Personal profile

Research interests

My overall research aims are to develop and apply Mendelian randomization, and related analytical approaches, for cancer prevention and improving outcomes for cancer patients. I currently lead the Cancer Progression and Drug Target research theme of ICEP. The overall aim of this theme is to apply Mendelian randomization techniques to identify potential therapeutic targets for improving survival in cancer patients, using a variety of data sources, including published datasets and new analyses within UK Biobank and international cancer consortia.

I also lead the Fatty Acids in Cancer Mendelian randomization Collaboration (FAMRC). The aim of the FAMRC is to assess the causal relevance of fatty acids for risk of various cancer types through Mendelian randomization, using summary genetic data on 87 cancer types generated in analyses of up-to 609,863 cancer cases and 1,311,961 controls from 52 separate studies, consortia or biobanks. It is hoped that the results of the FAMRC will help strengthen the evidence base for public health policy on cancer prevention through dietary or pharmaceutical interventions on fatty acid pathways.

Prior to the FAMRC, I led the Telomeres Mendelian Randomization Collaboration, a large international research effort to systematically appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases. Using summary genetic data from GWAS for 35 cancers and 48 non-neoplastic diseases, corresponding to >400,000 cases and >1,000,000 controls, my research showed that longer telomeres are likely to increase risk for several cancers but to reduce risk for some non-neoplastic diseases, particularly cardiovascular diseases. These results probably reflect an evolutionary trade-off for greater resistance to cancer at the cost of greater susceptibility to degenerative diseases. This risk trade-off may require careful consideration in any clinical applications based on telomere length, e.g. as a tool for risk prediction or as an intervention target for disease prevention.

In order to support the application of Mendelian randomization approaches for identifying intervention targets for disease prevention, I also co-lead the development of MR-Base [Gib Hemani, Tom Gaunt and Jie (Chris) Zheng]: a platform that integrates summary data from thousands of genome-wide association studies in the MRC IEU Open GWAS database with an application programming interface, webapp and R packages that automate analyses (; Hemani et al eLife 2018). Our motivation for developing MR-Base was the knowledge that genetic data can be difficult to access and that Mendelian randomization methods, particularly sensitivity analyses for appraising analytical assumptions, are evolving rapidly. These considerations make implementation of Mendelian randomization challenging and time-consuming even for expert users of the approach.


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