A key challenge for any meta-analysis is to avoid selection biases. Biases arise if only some data are available for inclusion in the meta-analysis calculations and availability is dependent on the nature of the results. Publication bias, selective outcome and analysis reporting bias are extensively discussed in the traditional meta-analysis literature [12], but the ways that they may operate in GWA studies and whether GWA studies may have some particular immunity to them is poorly understood. In theory, a major advantage of GWA studies is their agnostic approach which allows comprehensive coverage of the genome [13]. However, if one were to simply increase the number of analyses, but still focus on making available only the most favourable results, selection bias could be detrimental. Conversely, if GWA studies are coupled with full availability of all the produced data and accompanying analyses, selection biases would be minimized. Consortia may have full availability of the entire datasets across all their participating teams and can also benefit from the implementation of common, standardized (or at least harmonized) approaches to data collection, definitions, measurements, and
