Major Depression: The “Chemical Imbalance” Pillar Is Crumbling—Is the Genetics Pillar Next?

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The Pillars of Biopsychiatry

In a widely discussed July, 2022 analysis, psychiatrists Joanna Moncrieff, Mark Horowitz and colleagues reviewed numerous studies and found “no consistent evidence of there being an association between [the neurotransmitter] serotonin and depression, and no support for the hypothesis that depression is caused by lowered serotonin activity or concentrations.”1

The response by supporters of mainstream psychiatry was at times marked by personal attack and distortion, and other times by statements from academic psychiatrists that Moncrieff et al. found nothing new, and that psychiatry has known for many years that serotonin levels are not associated with depression. Yet as Robert Whitaker showed, psychiatry continued to promote the serotonin chemical imbalance story after knowing it was wrong, and “pharmaceutical companies, and academic psychiatrists…told us a story that their own research had shown to be false, and they did so because it benefitted guild interests and the financial interests of pharmaceutical companies.”

If psychiatry’s serotonin and “chemical imbalance” pillars are now crumbling, the genetic predisposition (heritability) pillar remains in place—for now. In this article I review the evidence that psychiatry ceaselessly puts forward in support of the “heritability” of major depression (hereafter, “MD”). I will first describe MD genetic studies based on families, twins, and adoptees, and then finish with a more detailed critical evaluation of MD molecular genetic studies, which have failed to discover genes shown to cause MD. The “genetics of depression” story I will tell differs fundamentally from the story told in most textbooks, academic review articles, popular media accounts, and online sources.

DNA particles and diffused glowing lines, 3d renderingFamily and Adoption Studies

The American Psychiatric Association sees MD as a genetically based “serious medical illness,” for which “brain chemistry may contribute.” Critics have challenged these claims, and some have questioned the validity and reliability of the MD diagnosis itself. Inter-rater reliability refers to the ability of psychiatrists to agree on a diagnosis. MD reliability is low (inter-rater reliability kappa = .25), and has been decreasing. A diagnosis must be reliable in order to be valid. If MD cannot be reliably identified, it cannot be a valid diagnosis. (Although reliability is a prerequisite for validity, a reliably identified condition must still be validated by other means.) Therefore, research based on a diagnosis of “major depressive disorder” or a similar condition begins on shaky ground.

Although mainstream outlets and the general public often get this important point wrong, most genetic researchers and their critics are in agreement that the results of MD family studies (depression “running in the family”) cannot be interpreted genetically, because family members share common environments as well as common genes. MD adoption studies have been carried out in an attempt to separate these influences, but their flaws led top psychiatric genetic researchers Jonathan Flint and Kenneth Kendler to conclude in 2014, “Surprisingly, no high-quality adoption study of MD has been performed, so our evidence of the role of genetic factors in its etiology comes solely from twin studies.”

A subsequent 2018 MD adoption study by Kendler and colleagues, based on Swedish adoptees and families, was subject to the problems and potential confounds that characterize psychiatric adoption research. These problem areas include adoption agencies’ typically selective and non-random adoption placements, late separation, late placement, range restriction and the screening of adoptive families for psychological and financial stability, and shared prenatal environment. It is likely that some adopted children experienced attachment-rupture trauma, emotional suffering, loneliness and neglect, and other adverse childhood conditions that can lead to psychological problems later in life.

Kendler and colleagues’ 2018 adoption study was based on the records of over 14,000 adult adoptees obtained from Swedish population registers. Children were placed in their adoptive homes up to five years of age (late placement, probable late separation). Diagnoses were taken from hospital and medical records found through the registers. The researchers concluded, “The parent-offspring resemblance for treated MD arises from genetic factors and rearing experiences to an approximately equal extent.” They calculated a modest 16% MD heritability estimate. However, the study’s MD rate among adoptees was 50% higher than among people who grew up in intact families (15.6% vs. 10.2%), meaning that adoptees and non-adoptees constituted distinct populations in relation to MD. It follows that the study’s findings cannot be applied (generalized) to people who grew up in intact families. Due to the above-mentioned problems related to both the Kendler study and psychiatric adoption studies in general (including the reliability/validity issue), like the earlier investigations the 2018 Kendler et al. adoption study results cannot be interpreted genetically.

Twin Studies

If a genetic theory of behavior depends on twin study data, the theory is in serious trouble. Based on twin study results, biopsychiatry estimates MD heritability in the 30%-40% range. (I make a distinction between psychiatry and biopsychiatry, while being aware that biological and genetic approaches currently dominate psychiatry. The psychiatric genetics field is a major component of biopsychiatry.) Genetic theories in psychiatry are based on studies using reared-together twin pairs. Other than anecdotal reports on individual pairs, there are no reared-apart twin studies in psychiatry, even though psychiatric texts at times say that there are.

Psychiatric twin studies use the classical twin method, which compares the concordance rates or behavioral correlations of reared-together MZ (monozygotic, identical) versus reared-together same-sex DZ pairs (dizygotic, fraternal). MZ pairs are assumed to share 100% of their segregating genes, whereas DZ pairs are assumed to share on average 50%. The results of MD twin studies show that MZ pairs resemble each other more for MD compared with same-sex DZ pairs at a statistically significant level. I will designate this finding “rMZ > rDZ” (with r representing the behavioral correlation).

All sides of the genetics of depression debate expect a twin study finding of rMZ > rDZ. The main disagreement centers on how this expected-by-all finding should be interpreted.

Genetic interpretations of rMZ > rDZ require acceptance of the long-controversial MZ-DZ “equal environment assumption,” also known as the “EEA.” According to the EEA, MZ and same-sex DZ pairs grow up experiencing roughly equal environments, and the only behaviorally relevant factor distinguishing these pairs is their differing degree of genetic relationship to each other (100% vs. an average 50%). This key assumption is obviously false, however, since when compared with same-sex DZ pairs, MZ pairs grow up experiencing

  • Much more similar treatment by parents and others, including being dressed alike
  • More similar physical and social environments, including spending more time together, attending classes together, and having common friends and peer groups
  • More similar treatment by society due to their sharing a very similar physical appearance
  • A greater tendency to model their behavior on each other
  • Identity confusion and a much stronger level of emotional attachment to each other

Most modern twin researchers concede the point that MZ environments are more similar. For example, in a 2014 article by criminology twin researcher J. C. Barnes and colleagues, ironically written in defense of twin research, the authors properly recognized, “Critics of twin research have correctly pointed out that MZ twins tend to have more environments in common relative to DZ twins, including parental treatment…closeness with one another…belonging to the same peer networks…being enrolled in the same classes…and being dressed similarly.”

Despite recognizing that MZ and DZ twin pairs grow up experiencing very different environments, twin researchers have used eight different arguments in support of the EEA. In my forthcoming book Schizophrenia and Genetics: The End of an Illusion (Routledge, 2023), I examine each of these eight arguments and show that none holds up (a partial examination of these arguments can be found here). Because the EEA is false, the results of a psychiatric twin study finding rMZ > rDZ can be explained by non-genetic factors. Decades of studies designed to “test” the EEA have failed to alter this basic conclusion.

In a 2000 MD “review and meta-analysis” based on twin study data, leading genetic researchers Patrick Sullivan, Michael C. Neale, and Kendler calculated a 37% MD heritability estimate based on the greater MZ versus DZ resemblance for depression. Sullivan and colleagues sensibly did not claim that MZ and DZ environments are equal, and like most authors of the six depression twin studies they analyzed, they sidestepped the twin method’s unequal environments problem by defining the EEA in its “trait-relevant” form: “The critical equal environment assumption,” they wrote, “posits that monozygotic and dizygotic twins are equally correlated in their exposure to environmental events of etiologic relevance to major depression” (emphasis added).

A principle of science, however, is that the burden of proof falls on people making a claim, not on their critics. Therefore, MD twin researchers using this trait-relevant definition of the EEA—and not their critics—are required to identify the specific and exclusive trait-relevant environmental factors involved in a diagnosis of major depression. Until this happens, and until they then determine that MZ and DZ pairs were similarly exposed (or not exposed) to these factors, the EEA as conceptualized by Sullivan and colleagues fails completely. Because the EEA is false, MD twin study and twin-study-based meta-analysis results cannot be interpreted genetically.

Heritability

Biopsychiatry is confronted with another major predicament. It relies on the production and accuracy of heritability estimates (h2) that range from 0% to 100%, but these estimates are based on a string of questionable assumptions. One of these assumptions is the long-disputed idea that genetic and environmental factors are independent from each other (additive) and do not interact. In a 2022 analysis, sociologist Nicolas Robette and colleagues examined the assumptions that heritability estimates are based upon, and concluded, “None of the hypotheses inherent in heritability estimates are verified in humans.” This is a strong statement that, if true, should lead to the abandonment of heritability estimates in psychiatry and other behavioral science fields.

The heritability concept was developed in the 1930s as a tool to help predict the results of selective breeding programs of farm animals, such as milk production in cows. Since the 1960s, h2 has been extended by behavioral researchers and others into a measure of the “relative importance” of genetic and environmental influences on various psychiatric conditions, and behavioral characteristics such as IQ and personality. Critics generally object to h2 being used in this way, in part because nature and nurture influences interact with each other, meaning that it is not possible to separate and partition these influences. This leads to a rejection of “variance explained by” descriptions of the causes of psychiatric conditions.

Heritability estimates do not indicate the strength or weakness of potential genetic influences, or imply anything about changeability. Psychologist David Moore and David Shenk wrote in “The Heritability Fallacy” that the “term ‘heritability,’ as it is used today in human behavioral genetics, is one of the most misleading in the history of science.” A strong statement that may well be true.

Major Depression Molecular Genetic Research

Like other psychiatric diagnoses, the decision to perform major depression molecular genetic research was based on the belief that earlier family, twin, and adoption studies produced indisputable evidence in favor of substantial “heritability.” This is the fundamental error of MD gene-finding strategies. Because family, twin, and adoption studies have failed to provide such evidence, there is no good reason to assume that genes for depression even exist. Future historians may well conclude that the search for non-existent genes was a scientific folly of epic proportions.

When assessing MD gene discovery claims, we should keep these additional points in mind.

  1. We have seen that the reliability (and therefore the validity) of a diagnosis of MD is questionable. For this reason alone, even well-performed large-sample molecular genetic studies can produce false-positive results.
  2. Claims based on molecular genetic research refer to “associations” between MD and particular genetic variants or genomic regions (“loci”), not on the discovery of genes shown to cause the condition (more below).
  3. We must view recent gene association or discovery claims in the context of decades of similar claims that fell by the wayside. Whatever mainstream investigators write now about their own or others’ past failures, when these false-positive reports were being published, they often wrote of discovery, and the beginning of a new era.
All Noise, All False Positives, All Junk”: MD Linkage and Candidate Gene Studies

The three main (at times overlapping) eras of psychiatric molecular genetic research, which dates back to the 1960s, have been the linkage, candidate gene association, and the current GWAS/PRS eras (“genome-wide association study”/“polygenic risk score”). Another area of research focuses on potential “rare risk variants” such as copy number variants, or “CNVs.” Although claims of CNV-MD gene associations have appeared in recent years, I will focus on molecular genetic studies using the candidate gene, GWAS, and PRS approaches.

Psychiatric candidate gene researchers generate hypotheses about a diagnosis, and then identify “candidate” genes that might play a role in causing it. Genes become MD candidates based on their role in influencing brain functions believed to be related to the diagnosis. Flint and Kendler reported that as of 2013, more than 1,500 MD candidate gene association studies had been published, and almost 200 genes had been tested. Similar to the linkage era, however, the candidate gene era in the behavioral sciences is now widely recognized to have been, as leading behavioral genetic researcher Robert Plomin conceded in 2018, a “flop.”2

In a 2019 analysis appearing in the American Journal of Psychiatry, behavioral geneticists Richard Border, Matthew Keller and colleagues concluded that findings from the MD candidate gene era “are likely to be false positives”:

“The study results do not support previous depression candidate gene findings, in which large genetic effects are frequently reported in samples orders of magnitude smaller than those examined here. Instead, the results suggest that early hypotheses about depression candidate genes were incorrect and that the large number of associations reported in the depression candidate gene literature are likely to be false positives.”

In a subsequent interview, Keller asked, “How on Earth could we have spent 20 years and hundreds of millions of dollars studying pure noise?” A similar question could be asked in relation to schizophrenia candidate gene research.

An example of earlier candidate gene era excitement is found in a 2009 academic journal article entitled “The Role of Serotonin in the Pathophysiology of Depression: As Important as Ever.” In this publication psychiatrist Charles Nemeroff and Michael Owens reviewed and updated their 1994 “citation classic” article describing what they believed was a big serotonin gene discovery: “One of the most exciting findings is the importance of SERT [serotonin transporter] polymorphisms [gene variants] in vulnerability to depression, and the interaction of this genetic marker with environmental factors.” Both authors reported paid advisory roles with and research funding from several drug companies, and Nemeroff reported stock ownership in six related companies. At the height of the candidate gene era an article appeared in a major mass media outlet wondering out loud whether people with depression are morally obligated to “forgo bearing children in order to avoid passing on their ‘bad’ genes.” The genetic predisposition and serotonin theories of MD have been linked for many years.

Psychologist Stuart Ritchie recalled in 2020 that when he was an undergraduate student between 2005 and 2009, “candidate gene studies were the subject of intense and excited discussion. By the time I got my PhD in early 2014, they were almost entirely discredited.” For Ritchie, who otherwise strongly supports behavioral genetic research and theories, “reading through the candidate gene literature is, in hindsight, a surreal experience: they were building a massive edifice of detailed studies on foundations that we now know to be completely false.”3

The Most Famous Candidate Gene-Environment Link of Them All. A highly publicized MD-candidate-gene link was put forward in a widely cited 2003 study by Avshalom Caspi and colleagues (according to Google Scholar, cited over 10,400 times as of August, 2022, or about 550 citations per year over 19 years), who concluded that people experiencing “stressful life events” are more likely to be diagnosed with depression if they carried 5-HTTLPR, a variant genetic sequence within the SLC6A4 gene that encodes a protein that transports serotonin within neuronal cells. For many people, the Caspi study provided a sensible explanation for the causes of depression, where life events and genetic predisposition combined to explain why some people become depressed, while others do not. However, despite the publication of at least 450 research papers about this genetic variant, by 2018 or so it was clear that the 5-HTTLPR depression theory did not hold up.

The rise and fall of the 5-HTTLPR-depression link was described in psychiatric drug researcher Derek Lowe’s aptly-titled 2019 Science article, “There Is No ‘Depression Gene.’” The depression candidate gene literature, he wrote, turned out to be “all noise, all false positives, all junk.” A 2019 online article by a psychiatrist using the pen-name Scott Alexander documented years of subsequently unsubstantiated 5-HTTLPR-depression claims in the scientific literature, and how the media popularized these claims by calling “5-HTTLPR and a few similar variants ‘orchid genes,’ because orchids are sensitive to stress but will bloom beautifully under the right conditions.” Who could say a bad word about orchids? Alexander summed up the 5-HTTLPR debacle as follows:

“First, what bothers me isn’t just that people said 5-HTTLPR mattered and it didn’t. It’s that we built whole imaginary edifices, whole castles in the air on top of this idea of 5-HTTLPR mattering. We ‘figured out’ how 5-HTTLPR exerted its effects, what parts of the brain it was active in, what sorts of things it interacted with, how its effects were enhanced or suppressed by the effects of other imaginary depression genes. This isn’t just an explorer coming back from the Orient and claiming there are unicorns there. It’s the explorer describing the life cycle of unicorns, what unicorns eat, all the different subspecies of unicorn, which cuts of unicorn meat are tastiest, and a blow-by-blow account of a wrestling match between unicorns and Bigfoot.”

So ends the sorry and expensive MD candidate gene story. Despite the expenditure of hundreds of millions of dollars on the depression studies alone, and despite genetic researchers’ sincere and admirable desire to prevent and alleviate human suffering, the behavioral science candidate gene era turned out to be, in the words of our planet’s top behavioral geneticist, “a flop.”4

Genome-wide Association (GWAS) and Polygenic Risk Score (PRS) Studies

Given the failure of family studies, twin studies, adoption studies, linkage studies, candidate gene studies, and rare variant studies to produce scientifically acceptable evidence that disordered genes play a role in causing MD, supposedly “hypothesis-free” GWAS/PRS research has become the last hiding place of potential MD “heritability.” GWAS researchers attempt to identify single-nucleotide polymorphisms or “SNPs” (pronounced “snips” by those in the field). These variants, numbering in the millions and curated in an ever-growing digital catalogue available to researchers, are considered “common” minority variants of genes present in at least 1% of the population. Because multiple comparisons are made, the GWAS significance threshold is very high, usually 5 × 10−8. A PRS study combines statistically significant and non-significant individual SNP hits to produce a polygenic (composite) risk score. Polygenic risk scores have been described as “constructed as a weighted sum of risk allele counts using effect sizes estimated from GWAS as the weights.” They are expressed as a percentage.

As GWAS pioneer Jonathan Flint, Ralph Greenspan, and Kendler repeatedly stressed in their 2020 book How Genes Influence Behavior (2nd ed.), “A GWAS does not find association with a gene.” A GWAS finds associations with a locus, which “is a geneticist’s term for place—a place in the genome where the genetic variant is found….If the variant found by a GWAS altered a coding region, as was initially hoped, then it would be straightforward to say which genes were involved in the trait under investigation. But GWAS hits turned out not to be coding for SNPs.”5

To repeat: A GWAS does not identify causative genes, and a “gene association” points to a correlation or to a chance finding, not to a cause. The classic example of a correlation not implying cause is that if red-haired people in a given society are persecuted, and for this reason alone many red-haired people suffer from depression, this indicates only that genes for red hair are associated with depression, not that they cause depression.

In 2014, Flint and Kendler recognized the “failure” of the nine GWASes published up to that time. Since then, a few studies have produced GWAS SNP hits that psychiatry and the media now put forward as solid MD gene associations. However, psychiatric GWAS/PRS studies have been the subject of controversy for several reasons. I will mention a few of the problem areas.

“Associated With” “Caused By.” As we saw, a GWAS identifies regions of the genome (“hits”) “associated with” a condition. It does not identify genes that cause it, and “associated with” does not mean “caused by.”

Population Stratification Confounds. GWAS/PRS findings are subject to the confounding influence of population stratification (“pop strat”), which can lead to spurious findings (explained here, here, here, and here). Briefly, population stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry, rather than association of genes with disease. No generally accepted remedy for pop strat has been found, although many have been proposed and attempted.

Dependence on Heritability Estimates. Heritability estimates both justify and guide a GWAS. Researchers assume that heritability estimates are important and roughly accurate, and that MD heritability is in the 30%-40% range. If a heritability estimate is inflated due to systematic bias, or if heritability estimates are meaningless in and of themselves (apart from their original purpose of helping predict the results of a selective breeding program), attempts to find causative genes will end up as expensive failures.

A Scientific Fishing Expedition? By definition, a scientific “fishing expedition” is a hypothesis-free method, where researchers base their conclusions on significant correlations that in the GWAS context pop up on a Manhattan Plot. According to an author writing in a clinical psychiatry publication, “The term fishing expedition is used to describe what researchers do when they indiscriminately examine associations between different combinations of variables not with the intention of testing a priori hypotheses but with the hope of finding something that is statistically significant in the data.” It could be argued that a GWAS is a type of fishing expedition, or even more, a massive gene-trawling juggernaut hauling in as much variation as possible. In 2016, behavioral geneticist Eric Turkheimer referred to the GWAS method as “unapologetic, high-tech p-hacking.”

Conflicts of Interest. Potential conflicts of interest exist when research, researchers, and institutions are funded by companies that profit from the promotion of biological and genetic explanations of depression. A large-sample GWAS claiming 178 significant loci-associations for MD, including replication of the findings in an independent sample, was published in 2021. Yale University’s Daniel Levey was the lead author, and the corresponding author was psychiatric researcher Murray B. Stein. Dr. Stein’s “competing interests” statement read (I marked companies that develop antidepressant drugs with an asterisk), “M.B.S. reports receiving consulting fees in the past 3 years from Acadia Pharmaceuticals*, Aptinyx*, Bionomics*, BioXcel Therapeutics*, Boehringer Ingelheim, Clexio Biosciences*, EmpowerPharm, Engrail Therapeutics*, Genentech/Roche, GW Pharmaceuticals, Janssen*, Jazz Pharmaceuticals and Oxeia Biopharmaceuticals.” The annual “consulting fee” income Dr. Stein received was not disclosed. The article said that he and a co-author “secured funding for this project.” The direct-to-consumer genetic testing company 23andMe played a significant role in this study, a company that stood to profit from the “discovery” of relevant MD genes. There is a symbiotic relationship between psychiatry, biopsychiatry, direct-to-consumer genetic testing companies, and the drug companies. All have a vital and mutual interest in convincing the public that psychiatric conditions are real brain-based diseases rooted in genetics, in need of medication like other diseases. As Robert Whitaker and others have shown, all share in the profits.

Other Unlikely GWAS Findings. The GWAS method has produced some questionable and even humorous “findings.” These include significant hits for behavioral characteristics that include getting concussions, self-reported childhood maltreatment, crying habits, female sexual dysfunction, food liking, household income, ice cream flavor preferences, loneliness, being a morning person, musical beat synchronization, regular attendance at a sports club, pub, or religious group, and white wine liking. Results of this type are obvious GWAS red flags, just as they were during the failed candidate gene era.

Polygenic Risk Score Cautions and Warnings. In an interview, veteran psychiatric genetic researcher Elliot Gershon described PRS as “sort of a mindless score,” and that “you can’t really tell anything from the polygenic risk factor.”6 In a detailed analysis, sociologist/criminologist Callie Burt described several potential PRS environmental confounds, and concluded that scores should be used “sparingly and cautiously with caveats placed front and center.” Historian of science Nathaniel Comfort warned that polygenic risk scores “are in no sense causal.” A group of genetic researchers concluded that polygenic scores are computed “under erroneous assumptions.” Medical researcher Keith Baverstock called polygenic risk scores “a dangerous delusion.”

Major Depression Genetic Research and the Replication Crisis in Science

Science is in the midst of a replication crisis (also known as the reproducibility crisis), meaning a crisis brought about by the discovery that some key findings across various scientific fields were probably non-findings resulting from research that was poorly performed, manipulated to match confirmation biases or funding source expectations, or even fraudulent. The traditional scientific research and publication process makes it possible for researchers to change various aspects of their study after reviewing their data, but before submitting their paper for peer review and publication. Science writer Ed Yong wrote a 2019 Atlantic article about how confirmation biases may have played a role in prolonging what Lowe called the “all noise, all false positives, all junk” MD candidate gene era:

“Many fields of science, from psychology to cancer biology, have been dealing with similar problems: Entire lines of research may be based on faulty results. The reasons for this so-called reproducibility crisis are manifold. Sometimes, researchers futz with their data until they get something interesting, or retrofit their questions to match their answers. Other times, they selectively publish positive results while sweeping negative ones under the rug, creating a false impression of building evidence.”

Such practices have led to increasing calls for research preregistration, where investigators would have the option or be required to submit their research rationale, hypotheses, design and analytic strategy, and planned data-collection stop point to a journal for peer review before they collect and analyze their data. Although “we may never be able to eliminate bias altogether,” wrote cognitive neuroscientist Chris Chambers, a “sure way to immunize ourselves against its consequences…is peer-reviewed study preregistration.”7

Yong saw the problems that led to the downfall of depression candidate gene research as characteristic of “an academic world that rewards scientists…for publishing papers in high-profile journals—journals that prefer flashy studies that make new discoveries over duller ones that check existing work.” Researchers “are rewarded for being productive rather than being right, for building ever upward instead of checking the foundations.” (The validity of twin studies question is an example of a “foundation” that molecular genetic researchers rarely check.) After enough (albeit weak) studies are published, according to Yong “they create a collective perception of strength that can be hard to pierce.” Hard to pierce, that is, until the entire false-positive structure comes crashing down.

Most likely, Stuart Ritchie’s 2020 evaluation of the behavioral candidate gene era will be the eventual evaluation of the behavioral and psychiatric GWAS/PRS era as well (emphasis added): “They were building a massive edifice of detailed studies on foundations that we now know to be completely false.”

Conclusions

I have shown that family, twin, adoption, and molecular genetic studies have failed to provide scientifically valid evidence that genes play a role in causing depression. Combined with the recent findings by Moncrieff and colleagues that serotonin is not associated with depression, the idea of MD as a medical condition is in serious trouble.

To understand the true causes of depression, we must focus on family (including abuse and trauma), social, and political environments, including racial, gender, class, and other types of oppression/discrimination. We must address people’s increasing social isolation and disconnection from each other, lack of meaning and purpose, consumerism, and fears of present or future calamities such as pandemics, climate change, and nuclear war. The idea of depression as a medical/genetic condition must be reevaluated, and non-medical prevention and intervention strategies should be promoted. This is the approach of the Power Threat Meaning Framework (PTMF), developed by psychologists Lucy Johnstone, Mary Boyle, and others.8 In a 2020 introductory book, the authors described the Framework’s overall message as follows:

“All forms of adversity and distress are more common in social contexts of inequality and other forms of deprivation, discrimination, marginalisation and injustice. This evidence does not support the individualisation of distress, either medically or psychologically. Instead, it implies the need for action, primarily through social policy, at the earliest possible point, before the destructive and self-perpetuating cycles are set in motion.”9

Psychiatry sees a depressed person and asks, “What is wrong with you?” The PTMF asks, as do most psychotherapists, “What happened to you?” Given the lack of evidence, terms such as “serotonin,” “chemical imbalance,” “brain disease,” “genetic predisposition,” “genes,” and “heritability” should not be found in the answer to either of these questions. As James Davies wrote, the medical model describes “suffering as being rooted in individual rather than social causes, leading individuals to think that it is them rather than the economic and social system in which they live that is at fault and in need of reform.”10

Psychiatry’s longstanding major depression chemical imbalance and brain disease claims used to support the medical model are now crumbling. The longstanding and related “depression as a heritable disorder” claim awaits its turn.

Show 10 footnotes

  1. I thank Mike Jones for providing helpful feedback on earlier drafts of this article. Mike’s extensive knowledge of psychiatric molecular genetic research was of great help. I also thank Jonathan Leo for providing helpful feedback. All views expressed in this article are my own, and any errors are my responsibility.
  2. Plomin, R., (2018), Blueprint: How DNA Makes Us Who We Are, Cambridge, MA: MIT Press, p. 224.
  3. Ritchie, S., (2020), Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth, Henry Holt and Company, pp. 140-141.
  4. For my review of Robert Plomin’s 2018 book Blueprint, see Joseph, J., (in press), A “Blueprint” for Genetic Determinism: An Appraisal of Robert Plomin’s Blueprint: How DNA Makes Us Who We Are, American Journal of Psychology. Advance online publication https://cpb-us-e1.wpmucdn.com/sites.ucsc.edu/dist/0/158/files/2021/10/FinalJay-Joseph-Review-of-Blueprint-Manuscript-AJP-Revised-9-17-2021.pdf
  5. Flint, J., Greenspan, R. J., & Kendler, K. S., (2020), How Genes Influence Behavior (2nd ed.), Oxford, UK: Oxford University Press, p. 79, emphasis in original.
  6. Kolker, 2020, Hidden Valley Road: Inside the Mind of an American Family, New York: Anchor, p. 254.
  7. Chambers, C., (2017), The Seven Deadly Sins of Psychology: A Manifesto for Reforming the Culture of Scientific Practice, Princeton, NJ: Princeton University Press, p. 174.
  8. For Power Threat Meaning Framework (PTMF) documents, see https://www.bps.org.uk/power-threat-meaning-framework
  9. Boyle, M., & Johnstone, L., (2020), A Straight Talking Introduction to the Power Threat Meaning Framework: An Alternative to Psychiatric Diagnosis, Monmouth, UK: PCCS Books, p. 105.
  10. Davies, J., (2021), Sedated: How Modern Capitalism Created Our Mental Health Crisis, London: Atlantic Books, p. 34.

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Mad in America hosts blogs by a diverse group of writers. These posts are designed to serve as a public forum for a discussion—broadly speaking—of psychiatry and its treatments. The opinions expressed are the writers’ own.

17 COMMENTS

  1. Sacramento Mayor Darrell Steinberg is firmly committed to the brain disease model. He says that everyone is on the Mental Illness Spectrum, and so it seems that everyone needs to be checking in with Thomas Insel’s auditor center via their cell phone.

    He had put his 13yo daughter in an out of state psychiatric hospital, and now in her 20’s, she still says that she has “Mental Illness”.

    Mental Health has been the cornerstone of Steinberg’s entire political career.

    You can’t reason with such a person. It really just comes down to power, legal, political, social, that is the only way one can stand up to such attacks.

    August 29, 25 min.
    https://www.youtube.com/watch?v=JkpBc48Au3Q

    Joshua

  2. Jay: Great article.

    I also enjoyed our recent little dust-up on Twitter with Bob Kolker, author of Hidden Valley Road:

    https://www.madinamerica.com/2020/08/kicking-straw-woman/

    Kolker’s sneering remarks about “Blaming Mommy and Daddy” in reference to individuals who have been raped, sodomized, and had God knows what else done to them when they were small and defenseless confirmed my belief that biological psychiatry is a field that has lost all sight of what means to be human.

    Be well.

  3. Thank you, Jay, for all your truthful research and writings regarding the scientific fraud of the psychiatric industry. I hope and pray you are able to destroy psychiatry’s fraudulent claims of genetic etiology.

    Especially given the reality of the iatrogenic etiology of – likely most of the DSM disorders – including “schizophrenia.” The positive symptoms of which can be created with the antidepressants and antipsychotics, via anticholinergic toxidrome. And the negative symptoms of “schizophrenia” can be created, via neuroleptic induced deficit syndrome.

    In Allen Frances’ own words, “It’s bullshit.” Thanks again for all your truth telling, Jay.

    • These claims of multi-gene associations are really just window dressing. It means absolutely nothing that 4% of those “diagnosed” with “ADHD” have a set of certain genes in problem, when only 2% of “normal” people have such an association. There are still 96% of the affected population who don’t even have the genes in question! Why does anyone take this crap seriously?

  4. They need to realize that in most cases depression is simply a symptom of another illness. It may be trauma, it may be your body responding to a pathogen, or simply you body telling you to rest. The current model is the equivalent of treating a fever without trying to find out what is causing the fever and the results are no different.

  5. Bravo! This is a very scientific and well researched blog.

    I would differ with the “pillar crumbling” analogy for the “chemical imbalance” theory AND for current psychiatric genetic theories.

    We cannot underestimate how much power psychiatry and their medical model wields in today’s world. They have become crucial “pillars” in propping up and sustaining a profit based capitalist system.

    In today’s world it is now quite possible for “alternate realities” to co-exist in the vast “marketplace of ideas.” Major systemic change will be required to bring down these “pillars ” of oppression.

    Richard

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