In 2006, the Human Genome Epidemiology Network (HuGENet) envisioned the development of ‘field synopses’ whereby succinct summaries of the evidence from genetic epidemiology in a particular field were produced by large consortia and published in peer-reviewed journals [Ioannidis et al. (2006) Nat Genet 38(1):3-5]. A second meeting in 2008 further defined a field synopsis as “a regularly updated snapshot of the current state of knowledge about genetic associations in a particular field of research defined by a disease, phenotype, or family of genes”. Ideally, field synopses [Khoury et al. (2009) Am J Epidemiol 170:269-279]:
1) are freely available to all;
2) incorporate the use of online databases curated by researchers linking genetic variants with the evidence of association;
3) use objective and transparent criteria for grading the credibility of the cumulative evidence;
4) summarise the information from the peer-reviewed literature; and
5) update the information on a regular basis.
The first field synopsis published – and source of the AlzGene Alzheimer’s disease genetic association database – was developed by Bertram et al. and is an excellent example [Bertram et al. (2007) Nat Genet 39(1)17-23].
A new field synopsis by Holmes et al. in the Journal PLoS ONE reviews the field of pharmacogenetics to quantify the scope and quality of available evidence to inform future research [Holmes et al. (2009) PLoS ONE 4(12):e7960.doi:10.1371/journal.pone.0007960]. The authors searched the Medline database for pharmacogenetic studies published before 2008, defining them as studies “in which the response (intended outcome/adverse reaction) to drug therapy was examined in relation to genetic variation (germline/somatic) in humans”. Having initially identified over 100,000 articles with their search (the large majority being reviews and commentaries), a total of 1,668 fulfilled all the inclusion criteria. The authors then extracted data from the full text of a randomly selected 10% (161) of these articles, with data being extracted from just the abstracts of the remaining articles.
Most articles reported results from prospective studies based in Europe or North America and largely focused on common diseases such as cancer and cardiovascular disease, as well as neurological and psychiatric disorders. Over 500 genes were investigated with 10 of these genes included in studies with over 10,000 participants. Thirty-one meta-analyses of 29 genes were identified, and a further 107 genes were identified as being the subject of at least four studies but lacking a meta-analysis. Despite some well conducted, high-quality research, small sample size was generally a problem showing little evidence of increase over time. Other problems with the literature included the use of surrogate (as opposed to more clinically relevant) outcomes, subgroup analyses with multiple hypothesis testing (possibly leading to lack of reporting for those hypotheses not reaching statistical significance), and highlighted a lack of research in other areas and populations. In almost 75% of cases, the reviewed studies provide nominally significant results (suggesting not all findings are real), although the authors do not focus on any actual meta-analysis results.
Comment: This field synopsis highlights a problem of large numbers of opinion-based articles such as reviews and commentaries over actual primary research studies. This has possibly contributed to the high expectation for the delivery of personalised medicine with little actual ‘delivery’ so far. The authors highlight current parallels with the problems faced by the field of common disease genetics a decade ago. At that time, large efforts were made to collate the evidence in a systematic and comprehensive manner, undertake large collaborative projects of primary research as well as conducting large meta-analyses, encourage greater transparency in reporting including publishing null findings, and place a greater importance on independent replication. Recent improvements in technology have also undoubtedly advanced research with genome-wide association studies producing more robust data. In addition to implementing these improvements in future pharmacogenetic study design, online databases like AlzGene provide continuously updated evidence snapshots to be available allowing researchers to focus on the knowledge gaps that emerge. By utilising all these advances and improvements in the gene-disease association field, researchers can develop the field of pharmacogenetics and help it to realise the goal of personalised medicine, although if ten years behind common complex disease genetics, there is still some way to go.