The Use of Genetic Research to Justify Racism

 This is a piece I wrote about the Buffalo mass shooter, who justified his actions in part by citing genetic studies. Some of these were obvious race science, but I am more focused on the “educational attainment” genetic study he cites, that is considered respectable by the scientific community and was heavily cited by Kathryn Paige Harden in her “Genetic Lottery” book (my review of that book, here).

Dog Breed Myths

“Thus, dog breed is generally a poor predictor of individual behavior and should not be used to inform decisions relating to selection of a pet dog.”

I generally find it annoying that, failing any real evidence of genetic causes of human behavior, people (including behavioral genetic scientists), point to dog breeds to demonstrate some validity to the concept, since dog “behavior” is even more subjective than for humans and is based on the dog’s owner’s opinions, and people can be influenced by breed perceptions. Moreover, the variability in size and build of dogs could have an influence on the behavior. 

It appears, however, according to this study, that a lot of claimed breed characteristics are myths. As anyone who has owned more than one dog of the same breed can attest, dogs, like humans, have their own personalities. 

Another ADHD "Meta-Analysis" Makes Genetic Claims for the Disorder, But Shows the Opposite.

 The latest ADHD GWAS is available in pre-print:

Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains

It is now formulaic to perform a GWAS "meta-analysis," rather than independently examining a new dataset. I put meta-analysis in quotes, because this is not really even what we have, since this new data which makes up half the data in the study has not been in a previous study. As I have noted repeatedly, this is problematic and I will touch on why in this critique. Let's get to the claims. 

 The meta-analysis identified 32 lead variants (r2 < 0.1) located in 27 genome-wide significant loci (Figure 1; Table 1, locus plots in Supplementary Data 1), including 21 novel loci. No statistically significant heterogeneity was observed between cohorts 

The first question you might ask is why these 21 novel loci were not noted in the previous GWAS for ADHD? The argument is that when you increase the number of cases, working with a higher N, you are more likely to pick up smaller correlations. The problem with that argument can be seen by the fact that there were 12 loci found significant previously and now only 6 of them are still significant. If we were talking about two entirely different studies, where the larger one picked up 6 out of 12 loci from the previous study, you might make some claims of a modest success and the authors seem to imply exactly this: 

Six of the previously identified 12 loci in the ADHD2019 study14 were significant in the present study (Table 1), and the remaining six loci demonstrated P-values < 8x10-4 

The problem here is that the data from the ADHD2919 study referenced above WAS INCLUDED IN THE CURRENT STUDY. It makes up about half the data, in fact. Thus we are not talking about independent replication, which apparently was not even attempted (or at least no such results were included). If you make the argument that increasing the case numbers identifies more significant loci, then why wouldn't you expect the previous 12 loci to be confirmed? Without even considering population stratification issues, if you have 12 loci with low p values for correlation, you are bolstering the dataset. The fact that half the loci did not retain significance should sound alarm bells. 

Similarly, it is assumed that increasing case size would increase the identified h2 heritability related to genes. Let's see how that turns out:

The SNP heritability (h2 SNP) was estimated to 0.14 (s.e. = 0.01), which is lower than the previously reported h2 SNP of 0.2214. The h2 SNP for iPSYCH (h2 SNP = 0.23; s.e. = 0.01) was in line with the previous finding, but lower h2 SNP was observed for PGC (h2 SNP = 0.12; s.e. = 0.03) and deCODE (h2 SNP = 0.081; s.e. = 0.014). Between-cohort heterogeneity in h2 SNP is not unusual and has been observed for other disorders like e.g. MDD <Major Depressive Disorder>.

One interpretation of this finding, apparently not occurring to the authors, is that the positive findings they have are little more than population stratification, and even in relatively homogenous (white European) cohorts, such pop strat loses its strength from one study to the next. It is a bit amusing that the counter to this is that it was observed in MDD, circularly assuming that both are valid. In other words, getting contradictory results for other diagnoses validates that it should be expected for ADHD. They, in fact, double down on this dubious argument:

The observation that previously identified loci may not reach genome-wide significance in a subsequent larger GWAS, has also been seen for other psychiatric disorders, e.g. bipolar disorder, where eight out of 19 loci were significant in a subsequent larger study.

It's hard not to laugh, and I'll point out that the "larger" GWAS for other disorders like bipolar disorder also had this contradiction even though they were also using data from the studies that first "discovered" the loci.  

Much of the rest of the study involved "enrichment," statistics, making the argument that cognitive related genes are more common among the significant loci. This is impossible to critique without access to the methods used. However, I would ask the authors to consider whether the 6 loci that did not remain significant were claimed to be enriched in previous studies? Is this an indication for the loci being valid, or is this an indication that these enrichment statistics are misguided?

 

 

 

Genetic Studies of Schizophrenia to Date Fail to Find Anything of Substance

 This Study compiles the results of genetic studies (GWAS) for Schizophrenia to date:

What genes are differentially expressed in individuals with schizophrenia? A systematic review (Merikangas et al.)

Despite the authors' claim that the review is "promising," it provides nothing of substance. Below are some excerpts. 

First they review the problem to date:

Though there have been more than 300 studies of gene expression in schizophrenia over the past 15 years, none of the studies have yielded consistent evidence for specific genes that contribute to schizophrenia risk. The aim of this work is to conduct a systematic review and synthesis of case–control studies of genome-wide gene expression in schizophrenia. 

There have been more than three hundred studies of gene expression in schizophrenia over the past 15 years, but to date there is no consistent evidence for clearly implicated genes from these findings.

Here are some of their findings: 

Of the top 160 genes, the majority of replicated findings were inconsistent in their reported direction of effect (n = 108 genes). This finding did not appear to follow a pattern based on the origin tissue or the expression measurement technology employed 

The GBP2 gene, which appeared in five studies, was reported to be upregulated in individuals with schizophrenia in four studies and downregulated in one. Of the 21 genes reported as significant by four studies, 19 had inconsistent directions of effect. Of the 138 genes that appeared in three studies, 88 had the inconsistent direction of effect.

In other words, even when they purported to find the same gene in more than one study, it is correlated higher or lower for that gene from one study to the next, suggesting random false positive findings. 

Of the 160 genes reported as significantly differentially expressed in three or more studies, none showed associations with rare variants in the SCHEMA data after correction for multiple comparisons. 

 

This review summarizes the literature on gene expression in schizophrenia and demonstrates the surprisingly small overlap in the genes reported across studies. Only 26 studies met our a priori inclusion criteria and were described here. The results of this review were unexpected, in that few genes were found in more than three studies, and the reported direction of effect was so variable. It was hoped that gene expression would help to explain the large number of genome-wide associated variants that are not found in genes and are theorized to be regulatory. With some exceptions described below, gene expression does not implicate the same genes that have been found by GWAS, CNV studies, or exome studies via SCHEMA.

As we would expect, the genes found in this study appear to be differentially expressed in the brain when compared to other body tissues. However, there are some unexpected results; notably, differential expression in the pancreas, subcutaneous adipose tissue, whole blood, liver, and lymphocytes. It is possible that the differential expression in blood, brain, and lymphocytes is due in some part to these being the tissues assayed in the transcriptome abundance studies summarized here.