Principal Investigator: Dr Tao Huang
Peking University, Beijing, ChinaTags: 44430, causality, ethnic difference, gene-environment interaction, Genetic Correlation, GWAS, Mendelian randomisation
Collaborator: Dr Zhonghua Liu, University of Hong Kong, Hong Kong
The rapid increase in complex chronic diseases such as obesity, type 2 diabetes, dyslipidemia, and cardiovascular disease and cancers, etc.) over the past several decades threatens human health. Although genetic association studies conducted in the past decade have dramatically improved our understanding of the genetic basis of complex chronic diseases, growing evidence has shown that the genetic variations and diet/lifestyle factors may act synergistically in affecting the disease risk. Moreover, the undeniable fact remains that most identified genetic variants only explain only small proportion of the heritability in many of these disease outcomes. This has driven the need for novel research to characterize the gene-environment interactions that will be useful in investigating biological pathways, and understanding heterogeneity in genetic associations across populations, predicting individual risk and choosing the best intervention for disease prevention based on the individual’s genotype.
Importantly, although large scaled cohort studies have found numerous associations between environmental factors or biomarkers and diseases, the causality behind theses associations remain unclear. Therefore, we are applying the UK biobank data to examine genome-wide variants environment interaction in relation to chronic diseases and explore the causal effects of exposure (diet, lifestyle, biomarkers) on chronic diseases, further we will use the China Kadoorie biobank (CKB) to compare the heterogeneity in genetic association and causal effects across populations. Increasing statistical power, gene, pathway-based analytic and genome wide approaches provides an effective means to integrate prior biological knowledge into association and interaction analyses and thus provide insights into biological mechanisms.
Aim 1, To conduct genome-wide variants-environment interaction analyses between and environmental exposures such as diet, lifestyle factors and biomarkers in relation to chronic diseases, cardiometabolic traits and ageing outcomes.
Aim 2, To conduct mendelian randomization analysis to examine the causal effect of environmental factors or biochemical markers on chronic diseases and aging outcomes.
Aim 3, To examine the genetic correlations between chronic diseases or aging outcomes and biochemical markers.
Given the current strategies for prevention and control of chronic diseases, one-size-fits-all diet and exercise recommendations are limited in scope and effectiveness. Therefore, identification of robust gene-environment interaction effects could be the first step for the promotion of precision or personalization of disease prevention and management. And of equal importance, using Mendelian randomization to screen and validate the causal effects of the risk factors is crucial for decision-makers to target the right modifiable risk factors.