Statistical methods to study the interplay between genetic and lifestyle factors using large-scale genetic data and a wide variety of lifestyle factors
This research project aims to develop statistical methods for large-scale genome-wide association studies that include a wide variety of lifestyle factors. We will combine genetic and non-genetic data using three main approaches: we will study whether genetics can predict important lifestyle factors (such as smoking behaviour, diet, exercise, and alcohol consumption); we will test for interactions between genetic and environmental factors; we will study the genetic architecture of quantitative traits (such as obesity-related traits, bone density measures, and red blood cell measures) with increased statistical power.
Genome-wide association studies have successfully identified thousands of genetic variants associated with human traits and diseases. The UK Biobank resource offers a unique opportunity to expand this knowledge by taking advantage of the size of the resource (numbers of individuals, amount of genetic information, and range and diversity of the non-genetic information available) so that for the first time we can examine the interactions, or interrelationships amongst the variables.
We will pursue this in three related directions: (i) using the resource to explore the interactions amongst genetic risk factors for baseline measures, traits and biomarkers; (ii) the role of genetic factors in lifestyle outcomes, and the role of, and interaction between, genetic and lifestyle/environmental, risk factors in susceptibility to disease and in other medically-relevant outcome variables (including many quantitative traits measured at baseline in UK Biobank, in addition to the biomarkers currently being measured); (iii) understanding the extent and nature of pleiotropy, that is, the extent to which the same genetic factors affect a range of different outcome variables.
Young AI, Wauthier F and Donnelly P (2016) Multiple novel gene-by-environment interactions modify the effect of FTO variants on body mass index, Nature Communications 7:12724 DOI: 10.1038/ncomms12724 https://www.nature.com/articles/ncomms12724
|Lead investigator:||Prof. Peter Donnelly|
|Lead institution:||University of Oxford|