Within Family Studies not Finding What They are Claiming

 More evidence that genome wide association studies (GWAS) find nothing but pop strat and noise. This study debunks the idea that you can look exclusively at family members to assess differences in genetic variation and definitively validate genetic correlations:

Interpreting population and family-based genome-wide association studies in the presence of confounding

The idea is that, since we are looking at family members (brothers, parent/sibling, etc.) and determining whether the relatives with a particular trait have higher polygenic scores (have more of the genetic variants correlated to a trait) versus those who do not have the trait, that will demonstrate that these genetic variations contribute to the person having the trait. 

One recent example is the so-called “Educational Attainment” GWAS. Before doing a within family analysis, they claimed that they found genetic variants that explained 13% of the genetic variance. When they did a within family analysis, this figure dropped down to 2 to 3% (the study does not provide an actual figure and the authors did not provide one at my request. This is an estimate I received from an expert in the field). Rather than focusing on the fact that the 13%  figure was demonstrably bloated, they pivoted to claiming that the 2 to 3% figure proved there was at least “some” genetic contribution to educational attainment. I think that this study suggests, though, that even this small percentage is possibly little more than pop strat and noise. 

After years of these studies, they have nothing at all to show for it. They nonetheless write books and advance careers making these spurious claims. The idea that “educational attainment” is genetic is harmful. It is irresponsible to continue making these claims and it is time to address the likelihood that “behavioral” traits do not have a significant genetic component.

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.

New Video on Psychiatric Twin Studies

 A second video with Jay Joseph, this time talking about his specialty: twin studies. We focus specifically on psychiatric twin studies and discuss heritability claims. 

New YouTube Channel with Jay Joseph

 Psychologist, author and twin study skeptic Jay Joseph and I are collaborating on a YouTube channel called “Genetic Illusions.” Our first video challenges claims of a genetic mechanism for schizophrenia.

Polygenic Risk Score is Absolutely Useless for Predicting Schizophrenia

 This Study  used the best polygenic risk score (PRS) to try and predict schizophrenia for a large group of individuals. Let's just cut to the chase, here:

For all outcomes investigated, the SCZ PRS did not improve the performance of predictive models, an observation that was generally robust to divergent case ascertainment strategies and the ancestral background of the study participants.

 At this point, it is denial to believe that PRS is ever going to have any real use for schizophrenia or other classified mental disorders. The reason for this is that these traits are not related to genetic variants. The entire premise of PRS is a flawed idea for behavioral genetics. Let me add that if you let your diagnosis be influenced by polygenic scores (which you shouldn't based on this study, but you know how these go), then you will create a self-fulfilling prophecy of PRS predicting schizophrenia. 

My Review of Kathryn Paige Harden's "The Genetic Lottery "

Every few years, the scant evidence for genetic determinism will be promoted and sold in book form. In 2018, it was Robert Plomin’s “Blueprint.”  The latest comes from psychologist and behavior geneticist, Kathryn Paige Harden, with her new book: “The Genetic Lottery - Why DNA Matters for Social Equality.” From the title alone, one can see that she will be selling a version of genetic determinism with a heart. To her credit, in contrast to Plomin, Harden addresses the ramifications of behavioral genetics’ historical association with eugenics in some detail, but her book is otherwise similar in substance to Blueprint (despite her own negative review of Blueprint). Both books spin polygenic scores as a savior for the failing field of behavioral genetics, with the dubious suggestion that these results are “causal.”  

In Plomin’s case, his fanaticism for polygenic scores could be written off as wishful thinking for a man at the end of his career, touting a perceived future of ever improving polygenic prediction. Harden, on the other hand, has had a few years to see the hype dwindle, with study after study noting the limitations of such scores. 

Harden’s primary focus is what is referred to as “educational attainment,” basically a simple measurement of how far someone goes in school, viewing it as a trait with some genetic basis. In truth, this “trait” is a bit of subterfuge, serving as a proxy for intelligence, while avoiding some of the controversy surrounding genetic studies of IQ (and their association with books like Charles Murray’s, “The Bell Curve”). 

Harden's writing style at times involves condescending oversimplification through analogy: “If a gene is a recipe, then your genome -  all the DNA contained in all of your cells - is a large collection of recipes, an enormous cookbook.” This quaint presentation of the subject suggests that she is targeting a lay audience, but I question whether those not already familiar with this kind of research would find this book engaging and these analogies do not appear to clarify the subject in a more comprehensible manner.

Books of this nature generally have the same two issues to tackle and Harden’s is no exception. The first is to sell the scientific evidence related to claims of a genetic basis for educational attainment and other behavioral traits. The second relates to the ethical and practical implications of this research. I will address her treatment of both issues here, beginning with the latter.

The “Genetics” of Schizophrenia

 I have several posts on here related to schizophrenia and the claimed genetics of this psychiatric disorder and I thought it would be useful to combine twin studies, genetic studies, and polygenic scores  in one post to give some perspective. Let me start with twin studies:

During medical school and my psychiatry residency training, the genetic nature of schizophrenia was often emphasized. The strongest evidence, we were informed, was that if an identical twin was diagnosed with schizophrenia, there was a 50% chance that the other identical twin would be diagnosed with schizophrenia. While this might leave some wondering why the other 50% do not get schizophrenia with an identical genetic profile, 50% is hard to just ignore.  Well, as it turns out, it isn’t really accurate. Most of the studies that claimed such high concordance rates are from well over a half century ago. At that time, there was far more institutionalization and the diagnostic criteria were not really the same. As twin study critic Jay Joseph points out, if you take more modern studies the concordance rates are far lower, with an overall concordance rate for such studies after 1963 of 23% (some might recall a figure of 28%, but this does not include the Finnish study noted below, which seems to have been “disappeared”). 

Depending on which of these studies you examine, that 23% figure might even be a bit inflated. Take, for example, a couple of the Scandinavian studies and although I may be accused of cherry-picking, these countries tend to have better national records to draw from, which include all twin pairs and all twin pairs that are concordant for schizophrenia, so I think they arguably are more accurate. I have recently posted on these studies. One is a Finnish study from 1984 and one is a more recent Danish study from 2018. I discussed them both at more length in previous blog posts, here and here. These studies both had a full twin registry available to them and were thus able to identify all individuals diagnosed with schizophrenia, as well as any which had a monozygotic or dizygotic twin and whether they were also diagnosed with schizophrenia. The Finnish study found an 11% concordance and the Danish study found a 14.8% concordance if you look at the actual numbers. These are shocking numbers for those of us who were led to believe that it was closer to 50%. 

Another Schizophrenia Twin Study That Only Looks Good When You Skim The Results

 In medical school, my Psychiatry Residency, and even the Psychiatry Board Exams, the concordance rate noted for Schizophrenia was 50%. The assumption here is that if one identical twin was diagnosed with schizophrenia, then the other one had a 50% chance of also being diagnosed with schizophrenia (I am told this is still the conventional wisdom). This is an impressive number, even if it doesn't explain why the other 50%, also genetically identical to their schizophrenic sibling is not also diagnosed with schizophrenia. Well, it appears this is far from accurate, as I've discovered when looking at the actual studies, which we admittedly rarely did in our training, as we filled our heads with the "facts" we needed to pass our training and board certification. It appears to be a bit of statistical sleight of hand. [Hat tip to Jay Joseph (blog linked in my blog roll) for looking at this study a bit past the abstract]:

Heritability of Schizophrenia and Schizophrenia Spectrum Based on the Nationwide Danish Twin Register

If one peruses the abstract of this study, you are met with this:

The probandwise concordance rate of SZ is 33% in monozygotic twins and 7% in dizygotic twins. We estimated the heritability of SZ to be 79%. 

Does that mean that if your identical twin has schizophrenia, you have a 33% chance of having schizophrenia? No, I don't think it does. Does it mean that you have a 79% chance with that stated heritability? No, it doesn't, either.

In fact, based on this study, if one identical twin is diagnosed with schizophrenia, the other was diagnosed with schizophrenia only 14.8% of the time. While that's higher than you would expect at random, it is a far cry from what you might think if you skim the study and feels a bit deceptive, really. So let's see where they come up with their figures.

Old Schizophrenia Twin Study That Tells a Different Story

 This study of Finnish Twins is originally from 1984:

Psychiatric Hospitalization in Twins

I think it makes some interesting points and I'm surprised I hadn't seen it before (Hat tip to Jay Joseph). Throughout my residency, I was told that there was a 50% concordance rate for schizophrenia among identical twins. I don't recall this study ever being referenced. It used hospitalization records and seems to have found a much lower concordance rate:

Pairwise concordance rates for schizophrenia (11.0% for MZ and 1.8% for DZ) seem to indicate great environmental influence (high proportion of discordant pairs) with apparent genetic liability (6.1-fold ratio in concordance between MZ and DZ pairs).

That's a surprisingly low figure. Perhaps because they used hospitalization records rather than interviews there was less bias or perhaps one twin wasn't hospitalized when the other was. 

Of course, one might jump on the fact that at least the concordance rate is significantly higher for MZ than DZ, even if not impressive. It's worth pointing out, though, that since doctors are regularly trained to take a family history and are more likely to diagnose someone with schizophrenia if they have a close relative with that diagnosis, that there is potential for inflation. 

I think such inflation would favor MZ twins in particular and this is an impressive point in the article:

Of the MZ pairs concordant for psychiatric hospitalization, 47% had lived together for their whole life time; of those discordant, 16% lived together. The corresponding figures for DZ pairs were 18% and 15%.

It is interesting that the MZ twins who lived together were more frequently diagnosed concordantly with schizophrenia, while not true of DZ twins. I am extrapolating, here, but it also appears that MZ twins are more likely to live together than DZ twins, which suggests some bonding that again brings into question the idea that MZ twins and DZ twins can be compared in this way (for more, see Jay Joseph's work on the EEA). 

Interesting Study Related to Cognitive Decline from Schizophrenia

 This study assessed whether cognitive decline from Schizophrenia has a genetic component. 

Schizophrenia polygenic risk predicts general cognitive deficit, but not cognitive decline in healthy older adults

In the early years of psychiatry, Schizophrenia was called "Dementia Praecox," a term coined by Emil Kraepelin, that described the deterioration of cognition associated with schizophrenia more so than the symptoms we normally associate with the disorder. From Wiki:

Dementia praecox (a "premature dementia" or "precocious madness") is a disused psychiatric diagnosis that originally designated a chronic, deteriorating psychotic disorder characterized by rapid cognitive disintegration, usually beginning in the late teens or early adulthood.

This term is no longer used, but the concept behind it is still accepted, that there is a progressive dementia among schizophrenic patients. The idea behind this study is that, assuming the polygenic model of schizophrenia holds true, if someone is not schizophrenic, but has a high polygenic score for schizophrenia (has a lot of the identified variants), then one might expect them to have some cognitive decline. That was not the case, as the study points out:

These results do not support the neo-Kraepelinian notion of schizophrenia as a genetically determined progressively deteriorating brain disease.

I think what this suggests is that schizophrenia, itself, is the cause of the cognitive deterioration, rather than the other way around.  Moreover, it challenges the polygenic model of schizophrenia and the idea of a "continuum" related to the number of susceptibility genetic variants, as Robert Plomin suggested in the book, "Blueprint.

Wake Up Call for Insomnia GWAS

Here is another GWAS, this time for insomnia, that I think buries the lead:

Genome-wide meta-analysis of insomnia in over 2.3 million individuals implicates involvement of specific biological pathways through gene-prioritization

Here's an alternate title:

Based on 1.3 million GWAS, the maximum variance explained was 2.6% and based on 2.3 million individuals the maximum variance explained seems to be only 2% !

- (Hat tip to Veera M. Rajagopal, twitter handle: @doctorveera, who might not really appreciate the hat tip)

Obviously, there is a problem here, when, even when finding novel loci by expanding your dataset, you are getting getting worse "variance explained" from your PRS. I think this suggests that they have already reached their peak, which seems to run in the 2 to 3% range for most behavioral traits. I will once again point out that even this number is suspect, since it is not compared to any null trait. They try to rationalize it by suggesting that that the added data (from 23andMe) was less stringently phenotyped, but you can't have it both ways. Expanding datasets does not appear to give us any more real insight. It just bumps up the number of loci meeting significance, which arguably just a collection of false positives.

As the datasets expands beyond just white Europeans, I suspect this will onlly further water down the success of these studies, since they will not be able to rely as much on pop strat to get correlations.

Bipolar Genetics Makes No Progress

Nice critique by Peter Simons of a genetic study for bipolar disorder among Han Chinese with the diagnosis  of Bipolar Disorder (original study here). A couple of excerpts:

The researchers analyzed thousands of Han Chinese people and found that genetics explained just 2.3% of whether they received a diagnosis of bipolar disorder (BD) or not...

However, it is unclear how tiny correlations like this—which affect but a tiny sample of the population studied and explain less than 3% of the risk for a diagnosis—could help researchers understand the supposed “biological etiology” of bipolar disorder. In fact, they rather show that more than 97% of the reason that someone gets a diagnosis is explained by factors other than biology. 

As I like to point out, even the 3% is quite suspect and is arguably noise and should be tested against a null trait to establish that the 3% is not the null.

The Unembarrassed Bot

 A shortcutting of the usual GWAS is a bot that simply cranks out a Manhattan plot with no further analysis. While, those who do traditional GWAS downplay it, there is really little difference between what they are doing and what the bot is doing other than some shoddy speculation and perhaps a bit of data cleaning, but the real issue is that the bot does GWAS that most would be too embarrassed to publish and these get a lot of hits. Take this one, for example, that ironically, without embarrassment, finds genetic variants for "worrying too long after embarrassment":

This should be a clear indication that silly false positives can be produced from anything you can ask on a questionnaire. In addition to the likelihood of some massive pop strat dependent on particular cultural backgrounds, what exactly is meant by "too long"? Is this a subjective opinion of the person or is it a specific amount of time? 

If You Can't Make it Happen for Schizophrenia

 This is an expansion of a previous Schizophrenia GWAS:

Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia

This is the PGC schizophrenia study. We hadn't really had an update since 2014. It appears they buried the lead with the usual false optimism. They went from 36,000 cases in the previous study to 69,000 in this one. We have been promised that polygenic risk scores (PRS) would explain more and more of the "missing heritablity" as the study sizes increased. Well, in this case, the PRS variance explained went from 3.4% to ... 2.6%. Of course, that 3.4% was apparently an error anyway.They also admit that their previous calculation of 3.4%, often cited in other papers, was calculated in error and was probably lower. Is that to make it look like the 2.6% is not that bad?

The fact of the matter is that this is a very bad result. This is not even a within family calculation, which one might expect to be very close to 0%. I think at this point, getting 2 or 3 percent of the variance explained is essentially a null finding, and I challenge any authors who claim otherwise to compare it to obvious null traits.

I might have more to say about this related to the loci they say reached significance, but can't find the old PGC data to compare it with directly. In any case, the only thing that increasing N does is bolster the number of "significant" loci and I expect none of these loci will independently meet statistical significance in any other study.

What this study really suggests, when you take away the spin, is that the entire model of a polygenic mechanism for schizophrenia is pie in the sky. This points to a larger problem, which is that if any psychiatric trait should be due to a physical (genetic) cause, one would think schizophrenia would be a sure thing. If you can't make it happen for schizophrenia, good luck making it happen for dubious diagnoses like ADHD or really any other psychiatric trait.

Genetic homogeneity does not reduce individuality (in fish)

 This Study takes genetically identical fish and puts them in indentical environments and demonstrates that they show a lot of individuality:

we find that (i) substantial individual variation in behaviour emerges among genetically identical individuals isolated directly after birth into highly standardized environments and (ii) increasing levels of social experience during ontogeny do not affect levels of individual behavioural variation. In contrast to the current research paradigm, which focuses on genes and/or environmental drivers, our findings suggest that individuality might be an inevitable and potentially unpredictable outcome of development.

 This, of course, is a fish study, but if even fish have such individuality, I think it is a good bet that the same can be said for humans.

Genes for Walking at a Brisk Pace (yes, this is real)

 Another absurd GWAS:

Genome-wide association study of self-reported walking pace suggests beneficial effects of brisk walking on health and survival

The fact that I see scientists in the field taking this seriously rather having some self-reflection is a good indication of the lack of common sense that drives these studies. I am not going to pick through it. I hereby just mock it.

Yet Another Study Showing that Polygenic Scores are Confounded by Population Stratification

 Another study related to height PGS:

Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies

It should at some point become clear that GWAS and their cousin, the "polygenic score" are little more than measures of population stratification and other such issues in the population being studied (or the database being used). In this case, here is the money shot:

More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

I would be grateful if someone could tell me what interpreting with caution would look like? How about stop making these interpretations, instead? 

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).

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). 

Parental Wealth is Pop Strat

 This paper discusses how parental wealth is a strongly prone to assortative mating. 

...parental wealth homogamy is high at the very top of the parental wealth distribution, and individuals from wealthy families are relatively unlikely to partner with individuals from families with low wealth. Parental wealth correlations among partners are higher when only parental assets rather than net wealth are examined, implying that the former might be a better measure for studying many social stratification processes. Most specifications indicate that homogamy increased in the 2000s relative to the 1990s, but trends can vary depending on methodological choices. The increasing levels of parental wealth homogamy raise concerns that over time, partnering behavior has become more consequential for wealth inequality between couples.

The reason this is important in terms of genetic studies, is that it creates population stratification that will no doubt present itself as genetic correlations, giving the impression that genes exist for income (yes such studies have been done), as well as other things like educational attainment, when all that is really happening is that the rich are keeping it in the family, so to speak.  They noted that it was less common, but still an issue in Denmark, where the study was done, but worse in other countries (US and UK, for example). Thus these GWAS that purport to show such genetic correlations are likely really demonstrating that certain social/ethnic groups have an unfair proportion of wealth. By this token, if you were going to use "polygenic scores" for educational attainment to decide who should get into an elite school, it would more fair for those with the lowest scores to be given preference...

The Tarot of Reading Neuroimaging

 I'm linking to this piece discussing the lack of consistency among researchers reading and interpreting Neuroimaging studies, because I think this highlights why it is akin to phrenology or perhaps Tarot card readings. 

 Research Teams Reach Different Results From Same Brain-Scan Data 

 "When 70 independent teams were tasked with analyzing identical brain images, no two teams chose the same approach and their conclusions were highly variable. "

The fact that these are static neuroimages gives the impression of some sort of consistent, definitive interpretation. But Tarot Cards are also the same deck no matter who is laying out the cards. "It's in the cards," they will say. But, in truth, it is in the card reader. 

 

My Four Laws of the Behavioral Genetics Fallacy

 I discussed these in more length, here as a response to Eric Turkheimer's Three Laws of Behavior Genetics. But just wanted to lay them out in one short post (credit Turkheimer for the second, which is his third).

My Four Laws of the Behavioral Genetics Fallacy:

1. Any behavioral trait studied within a society will be correlated genetically to specific subpopulations, regardless of whether these genetic correlations are directly related to the trait.

2. A substantial portion of the variation in complex human behavioral traits is not accounted for by the effects of genes or families.

3. Differences in human behavior, intelligence and personality are not accounted for by structural or functional differences in the brain.

4. Advancements in understanding human behavior and psychology require inner exploration from the scientist, the subject or both.

Some Comments on "The Three Laws of Behavior Genetics" (and the two other laws)

Twenty years ago, Eric Turkheimer wrote an often cited paper titled:

Three Laws of Behavior Genetics and What They Mean

This paper is still often cited today and perhaps has taken on a life of its own, with a more deterministic interpretation than Turkheimer apparently intended and for which he recently clarified in a blog post his original intent. Nonetheless, much of the criticism of his paper comes from those in the genetic determinism camp, with the extremes being the "race scientist" crowd. So, since I sit on the other end of the see saw from the genetic determinists with Turkheimer poised somewhere in the middle, I will weigh in with my own thoughts about his three laws, as well as the two additional non-Turkheimer laws added into the soup. In the process of this, I will posit my own Four Laws of the Behavioral Genetics Fallacy. First, let's lay out the three laws that Turkheimer posits:

First Law. All human behavioral traits are heritable.

Second Law. The effect of being raised in the same family is smaller than the effect of genes.

Third Law. A substantial portion of the variation in complex human behavioral traits is not accounted for by the effects of genes or families.

I agree with perhaps one and a half of these laws. I’ll start with my half agreement with the First Law. For starters, I take issue with the use of the term “heritable,” because the term predates genetics and has had many different meanings and interpretations over the years, as this article points out:

The term ‘heritability,’ as it is used today in human behavioral genetics, is one of the most misleading in the history of science. Contrary to popular belief, the measurable heritability of a trait does not tell us how ‘genetically inheritable’ that trait is. Further, it does not inform us about what causes a trait, the relative influence of genes in the development of a trait, or the relative influence of the environment in the development of a trait.

Genetic Prediction of Schizophrenia via Polygenic Risk Score Has No Clinical Utility

 A new study from Schizophrenia Bulletin tested various risk factors on a group of individuals in a Netherlands. In addition to other risk factors, they used Polygenic Risk Score (PRS) developed from previous studies. This summarizes the results:

We calculated the relative contribution of each (group of) risk factor(s) to the variance in (change in) mental health. In the combined model, familial and environmental factors explained around 17% of the variance in mental health, of which around 5% was explained by age and sex, 30% by social circumstances, 16% by pain, 22% by environmental risk factors, 24% by family history, and 3% by PRS for schizophrenia (PRS-SZ). Results were similar, but attenuated, for the model of mental health change over time. Childhood trauma and gap between actual and desired social status explained most of the variance.

 This is a weak result all around, but particularly bad was the PRS which had a predictive success of 0.5 % (3% of 17%). Thus, just knowing a person's age and sex was almost twice as predictive as the PRS. If the person had a history of pain or other medical complaints, that alone was 4 times more predictive for schizophrenia. This is simply a dismal failure and the continued hope that this will be good enough to be clinically useful is little more than wishful thinking. Realistically, to be clinically useful, it would have to be 25 to 50 times better than this and I am guessing it has come close to peaking.

Yet More UK BioBank Pop Strat Issues Noted.

 There are so many studies coming out noting population stratification issues that it is hard to keep track. This is an interesting preprint looking at CAD and BMI:

Fine-scale population structure confounds genetic risk scores in the ascertainment population

From the Abstract:

we investigated the accuracy of two different GRS across population strata of the UK Biobank, separated along principal component (PC) axes, considering different approaches to account for social and environmental confounders. We found that these scores did not predict the real differences in phenotypes observed along the first principal component, with evidence of discrepancies on axes as high as PC45. These results demonstrate that the measures currently taken for correcting for population structure are not sufficient, and the need for social and environmental confounders to be factored into the creation of GRS. 

One interesting aspect of this study, I think, is that it highlights how it can be necessary to have a good working knowledge of the population you are studying.  This plot is striking in that respect:

This was confined only to white European descent, but still had this kind of stratification. A larger point here is that more and more pop/strat issues arise, many of which were not accounted for in earlier studies and perhaps should lead to corrections. Moreover, for those doing GWAS in the future, particularly in the UK BioBank, it is worth having a bit of skepticism that at least some of what you are seeing is pop/strat that has yet to be recognized.

 

Another Paper Related to Pop Strat issues for GWAS

Another study discussing pop strat issues:

Demographic history impacts stratification in polygenic scores

Points out more issues with population stratification:

We show that when population structure is recent, it cannot be fully corrected using principal components based on common variants—the standard approach—because common variants are uninformative about recent demographic history.

They further note some limitations with sibling based studies:

While sibling-based association tests are immune to stratification, the hybrid approach of ascertaining variants in a standard GWAS and then re-estimating effect sizes in siblings reduces but does not eliminate bias. 

As I've argued previously, the "immune to stratification" point is not necessarily true secondary to factors like varying ages of the siblings and selections biases of the databases. Nonetheless, if using sibling studies  "reduces but does not eliminate bias," and they are bringing the variance explained down to 2 or 3 %, then arguably they are scraping along near the null. So, far from showing that some of the variance explained is retained in sibling studies, it might suggest that there is no real genetic component found.

Finally, it's worth pointing out that despite the growing number of studies showing pop/strat issues in the UK Biobank and other such databases, no one has taken it upon themselves to reevaluate their previous, published GWAS results in light of this. It's as if they are grandfathered in.

Depression genetic study finds nothing.

This study:

Analysis of 50,000 exome-sequenced UK Biobank subjects fails to identify genes influencing probability of psychiatric referral

Speaks for itself. There is, of course, the  usual hope for the future:

  It seems unlikely that depression genetics research will produce findings that might have a substantial clinical impact until far larger samples become available.

There is simply no reason to continue believing at this point that such genetic variants will be found. They simply don't exist. There needs to be a cutoff at which point this would be acknowledged, or this shell game will never end.

Thought Experiment on Genetics and Society

I did something like this on Twitter, but will expand it here:

Let's say we live in a society where all the citizens are genetically identical (1 male and 1 female genetic code) and further that progeny, through laboratory manipulation or the like, retain the same genetic code from one generation to the next:

Will there still be a social hierarchy in such a society? Wouldn't a society require professionals and a working class. If it was along the lines of our current system, some would be doctors and some janitors and some field workers. Would those born to wealthy and well educated families have a leg up in also achieving educational and professional success? Might one also expect some homeless, some people with substance abuse problems, some people who are unhealthy? Some who turn to a life of crime? Some who would be humanitarians? Would there not eventually be wars and some way in which groups would be prejudiced towards other groups? Would people be more alike, or would they go out of their way to differentiate and be even more diverse in personality?

This is all quite obvious, isn't it? Gene hunting is not going to uncover human nature. We are human beings first. For the most part, genes simply display the costume that each of us wears. The exceptions to this fact are simply that: exceptions.  Marking people's personal traits by identifying generally unrelated genetic variations does little more than create meaningless divisions in our society and a perception of humans as genetic automatons.

"Educational Attainment" and the Wobbly Null

New study related to genetic studies of Educational Attainment:

Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses

Like a previous study, it makes the point that within family analysis significantly "attenuates" the educational attainment, in this case related to correcting for height. Here's the rub, though. In the previous study, the fact that EA was significantly diluted by within family results was somehow lauded as a demonstration along these lines: "At least there is still something, so it proves there is some genetic component to EA." This study, however seems to take the opposite approach:

The Mendelian randomization estimate using the sample of unrelated individuals implied that each 10 cm increase in height caused an increase of 0.17 (95%CI: 0.14–0.20, p-value = 8.5 × 10−26) years of education. After allowing for a family fixed effect, the Mendelian randomization estimate was greatly attenuated suggesting little evidence of a causal effect of height on education

In this case, the attenuation was taken as evidence of a null value, to demonstrate that they were able to get the pop strat out of the picture. However, if something like height has even a small effect on EA and the tiny results for genetic variants for EA after within family analysis, then it's worth asking whether there are any actual genetic variants related to someone being better or smarter in a way that allows them to get more education (c'mon, this should be obvious), or whether there are just a few physical confounders giving us the slight variance accounted for. You can't have it both ways.

Genes for Substance Abuse Has Made No Progress, but Unjustified Optimism Continues

Yet another genetic study of substance abuse:

Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples

Highlights:

These PRSs explain ~2.5–3.5% of the variance in AUD (across FT12 and COGA) when all PRSs are included in the same model. 

...usefulness for identifying those at increased risk in their current form is modest, at best 

This was from an all white European sample, ftr, with the assumption that pop strat is accounted for. One can assume, as has been the case, that such pop strat will be found and water this down to next to nothing. That said, is the null 0% or is 2 or 3 % about as low as you can get? I'd be happy to see an example in which a PRS does worse than this.

So is the conclusion that perhaps we are barking up the wrong tree? Of course not:

 Improvement in predictive ability will likely be dependent on increasing the size of well-phenotyped discovery samples. 

The shell game continues...

Nice piece on genetic correlation vs causality

This piece:

What Causes Genes?A genetic association doesn't necessarily mean a genetic cause.

Gives a good overview of why genetic correlations don't necessarily live up to their billing. (From Jaime Derringer, Ph.D.).

Addendum: As a reader mentions, this piece was apparently inspired by this study:
Population phenomena inflate genetic associations of complex social traits.

From that paper:

In conclusion, our results demonstrate some of the causal structures that may bias univariate and bivariate genetic estimates such as heritability and genetic correlations, particularly when applied to complex social phenotypes. 

Study Showing Weakness of PGS, even within ancestry

I already put up a blog post on the preprint of this new study last year:

Variable prediction accuracy of polygenic scores within an ancestry group

Here is the Abstract:

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use. 

Damning on its face, but the authors appear to not want to give up the ship, and give only a few passing mentions of pop/strat and other confounding issues with these large genetic databases. At what point do you reject the model if the studies aren't giving you the expected results? Time will tell...

More Bias in DNA databanks.

This study:
Genetic analyses identify widespread sex differential participation bias
is yet another example of the bias problems in these large consumer and other databases. This one looked at several, including 23andMe and the UK Biobank.
With 23andMe, a GWAS just for "male vs. female" had 150 "signficant" loci and many of these loci were previously correlated to complex traits from other GWAS that used the database. This is a problem, because it suggests that many of the previously discovered loci for particular traits might actually just be an indication of bias in the databank and have no causal relationship to the trait as the authors point out:

Finally, we demonstrate how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.

A broader problem related to this is... Every GWAS performed to date using the biased databank, since this form of bias was not recognized when those studies were performed. I don't expect it to happen, of course, but this should lead to a reevaluation of any GWAS previously performed using the database with a correction that will further dwindle the results. Sex differences is an easy to recognize bias to test for, but there are no doubt many more that remain unrecognized and the fact of the matter is, that you will never  be completely sure you have eliminated them all, so you can never say for sure whether you are finding anything but noise in these studies (I think that is the case, for the record, with behavioral genetic phenotypes). So in addition to population stratification issues in these studies, which also never seem to be fully recognized, the databases themselves have their own stratification issues.

Interesting Update: Another study just came out that incidentally looked at the same thing in the UK BioBank. This one found NO hits. I think this is likely a good demonstration of how participation bias created a very large number of false positives (23andMe) vs. the UK BioBank, which perhaps didn't have the same participation bias and shows that a large number of "significant" hits can be produced simply with noise. Again, we are left with the question of whether anything from these studies are true genetic correlations.

The Trickle down of GWAS to Race Science

I like to point out that many of the genetic studies related to "IQ," "g" and "Educational Attainment," whether or not their intentions were good, tend to attract racists of varying degree, from the smooth-talking race scientists down to white nationalists and overt racists trying read the study as a whites are smarter than blacks because of their genes misinterpretation (leaving aside the fact that most of the studies are unreplicatable). This study which examines which people tend to pick up particular studies on social media sites like Twitter quantifies this and notes:

Our study provides conclusive quantitative evidence that white nationalists and adjacent communities are engaging with the scientific literature on Twitter. Not only are these communities a ubiquitous presence in the social media audience for certain research topics, but they can dominate the discourse around a particular preprint and inflate altmetric indicators.

Often, once this process begins, the scientists involved in the study and other experts in the field attempt to debunk this misappropriation of the science. Unfortunately, this does little more, in my view, than amplify the debate in a "both sides" dichotomy, effectively giving credence, or at least attention, to the racist views. While scientists will try to defend or find a use for such studies to justify their existence, these are often a reach and fall flat, leaving one to ask what purpose they serve other than to energize racists? 

Genes for Getting Beaten Up or Mistreated as a Child (Yes, this is a real study)

This is an actual genetic study to which some people are proud to have their attached:

Genomic influences on self-reported childhood maltreatment

The study failed to replicate (big surprise), and is entirely bogus and I could go through it and pick it apart but, instead, I am just going to say that the implication here is that there are genes for getting beaten up as a kid. I guess, that's what they are trying to say, anyway. I am just going to say that I find this disgusting and refuse to even engage with it further. The authors should be ashamed of themselves for printing it. I'll also point out that this is another example of a UK Biobank study where the application for the use of the database was deliberately vague. If you need to use subterfuge to get your study printed, then you are being doubly unethical.  Hopefully, as noted in my previous post, this type of drivel will be prevented from further use of the UK Biobank.

My Letter to the UK Biobank

I will update with any reply from them: Update: Reply after the fold - exactly what you would expect ... Double Update: I have deleted the tepid e-mail I received and attached the e-mail that the UK Biobank sent to researchers which appears to lay down the hammer on these shenanigans. Kudos to the UK Biobank and I hope that their actions will match the sentiment of their e-mail.

My Letter:
To Whom it May Concern:

I am writing this letter to express my concern over an apparent misuse of the UK Biobank. I am referring specifically to this study: https://www.nature.com/articles/s41467-019-13585-5

Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income

It is my understanding that the UK Biobank was conceived to identify health issues related to genetics and one would presume that the majority of those who have provided DNA and other information about themselves, did so altruistically, with the understanding that it would be put to such use in order to aid in the discovery of new disease treatments and to improve health care for the citizens of the UK and beyond. Therefore, I find it rather disturbing that a study that purports to find genes related to a person’s income is given the use of the UK Biobank to make such an analysis. Such studies contain more than a hint of eugenics, and promote the harmful impression that a person’s income is somehow related to their genetic endowment which, among other issues, has political implications related to the current economic system, its validity and fairness.

This study appears to be related to other studies that have utilized the UK Biobank, with many of the same authors, that try to identify genes for “intelligence” and “educational attainment.” Clearly, such studies have created a slippery slope for this type of dubious science to seep into general acceptance and I think they cloud the original intent and purpose of the UK Biobank. Such studies have a sordid history, often embraced by those with a racist agenda, and judging from the interest it is garnering on social media sites, this study is no exception. One might wonder whether the Biobank volunteers would reverse their consent if they understood that their DNA information was being accessed for these dubious purposes.

Moreover, while such studies are nicely self-serving for the highly educated, high-income scientists who perform them, it would probably come as no surprise to most in the UK that the genes particular individuals possess have an influence on how much education and income they receive, considering the long history of social class stratification and the likelihood that such genes are nothing more than identifiers of particular social and racial categorizations, wherein their “income” is heavily influenced by which of these categories they are identified with, rather than some sort of magical genes that provide an entrepreneurial advantage for a select few. There is simply no reason that such studies should be given access to the UK Biobank. They create harmful and erroneous perceptions and divisions and turn the UK Biobank into a political entity rather than an aid to human well being.

In addition, and most importantly, it appears that the authors of this study deliberately misrepresented their intent, claiming that their study would be used to determine “The relationship of cognitive function and negative emotions with morbidity and mortality: an aetiological investigation.” This does not appear to be at all representative of the study they performed, as even the title of the study makes clear. They also apparently applied for an extension with the following rationale:

“One outcome we are also interested in exploring in relation to prior cognitive function and other factors is dementia. For instance, we would like to investigate the extent to which prior cognitive function helps predict later onset of vascular dementia independently of other risk factors. We have research experience in the cognitive epidemiology of dementia. This is not an outcome that we specified in our original application so I am writing to ask for approval to expand the scope of our project to include dementias as an outcome.”

Anyone reading this study can see that both of these descriptions have little to do with the true focus of the study and, in reality, are a complete misrepresentation. It seems clear that they were just gratuitously added in order to give the study the kind of authenticity it would need to secure the use of the UK Biobank, with the knowledge that it otherwise did not merit it, or more sinisterly, was part of an attempt to perform a study that would be otherwise viewed as ethically questionable. It is a fraudulent and hubristic maneuver, that shows a disdain for the intent and spirit of the UK Biobank, and is arguably scientific misconduct. Although this and other such studies will pay lip service to health and well-being issues, it appears much more likely that these issues are merely a “Trojan Horse” for their true intent, which is a scientific justification for societal privilege and elitism by way of genetic determinism.

I would like to suggest that the UK Biobank apply more scrutiny to studies of this nature, and ask you to consider preventing the authors responsible for this study from further access to the UK Biobank.

As noted, I removed the intitial response to my e-mail and have provided the e-mail sent out to UK Biobank researchers after the fold:

Perhaps We Have a Use for These GWAS, Afterall

In my last post, I briefly critiqued this study absurdly correlating genetics to income and offered a challenge to the authors. My opinion of GWAS is obvious to anyone who skims through this blog, but it occurs to me that perhaps we might have a use for GWAS, afterall, as I recently tweeted:

Here’s a different take: The extent to which you can correlate genes to income in a society, is a direct measure of the unfairness and class stratification of that society. 

If we assume, as I do, that most genetic correlations in the behavioral genetics realm are due to population stratification, then we know that any genetic correlations would demonstrate ways in which the society is stratified. This could be in obvious ways such as racial delineations, but might also include more subtle classist issues (He/She is not from the right family...) and would be an even better way to measure more covert discrimination. By the way, I think this is provable in the sense that other societies will have entirely different loci correlated to income, a fact that will cause a lot of mental gymnastics to explain away.
If we can't prove the causality of the genes flagged in such studies, shouldn't we assume that they are an indication, of an unfair stratification of the society? If we could rid ourselves of all such genetic commonalities, wouldn't that lead us to a true meritocracy? Therefore, wouldn't it make sense and be more fair to give job and college admission preferences to those with the LOWEST polygenic scores for income? As the very "not racist" individuals who embraced this study and took me to task on Twitter pointed out, shouldn't we pursue the truth wherever it happens to lead?