Ruth's primary research interests focus on the identification of genes and genetic loci contributing to the risk of obesity and related metabolic traits. She has been involved in gene - discovery since 2005, when ‘genome - wide association’ was introduced and has since actively contributed to many consortia that use this approach to identify genetic loci for a large number of metabolic traits. Increasingly, her gene - discovery work also focuses on the identification of low - frequency variants through the implementation exome - chip genotyping and sequencing projects, not only in individuals of white European descent, but also in those of African and Hispanic decent.
She is a member of steering committee of the GIANT (Genetic Investigation of ANTropometric Traits) consortium, led by Professor Joel Hirschhorn and is actively involved in the many working groups. She has set up the Genome - Wide Association Study (GWAS) consortia for body fat percentage, for leptin levels, and also for resting heart rate. Furthermore, she has been involved in the GWAS consortia for blood pressure (ICBP), lipids (GLGC), glucose and insulin (MAGIC), and type 2 diabetes (DIAGRAM), amongst others.
Besides gene-discovery, she uses epidemiological methods to follow - up on established loci with the aim to elucidate the pathways through which they increase risk of metabolic disease. Furthermore, her work also assesses the public health implications of the established loci by examining their predictive value and their interaction with lifestyle factors such as diet and physical activity.
Research Interests:
- Gene-discovery for obesity and related metabolic traits
- Role of low-frequency variants in metabolic traits
- Genetic contribution to obesity and related metabolic traits in individuals of African and Hispanic origin
- Gene-environment interaction to study the influence of lifestyle on the genetic susceptibility to obesity and related metabolic traits
- The value of genetic prediction for obesity and related metabolic disease
- Use of epidemiological methods to gain insights in the pathways that connect genetic loci to increased risk of disease
