Editorial: systematic reviews and meta-analyses of gene association studies

In this short note we outline some of the considerations needed for the submission of systematic reviews and meta-analyses of candidate gene association studies. These points are aimed at providing guidance to both submitters of papers and the referees whose work Biomarkers depends upon. The genomic era has produced a torrent of data which will only increase. One consequence is that systematic reviews and meta-analyses exploring associations between genetic variants or epigenetic marks with disease are also going to increase. Biomarkers will continue to publish good studies and encourages submission but is aware both of the potential volume of studies and the need for these to undergo rigorous review. Meta-analysis is a statistical method which combines the results from independent studies. It can provide a more precise estimate of an effect, weighting them by their size. The argument for meta-analyses is that they decrease random errors and increase statistical power (Trikalinos et al., 2008). The initial use of meta-analysis was for the combination of clinical trial data; it has subsequently been extend into observational studies (Egger et al., 2001) and more recently into candidate gene association (Lee, 2015) and genome-wide association studies (GWAS) (Thompson et al., 2011). The citation impact of meta-analysis of such studies is high (Patsopoulos et al., 2005). The validity of the analysis, however, relies heavily upon the quality of the systematic review which provides the data. We have previously given guidance to the considerations we expect to be taken into account in the design and statistical analyses of the studies that are published in Biomarkers (Lovell, 2012). In this short note we outline some of the considerations needed for the submission of systematic reviews and metaanalyses of candidate gene association studies. These points are aimed at providing guidance to both submitters of papers and the referees whose work Biomarkers depends upon. The first and most important point is that, surprisingly to some, the main complexity of such studies is not their statistical analysis but rather the biological thinking that goes into the framing of an appropriate question, the collation of suitable data and then, the discussion and drawing of conclusions. To the non-statistician, the statistical jargon of, for instance, fixed or random effect models, forest plots, I values and DerSimonian and Laird approaches, can be daunting and intimidating. Certainly any statistical analysis should be appropriate and correctly carried out; however, most of the analyses can be carried out using standard software. Although there may seem to be much statistical analysis in these types of paper, in practise, once the data has been accumulated in the correct way all the analyses and figures can effectively be produced using the data in a very short time using a standard set of software code. Proponents argue that systematic reviews are to be preferred to traditional narrative reviews because they are more rigorous, objective and transparent. The questions that really arise are: what was the justification for the review (i.e. why was it done) and what were the criteria used for assessing the quality of studies to be reviewed and the inclusion/exclusion criteria? Effects in genetic association studies are mostly small is size and vulnerable to biases. The potential exists, especially for genetic variants, for many systematic reviews/ meta-analysis to be carried out where there are no biological hypotheses but which would produce what looks like a very comprehensive scientific analysis. However, these will be vacuous because the justification for and the interpretation of the study is where the ‘‘innovative thinking’’ needs to be done. Quality criteria with scoring can provide a more objective approach to the selection of studies and a reduction of the risk of bias. Quality scoring is a controversial topic (Greenland, 1994; Juni et al., 2001) and there are many methods; Juni et al. noting, for instance, 25 different scales. One (widely used) method is the Newcastle-Ottawa scale which grades studies based on eight items (Downs & Black, 1998; Wells et al., 2016). The criteria for inclusion and exclusion on quality considerations should be explicit, objective and transparent. The nature of the data collection process is important. Data collected from the literature is relatively easy to obtain at a relatively low cost but is more likely to be subject to biases than that assembled through a consortium of investigators working together with appreciable standardisation of procedures such as methods of genotyping and definition of phenotypes. The latter is the preferred option of Sagoo et al. (2009) and Evangelou & Ioannidis (2013). Reproducible and independent replication of studies is increasing recognised as an important part of the scientific process. It is important to appreciate that a meta-analysis is not replication. This is especially so if the initial study that generated the interest is included. If this study was large it may also be influential in the results of the meta-analysis. There is also the danger of bias being introduced because of the ‘‘Winner’s curse’’. The systematic review/meta-analysis may be carried out because a specific study has indicated an association. In many cases studies that ‘‘flag up’’ in this way are often ‘‘exaggerations’’ or represent results at the upper end of the