Master of Science, Imperial College of Science, Technology & Medicine (2008)
Master of Engineering, University of Cambridge (2007)
Doctor of Philosophy, University of Cambridge (2012)
Sylvia Plevritis, Postdoctoral Research Mentor
Current Research and Scholarly Interests
My research involves both the development of novel machine learning methods and their application to data analysis problems in biology.
- Relational Learning and Network Modelling Using Infinite Latent Attribute Models IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2015; 37 (2): 462-474
- Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2015; 37 (2): 271-289
Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants.
American journal of human genetics
2014; 95 (3): 245-256
Recent and rapid human population growth has led to an excess of rare genetic variants that are expected to contribute to an individual's genetic burden of disease risk. To date, much of the focus has been on rare protein-coding variants, for which potential impact can be estimated from the genetic code, but determining the impact of rare noncoding variants has been more challenging. To improve our understanding of such variants, we combined high-quality genome sequencing and RNA sequencing data from a 17-individual, three-generation family to contrast expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) within this family to eQTLs and sQTLs within a population sample. Using this design, we found that eQTLs and sQTLs with large effects in the family were enriched with rare regulatory and splicing variants (minor allele frequency < 0.01). They were also more likely to influence essential genes and genes involved in complex disease. In addition, we tested the capacity of diverse noncoding annotation to predict the impact of rare noncoding variants. We found that distance to the transcription start site, evolutionary constraint, and epigenetic annotation were considerably more informative for predicting the impact of rare variants than for predicting the impact of common variants. These results highlight that rare noncoding variants are important contributors to individual gene-expression profiles and further demonstrate a significant capability for genomic annotation to predict the impact of rare noncoding variants.
View details for DOI 10.1016/j.ajhg.2014.08.004
View details for PubMedID 25192044
View details for PubMedCentralID PMC4157143
- Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues PLOS GENETICS 2014; 10 (5)
- Gene expression changes with age in skin, adipose tissue, blood and brain GENOME BIOLOGY 2013; 14 (7)
- Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression BAYESIAN ANALYSIS 2013; 8 (4): 837-881