The importance of iterative analysis and reporting

Iterative analysis refers to the practice of revisiting and reanalysing patient data (both clinical and genomic data) where a disease-causing variant could not be identified initially. This is a very important part of the variant interpretation process where a genetic variant was not initially identified for a specific case.

Scientific advances that change our understanding of the clinical significance of genetic variants happen rapidly. This includes knowledge discoveries applicable to biological function, new gene-disease association discovery (especially important for conditions such as developmental disorders where gene discovery is rapid), as well as growing population databases that include more representative and diverse datasets. This is a particularly important step for those working with diverse populations where there is a lack of information. Using this new information in our quest to interpret genetic variants can lead to new discoveries and new diagnoses.

The benefit of iterative analysis is illustrated by the DDD study, a large research study focused on advancing clinical genetic practice for children with developmental disorders. This study analysed genomic data from exome sequencing for more than 1,000 children, and in 2014 reported a diagnostic yield of 27%. When the data was reanalysed in 2018 the DDD researchers were able to diagnose an additional 182 individuals, taking their overall diagnostic yield to 40%.

Iterative analysis involves several steps, including reinterpretation, which refers to the re-evaluation and possible reclassification of genetic variants regarding their pathogenicity (Figure 1). This is usually achieved by considering new or updated evidence and by using the most recent guideline frameworks. A variant can then be reclassified based on this re-evaluation. Re-sequencing or recalling variants from existing data with new bioinformatics tools are other ways that iterative analysis can be performed.

Analysis of the genomic sequence and interpretation through automation and through iterative discussions with laboratory scientists > Reporting to screening providers and possibility to participants.”> Click to enlarge

Figure 1. The iterative analysis and reporting cycle. Source: Journal of Public Health.

Although many studies have now shown that iterative analysis is beneficial, it is also clear that there are considerations for the feasibility of this practice. This is a specialist and labour-intensive task, and may not always be practical, especially in resource-constrained settings.

In the comments, tell us about your thoughts on how feasible iterative analysis would be in your setting.