DNA turning into silhouette representing genetics, AI and personalized health.

Decoding Obesity: Can Deep Learning Unlock the Secrets in Our Genes?

Beau Callahan in Health & Wellness June 2025 • 4 min read.

"A new approach combines genetic studies with AI to predict obesity risk with unprecedented accuracy. Is this the future of personalized health?"

Obesity is a global health crisis, with rates soaring and impacting millions worldwide. The World Health Organization (WHO) recognizes obesity as a major mortality risk factor, contributing to approximately 2.8 million deaths each year. Beyond the human toll, obesity places a huge burden on healthcare systems. In the UK alone, the National Health Service (NHS) spent an estimated £5.1 billion on obesity-related issues in 2006-07.

While lifestyle factors such as diet and exercise play a significant role, genetics also have a huge influence on a person's susceptibility to obesity. Studies on twins and families show that up to 70% of the variation in body mass index (BMI) can be attributed to genetic differences. This means that some people are simply genetically predisposed to gain weight more easily than others.

Genome-wide association studies (GWAS) have emerged as a powerful tool for exploring the genetic basis of complex conditions like obesity. GWAS involve scanning the entire genome for single nucleotide polymorphisms (SNPs), which are variations in a single DNA building block. By analyzing the SNPs of thousands of people, scientists can identify genetic markers associated with an increased risk of obesity.

How Deep Learning Cracks the Genetic Code of Obesity

DNA turning into silhouette representing genetics, AI and personalized health.

Traditional GWAS methods typically analyze each SNP independently, overlooking the complex interactions between genes. Deep learning, a type of artificial intelligence, offers a new way to analyze this data. Deep learning models can identify intricate patterns and relationships within vast datasets, potentially uncovering the complex interplay of genes that contribute to obesity.

Researchers have combined GWAS data with deep learning to predict obesity with greater accuracy. The approach involves training a deep learning model on a dataset of SNPs from individuals with and without obesity. The model learns to identify the specific combinations of SNPs that are most predictive of the condition.

Data Collection: The study used data from the database of Genotypes and Phenotypes (dbGaP), including participants from the MyCode Community Health Initiative.
Quality Control: Rigorous quality control measures were applied to the data, including filtering out individuals with inconsistent sex information, removing related or duplicate samples, and excluding SNPs with low minor allele frequency or high missing rates.
Association Analysis: Statistical association testing was conducted between individual SNPs and obesity using logistic regression, an additive model was employed.
Deep Learning Implementation: A multi-layer feedforward neural network was implemented, trained using labelled training samples from case-control genetic data. The network's performance was evaluated using metrics like sensitivity, specificity, AUC, Gini coefficient, log loss, and MSE.

The results of the study showed that deep learning models could predict obesity with high accuracy, outperforming traditional methods that analyze SNPs in isolation. These findings suggest that deep learning can effectively capture the cumulative effect of less significant genetic variants and their overall contribution to the condition.

The Future of Obesity Prediction and Prevention

These findings suggest that single SNP analysis fails to capture the cumulative effect of less significant variants and their overall contribution to the outcome. The application of GWAS in case control setups have resulted in a plethora of significant genetic variants associated with complex disease phenotypes. However, the predictive capacity of these genetic markers is weak since this approach is based on single-locus analysis, omitting the existence of interactions between loci.

About this Article -

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This article is based on research published under:

DOI-LINK: 10.1109/ijcnn.2018.8489048, Alternate LINK

Title: Deep Learning Classification Of Polygenic Obesity Using Genome Wide Association Study Snps

Journal: 2018 International Joint Conference on Neural Networks (IJCNN)

Publisher: IEEE

Authors: Casimiro A. Curbelo Montaez, Paul Fergus, Almudena Curbelo Montaez, Abir Hussain, Dhiya Al-Jumeily, Carl Chalmers

Published: 2018-07-01

Everything You Need To Know

To what extent do genetics influence obesity, and how does this understanding impact approaches to weight management?

Obesity's genetic component is significant, with studies suggesting that up to 70% of the variation in body mass index (BMI) can be attributed to genetic differences. This means that genetics play a substantial role in determining an individual's susceptibility to gaining weight. While lifestyle factors are important, a person's genetic makeup can predispose them to obesity.

How do Genome-wide association studies (GWAS) contribute to our understanding of the genetic basis of obesity?

Genome-wide association studies (GWAS) scan the entire genome for single nucleotide polymorphisms (SNPs) to identify genetic markers associated with an increased risk of obesity. By analyzing the SNPs of thousands of people, scientists can pinpoint specific genetic variations that are more common in individuals with obesity. These studies help uncover the genetic factors that contribute to the condition.

How does deep learning enhance the analysis of genetic data related to obesity, and what advantages does it offer over traditional methods?

Deep learning offers a new approach by analyzing GWAS data, identifying intricate patterns and relationships within vast datasets. Unlike traditional methods that analyze each SNP independently, deep learning models can uncover the complex interplay of genes that contribute to obesity. This allows for a more holistic understanding of the genetic factors involved.

What were the key steps involved in the study that combined GWAS data with deep learning to predict obesity, and why were these steps important?

The study involved several key steps: Data Collection from the database of Genotypes and Phenotypes (dbGaP), rigorous Quality Control measures to ensure data accuracy, Association Analysis using logistic regression to identify links between individual SNPs and obesity, and Deep Learning Implementation using a multi-layer feedforward neural network. These steps are crucial for training and evaluating the deep learning model's ability to predict obesity.

What are the implications of the study's findings on how we understand the genetic contributions to obesity, and how could this influence future research?

The findings suggest that single SNP analysis fails to capture the cumulative effect of less significant variants and their overall contribution to the outcome. By using deep learning, researchers can effectively capture the cumulative effect of these less significant genetic variants, offering a more comprehensive understanding of their combined impact on the condition. This highlights the limitations of traditional methods that focus solely on individual SNPs.