Human Mendelian disease in Saudi Arabia is both significant and challenging.
Next-generation sequencing (NGS) has resulted in important discoveries of the genetic
variants responsible for inherited disease. However, the success of clinical genomics
using NGS requires accurate and consistent identification of rare genome variants.
Rarity is one very important criterion for pathogenicity. Here we describe a model to
detect variants by analyzing allele frequencies of a Saudi population. This work will
enhance the opportunity to improve variant calling workflow to gain robust frequency
estimates in order to better detect rare and unusual variants which are frequently
associated with inherited disease.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/662641 |
| Date | 26 April 2020 |
| Creators | Alsaedi, Sakhaa |
| Contributors | Hoehndorf, Robert, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Gao, Xin, Gojobori, Takashi |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | 2021-04-20, At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2021-04-20. |
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