Skip to main navigation menu Skip to main content Skip to site footer

Special article

Vol. 144 No. 3334 (2014)

Current challenges in handling genetic data

  • Patricia R. Blank
  • Felix Gutzwiller
DOI
https://doi.org/10.4414/smw.2014.13998
Cite this as:
Swiss Med Wkly. 2014;144:w13998
Published
10.08.2014

Summary

In no other field of biomedicine has such revolutionary change taken place in recent decades as it has in molecular genetics. The accumulated knowledge in this field will not only enable clinicians to make new treatment decisions in future, but will also help to save on healthcare costs. A positive test result will be the prerequisite for carrying out targeted drug treatment (companion diagnostics). Specific molecular diagnostics provide doctors with additional information that was not previously available, enabling them to optimise treatment accordingly. At the same time, prognostic tests mean that targeted preventive measures can be taken. Highly informative non-invasive tests will enable early detection and prevention to play a greater role. Technological breakthroughs, such as high-throughput sequencing, will lead to a flood of data in the future. The challenge lies in the quality of interpretation, which means extracting useful information for doctor and patient.

Unlike data collection, interpretation is complex and expensive: it requires a high degree of expertise and a lot of resources. At the same time, experts stress that – as well as improvements in the accuracy and speed of data analysis – defined quality criteria must be generated for reliable interpretation of results. These challenges need to be tackled so that the population can benefit to the utmost from the opportunities offered by these developments: rapidly available and informative tests for targeted therapies based on high-quality data.

References

  1. Rehm, et al. ACMG clinical laboratory standards for next generation sequencing. Genet Med. 2013;15(9):733–47.
  2. Green, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med. 2013;15(7):565–74.
  3. Eggington, et al. A comprehensive laboratory-based program for classification of variants of uncertain significance in hereditary cancer genes. Clin Genet. 2013;8.
  4. Greenbaum, et al. Genomics and privacy: implications of the new reality of closed data for the field, PLoS Comput Biol. 2011;7(12).
  5. Gymrek, et al. Identifying Personal Genomes by Surname Inference. Science. 2013;339(6117):321–4.
  6. New NIH-funded resource focuses on use of genomic variants in medical care, Press release 2013, http://www.nih.gov/news/health/sep2013/nhgri-25.htm

Most read articles by the same author(s)