Abstract
The knowledge of genetic variants in genes involved in drug metabolism may be translated into reduction of adverse drug reactions, increase of efficacy, healthcare outcomes improvement and economic benefits. Many high-throughput tools are available for the genotyping of Single Nucleotide Polymorphisms (SNPs) known to be related to drugs and xenobiotics metabolism. DMETTM platform represents an example of SNPs panel to discover biomarkers correlated to efficacy or toxicity in common and rare diseases. The difficulty in analyzing the mole of information generated by DMETTM platform led to the development and implementation of algorithms and tools for statistical and data mining analysis. These softwares allow efficient handling of the omics data to validate the explorative SNPs identified by DMET assay and to correlate them with drug efficacy, toxicity and/or cancer susceptibility. In this review we present a suite of bioinformatic frameworks for the preprocessing and analysis of DMET-SNPs data. In particular, we introduce a workflow that uses the GenoMetric Query Language, a high-level query language specifically designed for genomics, able to query public datasets (such as ENCODE, TCGA, GENCODE annotation dataset, etc.) as well as to combine them with private datasets (e.g., output from Affymetrix® DMETTM Platform).
Highlights
In the era of precision medicine, the identification of germline variants related to the inter-individual variability observed in response to the same drug represents a great opportunity for evidence-based drug prescription
The identification of polymorphic variants with impact on phenotype has evolved through three approaches: (1) the candidate gene approach, where a small number of well-known PK or PD-related markers are tested in a small sample size; (2) the genome wide association study (GWAS) approach, a hypothesis-free method performed on large populations where high numbers of markers are simultaneously tested, identifying only common variants but with the need of stringent statistical correction [7]; (3) the pre-defined Single Nucleotide Polymorphisms (SNPs) panel approach which includes only thousands of candidate SNPs relevant pharmacogenes with putative importance
Genetic associations identified in the initial study need to be confirmed in an additional non overlapping study samples in order to replicate the statistically correlation between the same genetic variants and the trait of interest
Summary
In the era of precision medicine, the identification of germline variants related to the inter-individual variability observed in response to the same drug represents a great opportunity for evidence-based drug prescription. The identification of polymorphic variants with impact on phenotype has evolved through three approaches: (1) the candidate gene approach, where a small number of well-known PK or PD-related markers are tested in a small sample size; (2) the genome wide association study (GWAS) approach, a hypothesis-free method performed on large populations where high numbers of markers are simultaneously tested, identifying only common variants but with the need of stringent statistical correction [7]; (3) the pre-defined SNPs panel approach which includes only thousands of candidate SNPs relevant pharmacogenes with putative importance Advancement in technologies, such as generation sequencing (NGS), contemplate a diagnostic tool where whole genome or exome sequences are interrogated as comprehensive PGx genotyping tool in a rapid and large-scale DNA sequencing technology [8]. The latter might represent a new approach for biomarkers validation
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