The latest depression GWAS has come out. I will focus on just one result from it:
The European-only analysis identified 622 SNPs in 570 regions with a net change in the full meta-analysis of 65 (142 regions gained, 77 regions became non-significant).
It boggles the mind that hundreds of authors see this and don't realize that they are dealing with spurious correlations. This is exactly what you would expect in such a scenario. You add more data to your original dataset, and you will get more correlations, but if you are losing 77 regions when you are still using the old data, which should bolster your previous results, you have a big problem. Moreover, making hay of an overall gain is a bit silly, when you increase the N. This is exactly what you would expect if the data was spurious to begin with.
In addition, this should not be called a "meta-analysis," because there was never a GWAS done on new data before it was added to the old data. It is just an expansion of a known dataset, which is bad science for any number of reasons. About 10 years ago, the GWAS'ers stopped doing independent GWAS, because they were not getting any replications. Thus, they solved the replication crisis... They simply add to an ever-expanding, amorphous N by redoing the GWAS. Thus, you have no idea if a GWAS of the new data alone would have any replications from the previous (I'll take bets if anyone wants to challenge me).
It's also worth pointing out the misguided "enrichment analysis."
Our results confirm and extend previous findings showing the enrichment of expression signals in excitatory and inhibitory neurons.
Can you imagine discovering that 77 of the previous regions became non-significant, regions that you no doubt excitedly did an enrichment analysis on in the previous GWAS, and still thinking it's a good idea to do one for the new correlations?
Folks, you have a null result, again, and if you don't admit it, one can only speculate on whether this is a level of denial or dishonesty. I'm sorry but face the music.