Thanks for the reply, and my turn to apologize for misinterpreting your tone.

You asked initially what exactly I was saying, and here again how to square what I'm saying with my original comment. So, let me try to explain, and I would hope we're not on different sides of this as I think what I'm saying is reasonable, given the context.

I originally said: "GWA studies general should be treated with great caution. The way they work generally is based on a simple p-value test of association among outcome (in this case, depression) and all genes based on SNPs. There is a high degree of mere chance association and false positives. Most GWA studies leave a lot to be desired."

For context: genome-association studies have had a history of being blown out of proportion in the press. And often for outcomes which greatly affect people's lives. Depression is one such issue and it'd be a shame if people were led into thinking there is necessarily a great breakthrough here in understanding possible gene-linkages to depression outcomes. It'd also be a shame if the result was ignored. I tried to provide some praise to this paper for being fairly rigorous, but also note that GWAS studies should be treated with caution generally.

Why should GWAS studies be treated with caution generally? (Besides that results of any study should be treated with some degree of caution.)

Well, firstly, GWAS is a fairly nascent field. Unlike physics or even particle physics, it hasn't had that much time to mature. This is doubly so when applying GWAS to mental health. I'm sure the paper covers some of these risks (or at least it hopefully does)... but relying on self-reporting introduces potential selection bias in the population sample, as does using people seeking help vs a general population study. Quickly reading the paper, it looks like it relied on self-reports and analyzed only people who'd been diagnosed with major depression (meaning they'd sought help). We should be cautious in over-generalizing based on this.

Secondly, GWAS has had a rough history of overstating results and misapplying analyses. It's much better today than it was even, say, 5 years ago. Ioannidis and others made some heroic efforts to convince the field to clean up it's act, in effect starting ~7-8 years ago.

Thirdly, there is a historical pattern of results in this field being overhyped in the press.

Finally, there are active and sometimes heated debates in the field about how best to do GWAS. This is getting worse as a high-throughput, low-cost full-genome sequencing comes online to a greater and greater degree and SNP-based data sets fall by the wayside. Some question taking a frequentist approach at all in the face of such a huge degree of multiple testing. Others call for much, much higher requirements for holdout data sets, cross-validation, and replication before a study is published or considered final, especially when dealing with things like mental health (and their likely application to the field of pharmacology).

This is serious stuff that could end up affecting people's mental health treatments and lives, so caution is warranted. Especially given the field's relative nascence, self-admitted history of publishing low-quality results, the rapidly changing techniques, and the fact that there are ongoing debates within the field of how best to do GWAS analysis and how to effectively replicate results.