A Powerful New Computational Tool Reveals How Genetic Interactions Shape Human Traits

Scientists have long known that genes do not work alone. Instead, they interact with one another in complex ways to influence how our bodies function and how traits develop. Yet, despite this understanding, actually detecting and measuring these interactions across the entire human genome has remained a major scientific challenge. A new study from researchers at the University of California, Los Angeles (UCLA) changes that by introducing a computational method capable of uncovering genetic interactions at an unprecedented scale.

Published in Nature Genetics, the study presents a tool called FAME, short for Fast Aggregated Marginal Epistasis. This method allows researchers to detect how a person’s overall genetic background can modify the effects of individual genetic variants on complex traits such as cholesterol levels, liver enzyme activity, and hormone levels.

Why Genetic Interactions Matter More Than We Thought

Many common human traits, including body mass index (BMI), cholesterol, and various metabolic markers, are influenced by thousands of genetic variants. Traditional genetic studies, such as Genome-Wide Association Studies (GWAS), typically examine how single variants affect a trait on their own. While this approach has identified many important genetic signals, it often falls short of explaining why people with similar genetic risk factors can have very different health outcomes.

This gap exists because genetic variants can interact with one another, a phenomenon known as epistasis. In simple terms, the effect of one gene can depend on the presence or absence of many other genes. These interactions may help explain why genetic predictions of disease risk sometimes perform worse than expected and why personalized medicine has proven more complex than initially hoped.

The problem is not a lack of interest, but a lack of tools. Detecting gene–gene interactions across hundreds of thousands of individuals and millions of variants requires enormous computational power and clever statistical approaches. Until now, most methods either could not scale or were unable to detect interactions with small effects.

How the FAME Method Works

FAME takes a fundamentally different approach from traditional interaction studies. Instead of searching for specific pairs of genetic variants that interact with each other, FAME asks a broader question: does the effect of a single genetic variant on a trait change depending on the rest of the genome?

In other words, rather than testing variant A against variant B, FAME evaluates whether variant A’s influence is modulated by all other genetic variants combined. This strategy allows the method to capture thousands of weak interaction effects that would be invisible when examined individually.

To make this possible, the researchers developed sophisticated mathematical techniques that dramatically reduce the computational burden. What would normally take years of computing time can now be done in a matter of hours, even on biobank-scale datasets. This efficiency is what makes FAME truly groundbreaking.

Applying FAME to the UK Biobank

To test the power of their method, the researchers applied FAME to data from the UK Biobank, one of the largest genetic and health databases in the world. The dataset included genetic and trait information from nearly 300,000 individuals of European ancestry, making it ideal for large-scale analysis.

Using FAME, the team identified 16 clear instances of genetic interaction effects across a range of traits. These included traits related to cholesterol, liver enzymes, and testosterone levels. While the interaction effects were generally small, many of them were actually larger than the effects of the same variants when analyzed alone, highlighting how much information is missed when interactions are ignored.

Importantly, several of these interaction signals were successfully replicated in an independent dataset. Replication is a critical step in genetics research, and this result strengthens confidence that the detected interactions are real and not statistical artifacts.

Why These Findings Are Important

One of the most striking outcomes of the study is the demonstration that genetic background can meaningfully alter how individual variants influence traits. This reinforces the idea that genetic effects are context-dependent and that no variant acts in isolation.

For precision medicine, this insight is especially valuable. If genetic interactions play a significant role in shaping traits and disease risk, then future prediction models will need to account for them. Tools like FAME provide a pathway toward more accurate genetic risk assessments and a deeper understanding of biological mechanisms.

Expanding the Scope of Genetic Interaction Research

While this study focused on common genetic variants and a population that was largely white and UK-based, the researchers are already planning the next steps. One major goal is to extend FAME to analyze rare genetic variants, which are often harder to study but can have strong biological effects.

Another priority is applying the method to disease traits, rather than just quantitative measurements like cholesterol levels. Doing so could reveal new interaction-driven mechanisms behind complex diseases such as diabetes, cardiovascular disease, or autoimmune disorders.

The team also aims to investigate how genetic interactions differ across diverse populations. This is especially important, as most large genetic studies have historically focused on individuals of European ancestry, limiting the global applicability of their findings.

A Closer Look at Epistasis in Human Genetics

Epistasis has been a topic of interest in genetics for decades, but its role in human traits has remained controversial. Some researchers have argued that interaction effects are too small to matter, while others believe they hold the key to understanding missing heritability.

What this study shows is that epistasis may not be rare or insignificant—it may simply be distributed across the genome in subtle ways. By aggregating these effects, FAME reveals a layer of genetic architecture that was previously hidden.

This does not mean that traditional GWAS approaches are obsolete. Instead, it suggests that additive genetic effects and interaction effects should be studied together to gain a more complete picture of how traits develop.

The Bigger Picture for Genetics and Medicine

Beyond its immediate findings, the development of FAME represents a shift in how researchers can think about genetic data. With biobanks growing larger and more detailed every year, tools that can efficiently analyze complex interactions will become increasingly important.

As computational methods continue to evolve, they will help bridge the gap between genetic variation and biological function. Ultimately, this could lead to better disease prevention strategies, more precise treatments, and a clearer understanding of why humans vary so much in health and disease.

Research Paper:
https://www.nature.com/articles/s41588-025-02411-y