Precision genetics to improve Asthma prediction, diagnosis and treatment in children

Asthma is a prevalent respiratory condition in children and adults globally, with the prevalence in NZ among the highest in the developed world. The 2016/17 national health survey reported that 12% (459,000) of adults (≥ 15 years old) and 14% (114,000) of children had doctor diagnosed asthma for which they were currently taking medication.

The rate of death due to asthma in New Zealand is nearly four times higher than the global rate for children aged 10-14 years. And the proportion of disability adjusted life years due to asthma in New Zealand is 3.6 times higher than the global rate for children the same age.

Genome Wide association (GWA) studies have shown that there are more than 120 genetic variants (SNPs) associated with asthma across the human genome. However, to date very little is known about the functional roles of most asthma-associated genetic variants. The majority of SNPs that are important for asthma have already been identified. The bottle neck in understanding and utilizing this genetic information is the lack of understanding of the biological effects of genetic variation.

This PhD project aims to use precision genetics to improve asthma prediction, diagnosis and treatment. It will use information on genetic variation and longitudinal cohort data to develop precision approaches to asthma in children. The project will be supervised by Associate Professor Justin M. O’Sullivan and Dr Tayaza Fadason within a very productive and supportive international lab group. You will use computational techniques to integrate new and existing spatial and epigenetic data to understand the link between the genotype and asthma in children.

We aim to create a process that will enable the information on an individual’s genetic variation to transition quickly from the research arena to clinical utility. To do this we will use computational methods to integrate information on the 3-dimensional structure of DNA, expression quantitative trait loci (eQTLs) and other co-localizing features (e.g. methylation) that control gene expression. We will then use longitudinal cohorts to predict how asthma SNPs work together and in which tissues they have the greatest effect. Calculating the tissue specific burden of these changes will enable us to predict who is most at risk of developing asthma. This work will be validated in a separate cohort.