Cardiometabolic disorders such as coronary artery disease-(CAD), obesity, and type-2 diabetes (T2D) represent the top leading causes of mortality worldwide. These disorders are highly interconnected and the recent rise in their prevalence poses elevated health burden and calls for a better understanding of the mechanisms underlying them. Thereby, more effective prevention and treatment strategies can be developed.
In the past decades, genome-wide-association studies(GWAS), which represent a particular type of big-biological data resources, have emerged as a revolutionary tool to uncover genetic risk variants associated with complex human-diseases e.g. CAD, T2D. Despite GWAS datasets have crucially improved our understanding in disease progression, several critical questions remain to be addressed: (i)GWASs can only capture a limited portion of genetic heritability, implicating many additional causal genetic risk factors remain to be uncovered; (ii)how these genetic signals communicate and altogether contribute to disease pathogenesis through perturbing certain biological mechanisms or gene-gene-interaction subnetworks remains to be explored. Tissue-specific transcriptome datasets, which represent another type of biological-data and are highly associated to environmental factors (e.g. nutrition), have been collected from human samples to study complex diseases. Transcriptome can be used for inferring tissue-specific gene regulatory networks, thereby can improve our understanding of common diseases by complementing our findings from GWAS data. Hence, proposing an integrative genomics approach can address the knowledge gaps that is raised from using a single type of biological-data. We propose to leverage big-biological datasets and implement an integrative genomics approach using deep neural networks, to identify molecular mechanisms contributing to the cardiometabolic disorders. Most of the previous studies focused on individual types of data, one at a time (e.g. a few top significant GWAS signals were predicted using statistical approaches, which only provide an incomplete view of disease pathogenesis), whereas an integrative approach can utilize the power of collaborative use of various datatypes. In particular, this integrative approach will merge the summary statistics from the disease-associated GWASs and tissue-specific gene network models through the expression quantitative-trait-loci signals, to prioritize the disease-associated subnetworks and their hub-genes that are potentially driving the disease progression. Hence, we purpose, not only elucidating the disease-mechanisms, but also predicting the (potential regulator) hub-genes in these mechanisms. Therefore, our study may provide valuable guidance for testing new drug candidates for cardiometabolic research, which target disease-associated genes found from our study. This interdisciplinary design incorporating computer science with genetics and medicine can significantly raise the quality of human lives.
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Eligibility and How to Apply:
Please note eligibility requirement:
· Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
· Appropriate IELTS score, if required.
For further details of how to apply, entry requirements and the application form, see
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF22/…) will not be considered.
Start Date: 1 October 2022
Northumbria University takes pride in, and values, the quality and diversity of our staff and students. We welcome applications from all members of the community.
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