How would you summarize your study for a lay audience?
Our study introduces a new tool called FUSE (Functional Substitution Estimation) that helps scientists better understand how changes in genes affect proteins. Genetic variations can change the way a protein works, potentially leading to disease. New CRISPR-based experiments can help us understand the impact of genetic variations by installing these changes in cells’ DNA. FUSE combines data from many of these experiments to more accurately predict the impact of specific genetic changes, even for mutations that have not yet been tested. This progress may provide better evidence for understanding the effects of genetic variations, which may ultimately help doctors identify harmful mutations more effectively, leading to improved patient care and personalized treatments.
What knowledge gap does your study help fill?
In this study, we aimed to improve the precision of interpretation of how genetic mutations affect protein function, which is crucial for understanding disease risk. High-throughput functional screening assays, such as deep mutational scanning, generate large amounts of data about how mutations affect proteins. However, individual measurements from these assays can be noisy due to experimental variability, making it difficult to accurately estimate the effect of each variation.
What prompted you to pursue research in this area?
In recent years, there has been a significant increase in studies aimed at experimentally measuring the impact of genetic variations on the human genome. These efforts have produced hundreds of thousands of screening results from high-throughput functional assays. However, each individual measurement can be affected by statistical noise and experimental variability, which can limit the precision of the estimates when considered individually.
Motivated by this challenge, our research teams recognized the opportunity to improve the accuracy of these variant effect estimates by collectively analyzing the vast amount of available data. By integrating results from numerous studies, we aimed to reduce the inherent noise in individual experiments and improve the reliability of each estimate.
Our two labs combined computational and experimental expertise to tackle complex problems in genomics. This synergy allowed us to develop a new approach that leverages both computational methods and experimental data to refine the findings. Our goal was to create a tool that not only advances our understanding of genetic variation, but also provides a valuable resource for the scientific community, ultimately contributing to improved patient care and personalized medicine.
What methods or approaches did you use?
To address this, we developed FUSE. We collected and integrated data from over 100 functional experimental control datasets covering multiple genes. By collectively analyzing this extensive data set, FUSE reduces noise and improves the accuracy of functional impact estimates for each variant. We also generated a new amino acid substitution matrix called FUNSUM, derived from high-quality, noise-free data, which helps fit expected functional effects at the residue level.
What did you find?
Our findings showed that FUSE significantly enhances the reliability of functional estimates, improves classification of pathogenic and benign variants in clinical databases such as ClinVar, and better predicts disease risk in patients with rare variants, as shown using data from Biobank of the United Kingdom.
What are the implications?
Our work has important clinical implications for patient care and precision medicine. By providing more accurate estimates of the effects of genetic variants, FUSE can help clinicians and genetic counselors better distinguish between deleterious and benign mutations, particularly those currently classified as “variations of uncertain significance” (VUS). This improved interpretation can lead to more accurate diagnoses, personalized risk assessments, and informed decision-making about prevention strategies and treatment options for patients.
Furthermore, by introducing the results of unscreened variants where some screening has already been performed nearby, FUSE addresses gaps caused by limitations in experimental analyses, expanding the range of variants that can be assessed. This means that we can provide reliable estimates of functional effects even for mutations that have not been directly tested in the laboratory.
What are the next steps?
Next steps include applying FUSE to a wider range of functional control approaches, such as emerging platforms such as basic and basic processing. We aim to further measure the strength of the clinical support provided by our imputed functional scores that have not been initially examined. By working with the broader scientific and medical communities, we hope to integrate FUSE into existing tools and databases, ultimately helping to improve patient outcomes through enhanced precision medicine.
Source:
Journal Reference:
Yu, T., et al. (2024). FUSE: Improving the estimation and imputation of variation effects in functional screening. Cellular Genomics. doi.org/10.1016/j.xgen.2024.100667.
