B3548 - Investigating the influence of natural selection on metabolites - 28/05/2020

B number: 
Principal applicant name: 
Tom Gaunt | MRC IEU University of Bristol (United Kingdom )
Charlie Hatcher , Dr Santi Rodriguez , Dr Josine Min , Dr Daniel Lawson
Title of project: 
Investigating the influence of natural selection on metabolites
Proposal summary: 

Environmental and genetic factors both play a role in shaping individual variation. Characteristics that increase an individual’s chance of survival and reproduction are more likely to be passed onto the next generation. This process is known as natural selection and it is reflected at the genetic level. Negative selection is a form of natural selection whereby rare genetic factors with harmful effect on survival and reproduction are removed from populations. Recent studies have found evidence of negative selection acting on complex traits such as body mass index (BMI), blood pressure and height.

This project will explore how natural selection influences molecular traits. Metabolites are intermediates or end products of biological processes and they are linked to numerous diseases. Gaining a better understanding of how metabolites are related to survival and reproduction will enable researchers to prioritise specific research avenues. This prioritisation may improve human health.

Impact of research: 
This work will investigate whether selection at the complex trait level is reflected at the molecular trait level (specifically metabolites), thus enabling us to better understand the relationship between complex traits, molecular traits and fitness. Understanding selection on molecular traits such as metabolites will ultimately help to identify biological pathways that can be intervened on to prevent and/or cure disease. Additionally, detecting regions of the genome under selection can also be used to help prioritise GWAS hits.
Date proposal received: 
Wednesday, 27 May, 2020
Date proposal approved: 
Thursday, 28 May, 2020
Genetic epidemiology (including association studies and mendelian randomisation), Statistical methods, Genetic epidemiology