Difference between revisions of "NGS"
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==Challenges== | ==Challenges== | ||
| − | * | + | * Bad coverage of pharmacogenes. Notably, the lack of intronic variants in WES. |
| − | * | + | * Haplotype calling is challenging due to short reads. NGS requires ''in silico'' haplotype estimation. Since GATK 3.3, the [https://software.broadinstitute.org/gatk/documentation/tooldocs/3.8-0/org_broadinstitute_gatk_tools_walkers_haplotypecaller_HaplotypeCaller.php Haplotype Caller] assemble contigs of 300 base pairs, but it does not consider the phasing of the reads. Haplotype phasing can be obtained by Trio analysis, or estimated by imputation methods [http://faculty.washington.edu/browning/beagle/beagle.html Beagle] or [http://dx.doi.org/10.1038/ng.3679 Eagle2] or [https://doi.org/10.1371/journal.pgen.1004234 SHAPEIT]. Other possible options at Oslo University Hospital are long read technology from [https://www.sequencing.uio.no/services/pacbio.html PacBio] or synthetic long reads from [https://www.sequencing.uio.no/news/2019/10x%20Genomics%20is%20here%21 10x Genomics] |
| − | * HLA-typing require special software | + | * Difficult variant calling in homologous regions, such as CYP2D6. I.e. regions with copy number variations (CNV) or pseudogenes. The CYP2D6 genotyping tool that was used by [https://github.com/PharmGKB/PharmCAT/wiki PharmCAT] is [https://www.nature.com/articles/npjgenmed201639 Astrolabe]. We intend to use the more recent program [https://github.com/inumanag/aldy Aldy]. |
| + | * HLA-typing require special software. There are many options. [https://doi.org/10.1002/cpt.411 Yang et al.] proposed to use [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). [https://doi.org/10.1101/356204 Reisberg et al.] proposed [https://doi.org/10.1371/journal.pone.0064683 SNP2HLA] for WGS. The candidate that we are investigating closer is [https://github.com/humanlongevity/HLA xHLA]. | ||
| + | * New variants are discovered, and needs to be [[gene function|functionally assessed]]. | ||
==Solutions== | ==Solutions== | ||
| − | [[PGx in Estonia| | + | [[PGx in Estonia|A solution for genotyping biobank data]] has been investigated 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]. |
| − | == | + | An excellent solution for genotyping a collection of ADME (absorption, distribution, metabolism and elimination) genes for individual patients is Aldy, which is fast, accurate and relatively simple to use. |
| + | |||
| + | ==Interesting publications== | ||
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| University of Washington || [https://doi.org/10.1097/FPC.0000000000000202 PGRNseq: A Targeted Capture Sequencing Panel for Pharmacogenetic Research and Implementation] || Targeted PGx panel with 84 genes | | University of Washington || [https://doi.org/10.1097/FPC.0000000000000202 PGRNseq: A Targeted Capture Sequencing Panel for Pharmacogenetic Research and Implementation] || Targeted PGx panel with 84 genes | ||
|- | |- | ||
| − | | University of Tartu || [https://doi.org/10.1101/356204 Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions] || Well-described pipeline for PGx on biobank data | + | | University of Tartu || [https://doi.org/10.1101/356204 Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions] || Well-described pipeline for PGx on biobank data. WES cannot be used for PGx (important variants missing, imputation and CNV calling difficult) |
|} | |} | ||
Latest revision as of 08:51, 27 February 2019
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
- Bad coverage of pharmacogenes. Notably, the lack of intronic variants in WES.
- Haplotype calling is challenging due to short reads. NGS requires in silico haplotype estimation. Since GATK 3.3, the Haplotype Caller assemble contigs of 300 base pairs, but it does not consider the phasing of the reads. Haplotype phasing can be obtained by Trio analysis, or estimated by imputation methods Beagle or Eagle2 or SHAPEIT. Other possible options at Oslo University Hospital are long read technology from PacBio or synthetic long reads from 10x Genomics
- Difficult variant calling in homologous regions, such as CYP2D6. I.e. regions with copy number variations (CNV) or pseudogenes. The CYP2D6 genotyping tool that was used by PharmCAT is Astrolabe. We intend to use the more recent program Aldy.
- HLA-typing require special software. There are many options. Yang et al. proposed to use Polysolver for whole exome sequencing (WES) or OptiType for whole genome sequencing (WGS). Reisberg et al. proposed SNP2HLA for WGS. The candidate that we are investigating closer is xHLA.
- New variants are discovered, and needs to be functionally assessed.
Solutions
A solution for genotyping biobank data has been investigated by Reisberg et al. in their article Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions.
An excellent solution for genotyping a collection of ADME (absorption, distribution, metabolism and elimination) genes for individual patients is Aldy, which is fast, accurate and relatively simple to use.
Interesting 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. WES cannot be used for PGx (important variants missing, imputation and CNV calling difficult) |
