A core activity in science is collecting data to develop and evaluate theories about the way things work. To explore research questions, investigators design studies and then look at the resulting data to find meaning in the context of randomness. Statistics is the science of learning from data, and provides a principled framework and powerful set of methods to help with study design and analysis.
As a statistical geneticist, my interests lie at the interface with the genome sciences. A central theme of my research is that the variation in the DNA sequences of individuals reflects their underlying genealogical relationships. These relationships can tell us about individual predisposition to inherited traits, and so are of use in mapping the genomic location of DNA variants that contribute to disease. The functions of the genes or gene-regulatory regions to which these variants belong can provide insight into potential treatments. Another theme is that the data measured by high-throughput 'omic technologies can be integrated with clinical data to refine phenotypes and gain insight into the genetic predisposition to a disorder. However, with big data come issues in statistical interpretation. Does the way the data have been collected allow us to answer our research questions? What are the relevant patterns and what are blind alleys that result from unrecognized biases in sampling? What are the sources of variation in our data? Are we seeing merely chance patterns in random data that has no systematic component?
To understand the inherited predisposition to disease, we can incorporate fundamental genetics principles and statistical thinking into our study designs, models and analysis methods. Recent developments in statistical computing and Bayesian modelling of data structures with complex dependencies have enabled and enriched this effort. Additionally, advances in high-dimensional data analysis have enabled the integration of 'omic, imaging and clinical data, in recent collaborations with neuro-imaging and pediatrics experts. Broadly, my focus is on developing, applying, extending and evaluating techniques of statistical analysis for understanding the inherited predisposition to traits, while taking into account the way the data have been sampled, the randomness and dependencies in the data, and the genetics principles underlying the research problem.
I am part of the Statistical Genetics Working Group within the Department of Statistics and Actuarial Science; please follow this link to learn more about my research.
A list of courses I have taught is available here