Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study.

PURPOSE: The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping. METHODS: Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response-related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record. RESULTS: For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping. CONCLUSION: Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources.

Correction to: Recommendations for performance optimizations when using GATK3.8 and GATK4.

Following publication of the original article [1], the author explained that Table 2 is displayed incorrectly. The correct Table 2 is given below. The original article has been corrected.

Recommendations for performance optimizations when using GATK3.8 and GATK4.

BACKGROUND: Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. RESULTS: We re-evaluated multiple options, such as threading, parallel garbage collection, I/O options and data-level parallelization. Additionally, we considered the trade-offs of using GATK3.8 and GATK4. We found optimized parameter values that reduce the time of executing the best practices variant calling procedure by 29.3% for GATK3.8 and 16.9% for GATK4. Further speedups can be accomplished by splitting data for parallel analysis, resulting in run time of only a few hours on whole human genome sequenced to the depth of 20X, for both versions of GATK. Nonetheless, GATK4 is already much more cost-effective than GATK3.8. Thanks to significant rewrites of the algorithms, the same analysis can be run largely in a single-threaded fashion, allowing users to process multiple samples on the same CPU. CONCLUSIONS: In time-sensitive situations, when a patient has a critical or rapidly developing condition, it is useful to minimize the time to process a single sample. In such cases we recommend using GATK3.8 by splitting the sample into chunks and computing across multiple nodes. The resultant walltime will be nnn.4 hours at the cost of $41.60 on 4 c5.18xlarge instances of Amazon Cloud. For cost-effectiveness of routine analyses or for large population studies, it is useful to maximize the number of samples processed per unit time. Thus we recommend GATK4, running multiple samples on one node. The total walltime will be ∼34.1 hours on 40 samples, with 1.18 samples processed per hour at the cost of $2.60 per sample on c5.18xlarge instance of Amazon Cloud.

Sentieon DNASeq Variant Calling Workflow Demonstrates Strong Computational Performance and Accuracy.

As reliable, efficient genome sequencing becomes ubiquitous, the need for similarly reliable and efficient variant calling becomes increasingly important. The Genome Analysis Toolkit (GATK), maintained by the Broad Institute, is currently the widely accepted standard for variant calling software. However, alternative solutions may provide faster variant calling without sacrificing accuracy. One such alternative is Sentieon DNASeq, a toolkit analogous to GATK but built on a highly optimized backend. We conducted an independent evaluation of the DNASeq single-sample variant calling pipeline in comparison to that of GATK. Our results support the near-identical accuracy of the two software packages, showcase optimal scalability and great speed from Sentieon, and describe computational performance considerations for the deployment of DNASeq.

Empowering Mayo Clinic Individualized Medicine with Genomic Data Warehousing.

Individualized medicine enables better diagnoses and treatment decisions for patients and promotes research in understanding the molecular underpinnings of disease. Linking individual patient's genomic and molecular information with their clinical phenotypes is crucial to these efforts. To address this need, the Center for Individualized Medicine at Mayo Clinic has implemented a genomic data warehouse and a workflow management system to bring data from institutional electronic health records and genomic sequencing data from both clinical and research bioinformatics sources into the warehouse. The system is the foundation for Mayo Clinic to build a suite of tools and interfaces to support various clinical and research use cases. The genomic data warehouse is positioned to play a key role in enhancing the research capabilities and advancing individualized patient care at Mayo Clinic.

Thiopurine S-methyltransferase pharmacogenetics: variant allele functional and comparative genomics.

Thiopurine S-methyltransferase (TPMT) catalyses the S-methylation of thiopurine drugs. Genetic polymorphisms for TPMT are a major factor responsible for large individual variations in thiopurine toxicity and therapeutic effect. The present study investigated the functional effects of human TPMT variant alleles that alter the encoded amino acid sequence of the enzyme, TPMT*2, *3A, *3B, *3C and *5 to *13. After expression in COS-1 cells and correction for transfection efficiency, allozymes encoded by these alleles displayed levels of activity that varied from virtually undetectable (*3A,*3B and *5) to 98% (*7) of that observed for the wild-type allele. Although some allozymes had significant elevations in apparent Km values for 6-mercaptopurine and S-adenosyl-L-methionine (i.e. the two cosubstrates for the reaction), the level of enzyme protein was the major factor responsible for variation in activity. Quantitative Western blot analysis demonstrated that the level of enzyme protein correlated closely with level of activity for all allozymes except TPMT*5. Furthermore, protein levels correlated with rates of TPMT degradation. TPMT amino acid sequences were then determined for 16 non-human mammalian species and those sequences (plus seven reported previously, including two nonmammalian vertebrate species) were used to determine amino acid sequence conservation. Most human TPMT variant allozymes had alterations of residues that were highly conserved during vertebrate evolution. Finally, a human TPMT homology structural model was created on the basis of a Pseudomonas structure (the only TPMT structure solved to this time), and the model was used to infer the functional consequences of variant allozyme amino acid sequence alterations. These studies indicate that a common mechanism responsible for alterations in the activity of variant TPMT allozymes involves alteration in the level of enzyme protein due, at least in part, to accelerated degradation.

Designing and implementing special-purpose databases: lessons from the pharmacogenetic network.

The Pharmacogenetics Research Network, which has the long-term goal of genotype-phenotype correlation related to pharmacotherapy, mandates timely electronic publication of results by participating research groups through submission to PharmGKB, the consortium's repository database. Because informatics expertise across groups varies, many groups need help in managing their own data and in generating electronic submissions. To assist these operations, we perform a needs assessment to determine an optimum database implementation strategy, which varies from standalone microcomputer database application to Web-based solutions, depending on the group and problem scope. Solution implementation is coupled with transfer of expertise through hands-on training, so as to reduce the groups' long-term dependence on us. Where multiple groups face common problems, such as managing genotyping data or clinical study support, we have devised generic software that can be reused in its entirety by individual groups, or customized with modest effort.