Development of high-dimensional data-adaptive causal inference methods to unravel the role of genetics in determining heart rhythm

The increased availability of information collected in human medical studies is transforming the nature of biomedical research. Thousands of genetic variants as well as hundreds of other detailed biological quantities (biomarkers, metabolites, blood electrolytes) are increasingly available in addition to more traditional epidemiological information such as age, gender and blood pressure in many large datasets such as UK-Biobank. Statistical methods for analysing genomics data have focused on associating genes with particular biological outcomes, or on associating biological markers with the same outcomes. Often, however, the scientific question of interest is whether a particular biomarker mediates the effect of the genetic variant on the outcome or disease. The focus of this research is therefore to improve our understanding of the causal relationships in these "OMICS" data-sets. This project aims to develop methods to study mediation in such settings, motivated by cardio-genetics studies, seeking to identify genetic variations associated with changes in heart rhythm. It is often unclear if these genes affect the heart rhythms through their effects on the heart ion channels or through other pathways. Finding which electrolytes (e.g. sodium, potassium) lie on important pathways from genes to heart disease, could help target new drugs, as well as new indications for existing drugs. Inference of such "networks" (chains between several genes, several electrolytes, and heart rhythms) is made more challenging by the large number of variables. Sophisticated techniques are required to account for confounding among these variables. Novel machine learning methods will be incorporated into the causal inference methodology, in order to overcome the problems of working with high-dimensional data sets. Although this project will focus on cardio-genetic exposures, the methods developed will be widely applicable, allowing researchers to use genomics and other big omics data sets to their full potential.