assumed to be latent constructs, that means they can not be observed directly from the data, and are measured through observed indicators. This is an important assumption in both neuroimaging, behavioural and genetic studies, and it is one of the reasons why BiGen is flexible and can easily be extended to include more indicators and latent constructs in the context of brain-related diseases. 2021-01-20T16:21:13Z 2021-01-20T16:21:13Z 2020 2021-01-20T16:20:11Z Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/32609 eng application/pdf Department of Clinical Laboratory Sciences Faculty of Health Sciences |
| spellingShingle | Integrative Biomedical Sciences Elsheikh, Samar Salah Mohamedahmed Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases |
| thesis_degree_str | Doctoral |
| title | Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases |
| title_full | Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases |
| title_fullStr | Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases |
| title_full_unstemmed | Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases |
| title_short | Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases |
| title_sort | integration of multi omic data and neuroimaging characteristics in studying brain related diseases |
| topic | Integrative Biomedical Sciences |
| url | http://hdl.handle.net/11427/32609 |
| work_keys_str_mv | AT elsheikhsamarsalahmohamedahmed integrationofmultiomicdataandneuroimagingcharacteristicsinstudyingbrainrelateddiseases |