Genome-wide association studies: Progress and potential for drug discovery and development

National Center for Genome Resources, 2935 Rodeo Park Drive East, Santa Fe, New Mexico 87505, USA.
Nature Reviews Drug Discovery (Impact Factor: 41.91). 04/2008; 7(3):221-30. DOI: 10.1038/nrd2519
Source: PubMed


Although genetic studies have been critically important for the identification of therapeutic targets in Mendelian disorders, genetic approaches aiming to identify targets for common, complex diseases have traditionally had much more limited success. However, during the past year, a novel genetic approach - genome-wide association (GWA) - has demonstrated its potential to identify common genetic variants associated with complex diseases such as diabetes, inflammatory bowel disease and cancer. Here, we highlight some of these recent successes, and discuss the potential for GWA studies to identify novel therapeutic targets and genetic biomarkers that will be useful for drug discovery, patient selection and stratification in common diseases.

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Available from: Stephen F Kingsmore, Jan 04, 2014
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    • "Resources provided by HapMap can aid the implementation of genome-wide association studies (GWAS). GWAS is a powerful method used to identify complex disease associated common genetic variants by statistically analyzing the differences between sequences of normal people and patients at a whole genome level on SNP arrays [17] [19]. Moreover, The Cancer Genome Atlas (TCGA, http :/ /cancergenome .nih "
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    ABSTRACT: DNA, RNA and protein are three major kinds of biological macromolecules with up to billions of basic elements in such biological organisms as human or mouse. They function in molecular, cellular and organismal levels individually and interactively. Traditional assays on such macromolecules are largely experimentally based, which are usually time consuming and laborious. In the past few years, high-throughput technologies, such as microarray and next generation sequencing, were developed. Consequently, large genomic datasets are being generated and computational tools to analyzing these data are in urgent demand. This paper reviews several state-of-the-art high-throughput methodologies, representative projects, available databases and data analytic tools at different molecular levels. Finally, challenges and perspectives in processing biomedical big data are discussed.
    02/2015; 36(1). DOI:10.1016/j.bdr.2015.02.005
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    • "Single-nucleotide polymorphisms (SNPs) are alterations in DNA at the single base level that are the most frequent variations in human genome (Kwok et al., 1996; Collins et al., 1998). The high density and distribution of SNPs make them suitable for association studies, population genetics, and indirect diagnosis (Brookes, 1999; Kingsmore et al., 2008). Many techniques for genotyping had been designed based on polymerization such as allele-specific primers and tetraprimer amplification refractory mutation system (ARMS) polymerase chain reaction (PCR) (Ugozzoli and Wallace, 1991; Ye et al., 2001). "
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    ABSTRACT: Objectives: Genotyping of single-nucleotide polymorphisms (SNPs) has been applied in various genetic contexts. Tetra-primer amplification refractory mutation system (ARMS) polymerase chain reaction (PCR) is reported as a prominent assay for SNP genotyping. However, there were published data that may question the reliability of this method on some occasions, in addition to a laborious and time-consuming procedure of the optimization step. In the current study, a new SNP genotyping method named modified tetra-primer ARMS (MTPA) PCR was developed based on tetra-primer ARMS PCR. Design and methods: The modified method has two improvements in its instruction, including equalization of outer primer and inner primer strength by additional mismatch in outer primers, and consideration of equal annealing temperature of specific fragments more than melting temperature of primers. Advantageously, a new computer software was provided for designing primers based on novel concepts. Results: The usual tetra-primer ARMS PCR has a laborious process for optimization. In nonoptimal PCR programs, identification of the accurate genotype was found to be very difficult. However, in MTPA PCR, equalization of the amplicons and primer strength leads to increasing specificity and convenience of genotyping, which was validated by sequencing. Conclusions: In the MTPA PCR technique, a new mismatch at -2 positions of outer primers and equal annealing temperature improve the genotyping procedure. Together, the introduced method could be suggested as a powerful tool for genotyping single-nucleotide mutations and polymorphisms.
    Genetic Testing and Molecular Biomarkers 02/2015; 19(3). DOI:10.1089/gtmb.2014.0289 · 1.46 Impact Factor
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    • "Considering the limitation of current molecular data, in this study, we did not explore the molecular mechanisms underlying the adverse drug interactions of SCZ drugs. However, a large volume of genome-wide molecular neuropharmacology data, such as microarray gene expression [31] and genome-wide association studies [32], is available, and more large-scale data will be available in the near future due to the rapid advances in genome-wide technologies and strong support from pharmacology communities. Therefore, it is possible and necessary to develop novel detection methods for investigation of adverse DDIs based on the molecular data. "
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    ABSTRACT: ANTIPSYCHOTIC DRUGS ARE MEDICATIONS COMMONLY FOR SCHIZOPHRENIA (SCZ) TREATMENT, WHICH INCLUDE TWO GROUPS: typical and atypical. SCZ patients have multiple comorbidities, and the coadministration of drugs is quite common. This may result in adverse drug-drug interactions, which are events that occur when the effect of a drug is altered by the coadministration of another drug. Therefore, it is important to provide a comprehensive view of these interactions for further coadministration improvement. Here, we extracted SCZ drugs and their adverse drug interactions from the DrugBank and compiled a SCZ-specific adverse drug interaction network. This network included 28 SCZ drugs, 241 non-SCZs, and 991 interactions. By integrating the Anatomical Therapeutic Chemical (ATC) classification with the network analysis, we characterized those interactions. Our results indicated that SCZ drugs tended to have more adverse drug interactions than other drugs. Furthermore, SCZ typical drugs had significant interactions with drugs of the "alimentary tract and metabolism" category while SCZ atypical drugs had significant interactions with drugs of the categories "nervous system" and "antiinfectives for systemic uses." This study is the first to characterize the adverse drug interactions in the course of SCZ treatment and might provide useful information for the future SCZ treatment.
    09/2013; 2013(2):458989. DOI:10.1155/2013/458989
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