Frequencies of pharmacogenomic alleles across biogeographic groups in a large-scale biobank

Pharmacogenomics (PGx) is an integral part of precision medicine and contributes to the maximization of drug efficacy and reduction of adverse drug event risk. Accurate information on PGx allele frequencies improves the implementation of PGx. Nonetheless, curating such information from published allele data is time and resource intensive. The limited number of allelic variants in most studies leads to an underestimation of certain alleles. We applied the Pharmacogenomics Clinical Annotation Tool (PharmCAT) on an integrated 200K UK Biobank genetic dataset (N = 200,044). Based on PharmCAT results, we estimated PGx frequencies (alleles, diplotypes, phenotypes, and activity scores) for 17 pharmacogenes in five biogeographic groups: European, Central/South Asian, East Asian, Afro-Caribbean, and Sub-Saharan African. PGx frequencies were distinct for each biogeographic group. Even biogeographic groups with similar proportions of phenotypes were driven by different sets of dominant PGx alleles. PharmCAT also identified "no-function" alleles that were rare or seldom tested in certain groups by previous studies, e.g., SLCO1B1∗31 in the Afro-Caribbean (3.0%) and Sub-Saharan African (3.9%) groups. Estimated PGx frequencies are disseminated via the PharmGKB (The Pharmacogenomics Knowledgebase: www.pharmgkb.org). We demonstrate that genetic biobanks such as the UK Biobank are a robust resource for estimating PGx frequencies. Improving our understanding of PGx allele and phenotype frequencies provides guidance for future PGx studies and clinical genetic test panel design, and better serves individuals from wider biogeographic backgrounds.

How to Run the Pharmacogenomics Clinical Annotation Tool (PharmCAT)

Pharmacogenomics (PGx) investigates the genetic influence on drug response and is an integral part of precision medicine. While PGx testing is becoming more common in clinical practice and may be reimbursed by Medicare/Medicaid and commercial insurance, interpreting PGx testing results for clinical decision support is still a challenge. The Pharmacogenomics Clinical Annotation Tool (PharmCAT) has been designed to tackle the need for transparent, automatic interpretations of patient genetic data. PharmCAT incorporates a patient's genotypes, annotates PGx information (allele, genotype, and phenotype), and generates a report with PGx guideline recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and/or the Dutch Pharmacogenetics Working Group (DPWG). PharmCAT has introduced new features in the last 2 years, including a variant call format (VCF) Preprocessor, the inclusion of DPWG guidelines, and functionalities for PGx research. For example, researchers can use the VCF Preprocessor to prepare biobank-scale data for PharmCAT. In addition, PharmCAT enables the assessment of novel partial and combination alleles that are composed of known PGx variants and can call CYP2D6 genotypes based on single and deletions in the input VCF file. This tutorial provides materials and detailed step-by-step instructions for how to use PharmCAT in a versatile way that can be tailored to users' individual needs.

Pharmacogenomics Clinical Annotation Tool (PharmCAT)

Pharmacogenomics (PGx) decision support and return of results is an active area of precision medicine. One challenge of implementing PGx is extracting genomic variants and assigning haplotypes in order to apply prescribing recommendations and information from the Clinical Pharmacogenetics Implementation Consortium (CPIC), the US Food and Drug Administration (FDA), the Pharmacogenomics Knowledgebase (PharmGKB), etc. Pharmacogenomics Clinical Annotation Tool (PharmCAT) (i) extracts variants specified in guidelines from a genetic data set derived from sequencing or genotyping technologies, (ii) infers haplotypes and diplotypes, and (iii) generates a report containing genotype/diplotype-based annotations and guideline recommendations. We describe PharmCAT and a pilot validation project comparing results for 1000 Genomes Project sequences of Coriell samples with corresponding Genetic Testing Reference Materials Coordination Program (GeT-RM) sample characterization. PharmCAT was highly concordant with the GeT-RM data. PharmCAT is available in GitHub to evaluate, test, and report results back to the community. As precision medicine becomes more prevalent, our ability to consistently, accurately, and clearly define and report PGx annotations and prescribing recommendations is critical.

Phased whole-genome genetic risk in a family quartet using a major allele reference sequence

Whole-genome sequencing harbors unprecedented potential for characterization of individual and family genetic variation. Here, we develop a novel synthetic human reference sequence that is ethnically concordant and use it for the analysis of genomes from a nuclear family with history of familial thrombophilia. We demonstrate that the use of the major allele reference sequence results in improved genotype accuracy for disease-associated variant loci. We infer recombination sites to the lowest median resolution demonstrated to date (<1,000 base pairs). We use family inheritance state analysis to control sequencing error and inform family-wide haplotype phasing, allowing quantification of genome-wide compound heterozygosity. We develop a sequence-based methodology for Human Leukocyte Antigen typing that contributes to disease risk prediction. Finally, we advance methods for analysis of disease and pharmacogenomic risk across the coding and non-coding genome that incorporate phased variant data. We show these methods are capable of identifying multigenic risk for inherited thrombophilia and informing the appropriate pharmacological therapy. These ethnicity-specific, family-based approaches to interpretation of genetic variation are emblematic of the next generation of genetic risk assessment using whole-genome sequencing.

An individual's genetic profile plays an important role in determining risk for disease and response to medical therapy. The development of technologies that facilitate rapid whole-genome sequencing will provide unprecedented power in the estimation of disease risk. Here we develop methods to characterize genetic determinants of disease risk and response to medical therapy in a nuclear family of four, leveraging population genetic profiles from recent large scale sequencing projects. We identify the way in which genetic information flows through the family to identify sequencing errors and inheritance patterns of genes contributing to disease risk. In doing so we identify genetic risk factors associated with an inherited predisposition to blood clot formation and response to blood thinning medications. We find that this aligns precisely with the most significant disease to occur to date in the family, namely pulmonary embolism, a blood clot in the lung. These ethnicity-specific, family-based approaches to interpretation of individual genetic profiles are emblematic of the next generation of genetic risk assessment using whole-genome sequencing.

Clinical assessment incorporating a personal genome

BACKGROUND

The cost of genomic information has fallen steeply, but the clinical translation of genetic risk estimates remains unclear. We aimed to undertake an integrated analysis of a complete human genome in a clinical context.

METHODS

We assessed a patient with a family history of vascular disease and early sudden death. Clinical assessment included analysis of this patient's full genome sequence, risk prediction for coronary artery disease, screening for causes of sudden cardiac death, and genetic counselling. Genetic analysis included the development of novel methods for the integration of whole genome and clinical risk. Disease and risk analysis focused on prediction of genetic risk of variants associated with mendelian disease, recognised drug responses, and pathogenicity for novel variants. We queried disease-specific mutation databases and pharmacogenomics databases to identify genes and mutations with known associations with disease and drug response. We estimated post-test probabilities of disease by applying likelihood ratios derived from integration of multiple common variants to age-appropriate and sex-appropriate pre-test probabilities. We also accounted for gene-environment interactions and conditionally dependent risks.

FINDINGS

Analysis of 2.6 million single nucleotide polymorphisms and 752 copy number variations showed increased genetic risk for myocardial infarction, type 2 diabetes, and some cancers. We discovered rare variants in three genes that are clinically associated with sudden cardiac death-TMEM43, DSP, and MYBPC3. A variant in LPA was consistent with a family history of coronary artery disease. The patient had a heterozygous null mutation in CYP2C19 suggesting probable clopidogrel resistance, several variants associated with a positive response to lipid-lowering therapy, and variants in CYP4F2 and VKORC1 that suggest he might have a low initial dosing requirement for warfarin. Many variants of uncertain importance were reported.

INTERPRETATION

Although challenges remain, our results suggest that whole-genome sequencing can yield useful and clinically relevant information for individual patients.

FUNDING

National Institute of General Medical Sciences; National Heart, Lung And Blood Institute; National Human Genome Research Institute; Howard Hughes Medical Institute; National Library of Medicine, Lucile Packard Foundation for Children's Health; Hewlett Packard Foundation; Breetwor Family Foundation.

An XML-based interchange format for genotype-phenotype data

Recent advances in high-throughput genotyping and phenotyping have accelerated the creation of pharmacogenomic data. Consequently, the community requires standard formats to exchange large amounts of diverse information. To facilitate the transfer of pharmacogenomics data between databases and analysis packages, we have created a standard XML (eXtensible Markup Language) schema that describes both genotype and phenotype data as well as associated metadata. The schema accommodates information regarding genes, drugs, diseases, experimental methods, genomic/RNA/protein sequences, subjects, subject groups, and literature. The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB; www.pharmgkb.org) has used this XML schema for more than 5 years to accept and process submissions containing more than 1,814,139 SNPs on 20,797 subjects using 8,975 assays. Although developed in the context of pharmacogenomics, the schema is of general utility for exchange of genotype and phenotype data. We have written syntactic and semantic validators to check documents using this format. The schema and code for validation is available to the community at http://www.pharmgkb.org/schema/index.html (last accessed: 8 October 2007).

The pharmacogenetics and pharmacogenomics knowledge base: accentuating the knowledge

PharmGKB is a knowledge base that captures the relationships between drugs, diseases/phenotypes and genes involved in pharmacokinetics (PK) and pharmacodynamics (PD). This information includes literature annotations, primary data sets, PK and PD pathways, and expert-generated summaries of PK/PD relationships between drugs, diseases/phenotypes and genes. PharmGKB's website is designed to effectively disseminate knowledge to meet the needs of our users. PharmGKB currently has literature annotations documenting the relationship of over 500 drugs, 450 diseases and 600 variant genes. In order to meet the needs of whole genome studies, PharmGKB has added new functionalities, including browsing the variant display by chromosome and cytogenetic locations, allowing the user to view variants not located within a gene. We have developed new infrastructure for handling whole genome data, including increased methods for quality control and tools for comparison across other data sources, such as dbSNP, JSNP and HapMap data. PharmGKB has also added functionality to accept, store, display and query high throughput SNP array data. These changes allow us to capture more structured information on phenotypes for better cataloging and comparison of data. PharmGKB is available at www.pharmgkb.org.

Integrating large-scale genotype and phenotype data

With the completion of the Human Genome Project, a new emphasis is focusing on the sequence variation and the resulting phenotype. The number of data available from genomic studies addressing this relationship is rapidly growing. In order to analyze these data as a whole, they need to be integrated, aggregated and annotated in a timely manner. The Pharmacogenetics and Pharmacogenomics Knowledge Base PharmGKB; (<www.pharmgkb.org>) assembles and disseminates these data and their associated metadata that are needed for unambiguous identification and replication. Assembling these data in a timely manner is challenging, and the scalability of these data produce major challenges for a knowledge base such as PharmGKB. However, it is only through rapid global meta-annotation of these data that we will understand the relationship between specific genotype(s) and the related phenotype. PharmGKB has confronted these challenges, and these experiences and solutions can benefit all genome communities.

A resource to acquire and summarize pharmacogenetics knowledge in the literature

To determine how genetic variations contribute the variations in drug response, we need to know the genes that are related to drugs of interest. But there are no publicly available data-bases of known gene-drug relationships, and it is time-consuming to search the literature for this information. We have developed a resource to support the storage, summarization, and dissemination of key gene-drug interactions of relevance to pharmacogenetics. Extracting all gene-drug relationships from the literature is a daunting task, so we distributed a tool to acquire this knowledge from the scientific community. We also developed a categorization scheme to classify gene-drug relationships according to the type of pharmacogenetic evidence that supports them. Our resource (http://www.pharmgkb.org/home/project-community.jsp) can be queried by gene or drug, and it summarizes gene-drug relationships, categories of evidence, and supporting literature. This resource is growing, containing entries for 138 genes and 215 drugs of pharmacogenetics significance, and is a core component of PharmGKB, a pharmacogenetics knowledge base (http://www.pharmgkb.org).

A personalized and automated dbSNP surveillance system

The development of high throughput techniques and large-scale studies in the biological sciences has given rise to an explosive growth in both the volume and types of data available to researchers. A surveillance system that monitors data repositories and reports changes helps manage the data overload. We developed a dbSNP surveillance system (URL: http://www.pharmgkb.org/do/serve?id=tools.surveillance.dbsnp) that performs surveillance on the dbSNP database and alerts users to new information. The system is notable because it is personalized and fully automated. Each registered user has a list of genes to follow and receives notification of new entries concerning these genes. The system integrates data from dbSNP, LocusLink, PharmGKB, and Genbank to position SNPs on reference sequences and classify SNPs into categories such as synonymous and non-synonymous SNPs. The system uses data warehousing, object model-based data integration, object-oriented programming, and a platform-neutral data access mechanism.