Advancing Genomic Diagnostics: A Approach to Targeting De Novo Loss-of-Function Variants

Genomic medicine has rapidly transformed the landscape of rare disease diagnosis. With the advent of large-scale sequencing projects such as the 100,000 Genomes Project (100KGP) in the UK, the potential for identifying genetic causes of rare diseases has significantly increased. However, despite these advances, challenges remain in ensuring that all potential diagnostic variants are identified, particularly when the analysis is restricted to predefined gene panels. A recent study, titled Targeting de novo loss-of-function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project, introduces a novel method aimed at addressing this issue, thereby improving diagnostic outcomes.

The Challenge of Genomic Diagnostics
The 100KGP was a initiative that integrated whole genome sequencing (WGS) into clinical practice within the UK’s National Health Service (NHS). While the project successfully diagnosed numerous patients, it relied heavily on predefined gene panels that were selected based on the patient’s phenotype. This approach, although efficient, carried the risk of missing pathogenic variants in genes that were not included in the panels. The study in question proposes an innovative solution to this problem by focusing on de novo loss-of-function (LoF) variants in highly constrained genes, which are more likely to be involved in disease.

DeNovoLOEUF: A New Diagnostic Strategy
The study introduced DeNovoLOEUF, a filtering strategy that utilizes the Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score. The LOEUF score quantifies the degree of constraint against loss-of-function mutations in genes, with lower scores indicating a higher likelihood that a LoF mutation in that gene would lead to disease. The DeNovoLOEUF method was applied to sequencing data from 13,949 rare disease trios in the 100KGP, filtering for rare, de novo LoF variants in genes with a LOEUF score below 0.2. This approach was aimed at identifying potentially pathogenic variants that might have been missed by traditional gene panel analyses.

Key Findings and Impact
The results of the study were striking. Out of 332 variants identified using the DeNovoLOEUF method, 98% were found to be diagnostic or partially diagnostic, meaning they were responsible for at least some of the patient's phenotype. The study highlighted that 39 diagnoses were missed by the standard 100KGP analysis, which are now being returned to patients. This finding underscores the potential of DeNovoLOEUF to enhance diagnostic rates and offer new insights into the genetic basis of rare diseases.

One of the significant advantages of the DeNovoLOEUF method is its high positive predictive value (PPV), which stood at 98%. This means that nearly all the variants identified by this method were genuinely linked to the disease, reducing the number of false positives that need further investigation. The method’s specificity makes it a valuable tool for clinicians and researchers, especially in large-scale genomic projects where the volume of data can be overwhelming.

Broader Applications and Future Directions
The implications of this study extend beyond the 100KGP. As WGS becomes increasingly integrated into routine clinical practice worldwide, methods like DeNovoLOEUF offer a scalable and efficient way to improve diagnostic yields. The study also explored the application of DeNovoLOEUF in non-trio data, demonstrating its utility in complex family structures and singletons, albeit with a slightly lower PPV. This broader application indicates that DeNovoLOEUF could be a powerful adjunct to current diagnostic protocols in various clinical settings.

Challenges and Limitations
While the focus of this study is on protein-coding regions, the authors acknowledge the limitations in understanding non-coding regulatory variants. These variants, which may alter gene expression without disrupting the protein sequence, are harder to detect and interpret. The authors suggest that future research will need to explore these regions with even larger datasets and improved functional annotations.

Conclusion
The study on DeNovoLOEUF represents a significant advancement in the field of genomic diagnostics. By targeting de novo loss-of-function variants in constrained genes, this method offers a highly specific and efficient way to improve diagnostic rates, particularly in large-scale projects like the 100KGP. As genomic medicine continues to evolve, tools like DeNovoLOEUF will be crucial in unlocking the full potential of WGS, ultimately leading to better outcomes for patients with rare diseases.

Reference:
Seaby, E.G., Thomas, N.S., Webb, A. et al. Targeting de novo loss-of-function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project. Hum Genet 142, 351–362 (2023).