GWASs in pharmacogenomics also present challenges. So far, most such studies have included far fewer participants (average approximately 200) than the very large sample sizes typical of GWASs of common diseases [13
]. Nevertheless, even small pharmacogenomics GWASs have been successful in identifying robust associations because reducing variability in the definition and ascertainment of exposure and outcome increases their power to detect clinically meaningful effects. Nevertheless, larger studies will be required to detect smaller or less common effects.
The absolute cost of GWASs in pharmacogenomics remains as high as in other fields, but in the context of drug RCTs and observational studies for side effects, the marginal cost of genotyping is small: as Roses recently pointed out [20
], the entire investment in DNA sample collection and genotyping up to now is only a small fraction of the currently estimated cost to develop a single drug. At present, investment in GWASs, pharmacogenomics and clinical trials of drug safety and efficacy are not coordinated. From 2001 to mid-March 2009, HuGE Navigator identified 2,967 articles on pharmacogenomics, of which only 299 (10%) were clinical trials and 12 (0.4%) were GWASs; these 12 articles accounted for just 4% of all GWASs [4
] (Figure ). A coordinated approach to GWASs in RCTs could lead to more efficient pharmacogenomic research. A first step would be to collect appropriate biological samples from all clinical trial participants and obtain their informed consent for future pharmacogenomic research studies.
Figure 1 Overlap of human genome epidemiology (HuGE), pharmacogenomics (PGx) and genome-wide association study (GWAS) literature, from 2001 to mid-March 2009. Numbers indicate the results obtained when PubMed and HuGE Navigator publication databases were searched (more ...)
Many drug adverse effects are rare, coming to light only after a drug becomes available for widespread use [21
]. Clinical trial participants are a key reference population for subsequent investigation of adverse effects in case-control GWASs. In addition, observational studies built on practice-based settings (such as the health maintenance organization (HMO) research network [22
]) can provide clues to adverse drug effects and differences in effectiveness in 'real' world settings, outside the restricted conditions of RCTs.
So far, only two GWASs have been conducted in drug clinical trials; each of these studies provides relevant insights for future research. A study of electrocardiographic abnormalities during iloperidone treatment of schizophrenia [24
] illustrated the feasibility of performing GWASs in a phase III clinical trial evaluating the efficacy, safety and tolerability of a novel drug. A report on statin-related myopathy [14
] demonstrated the efficiency of performing a nested case-control GWAS within a clinical trial.
Given their efficiency and potential for leading to useful clinical medicine and public health applications, it seems surprising that so few GWASs have been done in the field of pharmacogenomics, especially within clinical trials. The incentives for conducting such studies deserve closer evaluation. The US Food and Drug Administration has encouraged clinical trial sponsors to submit pharmacogenomic data, including GWASs, on a voluntary basis [25
]. The National Institutes of Health has recently established funding priorities and requirements for GWASs in government-funded clinical trials [26
]. In parallel, the prevailing method of reporting results - typically limited to novel 'GWAS hits' or to one candidate gene at a time - should be revisited. Recently, the investigators in the CATIE trial of antipsychotic therapy in schizophrenia demonstrated the feasibility of sharing complete pharmacogenomic study data and discussed the utility of this approach for the scientific community [27
]. Comprehensive reporting of GWAS results in standardized formats will enhance opportunities for evidence synthesis through meta-analysis [28
]. Thus, GWASs can not only identify novel associations for further study, but can help counter the selective reporting and pursuit of false positive findings that may occur when pharmacogenomic studies are limited to candidate genes [29