%0 Journal Article %J Genome Res %D 2023 %T An efficient genotyper and star-allele caller for pharmacogenomics. %A Hari, Ananth %A Zhou, Qinghui %A Gonzaludo, Nina %A Harting, John %A Scott, Stuart A %A Xiang Qin %A Steven E Scherer %A Sahinalp, S Cenk %A Numanagić, Ibrahim %K Alleles %K Genomics %K Genotype %K High-Throughput Nucleotide Sequencing %K Humans %K Pharmacogenetics %K Polymorphism, Single Nucleotide %K Sequence Analysis, DNA %X

High-throughput sequencing provides sufficient means for determining genotypes of clinically important pharmacogenes that can be used to tailor medical decisions to individual patients. However, pharmacogene genotyping, also known as star-allele calling, is a challenging problem that requires accurate copy number calling, structural variation identification, variant calling, and phasing within each pharmacogene copy present in the sample. Here we introduce Aldy 4, a fast and efficient tool for genotyping pharmacogenes that uses combinatorial optimization for accurate star-allele calling across different sequencing technologies. Aldy 4 adds support for long reads and uses a novel phasing model and improved copy number and variant calling models. We compare Aldy 4 against the current state-of-the-art star-allele callers on a large and diverse set of samples and genes sequenced by various sequencing technologies, such as whole-genome and targeted Illumina sequencing, barcoded 10x Genomics, and Pacific Biosciences (PacBio) HiFi. We show that Aldy 4 is the most accurate star-allele caller with near-perfect accuracy in all evaluated contexts, and hope that Aldy remains an invaluable tool in the clinical toolbox even with the advent of long-read sequencing technologies.

%B Genome Res %V 33 %P 61-70 %8 2023 Jan %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/36657977?dopt=Abstract %R 10.1101/gr.277075.122 %0 Journal Article %J J Mol Diagn %D 2022 %T CYP2C8, CYP2C9, and CYP2C19 Characterization Using Next-Generation Sequencing and Haplotype Analysis: A GeT-RM Collaborative Project. %A Gaedigk, Andrea %A Boone, Erin C %A Steven E Scherer %A Lee, Seung-Been %A Numanagić, Ibrahim %A Sahinalp, Cenk %A Smith, Joshua D %A McGee, Sean %A Radhakrishnan, Aparna %A Xiang Qin %A Wang, Wendy Y %A Farrow, Emily G %A Gonzaludo, Nina %A Halpern, Aaron L %A Nickerson, Deborah A %A Miller, Neil A %A Pratt, Victoria M %A Kalman, Lisa V %K Alleles %K Cytochrome P-450 CYP2C19 %K Cytochrome P-450 CYP2C8 %K Cytochrome P-450 CYP2C9 %K Genetic Testing %K Genotype %K Haplotypes %K High-Throughput Nucleotide Sequencing %K Humans %X

Pharmacogenetic tests typically target selected sequence variants to identify haplotypes that are often defined by star (∗) allele nomenclature. Due to their design, these targeted genotyping assays are unable to detect novel variants that may change the function of the gene product and thereby affect phenotype prediction and patient care. In the current study, 137 DNA samples that were previously characterized by the Genetic Testing Reference Material (GeT-RM) program using a variety of targeted genotyping methods were recharacterized using targeted and whole genome sequencing analysis. Sequence data were analyzed using three genotype calling tools to identify star allele diplotypes for CYP2C8, CYP2C9, and CYP2C19. The genotype calls from next-generation sequencing (NGS) correlated well to those previously reported, except when novel alleles were present in a sample. Six novel alleles and 38 novel suballeles were identified in the three genes due to identification of variants not covered by targeted genotyping assays. In addition, several ambiguous genotype calls from a previous study were resolved using the NGS and/or long-read NGS data. Diplotype calls were mostly consistent between the calling algorithms, although several discrepancies were noted. This study highlights the utility of NGS for pharmacogenetic testing and demonstrates that there are many novel alleles that are yet to be discovered, even in highly characterized genes such as CYP2C9 and CYP2C19.

%B J Mol Diagn %V 24 %P 337-350 %8 2022 Apr %G eng %N 4 %1 https://www.ncbi.nlm.nih.gov/pubmed/35134542?dopt=Abstract %R 10.1016/j.jmoldx.2021.12.011 %0 Journal Article %J Nat Commun %D 2018 %T Allelic decomposition and exact genotyping of highly polymorphic and structurally variant genes. %A Numanagić, Ibrahim %A Malikić, Salem %A Ford, Michael %A Qin, Xiang %A Toji, Lorraine %A Radovich, Milan %A Skaar, Todd C %A Pratt, Victoria M %A Berger, Bonnie %A Scherer, Steve %A Sahinalp, S Cenk %K Alleles %K Chromosome Mapping %K Cytochrome P-450 CYP2D6 %K DNA Copy Number Variations %K Genome, Human %K Genotype %K Genotyping Techniques %K High-Throughput Nucleotide Sequencing %K Humans %K Isoenzymes %K Phenotype %K Polymorphism, Genetic %K Sequence Analysis, DNA %K Software %X

High-throughput sequencing provides the means to determine the allelic decomposition for any gene of interest-the number of copies and the exact sequence content of each copy of a gene. Although many clinically and functionally important genes are highly polymorphic and have undergone structural alterations, no high-throughput sequencing data analysis tool has yet been designed to effectively solve the full allelic decomposition problem. Here we introduce a combinatorial optimization framework that successfully resolves this challenging problem, including for genes with structural alterations. We provide an associated computational tool Aldy that performs allelic decomposition of highly polymorphic, multi-copy genes through using whole or targeted genome sequencing data. For a large diverse sequencing data set, Aldy identifies multiple rare and novel alleles for several important pharmacogenes, significantly improving upon the accuracy and utility of current genotyping assays. As more data sets become available, we expect Aldy to become an essential component of genotyping toolkits.

%B Nat Commun %V 9 %P 828 %8 2018 Feb 26 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/29483503?dopt=Abstract %R 10.1038/s41467-018-03273-1