Difference between revisions of "NGS"
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* Variants in homologous regions are hard to capture. Notably, the genes CYP2D6 and CYP2A6 are challenging. | * Variants in homologous regions are hard to capture. Notably, the genes CYP2D6 and CYP2A6 are challenging. | ||
* HLA-typing require special software, e.g. [https://software.broadinstitute.org/cancer/cga/polysolver Polysolver] for whole exome sequencing (WES) or [https://github.com/FRED-2/OptiType OptiType] for whole genome sequencing (WGS) | * HLA-typing require special software, e.g. [https://software.broadinstitute.org/cancer/cga/polysolver Polysolver] for whole exome sequencing (WES) or [https://github.com/FRED-2/OptiType OptiType] for whole genome sequencing (WGS) | ||
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+ | ==Solutions== | ||
+ | [[PGx in Estonia|Solutions for PGx on NGS data]] are given by ''Reisberg et al.'' in their article [https://doi.org/10.1101/356204 Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions]. | ||
==Publications== | ==Publications== |
Revision as of 08:47, 23 August 2018
Next Generation Sequencing (NGS) is an interesting technology for PGx
A nice overview of the Requirements for comprehensive pharmacogenetic genotyping platforms was published by Volker Lauschke et al. They claim that rare variants account for 30-40% of functional variability in PGx. However they argue that pre-emptive PGx should only include validated variants, and rare variants should be investigated only when the patient experience unexpected drug response.
Challenges
- CNV and other haplotype calling is challenging due to short read. NGS requires in silico CNV and haplotype estimation. Haplotype calling can e.g. be performed by Beagle.
- Variants in homologous regions are hard to capture. Notably, the genes CYP2D6 and CYP2A6 are challenging.
- HLA-typing require special software, e.g. Polysolver for whole exome sequencing (WES) or OptiType for whole genome sequencing (WGS)
Solutions
Solutions for PGx on NGS data are given by Reisberg et al. in their article Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions.
Publications
Institution | Article | Comments |
---|---|---|
St. Jude Children’s Research Hospital | Comparison of Genome Sequencing and Clinical Genotyping for Pharmacogenes | WES and WGS can be used for PGx with in silico CNV calling and HLA calling |
National Human Genome Research Institute | Assessing the capability of massively parallel sequencing for opportunistic pharmacogenetic screening | Suggests developing tools for PGx based on WES and WGS |
University of Washington | PGRNseq: A Targeted Capture Sequencing Panel for Pharmacogenetic Research and Implementation | Targeted PGx panel with 84 genes |
University of Tartu | Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions | Well-described pipeline for PGx on biobank data |