Scientists have developed a powerful new tool to pinpoint genes most likely causally linked to diseases, potentially accelerating drug development and advancing our understanding of disease biology.
Research shows that drugs targeting genes identified in genetic studies are 2.3 times more likely to receive FDA approval than those without such genetic evidence. However, determining which genes are truly causal for disease remains a major challenge.
Now, researchers Marijn Schipper and Danielle Posthuma from the Complex Trait Genetics lab at CNCR have introduced FLAMES, a machine-learning tool designed to improve gene prioritization based on genetic discovery studies. Their study, published in Nature Genetics highlights how FLAMES outperforms existing methods by combining multiple approaches and large-scale genetic data to make more accurate predictions.
FLAMES was used to find schizophrenia-related genes, successfully prioritizing 180 genes from 255 known genetic regions. These genes were found to be linked to synaptic function and divided into two key groups: one related to early brain development and the other to processes occurring later in life.
As part of the BRAINSCAPES consortium, Professor Posthuma plans to apply FLAMES to uncover new insights into brain disorders. The tool is currently being used to study genes involved in alcohol addiction, anxiety, depression, insomnia, and Alzheimer’s disease.
FLAMES is freely available, allowing researchers worldwide to prioritize genes for any disease or trait with genetic data, opening new doors for medical breakthroughs.