Principal Investigator: Prof Zoltan Kutalik
Centre Hospitalier Universitaire Vaudois, IUMSP, Rte de al Corniche 10, Lausanne 1010, SwitzerlandTags: 16389, diet, GxE interaction, Mendelian randomization, obesity
Lead Collaborators: Tim Frayling
Collaborating Institutions and Addresses:
University of Exeter, Medical School, RILD Building, Barrack Road, Exeter EX2 5DW. United Kingdom.
Summary of research
Diet, obesity, GxE interaction, Mendelian randomization
Application Lay Summary:
1a: We would like to investigate how environmental factors (e.g. (reported) dietary intake and essential lifestyle factors) correlate with obesity/metabolic trait. We will establish which factors are causes and which are consequences of obesity (i.e. preventive measures) through Mendelian randomisation. Moreover we’d explore which of these environmental factors modify the effect of the genetic risk score on obesity/metabolic traits.
1b: The research aims at better understanding of obesity genetics and identify modifiable lifestyle factors causally influencing obesity and other metabolic traits
1c: We will apply statistical methods to explore the causal effect of obesity/metabolic traits on environment and visa versa. These methods require genetic data and lifestyle information. This will inform us which modifiable environmental factor are more likely causes/consequences of obesity. Furthermore, we’ll identify which of these factors modify association strength between body-mass-index associated SNPs and obesity.
1d: We need the entire cohort for this analysis as lifestyle and dietary factors are self reported, thus less reliable. In addition, since individual genetic variants have small impact on these lifestyle factors, we have to maximise the sample size to ensure robust estimates.
“The central trait of my application, BMI (obesity) has shown to be strongly connected to brain structures, but the direction of causality or common confounding has not been elucidated. We have shown [http://www.nature.com/mp/journal/v20/n1/full/mp2014145a.html] that a certain BMI-associated genomic rearrangement modulate grey matter volume. Thus we would like to see if this trend is general, or rather BMI- (or a relevant confounding such as diet, physical activity) change is what mostly drives grey matter structural changes. For this analysis I was already granted FA, ICVF, ISOVF, L1, L2, L3, etc. brain imaging data from the Biobank, but not grey matter. We have also obtained GWAS results on grey matter volume from the ENIGMA consortium in order to maximise power.”
Last updated May 31, 2017