A groundbreaking study has unveiled a powerful computational method capable of detecting how genes interact to influence complex human traits at an unprecedented scale. The method, detailed in a recent publication in Nature Genetics, was applied to extensive datasets that link individual genomes with traits, revealing significant interactions. The findings underscore the profound impact a person’s genetic background can have on how individual genetic variants affect their traits.
The announcement comes as researchers strive to deepen their understanding of genetic interactions, which are crucial for both basic biology and precision medicine. Such insights could clarify why individuals with the same genetic predisposition may experience different health outcomes or why genetic predictions of disease risk often fall short of accuracy. Historically, the lack of tools capable of handling the complexity and scale of large genomic datasets has limited evidence for these interactions.
Understanding Genetic Complexity
The study introduces a computational method named FAME, designed to detect and quantify genetic interactions. Traditional approaches have often focused on identifying pairs of genetic variants that interact to influence a trait, but these methods struggle to detect interactions with weak effects. FAME, however, identifies genetic variants whose effects are modulated by the entire genome, allowing it to aggregate weak effects into detectable interaction signals.
According to the researchers, aggregating interaction effects from hundreds of thousands of variants across the genome poses a significant computational challenge. FAME overcomes this by employing advanced mathematical techniques that streamline computational demands while maintaining accuracy.
Key Findings from the UK Biobank
Applying FAME to the UK Biobank, which contains genetic and trait data from nearly 300,000 individuals of European ancestry, the research team identified 16 instances of genetic interactions affecting traits such as cholesterol, liver enzymes, and testosterone levels. While these interaction effects are generally small, they often surpass the effect sizes observed when examining variants individually, as is common in Genome-Wide Association Studies.
“The interaction effects tend to be small, but they are often larger than the effect sizes identified by examining the variant by itself.”
Moreover, several interaction signals were replicated in an independent dataset, bolstering the robustness of these findings.
Future Directions and Broader Implications
The research team plans to extend FAME to explore rare genetic variants and disease traits, as well as to pinpoint interactions within the genome. Although the current study focused on a predominantly white UK population, future research aims to investigate how these interactions vary across diverse populations.
Meanwhile, experts in the field are optimistic about the potential applications of this new method. Sriram Sankararaman, a senior author and professor at UCLA, remarked, “This gives us a new window into how genes work together to influence traits.” Boyang Fu, the study’s lead author and a postdoctoral fellow at Harvard Medical School, added, “FAME allows us to test for genetic interactions across hundreds of thousands of people and millions of genetic variants in a matter of hours—something that was previously impossible.”
Study Details and Collaborations
The study, titled “A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic interaction effects,” was published in Nature Genetics. The research team includes contributors from Harvard University, UCLA, the University of Pennsylvania, and Carnegie Mellon University.
Funding for the study was provided by the NIH, NSF, and the UCLA Dissertation Year Fellowship, with no competing interests declared by the authors.
Looking Ahead
This development represents a significant advancement in the field of genetics, offering new insights into the complex interplay of genes. As researchers continue to refine and expand the capabilities of FAME, the potential for personalized medicine and improved disease prediction becomes increasingly attainable. The ongoing exploration of genetic interactions across diverse populations will likely yield further breakthroughs, enhancing our understanding of human biology and health.
