Abstract
Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.
Highlights
High-throughput genomics technology has made possible the era of precision medicine, an approach to healthcare that involves integrating a patient’s genetic, lifestyle, and environmental data and comparing these data to similar data collected for thousands of other individuals to predict illness and determine the best treatments
We give an overview of next-generation sequencing (NGS) technologies, bioinformatics processing of NGS data, bioinformatics approaches for identifying clinically actionable variants in sequence data, guidelines for maintaining high standards when generating genomic data for clinical use, bioinformatics infrastructures of studies aimed at implementing precision medicine, and methods for ensuring the security of genomic data
The Coriell Personalized Medicine Collaborative [66], the Clinical Pharmacogenetics Implementation Consortium [67], the Pharmacogenetics Working Group established by the Royal Dutch Association for the Advancement of Pharmacy [68], and the Evaluation of Genomic Applications in Practice and Prevention initiative sponsored by the Centers for Disease Control and Prevention [69] have independently developed similar processes for selecting candidate drugs, reviewing the published literature to identify drug-gene associations, scoring the evidence supporting associations between genetic variants and drug response, and interpreting the evidence to provide treatment guidelines
Summary
High-throughput genomics technology has made possible the era of precision medicine, an approach to healthcare that involves integrating a patient’s genetic, lifestyle, and environmental data and comparing these data to similar data collected for thousands of other individuals to predict illness and determine the best treatments. Clinical pharmacology research using electronic health record (EHR) systems has recently become feasible as EHRs have been implemented more widely [5] Studies such as the Electronic Medical Records and Genomics-Pharmacogenomics (eMERGE-PGx) project [6], GANI MED project [7], SCANB initiative [8], and Cancer 2015 study [9] have been designed to assess the value of next-generation sequencing (NGS) in healthcare. We give an overview of NGS technologies, bioinformatics processing of NGS data, bioinformatics approaches for identifying clinically actionable variants in sequence data, guidelines for maintaining high standards when generating genomic data for clinical use, bioinformatics infrastructures of studies aimed at implementing precision medicine, and methods for ensuring the security of genomic data. We discuss the need for the efficient integration of genomic information into EHRs
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