Archives

  • 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-06
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • 2024-04

    • Finally we expanded WGS analysis

    2018-11-15

    Finally, we expanded WGS analysis to include all human disease-causing hypoxia inducible factor annotated in the ClinVar database. This potentially represents the largest collection of genes relevant to human health and disease. As expected, expanding WGS analysis increased the number of LoF PPVs; therefore, we further restricted the WGS analysis to LoF SNVs. In total, WGS provided likely genetic cancer risk PPVs in 20.7% [95% CI: 12.6–31.1%] of non-BRCA1/2 clinic patients. A recent report using targeted gene panel testing provided similar cancer risk PPVs in 10.6% [95% CI: 6.1–16.9%] of non-BRCA1/2 patients (Kurian et al., 2014). Therefore, WGS may provide genetic risk predictions for more patients than targeted gene panel testing (p=0.048), though we expect that significant research efforts in larger patient cohorts will be needed before clinical WGS is widely adopted. We do note that restricting analyses to LoF SNVs (as for ClinVar genes) is very conservative and that such an approach would have failed to identify likely cancer risk PPVs in our initial analysis of 163 genes. Comprehensive WGS analysis methods are likely to improve as clinical WGS becomes more widely accepted, and this may require a concerted effort to integrate multi-center WGS results with detailed clinical data from large patient cohorts. There are other major challenges involved in translating WGS into the clinic, including questions of mutation penetrance, better understanding of the genes about which we understand little, and how to counsel patients who test negative or positive for an identified familial mutation in one of the less well understood genes (CHEK2, PALB2 or RAD51C are good examples of this). Recently we documented that even clinical laboratories are not interpreting the significance of variants consistently (Yorczyk et al., 2014). This increases the urgency for information sharing of variants and their classifications. The more patients we evaluate with detailed and precise family history, the better we will understand the clinical significance of individual genes and, possibly, specific gene mutations. We encourage all clinicians and researchers to use consents that facilitate wide sharing of de-identified data and to submit the results of their studies to public repositories like dbGaP. WGS reported here will be made available through the NCBI dbGaP repository. Finally, though WGS was performed in these patients, we limited our analysis to the protein-coding exome to facilitate interpretation of nonsynonymous variants. In addition to better clinical interpretation of nonsynonymous (and other) variants, technical improvements will be required to comprehensively detect variants other than SNVs. The overwhelming false-positive rate and lower quality and confidence for detecting insertions and deletions may contribute significantly to the lack of a better diagnosis rate using WGS. Though currently whole-exome sequencing may prove more cost-effective, careful consideration should be paid to understanding the cost–benefit of both methods as technologies continue to improve and sequencing costs decrease, including aspects of sequence uniformity and completeness as well as improvements in variant detection (Biesecker and Green, 2014). The results from our study highlight the ongoing discussion as to the appropriateness and use of WGS to balance our research goals to improve future patient care and risk awareness with the goal of improving our current patients\' overall health and well-being (Burke and Dimmock, 2014). Proponents of clinical WGS highlight the potential to identify not only genetic risks for the patient\'s primary diagnosis but also risks for other diseases or conditions that may benefit the patient, such as late-onset diseases or disease risk that may affect reproductive decision-making or family planning. However, contradictory opinions often revolve around the interpretation of sequence results and whether findings unrelated to the current clinical presentation should be returned to patients. While not discussed here, these questions should be addressed for WGS to have the most benefit for current and future patients.