Why Rare Genetic Variant Correlations Will Not Shed Light on Schizophrenia

 

As I have discussed in a previous blog post, despite claims of strong heritability from twin studies and tepid results from genetic studies to date, there has been little success in elucidating causal genetic architecture for schizophrenia. A recent article comments on this issue (30 years too late).

The Human Genome Project was undertaken primarily to discover genetic causes and better treatments for human diseases. Schizophrenia was targeted since three of the project`s principal architects had a personal interest and also because, based on family, adoption, and twin studies, schizophrenia was widely believed to be a genetic disorder. Extensive studies using linkage analysis, candidate genes, genome wide association studies [GWAS], copy number variants, exome sequencing and other approaches have failed to identify causal genes. Instead, they identified almost 300 single nucleotide polymorphisms [SNPs] associated with altered risks of developing schizophrenia as well as some rare variants associated with increased risk in a small number of individuals. Risk genes play a role in the clinical expression of most diseases but do not cause the disease in the absence of other factors. Increasingly, observers question whether schizophrenia is strictly a genetic disorder.

An argument that is often made to counter this three decade failure, is that schizophrenia might be caused by rare genetic variants that, to date, have not been identified in genetic studies, but might be in the future. Bolstering this premise is the fact that individuals with rare genetic disorders such as 22q11.2 Deletion Syndrome and Fragile X Syndrome have a much higher prevalence for the diagnosis of schizophrenia. Such genetic disorders have been fairly easy to identify and most of the individuals have other issues such as intellectual disabilities and seizures. These disorders generally involve larger portions of DNA than a single genetic variant, however, so it is also difficult to identify which genes might be responsible. 

Whole exome sequencing (WES) is an advance on GWAS that allows one to locate very rare variants that would not otherwise be recognized and also confer a larger risk of schizophrenia. The idea here is that one could then see specifically what these genes do and determine how they might be causal for schizophrenia.

 This paper discusses some of these rare variants ( SETD1A, CACNA1G, CUL1, GRIA3, GRIN2A, HERC1, RB1CC1, SP4, TRIO, XPO7, and AKAP11) and their functions in an effort to understand how they might be implicated in schizophrenia. On the surface, this may seem like a useful approach for identifying genetic causes of schizophrenia. There are a couple of problems with this , however. The first is that these genes do not just increase the risk of schizophrenia. There is generally significant pathology that, in my view, disqualify them from considerations when we are talking about the bulk of individuals with schizophrenia who do not have such rare variants. Let’s look at one of the genes noted above. CACNA1G is involved in calcium channel gates in neurons. So it has a neurological mechanism, which might seem like it gives credence to the idea that is can be causal for schizophrenia. The problem here is the fine print:

 Along with being a schizophrenia risk gene, CACNA1G is also associated with the risk of severe intellectual or developmental disability.

In fact, all of the rare variants noted in the paper also confer risk for developmental and intellectual disabilities. If one had a gene that only conferred a risk for schizophrenia and nothing else, the case would be stronger. However, the fact that these patients generally have other developmental and intellectual disablities is not a coincidence. It speaks to the reality of clinical psychiatry and needs to be looked at in a broader social context.

Patients with intellectual disabilities are often put into the mental health system due to behavioral issues and other coping difficulties that anyone has witnessed. These issues will get them into difficulties both in the home with those involved in their care and other social environments. The hope is that some intervention, generally medications, will help keep them under control and out of troubles that might affect their ability to find basic living arrangements, hold jobs, etc. As a psychiatrist, there is no clinical justification for just keeping someone medicated without an appropriate diagnosis in the Diagnostic and Statistical Manual (DSM). The number of diagnoses one can give that would justify the kind of medications being considered in such a scenario, which would be sedative antipsychotic medications like Haldol or Olanzapine, or mood stabilizers like lithium and Depakote are few.

So we have a situation where the questions will be structured (consciously or unconsciously) to achieve the goal. “Billy, did the voices tell you to break the window?,’ “Jane, do you feel like people are making fun of you?,” “Mike just had a mood outburst,” etc. Soon you have a patient who hears voices, is  paranoid and has mood issues. Then you give a diagnosis of schizophrenia, or more often, schizoaffective disorder. The fact of the matter is that these “symptoms” are simply not the same as someone with the classic diagnosis of schizophrenia, who hears actual voices speaking to him as opposed to some impulse, who has a paranoid conspiracy brewing in his head related to the CIA or the Masons or the like rather than the very real concern of someone with a mental disability that people are making fun of them. Likewise, someone with classic manic symptoms where they are awake for a week straight, believing they are millionaires and secretly married to a celebrity, etc., is not like someone having a temper tantrum.

Semantically, they can both be said to meet the criteria for schizophrenia, but these are two very different things and speaks to the limitations of the DSM. Moreover, if you take ten rare genetic variants that correlate both to schizophrenia and a mental disability, common sense should tell you that the mental disability is what is being labeled schizophrenia and not that they have both a mental disability and schizophrenia. Since schizophrenia has very specific symptoms (auditory hallucinations, paranoid delusions, etc.), it makes no sense to assume that ten different variants with entirely different neurological mechanisms all happen to lead to schizophrenia. This is not logically coherent.

What these studies are really showing, in my opinion, is one of the dirty little secrets of psychiatry and society, more generally, where if individuals are not able to cope within the acceptable structured milieu, then it falls on chemical sedation, to get them in the necessary boxes.

Is Behavioral Genetics a Null Field?

 On a whim, I signed up to present a poster at the The American Society of Human Genetics (ASHG) conference in DC. That, of course, required an actual article, so I wrote this up. It should be an easy read. It is written for a wider audience:

The Question That Must Be Asked: Is Behavioral Genetics a Null Field?

Within-Family now Fading

Within-Family PGS was said to prove definitive causal SNP’s and even though they generally give 1 or 2% of the variance explained, this was being held onto as proof of something, as Harden stated in her book, The Genetic Lottery,   “... the heritability of educational attainment is still not zero.”  Now that is (not surprisingly) being called into question with this simulation study:

A model for co-occurrent assortative mating and vertical cultural transmission and its impact on measures of genetic associations.

The study notes that GWAS are still beset by confounding, noting:

“Furthermore, we show that such inflation remains even when applying within-family based estimates.”

For the past 3 decades, behavioral genetic studies have relied on the fact that their assertions take a few years to be disproven and, by the time that happens, they have new assertions - rinse and repeat. 

 

Review of “Innate,” by Kevin Mitchell

“Innate,” by the neurogeneticist Kevin Mitchell, explores the case for a genetic and neurodevelopmental origin of individual differences in intelligence and other human character traits. As the title suggests, the book generally leans toward “nature” in the nature vs. nurture debate, and makes the assumption that “innate” implies a genetic origin, although with a more dynamic view of the path from gene to trait than one sees in Robert Plomin’s “Blueprint” or Katherine Paige Harden’s “Genetic Lottery” (links to my reviews of those books at the end of this review).

The question of the nature of individuals and how that nature arises has existed, in one form or another, for as long as human civilization, but took a specific turn in our own with the work of Charles Darwin or, more specifically, the work of his second cousin, Francis Galton, the eugenicist and polymath who applied Darwin’s evolutionary theories to human behavior and intelligence and actually coined the term “nature versus nurture.” 

Galton’s eugenic ideas have inspired quite a bit of misery and Mitchell rightly condemns these ideas. Nonetheless, he is often complimentary of Galton’s statistical  work related to trait heritability, which I find unfortunate. I don’t think one can simplistically separate this from Galton’s eugenic ideas, which were arguably the driving force behind his math, and which is still embraced by race-oriented “scientists” to this day. 

Pigeon-holing behavioral traits into mathematical boxes, so that traits like intelligence, extroversion and schizophrenia can be assessed in the same way we might assess traits like height, eye color, or other obvious physical features, or even milk production in cows is bizarre on its face and involves some unimaginative assumptions about the nature and complexity of human beings, while also ignoring ongoing philosophical debates and simplifies individual human nature down to an assumption that it must be related to differences in genetics and neurodevelopment. 

Mitchell uses the analogy of a robot being programmed, to explain his view of the mind, with  “computational algorithms of decision-making,” and  “neuromodulator circuits …tuned - they work differently in each of us, thus influencing the habitual behavior strategies we each tend to develop.”   Mitchell suggests that “brain circuits” develop with some variation in individuals that make “major contributions to our psychological traits.” None of this is demonstrable, and is the kind of theoretical understanding of the brain-as-computer you find in his field. Unfortunately, Mitchell largely sells it as a factual representation of the human mind, rather than his theoretical viewpoint, a recurring theme in this book.  I think he could use far more qualifiers when presenting his ideas.

“Geneticism”: The Making of a New ism

I cringed a bit when I first saw this paper:

Nurtured Genetics: Prenatal Testing and the Anchoring of Genetic Expectancies

Any time there is mention of applying polygenic scores, particularly for so-called “educational attainment,” it raises my concern. However, I think this paper makes an excellent point that I’d like to explore further, about the perception of a “magical” genetics, fostered by decades of dubious claims purporting to demonstrate a role for genes for traits such as intelligence, personality, and mental disorders. I, of course, challenge any such role and chalk most of the ever-changing genetic correlations noted in studies to population stratification related to class, race, geography and other such divisions of people. Even if I am completely correct about this, however, there is an unfortunate reality created by these continued pronouncements of a genetic basis for something like educational attainment, noted in the paper:

1. Primacy Effect: Presenting polygenic scores for traits, as the first units of information about a child, will lead parents to assign undue weight to that genetic information.
2. Anchoring and Adjustment Heuristic: Parents informed about a future child's genetic predispositions (before birth) will form "genetic expect-ancies" (i.e., expectations created on the basis of polygenic scores), and will be less amenable to updating those expectancies based on subsequent environmental information compared to those informed post-birth).
   
3. Nurtured Genetics Effect: Parents will search to confirm or disconfirm their genetic expectancies and in doing so, they will be exposing their child to environments conducive to the actualization of their genetic expectancies.

Even if these polygenic scores are meaningless related to educational attainment (and, they are), they will still matter for educational attainment, because they will change the perception for individuals. This is not just for parents, but for the individual, who is now born with an expectation. If you don’t think you have the genes for getting a high level education, because you have been told this, then you are less likely to pursue higher education. If you don’t have the genes for “musical ability,” you might be less likely to pursue music, etc. So in, say, one or two generations, if these polygenic scores were widely used, you might essentially make the polygenic score valid, as people pursue what they are told by these scores to pursue and their parents guide them in that direction. 

I’ve pointed this out in the past related to psychiatric diagnoses, where it is often noted that a family history of a particular mental disorder will increase the likelihood that you will be diagnosed with that disorder. Well, sure, since psychiatrists are trained to give weight to family history when making a diagnosis, you would be more likely to get a particular diagnosis if your parent or sibling has that diagnosis than a person with the same symptoms who does not have a such a family history. So if you read that “studies show” that those with a family history of bipolar disorder are more likely to be diagnosed with bipolar disorder, it might take on a different meaning with this in mind.

Another concern would be if polygenic scores are accepted on an institutional level, where they are used to make decisions affecting the future of individuals. If this sounds like science fiction, Robert Plomin, a well known behavioral geneticist stated explicitly in his book, “Blueprint,” that, in the future, elite school selection should be based in part on “inherited DNA differences.” If such were the case, it would be a matter of time before people would take into consideration what DNA their potential spouse has and the likelihood that their children would have a high polygenic score for educational attainment.

In such a scenario, polygenic scores would reinforce classist and racist social structures, keeping those already more likely to get the benefit of a higher education locked in by their genetics, even if the genetic variants used to create a polygenic score have absolutely no real effect on a person’s ability to traverse higher education! This could create an extension of classism and racism, that one might call “geneticism,” that will be its own prejudice and compound other prejudices. Clearly, there is an incentive for those in a more privileged class to use polygenic scores to effectively help reinforce an aristocratic hierarchy and this is another example of the dangers of using polygenic scores for decisions relating to the future of individuals.

Study Shows That Genetically Identical Fish Can Have Lasting Behavioral Differences.

 This is an interesting study:

The Emergence and Development of Behavioral Individuality in Clonal Fish

 The study monitored the “behavior” of a specific clonal strain of a species of fish (Poecilia Formosa). They noted that, despite the fish all being genetically identical, they exhibited varying behavior (swimming speed, how active, etc.). 

Our findings show that substantial behavioral individuality is already present at the very first day of life after birth among genetically identical individuals, suggesting that pre-birth processes like pre-birth developmental stochasticity and/or maternal effects might play considerably more important roles in shaping behavioral individuality than commonly thought.

This variability only strengthened as the fish got older. From my perspective, the interesting thing is the extent to which this suggests behavior is not that defined by genetics, at least in fish. One might assume that this would extend to the more complex behaviors of humans, though, which begs the question as to how much influence genetic variants can have in human behavior for even one generation, much less be identified in genetically different individuals who merely share some common genetic variants. The math just doesn’t seem to be there for high heritability, if there is any heritability at all.

It would be interesting to see this experiment repeated with genetically varied fish and see if there is any more variation in their behavior (assuming they are physically the same size and shape, etc.) than you see with genetically identical fish. My guess is that it would be minimal. 

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.