> > Yes, pre-Ioannidis is the period I was referring to. Things cleaned up a lot in 2012+. What you said does not conflict with what I said.. > A candidate-gen

> > Yes, pre-Ioannidis is the period I was referring to. Things cleaned up a lot in 2012+. What you said does not conflict with what I said.. > A candidate-gene study != GWAS. It's particularly bizarre to criticize GWASes for the sins of candidate-gene study when GWASes were literally partially designed to avoid those problems. Obviously. I don't understand why you are referring to candidate-gene studies, as I haven't mentioned them at all. Are you saying that GWASs did not exist prior to 2012? Are you saying that GWASs somehow have not had to develop more rigorous analyses, change their standards for publication, or discussed and improved replication techniques in recent years? That somehow false positives, FDR, and FWER are of no concern in any published GWAS study today? That early GWA studies didn't struggle with effect sizes and power or that there aren't plenty of examples of published GWA studies that are underpowered or even which published spurious results? That there aren't fundamental questions about the assumptions underlying most GWAS analyses (eg potential epistasic effects vs common GWAS assumptions of SNP independence / additive effects... which is a big question in the field currently). I'm talking about GWA studies. And I'm just pointing out well-known issues the field has wrestled with. For example, here are a couple of the early, fairly influential papers overviewing issues with GWAS (not candidate-gene studies): 2008.. http://jama.jamanetwork.com/article.aspx?articleid=181647 http://jama.jamanetwork.com/article.aspx?articleid=181647 "GWA studies are an important advance in discovering genetic variants influencing disease but also have important limitations, including their potential for false-positive..." This and this paper, 2009: https://projecteuclid.org/download/pdfview_1/euclid.ss/1271770349 https://projecteuclid.org/download/pdfview_1/euclid.ss/12717... both helped move the field forward in more rigorously examining analysis and defining how replication could be achieved. These are helpful papers which moved the field forward substantially and didn't just point out issues then-currently facing the field, but also pointed out solutions (many of which were subsequently adopted). It demonstrates that GWAS has struggled with such issues. I imagine you are familiar with some of this, as you mentioned Ioannidis. So... what are you even saying here? Power, effect sizes, false positives, FDR, FWER, etc have been major issues since GWAS' early days, and despite a great deal of progress over the past few years, it's still an ongoing area of discussion, research and debate in the field. When I say caution is warranted and that we shouldn't over-generalize results, that's because the field has learned this the hard way. > GWASes were literally partially designed to avoid those problems. Yes, they were. And also, as I'm saying, they suffered from analytical and publishing issues for a number of years, despite being designed to address some of the early issues in single gene association studies. In the past few years, things have gotten much better. But there is still cause for concern and a lot of ongoing debate in the field. > Don't equivocate. If you have criticisms of actual GWASes as they are run and good reason to doubt that the hits are noise and will not replicate in well-powered followups (contrary to what we actually see, in this GWAS and others...), give them; don't swap in criticisms of candidate-gene studies and pretend they're the same thing, because they're not. I'm in no way equivocating, and you are the individual here discussing candidate-gene studies, not me. I have no idea why you're referencing them when I'm talking specifically about GWA studies. GWA studies, currently, today, as of right now... still have issues with FDR and FWER at a base level, despite improvements in analytical rigor. With study power. With changing data collection techniques and quality control. With population sample bias. With concern for heterogeneity when applied to difficult-to-definitively-diagnose or subjectively defined traits (such as mental health diseases) in particular. With myriad issues. It's still a relatively nascent, immature field and it is absolutely silly and unhelpful to dismiss a call for caution in interpretation, especially in the light of public press which is historically prone to jump to conclusions, under-report detail, under-qualify nuance, and overstate results. If you want some recent examples of this debate in the field regarding GWAS, not candidate-gene studies, see a number of articles from even the past few years, including the following couple examples: 2013: http://www.nature.com/nrg/journal/v14/n7/pdf/nrg3457.pdf http://www.nature.com/nrg/journal/v14/n7/pdf/nrg3457.pdf This is also in Nature.. and it was published because the editors in the field thought it held an important message for the field... despite what you seem to be saying here. 2016: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4756503/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4756503/ "Identifying disease-associated SNPs facilitates the clinically relevant task of identifying higher-risk individuals. However, the large amount of reclassification that we demonstrated in individuals initially classified as Higher Risk but later as Average Risk or Lower Risk, suggests that caution is currently warranted in basing clinical decisions on common genetic variation for many complex diseases." [Emphasis added] This example is very on the nose in calling for exactly the same sort of caution that I'm calling for here, and which you for some reason seem to be disputing the merit of. This paper specifically addresses problems that arise when different numbers of SNPs are used in calculated genetic risk scores, as well as other issues common in the field today. Now, I am not saying the GWAS are generally spurious or in any way bad science. I am saying we should treat them with caution, because it is a nascent field. Because there are a lot of underlying analytical assumptions and heavy analytical techniques involved in deriving results. Because the field has already seen a lot of flux over its short lifespan. Because there has been some incidence of spurious publication in the past. Because there are active and ongoing debates in the field about quality control, replication, analytical technique, etc. Because there are issues with population sampling. Because pathways aren't well-understood in many cases. And because, as I initially said, it's easy to get false positives here. It's hard to get appropriate power with a high degree of quality control. It's hard to get fully independent replication data of appropriate power. The field has challenges (against which brilliant and valiant efforts have and are being made), yet those challenges and qualifying statements rarely come to the fore in the public press regarding GWA studies. Are you actually disagreeing that caution is duly warranted, or are you just picking at nits?