Analysis of dental genomic data

Dnr:

SNIC 2016/1-449

Type:

SNAC Medium

Principal Investigator:

Dmitry Shungin

Affiliation:

Umeå universitet

Start Date:

2016-10-31

End Date:

2017-11-01

Primary Classification:

30302: Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi

Webpage:

Allocation

Abstract

Two major dental diseases (caries and periodontitis) are highly prevalent worldwide and substantially contribute to disease burden. Both diseases have substantial heritable component with 51% and 70% of population variances attributed to genetic factors for caries and periodontitis respectively. Unfortunately, details of genetic architecture and related pathogenesis for both diseases are still poorly understood, although some pieces of disease mosaic have been studied for decades. A tremendous success has been made in recent seven years to identify region of human genome that harbor genetic variants associated with a variety of cardio-metabolic disease traits, including diabetes-related traits, obesity and cardio-vascular diseases. We were an active part of research teams for a number of such studies including leading role in the latest efforts to identify novel genes implicated in obesity, which was published in Nature (Shungin et al. Nature 2015). In contrast to major cardio-metabolic diseases, no large-scale genetic association studies have been performed for dental diseases, mostly due to lack of relevant dental data. Thus, the aim of the present project is to discover novel genetic variants for dental caries and periodontal disease using genome-wide data as well as to provide new insights into molecular mechanisms behind dental diseases using a variety of bioinformatical approaches that combine genetic results with expression, epigenetic, proteomic and other omics data. We will use estimates of genetic effects from a collection of dental studies (GLIDE), totaling up to 70,000 individuals with data on up to 20 million genetic variants, to perform large-scale multi-ethnic meta-analysis of genetic data followed by bioinformatical analyses. The project is a collaboration between 13 research groups from all over the world, lead from Umeå University. This proposal is a request to use HPC2N resources to perform a battery of several bioinformatics analyses in this data.