Investigation of Respiratory Genotype-Phenotype Interactions Using Topological Data Analysis.

Asthma and COPD are heterogeneous respiratory diseases, affected by a combination of environmental and genetic factors, and among the leading causes of morbidity and mortality worldwide as they can present with severe effects on lung function. In recent years, developments have allowed for the investigation of the genetic basis of lung function and the development of respiratory disease. Topological data analysis (TDA) offers a framework for analysis of data using techniques from the field of topology. Mapper algorithm is one of analytical method of TDA that is seeing increase use over recent year in a wide range of scientific ?fields. It provides reduced dimension representations for data that is high-dimensional in the form of a graph, allowing for their visualisation. This thesis explores how genetic variants associated with lung function can be used as a basis of genetic pro?ling and the generation of features that are analysed by Mapper algorithm to generate respiratory phenotype visualisations in large populations.

To this end an analytical methodology is presented, bridging techniques from genetic epidemiology and topological data analysis. The UK Biobank dataset (N~500k) is reviewed extensively with respect to relevant respiratory diseases. Relevant information and phenotypes are mined and combined to create stratified respiratory phenotypes, with a focus around asthma and COPD. Mapper is applied to investigate genotype-phenotype interactions within the UKB population. Population networks are generated and used as discovery guides to identify subgroups within the UKB sample that present with complex traits.