Harnessing the power of old data: exome sequencing reanalysis on a Manitoba cohort
| dc.contributor.author | Athey, Taryn | |
| dc.contributor.examiningcommittee | Carriles, Claudia (Biochemistry and Medical Genetics) Hu, Pingzhao (Biochemistry and Medical Genetics) Hartley, Taila (Pediatrics and Child Health) | en_US |
| dc.contributor.supervisor | Frosk, Patrick (Biochemistry and Medical Genetics) | en_US |
| dc.date.accessioned | 2020-07-14T19:18:03Z | |
| dc.date.available | 2020-07-14T19:18:03Z | |
| dc.date.copyright | 2020-07-13 | |
| dc.date.issued | 2020 | en_US |
| dc.date.submitted | 2020-07-13T13:13:49Z | en_US |
| dc.degree.discipline | Biochemistry and Medical Genetics | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Rare disorders are thought to affect 1 in 12 Canadians, yet more than 50% of these patients currently do not have a diagnosis. Undiagnosed and misdiagnosed patients with rare disorders cause a significant burden to the healthcare system and the uncertainty often greatly affects their quality of life. Exome sequencing (ES) is an unbiased genetic testing method that sequences most of the known coding regions in the genome. ES is used when targeted methods have failed to provide a diagnosis and has been found to lead to a definitive diagnosis in 25-50% of cases, depending on the criteria used for case selection. Clinical ES is limited by the bioinformatics method used to call variants, the interpretation of those variants, and the knowledge of gene-disease associations at the time of interpretation. Therefore, it is not surprising that systematic reanalysis of ES data at regular intervals has been shown to provide a diagnosis in an additional 10-15% of cases. ES reanalysis is not routinely done for Manitoba patients; for this reason, we have developed a pilot project to reanalyze the ES data for patients who previously had non-diagnostic ES. We recruited 33 participants from 25 families who have received ES that failed to provide a definitive genetic diagnosis. The raw ES data for each participant was collected from the sequencing laboratories and variants were called using the bcbio-nextgen bioinformatics pipeline. Variants were annotated using the Ensembl Variant Effect Predictor. Custom filters were used to prioritize variants for review and pathogenicity of variants was assessed using the American College of Medical Genetics guidelines. We found candidate variants in 14 (56%) of the families analyzed, including 3 strong candidate variants and 6 variants in novel genes that have not previously been associated with disease. This study suggests that reanalyzing already generated ES data is an efficient way to increase genetic diagnoses for patients in Manitoba. As well, analysis of variants in genes with unknown function may lead to future gene discovery projects, adding to our overall knowledge of human genetics. | en_US |
| dc.description.note | October 2020 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/34776 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Exome sequencing | en_US |
| dc.subject | Reanalysis | en_US |
| dc.subject | Rare disease | en_US |
| dc.subject | Genetics | en_US |
| dc.title | Harnessing the power of old data: exome sequencing reanalysis on a Manitoba cohort | en_US |
| dc.type | master thesis | en_US |
| local.subject.manitoba | yes | en_US |