Wednesday, September 30, 2020

MAGMA- Licious, My new technique to create as many GWAS hits as you would like.

 Many of the recent Meta-analysis based GWAS used a technique called MAGMA in order to generate more hits. It should come as a surprise to no one that many of these hits (I'll say, all), were false positives as they openly admit in the title of their updated version of MAGMA:

A response to Yurko et al: H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives

The first thing that won't happen based on this, is a reassessment of the studies that used the previous version of MAGMA to increase the number of hits, which they now know for sure had a lot of false positives. It occurred to me, though, that if they really want more GWAS hits, I think I can deliver for them with my new technique called MAGMA-licious. We can, I believe, create as many hits as we want using this technique. It works a little like the Meta-analyses that are now commonly used in GWAS. As I have mentioned in many previous posts, these meta-analyses take new cohorts and simply add them to the old ones, to get a higher N and more hits. The beauty is that they get to keep the hits that previously reached significance, but did not quite make it when new data was added. As we know, the GWAS catalog never gives up on a hit once it's indelibly recorded.

So using this same idea, let's say that your meta-analysis now has 50 cohorts over 5 studies. Clearly, if we had not done GWAS on the cohorts in the order that we did, we would have found entirely different hits than we did, even if we end up with the same hits when the entire set is combined.

This is where MAGMA-Licious comes in. It works like this:

Sunday, September 20, 2020

GWAS Meta-analyis for Bipolar Disorder Gives Glowing Analysis, but is impossible to Interpret (Again)

 A brief review of this GWAS for Bipolar Disorder:

Genome-wide association study of over 40,000 bipolar disorder cases provides novel biological insights (Mullins et al. )

Like almost all the behavioral genetic GWAS studies, this one uses a meta-analysis, despite having new data added to previous data and the new data was never assessed (at least in print) independently. Thus, it is difficult to assess statistically what is success and what is failure, although it is filled with the usual accolades:

This GWAS provides the best-powered BD polygenic scores to date, when applied in both European and diverse ancestry samples. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

 Well, the best and the only, really. But, of course, I have a lot of questions. The first is related to their significant loci count, and for which I needed partial clarification from one of the authors, as I will discuss after the fold (click "read more" to continue).

Saturday, September 12, 2020

Weekend at Bernie's for Behavioral Genetics

Here is what I think is an attempt by Paige Harden at a behavioral genetics pivot:

“Reports of My Death Were Greatly Exaggerated”: Behavior Genetics in Postgenomic Era

On the contrary, I'd say that this is an attempt to prop up a corpse. The piece starts by basically burying "candidate gene" studies, which were the previous propped up corpse they spent a couple of decades convincing us was proof of genetic correlations for behavior (and personality and intelligence). Well, no self-reflection about the fact that something you were sure about for so long turned out to be nothing. It's easier to throw the past in the dustbin than consider the possibility that we are still working with dust. The candidate genes were largely killed by GWAS, which appears to have been their only useful function. We are now in the second wave of this, with GWAS and pgs largely in a death spiral, which was really not acknowledged by those in the field prior to this piece, to my knowledge. Thus, I am reporting their death, and I don't exaggerate. However, Harden does exaggerate here:

Overall, GWAS results have yielded two general lessons for psychology. First, traits of interest to psychologists are massively polygenic, meaning that they are associated with thousands upon thousands of genetic variants scattered throughout the genome, each of which has a tiny effect. This has been called the fourth law of behavior genetics (Chabris et al. 2015). Second, the aggregate predictive power of measured genetic variants, in some cases, rivals the predictive power of traditional social science variables, such as family socioeconomic status (SES) (Lee et al. 2018).