In pharmacogenomics studies, the analysis of gene-gene interactions increasingly plays a crucial role in characterizing a phenotypic trait that involves complex pharmacological mechanisms. Genetic association studies using individual genes may overlook the significant associations which can only be identified when the combinations of multiple genomic loci are employed. Indeed, by employing the candidate-gene approach and the genome-wide association study (GWAS) design, accumulating evidence reveals that certain genetic variants could affect clinical drug response and side effects for antipsychotic drugs in patients with schizophrenia, when gene-gene interactions are taken into account. This is particularly the case for schizophrenia therapeutics wherein pharmacogenomics has been intensively applied in the past two decades. The mainstay treatment for schizophrenia, a devastating disorder that affects nearly 1% of the world population, is the antipsychotic medications. Some of the most notable side effects of antipsychotic therapy include metabolic syndrome, obesity, tardive dyskinesia, restless legs syndrome, drug-induced parkinsonism, and drug-induced QT prolongation. This paper provides a synthesis of the findings from candidate gene studies of antipsychotics, with special emphasis on the importance of gene-gene interactions. For example, the interactions of two insulin-induced genes, designated INSIG1 and INSIG2, were found to be associated with metabolic syndrome in schizophrenic patients treated with antipsychotics. Furthermore, we summarize the pharmacogenomics studies with the GWAS approach for antipsychotic therapy. To this end, we discuss the recent advances in GWAS and gene-gene interaction studies conducted in Asia-Pacific populations by the public and private sectors. Finally, we address the strategic directions and challenges concerning pharmacogenomics studies of antipsychotics drawing from our experience both in the Asia-Pacific and internationally.
Keywords: Antipsychotic drug, Asia-Pacific, gene, –, gene interaction, genome-wide association study, metabolic syndrome, personalized medicine, single nucleotide polymorphisms
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