I am a statistical geneticist with wide ranging interests but am particularly interested in statistical methods development that ultimately facilitates predicting phenotype from genotype. Predicting phenotypes from genotypes is a central goal of personalized medicine, and if done accurately would have major benefits for human health. Increasingly, whole genome sequencing is used to identify pathogenic and hence clinically relevant mutations for patients with Mendelian diseases. However, assessment of risk among individuals without high impact variants, or in the presence of an incompletely penetrant variant, is lacking. Heritability studies suggest common variants contribute substantially to many rare and common diseases with large public health costs, and simulation studies suggest that large genome-wide association studies and polygenic scores may enable sufficiently discriminatory predictors to change standards of care to predict and prevent disease. However, there are a number of hurdles which must be overcome to make this vision a reality, which I seek to work on in my research
I received my DPhil in Genomic Medicine and Statistics at the University of Oxford in 2016. Afterwards, I worked for a year and a half at Genomics plc, and after that, as a research fellow at the Hospital for Sick Children in Toronto, Canada. Before coming to Oxford for the DPhil, I completed a Master’s Degree in Mathematics and Statistics, and worked for two years as a statistical genetic analyst, both at the University of Ottawa Heart Institute. Each of these research and work experiences has been primarily in statistical genetics.
Haplotype tagging reveals parallel formation of hybrid races in two butterfly species.
Meier JI. et al, (2021), Proceedings of the National Academy of Sciences of the United States of America, 118
A targeted amplicon sequencing panel to simultaneously identify mosquito species and Plasmodium presence across the entire Anopheles genus.
Makunin A. et al, (2021), Molecular ecology resources