My main research area is developing Bayesian methods in statistical genomics and epidemiology, in particular Bayesian hierarchical models and variable selection models. I have worked on several Bayesian models for analysing high-throughput molecular biology data, including gene expression microarrays, next-generation RNA-sequence data and metabolomics data. My current research is on methods for data integration and variable selection for multiple "omics" data sets.
I also work on methods in the Classical statistical framework, and apply these methods in genetic epidemiology and medical applications. I am particularly interested in variable selection and multiple testing issues.
I have a background in Mathematics and a PhD in Cosmology, where I worked on detecting non-Gaussianity in the cosmic microwave background and on analysis methods for Type Ia supernovae light curves.
- Statistical methodology: Highly structured stochastic systems; Bayesian hierarchical models; Variable selection and prediction; Bayesian model criticism; Methods for multiple testing.
- Statistical genomics and genetic epidemiology: Variable selection in high-dimensional modelling of genomics, epigenomics, transcriptomics, proteomics and metabolomics data.
- Molecular Biology: Statistical methods for modelling high-throughput molecular biology data, including microarray and sequencing data.
See below for Publications, Software and Presentations.
Papers in refereed journals:
et al., 2022, Exploring the causal effect of maternal pregnancy adiposity on offspring adiposity: Mendelian randomization using polygenic risk scores, Bmc Medicine, Vol:20, ISSN:1741-7015
et al., 2021, BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression, Journal of Statistical Software, Vol:100, ISSN:1548-7660, Pages:1-32
et al., 2021, Estimation of country-level incidence of early-onset invasive Group B Streptococcus disease in infants using Bayesian methods, Plos Computational Biology, Vol:17, ISSN:1553-734X
et al., 2021, A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery, Journal of the Royal Statistical Society Series C-applied Statistics, Vol:70, ISSN:0035-9254, Pages:886-908
et al., 2020, Understanding the cumulative risk of maternal prenatal biopsychosocial factors on birth weight: a DynaHEALTH study on two birth cohorts, Journal of Epidemiology and Community Health, Vol:74, ISSN:0143-005X, Pages:933-941