MAGMA- Licious, My new technique to create as many GWAS hits as you would like.

 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:

Instead of just adding the new cohorts and doing a full GWAS, Let's break them up, as if they were done separately in any kind of iteration. To start, Do each small cohort by itself and see if you get any hits. Then do all combinations of two cohorts, then three and so on, all the way up to the possible combinations of 25 cohorts each, so that we have every possible combination. Each time we do a GWAS, we will get new hits. I believe we can generate thousands of hits in this fashion and fill the GWAS Catalog with them. What a boon to science this would be. But, hey, with the computation power we now have, why stop there? Let's take all the data and make up our own new cohorts. We an then generate hundreds of thousands of new GWAS with the data we already have and no doubt create hits for just about every loci. After all, there is no particular reason other than the time sequence that we have done the GWAS to date, so we are being unfair to a lot of SNP's by being so rigid in the order we do these GWAS.