Professor of Molecular Epidemiology
If you’re interested in using your genetic data analysis skills to make exciting discoveries involving large-scale multi-omic data and/or cardiovascular disease using a range of well-powered international datasets, see our 2-year Genomic Analyst position: https://www.jobs.cam.ac.uk/job/33566/ Closes 19th April 2022
The Butterworth group is a small, friendly, highly collaborative international research group of post-docs and PhD students with a range of backgrounds and interdisciplinary skills. Our core interests involve genomics, multi-omics and cardiometabolic diseases. We are all passionate about using large-scale population datasets to improve understanding of biology and disease in order to ultimately improve health. We aim to publish high-quality reproducible research in leading journals and make broader contributions to the community by making our results and data openly available. See more about our research below……..
Genetic discovery: The Butterworth group’s main interests revolve around the identification of genetic variation linked with cardiovascular diseases, related phenotypes (eg, vascular risk factors such as lipids) and molecular ‘omics (eg, plasma proteomics and metabolomics) using SNP arrays and next-generation sequencing. Current efforts in this area include the CARDIoGRAMplusC4D 1M+ Hearts project, the 100,000 participant CHD Exome+ consortium and imputed GWAS studies in INTERVAL and UK Biobank. Increasingly we are also trying to address the ‘genomic diversity’ problem by building and analysing studies of diverse ancestries around the world, including the BELIEVE and BRAVE studies in Bangladesh, the MAVERIK study in Malaysia, and the SHINES study in Sri Lanka.
Human genetics to inform therapeutics: By relating informative genetic variants (eg, variants of known function) to disease outcomes and phenotypes, inference about the likely efficacy and safety profile of therapeutic agents (or potential therapeutic agents) can be made. For example, our work has highlighted the relationship of IL6R variants to risk of heart disease, raising the possibility of monoclonal antibodies being repurposed from inflammatory conditions to cardiovascular pathways. The group has worked closely with several large pharmaceutical companies (e.g. Merck, Novartis, Biogen, Sanofi) to help discover and evaluate pathways of potential therapeutic interest.
National and international consortia: Genetic and molecular epidemiology are inherently collaborative disciplines in which the most robust findings are produced by pooling data from across studies. We are closely involved in several major national and international initiatives to which we contribute data and play a leadership role, including:
- The HDRUK Multiomics Cohorts Consortium: Adam is the PI of this national consortium, which brings together 15 population cohorts from across the UK. The cohorts all have genomic data, linkage to electronic health records and some multi-omics data (e.g. transcriptomics, proteomics, metabolomics, lipidomics, epigenetics), allowing integrative molecular epidemiology studies of association, prediction and aetiology across common complex diseases;
- EPIC-CVD: Adam is the Scientific Coordinator of the EPIC-Heart/EPIC-CVD study, a pan-European study of incident coronary disease and stroke including participants from 23 centres across 10 European countries. This project has a wealth of genetic, biochemical and risk factor data, which are predominantly utilised for the study of gene-environment interaction, cardiovascular risk prediction, and genetic discovery;
- The International Hundred K+ Cohorts Consortium: the IHCC is a global consortium of large-scale population cohorts designed to address questions that single cohorts cannot reliably answer. As of 2021, the consortium involves >70 cohorts and a total of >52 million participants. Adam sits on the Scientific Strategy and Enhancements Committee of the IHCC.
To give a flavour of the different types of research we do, we have shared below some of our favourite publications from each of the 5 following categories:
a) Genetic discovery for molecular traits
Sun B et al. (2018) Genomic atlas of the human plasma proteome. Nature 558:73-79.
- By measuring ~3500 proteins in >3000 participants from the INTERVAL study, we were among the first groups to provide a thorough understanding of the genetic control of the plasma proteome, providing insights into the role of protein pathways in diseases from coronary disease to inflammatory bowel disease. Our summary statistics and underlying data are both publicly available: https://www.phpc.cam.ac.uk/ceu/proteins/
Astle WJ et al. (2016) Thousands of genetic variants modulate blood cell variation and function in humans. Cell 167(5):1415-1429.e19.
- We combined genetic data and haematological traits from ~170,000 participants in INTERVAL and UK Biobank to provide an in-depth understanding of the genetic regulation of >20 blood cell parameters, including many rare variant associations. Our findings enhanced understanding of the genetic architecture of blood cell traits, the genes that control haematopoiesis, and the links between blood cell parameters and complex diseases.
b) Genetic discovery of cardiovascular outcomes and traits
Aragam K et al. (2022) Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nature Genetics (in press).
- With colleagues at the Broad Institute, the Butterworth group co-led the most recent genetic discovery effort from the CARDIoGRAMplusC4D Consortium to identify new genetic associations with coronary artery disease. By amassing a dataset of >200,000 cases and >1 million controls, we were able to identify >250 association signals, including many discovered for the first time. We built a framework to integrate 8 predictors of causal genes, allowing prioritization of a putative candidate gene at >200 of these associations.
Nelson CP et al. (2017) Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nature Genetics 49(9):1385-1391.
Howson JMM et al. (2017) Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nature Genetics 49(7):1113-1119.
- In 2017 we co-led two parallel efforts using customised SNP arrays to identify novel risk loci for coronary artery disease, identifying a few dozen new genetic associations and implicating new genes and pathways in cardiovascular disease aetiology.
Surendran P et al. (2020) Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals. Nature Genetics 52(12):1314-1332.
Liu DJ et al. (2017) Exome-wide association study of plasma lipids in >300,000 individuals. Nature Genetics 49:1758-66.
Surendran P et al. (2016) Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nature Genetics 48(10):1151-61.
As well as identifying genetic associations with cardiovascular disease outcomes, we also have a keen interest in identifying loci associated with cardiovascular traits, such as lipids, blood pressure etc.
c) Therapeutic target prioritisation
Gaziano L et al. (2021) Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nature Medicine 27:668–676.
- To help prioritize therapeutic targets to treat COVID-19, we repurposed our proteome- and transcriptome-wide Mendelian randomization framework to interrogate thousands of genes and proteins. We identified several promising pathways that appeared to play a role in severity of COVID-19, some of which are already targeted by existing drugs (e.g. interferon pathways).
Zheng J et al. (2020) Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nature Genetics 52(10):1122-1131.
- We complemented approaches below relating to single targets by extending methods to look across thousands of proteins concurrently. By creating a pragmatic framework to integrate data from several proteomic GWAS, we could identify >200 protein-disease associations.
Burgess S et al. (2018) Association of LPA variants with risk of coronary disease and the implications for lipoprotein(a)-lowering therapies. JAMA Cardiology 3(7):619-27.
- To inform the design of phase-III trials of lipoprotein(a)-lowering treatments, we used genetic epidemiology and Mendelian randomisation approaches to predict the likely magnitude of cardiovascular risk reduction in short-term clinical trials. Our results suggested that only participants with high lipoprotein(a) levels were likely to derive sufficient benefit, so Novartis’ HORIZON trial has only enrolled participants with >70mg/dL of lipoprotein(a) at baseline.
Gregson J et al. (2016) Genetic invalidation of Lp-PLA2 as a therapeutic target: large-scale study of five functional Lp-PLA2 lowering alleles. European Journal of Preventive Cardiology 24(5):492-504.
- We used genetic data from multiple ethnic groups to understand why trials of Lp-PLA2 inhibitors had failed to show a benefit in reducing cardiovascular risk. Genetic epidemiology using common weak-effect alleles and rarer strong-effect alleles suggested that inhibiting Lp-PLA2 is not likely to lead to reductions in cardiovascular risk, invalidating Lp-PLA2 as a therapeutic target.
d) Cardiovascular disease association and prediction
Sofianopoulou E et al. (2021) Estimating dose-response relationships for vitamin D with coronary heart disease, stroke, and all-cause mortality: observational and Mendelian randomisation analyses. Lancet Diabetes and Endocrinology9(12):837-846.
- Large trials have suggested no benefit of vitamin D supplementation on cardiovascular risk in the general population, but observational epidemiology has shown non-linear associations of vitamin D levels with coronary heart disease, stroke and all-cause mortality risk. We used non-linear Mendelian randomisation approaches to show that there is likely to be a causal effect of vitamin D on disease risk, but only among those at low vitamin D levels. Our findings have implications for understanding of vitamin D’s effects on several major diseases, as well as for design of future trials.
Steur M et al. (2021) Dietary fatty acids, macronutrient substitutions, food sources and incidence of coronary heart disease: findings from the EPIC‐CVD case‐cohort study across nine European countries. Journal of the American Heart Association 10(23):e019814.
- Using data from the pan-European EPIC-CVD study involving >10,000 incident cases of coronary heart disease, we addressed the controversial question of the associations of dietary fatty acid intake with risk of heart disease. We found no strong associations of total fatty acids, SFAs, monounsaturated fatty acids, and polyunsaturated fatty acids with incident CHD. However, SFAs from different food sources had opposing directions, suggesting that food sources should be considered, not just macronutrients they contain.
Lassale C et al. (2017) Separate and combined associations of obesity and metabolic health with coronary heart disease: a pan-European case-cohort analysis. Eur Heart J ehx448.
- Previous research has suggested that there is a set of people who are the “metabolically healthy obese” who may be protected from heart disease despite their obesity. By using the pan-European EPIC-CVD study, we investigated the joint effects of metabolic health and obesity, showing that people who are obese but have ‘good’ metabolic health are still at higher risk of cardiovascular disease compared to those who are normal weight.
Kamat MA et al. (2019) PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics 35(22):4851-3.
Staley JR et al. (2016) PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32(20):3207-9.
- To facilitate rapid phenome-wide association scans based on publicly available GWAS summary statistics, we built the PhenoScanner tool, which instantly searches for variants and proxies and returns association data from a database of over 65 billion associations!
Stacey D et al. (2019) ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Res 47(1):e3.
- A key challenge in contemporary genetic epidemiology is to identify the effector genes for genetic association signals, translating statistical associations into actionable biology. We built and validated a tool (‘ProGeM‘) to prioritize likely causal genes for molecular association signals based on metabolomic data.
Following training in genetics at the University of Cambridge and genetic epidemiology at the University of Sheffield, Adam completed a PhD in meta-analysis of genetic association studies of coronary heart disease at the University of Cambridge. This research was carried out jointly with the PHG Foundation, the MRC Biostatistics Unit, and the Cardiovascular Epidemiology Unit.
Since his PhD, Adam has worked in the Cardiovascular Epidemiology Unit, initially as a post-doctoral Research Associate, from 2012 as a University Lecturer in Cardiovascular Epidemiology, from 2018 as a Reader in Molecular Epidemiology, and since 2021 as Professor of Molecular Epidemiology, leading a research group of pre-doctoral and post-doctoral scientists in genomic and molecular epidemiology.
Former members of the Butterworth group have gone on to achieve great things, in academia, industry, clinical medicine and other sectors! Previous group members include:
Laura Johnson: Director of Health @ the National Centre for Social Research (NatCen)
Ben Sun: Principal Clinical Scientist (Associate Medical Director) @ Biogen
Bram Prins: Associate Principal Scientist @ AstraZeneca
Praveen Surendran: Associated Director – Human Genetics and Computational Biology @ GSK
Marinka Steur: Senior Nutritional Scientist @ Erasmus MC
James Staley: Senior Computational Genetics Scientist @ UCB
James Blackshaw: Scientific Data Engineer @ the European Bioinformatics Institute