A revolution in the analysis of gene mutations

How would you summarize your research for a lay audience?

Our research introduces a new tool called FUSE (Functional Substitution Estimation), which helps scientists better understand how changes in genes affect proteins. Genetic variants can change how a protein works, potentially leading to disease. New CRISPR-based experiments can help us understand the effects of genetic variants by establishing these changes in the DNA of cells. FUSE combines data from many of these experiments to more accurately predict the effects of specific genetic changes, even for mutations that have not yet been tested. This progress could provide better evidence for understanding the impact of genetic variants, which could ultimately help doctors more effectively identify harmful mutations, leading to improved patient care and personalized treatment.

What knowledge gap does your research help to fill?

In this study, we aimed to improve the accuracy of interpretation of how genetic mutations affect protein function, which is critical to understanding disease risk. High-throughput functional screening assays, such as deep mutational scanning, provide a wealth of data on how mutations affect proteins. However, individual measurements of these assays may be noisy due to experimental variability, making it difficult to accurately estimate the effect of each variant.

What motivated you to pursue research in this field?

In recent years, there has been a significant increase in the number of studies aimed at experimentally measuring the impact of genetic variants on the human genome. These efforts have yielded hundreds of thousands of high-throughput functional assay screening results. However, each individual measurement can be affected by statistical noise and experimental variability, which can limit the precision of estimates when considered in isolation.

Motivated by this challenge, our research groups recognized the opportunity to improve the accuracy of these option impact estimates by collaboratively analyzing the vast amount of available data. By integrating results from multiple studies, we sought to reduce the noise inherent in individual experiments and increase the reliability of each estimate.

Our two laboratories have combined computational and experimental expertise to solve complex problems in genomics. This synergy allowed us to develop a new approach that uses both computational methods and experimental data to refine the results. Our goal was to create a tool that not only improves our understanding of genetic variants, 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 solve this problem, we developed FUSE. We collected and combined data from more than 100 functional experimental screening datasets covering numerous genes. By collaboratively analyzing this large 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 and denoised data, which helps to adjust for expected functional effects at the residue level.

What did you find?

Our results demonstrated that FUSE significantly increases the reliability of functional scores, improves the classification of pathogenic and benign variants in clinical databases such as ClinVar, and better predicts disease risk in patients with rare variants, as demonstrated by UK Biobank data.

What are the consequences?

Our work has significant clinical implications for patient care and precision medicine. By providing more accurate estimates of the impact of genetic variants, FUSE can help clinicians and genetic counselors better distinguish between deleterious and benign mutations, particularly those currently classified as “variants of undetermined significance” (VUS). This improved interpretation can lead to more accurate diagnosis, personalized risk assessment, and informed decisions about prevention strategies and treatment options for patients.

In addition, by introducing the consequences of untested variants where some selection has already occurred nearby, FUSE addresses gaps caused by limitations in experimental analyses, expanding the range of variants that can be evaluated. This means that we can provide reliable estimates of functional impact 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 screening approaches, such as new platforms such as basic and primary editing. We aim to further measure the strength of clinical support provided by our imputed functional measures, which were not initially validated. By collaborating with the broader scientific and medical communities, we hope to integrate FUSE into existing tools and databases, ultimately contributing to improved patient outcomes through improved precision medicine.