Characterization of population specific variation in non-coding genomic regions and application to Type 2 diabetes

Genome wide association studies (GWAS) are commonly used to identify associations between single nucleotide polymorphisms (SNPs) occurring within exons, the regions that code for proteins, and disease (e.g. coronary artery disease) or trait (e.g. high blood pressure). A typical GWAS study would investigate two study groups, for example, normal blood pressure and high blood pressure, and attempt to identify SNPs that are specific to one group and which are located within or nearby proteins that have known association with hypertension. GWAS have identified associations between SNPs and disease but only in a fraction of cases and new approaches are needed for cases lacking associations. In this work, we seek associations between SNPs that are located away from the protein coding regions genome and disease. However, these non-coding regions of the genome are vast (~98% of the genome) so we will focus on specific regions that are associated with microRNAs (miRNAs), short pieces of RNA (typically 22 nucleotides) that regulate gene expression. miRNAs are widely studied, but characterized only in terms of their sequence and genome location. Information related to their production and regulation mechanisms are generally disregarded. Moreover, there is virtually no consideration of population specific variation. In this project we plan to (i) develop a more comprehensive characterization that incorporates knowledge related to miRNA generation and regulatory mechanisms (ii) investigate how these properties vary amongst different populations (iii) apply this knowledge to Type 2 Diabetes in an attempt to identify population specific SNPs associated with the disease. A key part of this work is our collaborators, which include the Human Longevity Institute, the African Genome Resource Project and the T2D-GENES and GoT2D type 2 diabetes consortia. This will give us access to more than 100 000 genomes and associated datasets collected from a broad population range.

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The project combines computational biology, data mining, genetics, molecular biology and translational research. It will develop novel analytical methods to identify population specific variations in non-coding (NC) regions of the genome, and then apply them to the study of Type 2 Diabetes Mellitus (T2DM). Results will be verified and further investigated experimentally. The novelty of the work is that it hypotheses that, rather than coding regions, the NC regions of the genome that code for the miRNA biogenesis pathway can be used as a starting point for investigating population specific genetic association with disease. The work will lead to a deeper understanding of the miRNAome in the context of disease, and addresses both the issue of significant bias in population in genetic studies, as well as the rising significance of non-communicable diseases such as T2DM1. A key part of the project design is the creation of a multidisciplinary international consortium with the necessary experience and resources to support the proposal, with core expertise (methodology development, genetics & sequencing) located at the host department of Medical Genetics (AMG) at Oslo University Hospital (OUS). Our partners include the Human Longevity Institute (HLI), The African Genome Variation Project (AGVP) and the T2D-GENES and GoT2D consortiums (T2D-Genes/GoT2D). Our partners have published in high profile journals