Anna Hutchinson’s Webpage
I am a PhD student at the MRC Biostatistics Unit at the University of Cambridge (iCase studentship in collaboration with GlaxoSmithKline). My supervisors are Dr Chris Wallace and Dr David Wille and I am a member of the Wallace group and the Statistical Omics (SOMX) group.
Prior to starting my PhD, I completed a degree in Mathematics and Biology at Durham University (1st Class Honours). During my degree, I spent a year at the University of Calgary completing a variety of human genetics and statistics courses. During this time, I worked as a research assistant with Dr Ying Yan on the analysis of objectifying randomised clinical trials. I also interned at the Centre for Computational Biology at the University of Birmingham, working with Professor Jean-Baptiste Cazier to compare ovarian-breast cancer causing mutations between white British and Punjabi women. I am conscious of the Eurocentric biases of genetic studies and hope that more effort is placed on the under-represented non-European populations in the coming years.
Coming from a Natural Sciences background, I am interested in using Bayesian techniques to answer biological questions. My research involves determining the true coverage of credible sets in Bayesian fine-mapping genetic association studies. I also have interests in incorporating functional genomics data (such as genome segmentation data, Hi-C data and CRISPR data) with GWAS and fine-mapping results to improve our understanding of the biology of disease.
Improving the coverage of credible sets in Bayesian genetic fine-mapping (Hutchinson et al., 2020. PLOS Computational Biology https://doi.org/10.1371/journal.pcbi.1007829)
Abstract: Genome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some “coverage probability”. Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to re-estimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the “adjusted coverage estimate”. This is extended to find “adjusted credible sets”, which are the smallest set of variants such that their adjusted coverage estimate meets the target coverage. We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95% or 99% credible sets required the inclusion of more variants—a pattern matched in simulations of well powered GWAS. Crucially, our method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases.
See the R package webpage here: https://annahutch.github.io/corrcoverage/
Weekly summaries of my PhD work are posted here: https://annahutch.github.io/PhD/
Publications + Conferences + Talks
- Anna Hutchinson, Hope Watson and Chris Wallace (2020). Improving the coverage of credible sets in Bayesian genetic fine-mapping. PLOS Computational Biology: https://doi.org/10.1371/journal.pcbi.1007829
- Christophe Bourges, Abigail F Groff, Oliver S Burren, Chiara Gerhardinger, Kaia Mattioli, Anna Hutchinson, Theodore Hu, Tanmay Anand, Madeline W Epping, Chris Wallace, Kenneth GC Smith, John L Rinn and James C Lee (2020). Resolving mechanisms of immune‐mediated disease in primary CD4 T cells. EMBO Molecular Medicine: https://www.embopress.org/doi/10.15252/emmm.202012112
- SEGEG @ The Big Data Institute, Oxford – 6 November 2019
- Quantitative Genomics (QG19) ** BEST LONG TALK AWARD ** – 10 June 2019
- GSK Human Genetics Group Meeting – 6 June 2019
- Mathematical and Statistical Aspects of Molecular Biology (MASAMB) ** BEST STUDENT TALK AWARD ** – 26 April 2019
- Building Bridges in Medical Science (BBMS) (Poster)- 8 March 2019
- St Catharine’s College Graduate Symposium (Organiser) – 27 February 2019
I enjoy walking, travelling, board games (I am a ‘Settlers of Catan’ and ‘Splendor’ expert!) and meeting new people. I am a member of St Catharine’s College, where I enjoy my role as the MCR social secretary. I am also the PhD Student Rep at the MRC Biostatistics Unit.