Cas9 targeted nanopore sequencing with enhanced variant calling improves CYP2D6-CYP2D7 hybrid allele genotyping
CYP2D6 is a very important pharmacogene as it is responsible for the metabolization or bioactivation of 20 to 30% of the clinically used drugs. However, despite its relatively small length of only 4.4 kb, it is one of the most challenging pharmacogenes to genotype due to the high similarity with its...
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Veröffentlicht in: | PLoS genetics 2022-09, Vol.18 (9), p.e1010176-e1010176 |
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Zusammenfassung: | CYP2D6 is a very important pharmacogene as it is responsible for the metabolization or bioactivation of 20 to 30% of the clinically used drugs. However, despite its relatively small length of only 4.4 kb, it is one of the most challenging pharmacogenes to genotype due to the high similarity with its neighboring pseudogenes and the frequent occurrence of CYP2D6-CYP2D7 hybrids. Unfortunately, most current genotyping methods are therefore not able to correctly determine the complete CYP2D6-CYP2D7 sequence. Therefore, we developed a genotyping assay to generate complete allele-specific consensus sequences of complex regions by optimizing the PCR-free nanopore Cas9-targeted sequencing (nCATS) method combined with adaptive sequencing, and developing a new comprehensive long read genotyping (CoLoRGen) pipeline. The CoLoRGen pipeline first generates consensus sequences of both alleles and subsequently determines both large structural and small variants to ultimately assign the correct star-alleles. In reference samples, our genotyping assay confirms the presence of CYP2D6-CYP2D7 large structural variants, single nucleotide variants (SNVs), and small insertions and deletions (INDELs) that go undetected by most current assays. Moreover, our results provide direct evidence that the CYP2D6 genotype of the NA12878 DNA should be updated to include the CYP2D6-CYP2D7 *68 hybrid and several additional single nucleotide variants compared to existing references. Ultimately, the nCATS-CoLoRGen genotyping assay additionally allows for more accurate gene function predictions by enabling the possibility to detect and phase de novo mutations in addition to known large structural and small variants. |
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ISSN: | 1553-7404 1553-7390 1553-7404 |
DOI: | 10.1371/journal.pgen.1010176 |