Title | Evaluating systematic reanalysis of clinical genomic data in rare disease from single center experience and literature review. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Tan, NB, Stapleton, R, Stark, Z, Delatycki, MB, Yeung, A, Hunter, MF, Amor, DJ, Brown, NJ, Stutterd, CA, McGillivray, G, Yap, P, Regan, M, Chong, B, Fernandez, MFanjul, Marum, J, Phelan, D, Pais, LS, White, SM, Lunke, S, Tan, TY |
Journal | Mol Genet Genomic Med |
Volume | 8 |
Issue | 11 |
Pagination | e1508 |
Date Published | 2020 11 |
ISSN | 2324-9269 |
Abstract | BACKGROUND: Our primary aim was to evaluate the systematic reanalysis of singleton exome sequencing (ES) data for unsolved cases referred for any indication. A secondary objective was to undertake a literature review of studies examining the reanalysis of genomic data from unsolved cases. METHODS: We examined data from 58 unsolved cases referred between June 2016 and March 2017. First reanalysis at 4-13 months after the initial report considered genes newly associated with disease since the original analysis; second reanalysis at 9-18 months considered all disease-associated genes. At 25-34 months we reviewed all cases and the strategies which solved them. RESULTS: Reanalysis of existing ES data alone at two timepoints did not yield new diagnoses. Over the same timeframe, 10 new diagnoses were obtained (17%) from additional strategies, such as microarray detection of copy number variation, repeat sequencing to improve coverage, and trio sequencing. Twenty-seven peer-reviewed articles were identified on the literature review, with a median new diagnosis rate via reanalysis of 15% and median reanalysis timeframe of 22 months. CONCLUSION: Our findings suggest that an interval of greater than 18 months from the original report may be optimal for reanalysis. We also recommend a multi-faceted strategy for cases remaining unsolved after singleton ES. |
DOI | 10.1002/mgg3.1508 |
Alternate Journal | Mol Genet Genomic Med |
PubMed ID | 32969205 |
PubMed Central ID | PMC7667328 |
Grant List | UM1 HG008900 / HG / NHGRI NIH HHS / United States |