Publication
Core gene identification using gene expression
Claringbould, A., 2020, [Groningen]: University of Groningen. 243 p.Research output: Thesis › Thesis fully internal (DIV)

Documents
- Title and contents
Final publisher's version, 180 KB, PDF document
- Chapter 1
Final publisher's version, 210 KB, PDF document
- Chapter 2
Final publisher's version, 221 KB, PDF document
- Chapter 3
Final publisher's version, 388 KB, PDF document
- Chapter 5
Final publisher's version, 1.67 MB, PDF document
- Chapter 8
Final publisher's version, 354 KB, PDF document
- Appendices
Final publisher's version, 143 KB, PDF document
- Propositions
Final publisher's version, 53.4 KB, PDF document
DOI
While humans share most of their genetic code with one another, small differences in the DNA can have an impact on an individual’s risk of disease. Common genetic variants exert individually small effects on the development of a disease, but their combined impact is substantial. Although recent research has identified thousands of variants that are associated to complex traits, our understanding of the molecular mechanisms that eventually lead to disease is limited. One way to dive into the molecular changes that result from genetic variation, is to look at changes in gene activity (‘gene expression’). Each cell contains the same genetic code, but genes are only expressed when and where they are required. Research has shown that many disease-associated genetic variants also affect gene expression. Such a change in the expression of a gene can lead to an altered level of the protein it encodes, which in turn can be the start of a dysregulation in the system that can eventually develop into a disease. This thesis describes how gene expression patterns can be used to prioritise and describe the function of trait-relevant genes. The first chapters evaluate methodological considerations for doing gene expression research. Another study covers the systematic linking of genetic variation to gene expression in blood and the last research chapter describes a method for gene prioritisation that leverages the idea that multiple genetic variants converge onto disease-causing genes. These insights can be used to better understand disease and to identify potential drug targets.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution | |
Supervisors/Advisors |
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Award date | 2-Dec-2020 |
Place of Publication | [Groningen] |
Publisher | |
Publication status | Published - 2020 |
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