Researchers have developed a statistical model to better estimate how the interaction between genetic and environmental factors can affect obesity risk, potentially helping public health authorities to focus on specific environmental factors to mitigate this risk.
The study, “Quantification of the overall contribution of gene-environment interaction for obesity-related traits,” was published in the journal Nature Communications.
Most human traits and behaviors result from a complex interaction between a person’s genes and the surrounding environment.
Environmental effects may be prenatal or occur throughout life as a result of exposure to alcohol, tobacco, air or water pollution, or infections, and lifestyle habits such as diet, sleep patterns, and exercise.
Small differences in genes may make some people more prone to develop a condition (such as obesity) in a certain environment, in what is called gene-environment interaction.
Based on the total number of changes related to a disorder, genetic or environmental, researchers are investigating possible risk factors for developing a disease across different populations. This includes risk factors for obesity.
To decipher the role that one or multiple genes play as well as the environment’s influence, researchers have been using genome-wide association studies (GWAS), in which hundreds of small variations in the genome are identified and used to pinpoint genes linked to certain conditions, including obesity risk.
However, most methods do not account for the effects of multiple environmental conditions and genes acting together, or the gene-environment interaction. They require that the environment be measured accurately, which is challenging as many factors are difficult to define with precision — such as physical activity, accessibility of fast food, sleep, and diet, “which are all key factors in obesity, and suspected to interact with genetic risk,” the researchers wrote.
To overcome this challenge, researchers at the University of Lausanne, in Switzerland, followed a different approach to find the overall contribution of gene-environment interactions and their influence on obesity-related traits, such as body mass index (BMI), weight, fat mass, and fat-free mass in the body.
They developed a new statistical model that takes into account the effects of all environmental variables, without the need to measure any of them.
It calculates the overall contribution of the gene-environment interaction between a fixed genetic factor — a single nucleotide change, or a genetic risk score — and all environmental interactions. (Nucleotides are the building blocks of DNA).
In the model, the combination of all possible environmental variables is treated as a random effect and, as such, only data on traits (e.g. BMI) and genetic factors are required.
According to the team, extensive simulations demonstrated that the model provided unbiased interaction estimates and excellent coverage.
To assess the method’s usefulness, the scientists applied the model to estimate the contribution of gene-environment interactions to the variability in 32 obesity-related traits. Data from the UK Biobank were used for this purpose.
Results revealed that while a person’s genetic risk score — incorporating 376 genetic variants — explains 5.2% of the variations in BMI, interaction with the environment accounts for an additional 1.9%.
Although these are still early results, they suggest a substantial contribution of gene-environment interactions to nine obesity-related measures, with the strongest effects in leg bioelectrical impedance — a measure to estimate body fat — and trunk fat-free mass.
As many different environmental factors potentially interact with a genetic risk score, the model remains “rather general” for now, the researchers said.
“The proposed method could be used as a tool to establish the contribution of G × E [genetic-environment interactions] to different traits and subsequently prioritize those with substantial global interaction effect for follow-up,” the researchers wrote.
“Such traits may show particular potential for public health interventions, where the genetic predisposition could be modified the most by lifestyle changes,” they added.
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