Approved Research
Simultaneously Identifying and Estimating Gene-Environment Interactions for BMI
Lay summary
Obesity is a major public health issue worldwide. According to WHO, overweight is defined as a body mass index (BMI) over 25, and obesity is over 30. In 2016, there are almost 2 billion adults were estimated to be overweight, and 671 million adults with obesity. At least 2.8 million people died because of being overweight or obese in 2017. If current trends continue, it is expected that 1 billion adults will have obesity by 2025. Research has also shown that obesity decreases life expectations and affects both physical and psychosocial aspects of quality of life.
Various studies have shown that there are genetic effects behind BMI. In addition to genetic factors, environmental factors such as parental education level, length of physical activity per week, and being a smoker or non-smoker are also relevant. Importantly, the interactions between genetic factors and environmental factors, termed as Gene-Environment Interactions or GxE, are believed to have effects on BMI.
We propose to develop a novel approach that can simultaneously predict, identify important risk factors, and study the joint effects of GxE and genetic factors. The dimension of genetic factors can be over hundreds of thousands, which is often much higher than the number of subjects in a study. Including GxE interactions could increase the dimension of possible risk factors to millions. Given there are usually dozens of important risk factors, identifying them from millions of possible risk factors is like finding a needle in a haystack. The traditional genome-wide association studies (GWAS) only consider one genetic factor at a time and then repeat the testing procedure to analyze all genetic factors, ignoring the joint effects.
Overweight and obesity are of most interest rather than average BMI. We will focus on the high quantiles of BMI to study the high BMI values of most interest. Different quantiles will be used for different levels of overweight. We further propose to develop a new model to identify important GxE and estimate their effects for varying quantiles of BMI.
Our project plans to carry on for 3-4 years. Our research aims to identify possible risk factors including new GxE interactions for BMI and new genetic factors associated with BMI. This may provide insights for potential treatment. Our findings may further enable researchers or healthcare providers to evaluate obesity risk and give customized suggestions for patients.