Another GWAS meta-analysis that suggests replications when the opposite is the case

"Since the discovery of general cognitive ability (or ‘g’) in 1904..."  (When I read a sentence like this, I am already a bit leery of what will come next.)

This critique is for the following study: 

Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function.  Gail Davies, et al.

This study is a meta-analysis of several studies I have already critiqued, here, here and here, as well as some new cohorts.  If I understand correctly, the new ones are the CHARGE and COGENT cohorts, but  in any case, it does not appear that any of the new datasets were ever studied independently related to cognitive ability and were simply added to the N of the meta-analyis.  I have a problem with this, which hopefully will become obvious as you read this critique. 

GWAS/Meta-Analysis 78,308 (Sniekers): A critique of this study claiming numerous genetic correlations to intelligence

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence

 I had someone cite this study as strong evidence for specific genetic linkages to intelligence.  I'm getting a lot of, "A lot has changed in the field lately," types of comments, which are maybe a bit condescending, but not entirely off base.  To be honest, I'm surprised at how little change there has been since I was critiquing these studies 15 to 20 years ago.  They still jump to the same conclusions.  They still have no sense that their studies are producing false positives.  Some of the terminology has changed and, really, they are a bit overly technical and harder to read (or I'm just getting older).  Despite the fact that I disagree with the conclusions of this study, as I will lay out shortly, there is something to be said for dumbing down your points a bit, unless your goal is a bit of technical obfuscation (not accusing, just saying...).

Publishing Bias, but look a little closer

Over the past few decades, hundreds of "genetic links" have been "found", largely through genome-wide association studies, in which the researchers look at hundreds of different genetic loci to find 1 or 2 that are more highly correlated in their study group vs. their control group, whether this be for intelligence, schizophrenia, "novelty-seeking", etc.  As I've already mentioned, these studies tend to crank out a lot of false positives, which even the scientific community is coming to terms with, as can be seen here.  So, since almost all of the hundreds of alleged genetic linkages have not been replicated, we might expect to see a lot of studies in the literature that give negative results, refuting the previous positive results.   In actuality, although we occasionally do see that, it is the exception rather than the rule.

Meta-Analysis: Making your bad results good for fun and profit.

As mentioned in my last post, genome-wide association studies generated many positive "correlations" for genetic loci related to a particular disorder, that could not be consistently replicated.  This should have tipped them off that they were dealing with false positives.  It didn't, though.  They were determined to turn their lemons into lemonade.  They often did this by using meta-analysis.  The idea behind meta-analysis is that you combine the results of numerous studies, effectively increasing your sample size to give you a more accurate big picture of all the results.  I won't say that this approach is never useful in science, but in these instances, it was nothing more than an attempt to get a positive result.  There are two common approaches to using meta-analysis to bolster your negative results.  The first is focusing on a single genetic polymorphism.  Those performing the meta-analysis would focus their attention on a particular genetic locus for a particular disorder using all the published studies (worth noting that there may have been other studies, more likely with a negative result, that didn't get published).  Here is why you are going to create a false positive result: