Many of the recent Meta-analysis based GWAS used a technique called MAGMA in order to generate more hits. It should come as a surprise to no one that many of these hits (I'll say, all), were false positives as they openly admit in the title of their updated version of MAGMA:
A response to Yurko et al: H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives
The first thing that won't happen based on this, is a reassessment of the studies that used the previous version of MAGMA to increase the number of hits, which they now know for sure had a lot of false positives. It occurred to me, though, that if they really want more GWAS hits, I think I can deliver for them with my new technique called MAGMA-licious. We can, I believe, create as many hits as we want using this technique. It works a little like the Meta-analyses that are now commonly used in GWAS. As I have mentioned in many previous posts, these meta-analyses take new cohorts and simply add them to the old ones, to get a higher N and more hits. The beauty is that they get to keep the hits that previously reached significance, but did not quite make it when new data was added. As we know, the GWAS catalog never gives up on a hit once it's indelibly recorded.
So using this same idea, let's say that your meta-analysis now has 50 cohorts over 5 studies. Clearly, if we had not done GWAS on the cohorts in the order that we did, we would have found entirely different hits than we did, even if we end up with the same hits when the entire set is combined.
This is where MAGMA-Licious comes in. It works like this: