Genetic epidemiology

As part of a public health and epidemiological research programme, the Genetic Epidemiology Group explores genomic diversity and its impact on infectious and cardiometabolic risk factors among populations.

As well as providing new insights into disease aetiology, these studies offer a research framework to develop genetic tools for causal inference in epidemiology, informing strategies for disease prevention and treatment.

[Gordon Museum, Wellcome Images]

Background

Our research focuses on the integration of principles and procedures underlying epidemiology, genomics and public health.

The epidemiological transition from infectious to non-infectious diseases may, in part, reflect changes in the reciprocal influence of infectious and non-infectious factors. In this aetiological framework, epidemiological approaches can help describe and elucidate the determinants and consequences of the distribution and prevalence of infectious and metabolic risk factors, which may be specific to regions and countries. This information can be used to better plan health services that meet the needs of the population.

Within this epidemiological and public health context, our research uses emerging genome-wide genotyping and sequencing technologies to identify the biological mechanisms underlying the development of complex diseases and traits among populations. Collectively, these approaches can help inform strategies for disease prevention and treatment.

Research

Our research is currently based on three broad areas of investigation:

Genomics of lipid metabolism

Unequivocally, the study of rare and low frequency, functional genetic variants (SNPs and copy number variants) has played an important role in understanding lipid biology. Rare, highly deleterious mutations in the LDLR, APOB, PCSK9 and USF1 genes cause Mendelian forms of hypercholesterolaemia or combined dyslipidaemia and early onset coronary artery disease. Functional genetic mutations in LPL and APOA5 have provided insights into triglyceride metabolism. Importantly, studies of individuals with rare mutations in these genes have helped characterise the biological mechanisms underlying lipid metabolism and strategies for therapeutic intervention. Using genome wide approaches, we have identified novel, common genetic loci explaining additional variation in blood lipids and providing new insights into lipid biology. We are now using combination of whole genome and exome sequencing of human population samples to help identify lower frequency variants that may provide new insights into the potential biological role of these loci in lipid metabolism.

Mendelian randomisation studies

'Mendelian randomisation' - the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides an epidemiological approach that is much less susceptible to confounding by classical or environmental risk factors and excludes reverse causality as a possible non-causal explanation for the observed association between a biomarker and disease risk. In this research framework, the interrelation and consistency of associations among genetic variants that encode or regulate a biomarker, the expression or circulating levels of the biomarker, and disease risk may characterise the true magnitude of the relation between the biomarker and risk of disease (Figure 1). Reproducible evidence for statistical association between a genetic variant and biomarker provide the basis for a Mendelian randomisation study. Because of independent assortment, characteristics that may confound any association between a biomarker and disease are equally distributed among the relevant genetic variants. Thus a comparison of groups of individuals defined by a genetic variant, based on a Mendelian randomisation design, is equivalent to a randomised comparison, with only the relevant biomarker differing across the relevant genetic variant. Examining the relation between variants in the genome that show unequivocal and specific associations with the relevant biomarker and disease risk, is, therefore, a potential method of assessing whether a biomarker might be causally linked to disease. We are using this research framework to help assess whether some biomarkers are causal risk factors for type 2 diabetes and coronary artery disease.

Epidemiology, genomics and public health in Sub-Saharan Africa

Non-communicable diseases (NCDs) are rapidly becoming leading causes of morbidity and death in low- and middle-income countries, including those in sub-Saharan Africa. These epidemiological transitions characterise a complex change in the patterns of health and disease. They may also reflect varying associations among these patterns-the reciprocal influence of infectious and non-infectious factors. In this context, the need for good quality comparable data on disease burden and risk, to aid planning and implementation of primordial and primary prevention and control strategies for NCDs, is greatest among low- and middle-income countries. Within this public health framework, epidemiological approaches can help describe and elucidate the determinants and consequences of the distribution and prevalence of infectious and metabolic risk factors, which may be specific to regions and countries. Additionally, the integration of principles and procedures underlying population genetics and epidemiology has provided unparalleled opportunities to help understand the biological mechanisms underlying the aetiology of complex diseases. We are therefore contributing to the development of epidemiological resources in Sub-Saharan Africa, including in Uganda and Malawi to assess the burden of NCDs, and their interrelation with infectious factors, and conduct large scale genomic epidemiological studies.

Team

No team members listed

* quick link - http://q.sanger.ac.uk/genepid