Dataset Information

Calling Star Alleles With Stargazer in 28 Pharmacogenes With Whole Genome Sequences.

ABSTRACT: Variation in the enzymatic activity of pharmacogenes is defined by star alleles (haplotypes) comprised of single-nucleotide variants, small insertion-deletions, and large structural variants. We recently developed Stargazer, a next-generation sequencing-based tool to call star alleles for the clinically important CYP2D6 gene. Here, we present the utility of extending Stargazer to call star alleles for 28 pharmacogenes using whole genome sequencing (WGS) data. We applied Stargazer to WGS data from 70 ethnically diverse samples from the Genetic Testing Reference Materials Coordination Program (GeT-RM). These reference samples were extensively characterized by GeT-RM using multiple pharmacogenetic testing assays. In all 28 genes, Stargazer recalled 100% of star alleles (N = 92) present in GeT-RM's consensus genotypes (N = 1,559). Stargazer also detected star alleles not previously reported by GeT-RM, including complex structural variants. Our results demonstrate that combining WGS data and Stargazer enables automated, accurate, and comprehensive genotyping of pharmacogenes in the human genome.

SUBMITTER: Lee SB

PROVIDER: S-EPMC6896231 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Calling Star Alleles With Stargazer in 28 Pharmacogenes With Whole Genome Sequences.

Lee Seung-Been SB Wheeler Marsha M MM Thummel Kenneth E KE Nickerson Deborah A DA

Clinical pharmacology and therapeutics 20190726 6

Variation in the enzymatic activity of pharmacogenes is defined by star alleles (haplotypes) comprised of single-nucleotide variants, small insertion-deletions, and large structural variants. We recently developed Stargazer, a next-generation sequencing-based tool to call star alleles for the clinically important CYP2D6 gene. Here, we present the utility of extending Stargazer to call star alleles for 28 pharmacogenes using whole genome sequencing (WGS) data. We applied Stargazer to WGS data fro ...[more]

PMID: 31206625

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Project description:BackgroundThe last two human genome assemblies have extended the previous linear golden-path paradigm of the human genome to a graph-like model to better represent regions with a high degree of structural variability. The new model offers opportunities to improve the technical validity of variant calling in whole-genome sequencing (WGS).MethodsWe developed an algorithm that analyzes the patterns of variant calls in the 178 structurally variable regions of the GRCh38 genome assembly, and infers whether a given sample is most likely to contain sequences from the primary assembly, an alternate locus, or their heterozygous combination at each of these 178 regions. We investigate 121 in-house WGS datasets that have been aligned to the GRCh37 and GRCh38 assemblies.ResultsWe show that stretches of sequences that are largely but not entirely identical between the primary assembly and an alternate locus can result in multiple variant calls against regions of the primary assembly. In WGS analysis, this results in characteristic and recognizable patterns of variant calls at positions that we term alignable scaffold-discrepant positions (ASDPs). In 121 in-house genomes, on average 51.8±3.8 of the 178 regions were found to correspond best to an alternate locus rather than the primary assembly sequence, and filtering these genomes with our algorithm led to the identification of 7863 variant calls per genome that colocalized with ASDPs. Additionally, we found that 437 of 791 genome-wide association study hits located within one of the regions corresponded to ASDPs.ConclusionsOur algorithm uses the information contained in the 178 structurally variable regions of the GRCh38 genome assembly to avoid spurious variant calls in cases where samples contain an alternate locus rather than the corresponding segment of the primary assembly. These results suggest the great potential of fully incorporating the resources of graph-like genome assemblies into variant calling, but also underscore the importance of developing computational resources that will allow a full reconstruction of the genotype in personal genomes. Our algorithm is freely available at https://github.com/charite/asdpex .

Project description:BackgroundINDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.MethodsWe characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low-quality INDELs (7% vs. 51%).ResultsSimulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (53%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (84% vs. 57%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.ConclusionsOverall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (for example, capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Dataset Information

Calling Star Alleles With Stargazer in 28 Pharmacogenes With Whole Genome Sequences.

Publications

Calling Star Alleles With Stargazer in 28 Pharmacogenes With Whole Genome Sequences.

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