Abstract

The cost of DNA sequencing has now reached a level where it is economically viable for large scale sequencing programmes of thousands of volunteers to be undertaken. These data sets typically consist of patients genetic data, and a list of characteristics known as a ‘phenotype’. These data sets are then analysed using a technique known as a Genome wide association study (GWAS), to identify phenotypic characteristics that associate with genetics.

With the richness of data available today, we would like to investigate alternative analysis approaches for these data sets. These approaches can take advantage the fact that multiple phenotypes may be linked (e.g. Body Mass Index, and resting heart rate), and that the data is on a scale rather than bucketed into “has phenotype” or “does not have phenotype”.

This project would require investigation into other methods that have already been published, before proposing and designing new analysis approaches.