Evaluating and comparing sequence variant associations between the deCODE and UK Biobank datasets across a range of human traits

The last two decades have seen major progress in human genetics research in particular due to technological advancements and the success of the genome-wide association study method, testing for associations between sequence variations across the human genome and diseases or other traits such as height. Hundreds of variants have been found that associate with many phenotypes. However, much is still to be discovered. For example, the genetics of many diseases and other traits are still poorly understood, an example being heart failure. Possible explanations for this include small sample sets in the studies performed hitherto and diseases with complex definitions and classifications and ascertainment of traits. Also, while many sequence variants have been found that associate with risk of disease, how they associate (through which gene or mechanism) is unknown. Finally, little is still known about the interaction of genes with the environment. To address these issues, large genotyped datasets with rich phenotypic information are key.
Our research aim is to leverage two datasets, deCODE genetics from Iceland, and the UK Biobank, to further our understanding of the genetic underpinnings of health and disease. Both datasets are large population-based datasets with extensive phenotypic information that allow for thorough and complementary assessment of variant associations across a range of human traits. We will specifically address diseases for which the genetics are still poorly understood and explore the usefulness of re-defining phenotypes or defining subtypes. We will leverage the extensive expression and proteomics data available for the deCODE sample set to study the functional consequences of variants and thus establish the link between variants and their effect on disease mechanism. We will explore gene-environmental interaction utilizing the extensive longitudinal data available for both datasets, including many repeat measures.
We anticipate uncovering a number of variant associations with diseases and other traits, and in some cases providing in-depth characterization of variants with information on functional consequences. We expect results pertaining to both common and rare diseases, with regards to definitions of disease as well as genetic associations. We foresee the study shedding light on the pathophysiology of many diseases, including both genetic and environmental factors affecting disease development, progression and prognosis. Such information is paramount in improving disease prevention, diagnosis and treatment.