"Income" GWAS

 Yes, it's a genetic study that tries to identify genes associated with income. Yes, these people take themselves seriously and also seem oblivious to the eugenic implications of such a study. Yes, 71 authors, all presumably with Ph.D.'s think the following is good science:

The meta-analysis across the income measures led to a substantial increase in power, which allowed us to identify 162 loci tagged by 207 lead SNPs (Fig. 2). Of these loci, 88 were newly identified compared with the previously published GWAS household income result conducted in the UKB. 

The previous GWAS found 149 loci (that's not really accurate, but by some statistical manipulation I believe also used in this this study, they went from 30 to 149). The math here leads to the fact that although 88 novel loci were identified, 75 that were previously cited in the last study are no longer significant. Thus, while tripling the dataset, they barely broke even. This is a good sign that they are looking at false positive results and that there aren't really any reliable genetic correlations for income. This is the same stunt that was pulled in the depression GWAS I discussed recently.  If you significantly increase data size, you should expect far more associations, even if these are false positives, so the fact that only a few more are seen and many are lost suggests that the new data did not have the same pop strat working for it (it was not UK Biobank data). This is really bad science. And the amount of time they spend pouring over these false positive results is embarrassing. 

Depression GWAS spinning unfortunate result.

 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. 

Eugenicists that don't use the Word

 This filth is from a well-respected scientist in the field of behavioral genetics. I won't bother naming him, but let's read between the lines of this, which has no business being published.

Polygenic genome editing in human embryos and germ cells is predicted to become feasible in the next three decades. Several recent books and academic papers have outlined the ethical concerns raised by germline genome editing and the opportunities that it may present. To date, no attempts have been made to predict the consequences of altering specific variants associated with polygenic diseases. In this Analysis, we show that polygenic genome editing could theoretically yield extreme reductions in disease susceptibility. 

When you have nothing, you can say "is predicted to become feasible in the next three decades." This is little more than a setup for the scam of embryo editing.  It's sad to me, that eugenic propaganda like this worms its way into being published in Nature as an "analysis."