Genome-wide studies in pharmacogenomics: harnessing the power of extreme phenotypes

Abstract

PharmacogenomicsVol. 14, No. 4 Special Focus Issue: Genome-wide association studies in pharmacogenomics - ForewordFree AccessGenome-wide studies in pharmacogenomics: harnessing the power of extreme phenotypesDavid Gurwitz & Howard L McLeodDavid Gurwitz* Author for correspondenceDepartment of Human Molecular Genetics & Biochemistry, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel. Search for more papers by this authorEmail the corresponding author at gurwitz@post.tau.ac.il & Howard L McLeod* Author for correspondenceInstitute for Pharmacogenomics & Individualized Therapy, University of North Carolina, Chapel Hill, NC, USA. Search for more papers by this authorEmail the corresponding author at hmcleod@email.unc.eduPublished Online:25 Feb 2013https://doi.org/10.2217/pgs.13.35AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit Keywords: extreme phenotypesgenome-wide pharmacogenomic studiesmetabolomepersonal genomestranscriptomicsFour years ago this journal featured a theme issue on genome-wide association studies (GWAS) in pharmacogenomics. In our accompanying editorial we noted that “relatively few GWAS on drug response were so far published compared with the large numbers of disease risk GWAS” and called upon the pharmacogenomics research community to apply this powerful, hypothesis-free research approach for improving knowledge on drug safety and efficacy [1]. Our call seems to have been timely, as the past few years have witnessed an increase in pharmacogenomics-oriented genome-wide studies. Among PubMed-listed pharmacogenomics-related manuscripts, the fraction of those mentioning both ‘pharmacogenomics’ and ‘genome-wide’ as keywords have risen fourfold over the past few years, from 2.1% in 2006 to nearly 9% of total manuscripts published between 2009 and 2012 (Table 1). However, as in past years, only a tiny fraction (under 2%) of published genome-wide studies (in all areas) are concerned with pharmacogenomics (Table 1), a disappointingly low figure considering the huge societal burden of adverse drug events and ineffective therapeutics [2].Some of the recent pharmacogenomic genome-wide studies yielded novel insights. The genome-wide studies by Ge et al. on IL28B polymorphisms and antihepatitis C drug response [3], Daly et al. on HLA-B*5701 and flucloxacillin-induced liver injury [4], Baldwin et al. on paclitaxel-induced sensory peripheral neuropathy [5], and Brown et al. on temozolomide-associated cytotoxicity [6] have advanced our understanding of DNA sequence variations and drug response, and several pharmacogenomic tests are on their way to clinical practice [7]. The studies on toxic drug reactions in particular have extra power compared with typical GWAS. For example, the study by Daly et al. on flucloxacillin-induced liver injury, an extreme and rare drug response phenotype, included only 51 affected individuals and 282 controls, yet it reported the unprecedented odds risk ratio of 80.6 (p = 8.7 × 10-33 for carriers of HLA-B*5701) [4], while typical GWAS odds risk ratios are below 2.We propose that the extreme phenotypes approach should be prioritized for genome-wide pharmacogenomic studies, including those exploring genomic biomarkers for treatment efficacy as an alternative to enrolling very large random patient cohorts. This is based on both the likelihood of an enrichment for genomic signal due to the ‘Mendelian’ nature of the phenotype, the favorable study costs and reduced ‘noise’ arising from inaccurate phenotyping. Taken together, it seems far more effective to compare genome-wide data between ‘excellent drug responders’ versus nonresponders, compared with analyzing such data from huge patient cohorts representing a large spectrum of intermediate responders.The superior power of the extreme phenotype approach is exemplified by the recent study of Edmond et al., who identified DCTN4 as a modifier of chronic infection in cystic fibrosis among 91 individuals whose exomes were sequenced [8]. As a further example, expression levels of CHL1 were recently reported as tentative selective serotonin reuptake inhibitor (SSRI) antidepressants response biomarker, based on genome-wide transcriptomic profiling of lymphoblastoid cell lines representing merely 14 healthy unrelated individuals, seven from each phenotypic extreme of in vitro SSRI response [9]. Of note, CHL1 was subsequently reported as associated with SSRI-related dizziness based on genome-wide SNP arrays in a 100-fold larger cohort (n = 1432) of major depression patients [10]. The advantages of applying the extreme phenotypes approach for cancer research were pointed out over a decade ago [11], and the same considerations seem to apply for all areas of population genomics research. Indeed, recent numerical modeling studies demonstrated the superior power of sampling extreme phenotypes individuals compared with random cohorts [12,13].The majority of recently published pharmacogenomic research (over 90%) is still hypothesis driven (Table 1). This may in part reflect higher costs for array- or sequencing-based genome-wide studies, combined with the tendency to recruit larger patient cohorts, which often necessitates large consortia efforts. The first obstacle is diminishing thanks to reduced costs for DNA and RNA sequencing: prices of whole-genome sequencing are forecasted to decrease below US$1000 in a few years. Yet, personal genomes, that is, interpreted whole-genome sequences, are likely to remain far more costly than US$1000 owing to the associated bioinformatics costs, so that pharmacogenomics-oriented full-genome sequencing may not be around the corner. The second obstacle may be circumvented by applying more stringent patient phenotyping [14] and utilizing the extreme drug response phenotype approach, as noted above: excellent responders versus toxic responders for toxicity studies, and excellent responders versus nonresponders for efficacy studies. Obtaining more accurate drug response phenotype data may be assisted by longitudinal electronic health records [15], and also by detailed metabolome profiling of patients, which may provide further insights into disease subtypes and their drug response associations [16–18].Pharmacogenomic studies are shifting from the use of SNP arrays to transcriptomics (expression arrays or RNA sequencing), epigenomics and eventually to personal genomes [19,20]. Regardless of the genomic tools selected for a particular project, accurate patient phenotyping will always remain the most crucial part of projects aimed at gaining insights on genomic markers for drug safety and efficacy for the individual patient. Combining precise patient phenotyping with extreme phenotype sampling will further facilitate pharmacogenomic research.Table 1. PubMed-listed manuscripts including the terms ‘genome-wide’ and/or ‘pharmacogenomics’.TermYear 20052006200720082009201020112012Genome-wide (n)362267165906706711777127841338314412Pharmacogenomics (n)18012600250525992619227225222980Genome-wide and pharmacogenomics (n)485572112166199217238Pharmacogenomics and genome-wide/total genome-wide (%)1.30.821.21.61.41.61.61.6Pharmacogenomics and genome-wide/total pharmacogenomics (%)2.62.12.94.36.38.88.68.0Values are based on a PubMed search (7 February 2013) using the search terms ‘genome-wide’ and ‘pharmacogenomics’. For ‘pharmacogenomics’, PubMed includes manuscripts using the term ‘pharmacogenetics’ in the abstract or keywords. Of note, among the 238 manuscripts listed in PubMed in 2012 including both the terms ‘genome-wide’ and ‘pharmacogenomics’, only approximately one-third represent original research while the majority are reviews and commentaries. Only a minority of the 238 manuscripts are novel genome-wide studies (in others, the term ‘genome-wide’ was mentioned for other reasons, such as, reason for studying certain genes or suggestions for future research).Financial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.References1 Gurwitz D, McLeod HL. Genome-wide association studies: powerful tools for improving drug safety and efficacy. Pharmacogenomics10,157–159 (2009).Link, Google Scholar2 He YJ, McLeod HL. Ready when you are: easing into preemptive pharmacogenetics. Clin. Pharmacol. 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This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.PDF download