How psychiatric genetics can help to guide diagnostic practice and therapy

Recently, professor Stephen Faraone from SUNY Upstate University in the USA gave a webinar about genetic research in psychiatry (especially ADHD) and how this can help to better understand diagnosis and provide better treatment. In this blog I will share with you some highlights from this webinar.

  1. ADHD is a continuous trait in the population

ADHD is not something that you either have or don’t have. Rather, symptoms or characteristics of ADHD are present in the entire population, in varying severity. The system for psychiatric diagnoses is however based on categorical definitions that determine when a certain combination of symptoms and severity can be classified as a particular disorder. Although these categories can be of great help to provide public health data or determine insurance coverage, they often don’t really match individual cases. Hence there arise problems with heterogeneity, subtypes, subthreshold cases and comorbidity.

Genetic research has shown that psychiatric conditions such as ADHD are not caused by a few single genes, but rather by thousands or tens of thousands genetic variants that each contribute slightly to the ADHD risk. These so-called polygenic risk scores form a normal distribution across the entire population, with the majority of people having low polygenic risk scores (so a low to average risk of ADHD), while a small portion of individuals have a very low or very high risk. This adds to our understanding that ADHD is a continuous trait in the population.

Image from the webinar by prof. Stephen Faraone. The higher the number on the x-axis, the higher the genetic risk of having ADHD. Negative numbers mean reduced genetic risk of ADHD.

2. Comorbidity in psychiatry is the norm, rather than the exception

In the webinar, Stephen Faraone explains that in 90’s it was thought impossible that an individual can have both ADHD and depression. Now, we know better than that. There are substantial genetic correlations between different psychiatric disorders, meaning that the genes that increase the risk of for instance ADHD, also increase the risk of schizophrenia, depression, bipolar disorder, autism and tic disorder. This is further evidence that psychiatric conditions are not separate, categorial entities but rather arise from similar biological mechanisms.

3. Personalised medicine and pharmacogenetics are not yet sufficiently established to adopt widely and replace current medication on a broad scale

The second part of the webinar was about pharmacogenetic testing. This means that an individual’s genetic profile is used to determine whether a drug will be effective, and in what dose. Although this sounds promising, there is still a lot of discussion about the validity of such tests. This is due to varying results, differing protocols and large heterogeneity between studies. In some cases, pharmacogenetic testing can help to find the right treatment for an individual, for instance when this person is not responding well to regular treatment, but it is definitely not a fool-proof method yet. Better randomized controlled clinical trials are needed to improve reliability of these tests.

You can watch the full webinar here:

The genetics of having multiple mental health conditions

We know that psychiatric conditions have a strong genetic component. This means that genes play an important role in determining an individual’s risk or vulnerability to develop a psychiatric condition. However, there is evidence that there are genetic variants that increase the risk for multiple psychiatric disorders. This is called pleiotropy. Researchers of the “Cross-Disorder Group of the Psychiatric Genomics Consortium” have searched the entire genome of 727,000 individuals (of whom 233,000 were diagnosed with a psychiatric disorder) to identify genetic variants with such pleiotropy.

The researchers found one particular gene – called DCC – that increases vulnerability for all eight disorders that were investigated: ADHD, autism spectrum disorder, anorexia nervosa, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia and Tourette syndrome.

They also found more than 100 genetic variants that predispose to at least two psychiatric disorders, and around 20 variants that are associated with four or more. This means that the genes that contain these variants can be interesting to further understand why certain individuals are more vulnerable to develop psychiatric illnesses than others.

One of the researchers, professor Bru Cormand, explains more about this research in this blog.

Further reading: Cross-Disorder Group of the Psychiatric Genomics Consortium (2019): Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders. Cell, 179(7): 1469-1482.e11.

Professor Cormand is involved in the CoCA research consortium where he investigates the genetic overlap between ADHD, major depression, anxiety disorder, substance use disorder and obesity. To read more about this, see for instance this other blog by him and dr. Judit Cabana Dominguez.

The notorious evening chronotype and my master’s thesis

Almost every person, healthy or not, suffers from occasional problems with sleep and circadian rhythm. In the modern days of 24/7 smartphone use and transcontinental flights, our internal body clock is having a hard time adjusting to the external cues. For the persons suffering from mental health issues, their impaired sleep cycle can be one of the cornerstone problems of daily living. Sleep problems have been confirmed to be a first symptom, consequence, or even a cause of such psychiatric conditions as major depression, bipolar disorder, ADHD, autism, substance abuse, and even aggressive behaviour. Their strong relations, however, have not been studied systematically and broadly just yet.

Why study the circadian rhythm?

Circadian rhythm is our inner clock that regulates a lot of important processes in the human body, including the sleep/wake cycle, the release of hormones and even the way we process medicines. This clock is run by the brain region called the hypothalamus, which piles up a protein called CLK (referring to “clock”), during the daytime. CLK, in turn, activates the genes which make us stay awake, but also gradually increases the creation of another protein called PER. When we have a lot PER, it turns off CLK production and makes us ready to sleep. As CLK is getting lower, this causes a decrease in PER, so that the process starts again with elevating CLK waking us up. This cycle happens at around 24-hour intervals and is greatly influenced by so-called zeitgebers, or time-givers, like light, food, noise and temperature. When our retina neurons catch light waves, the suprachiasmatic nucleus in our brain stops the production of the hormone called melatonin that induces sleep and starts producing noradrenaline and vasopressin instead to wake us. This is the exact reason why you cannot fall asleep after watching a movie at night.

Figure 1. The smart protein CLK wakes us up and its friend PER gets us to sleep.

Sometimes our body clock fails to function, as in the case of jetlag when we feel bad after changing a time zone or social jetlag when we have to start work early at 8 am. It can go as far as a circadian rhythm disorder meaning you have either a delay or advancement of sleep phases or an irregular or even non-24-hour daily activities preference. However, in the general population, a small variation in the rhythm is quite normal and is usually referred to as a chronotype. It defines your preference of when to go to sleep and do your daily activities and is divided into 3 distinct versions. The radical points of these variations include a morning chronotype, or “larks”, who go somewhat 2-3 hours ahead of the balanced rhythm, and an evening chronotype, or “owls”, who are a little delayed. The larks feel and function better during the first half of the day and go to bed rather early, while the owls prefer to work in the evenings and go to bed and wake up naturally late. The third chronotype is the in-between, balanced version of these two.

Figure 2. The ‘owls’ seem to have questionable personalities and suffer from psychiatric conditions more often!

What’s my study about?

Previous research has shown that many psychopathologies are linked to an evening circadian preference. For my master thesis research, I am investigating whether we can identify specific profiles in sleep and circadian rhythm problems that are linked to specific mental health problems. There was even a curious study where researchers linked the Dark Triad personalities, which include people with tendencies for manipulation, lack of empathy, and narcissism, to the evening chronotype. Maybe this leaves some evidence for the famous quote that “evil does not rest”. However, there’s a great variation in sleep duration and perceived quality of sleep in patients with various diseases. We hope to divide such persons into more or less accurate groups with a sleep profile that would predict and aid the correct diagnosis of one or the other mental health condition.

The psychopathologies are included in our study as so-called dimensions, which look at each psychiatric syndrome not as with a norm/pathology cut-off but rather as a continuum of symptoms severity. This approach allows us to see if the sleep/circadian profile we identify refers to mental health in general or can be a distinguished part of a certain psychiatric condition. It might be that all dimensions, like depression and autistic spectrum disorders, have an evening chronotype and some non-specific sleep problems. Alternatively, we might find out that a person with symptoms of depression would sleep more or less than average and go to bed later, whereas a person with anxiety would go to sleep later as well but wake up at night very often despite an average summed up sleep duration.

The circadian rhythm changes throughout a lifetime from an early to an evening chronotype towards adolescence and then gradually shift back to the earlier preference with older age. Across the whole lifespan people constantly face varying quality of night sleep. Moreover, each psychiatric condition has a particular age of onset and sometimes changes its character with time. These are the reasons why our study will also look at how the sleep/circadian profiles change within the development phases from children (4-12 years) to adolescents (13-18) to adults (19-64) to the elderly (≥65) and if they affect males and females differently.

Why would it matter?

Should we discover distinct links between the profiles of sleep/circadian problems and certain conditions, other studies can then look into whether these profiles could be the reasons behind developing a mental health condition. It’d be interesting to finally learn what is a chicken and an egg in each profile-disease relation. For instance, should we really treat ADHD patients with melatonin and bright-light lamps instead of stimulants?

Figure 3. Maybe if we adopt a typical cat’s lifestyle, we get less mental health problems. 🙂

Dina Sarsembayeva is a neurologist and a research master’s student at the University of Groningen. She is using the data from the CoCa project to learn if the chronotypes and sleep problems can be turned into profiles to predict specific psychiatric conditions.

Further reading

  1. Walker, W. H., Walton, J. C., DeVries, A. C. & Nelson, R. J. Circadian rhythm disruption and mental health. Transl. Psychiatry 10, (2020).
  2. Logan, R. W. & McClung, C. A. Rhythms of life: circadian disruption and brain disorders across the lifespan. Nature Reviews Neuroscience vol. 20 49–65 (2019).
  3. Jones, S. G. & Benca, R. M. Circadian disruption in psychiatric disorders. Sleep Med. Clin. 10, 481–493 (2015).
  4. Taylor, B. J. & Hasler, B. P. Chronotype and Mental Health: Recent Advances. Curr. Psychiatry Rep. 20, (2018).

The genetic architecture of the brain

Genes play a big role in determining the architecture of our brain: the way it’s folded, the thickness of the outer layer, and the way different brain areas are connected. By combining data from all over the world, a large collaboration of researchers from the ENIGMA consortium has now identified almost 200 genetic variants that are involved in this brain architecture. These findings can help us to further understand the genetics of brain disorders. 

Our genes contain the blueprint of our bodies. They contain information about how our cells function, and they determine for instance the colour of our eyes and hair, or whether we like cilantro (coriander) and bitter tastes. For some traits we know very well how they are influenced by genes. Eye color for instance is coded by only a few genes. But for many other traits such as height and personality, many different genes are involved. In addition, other (non-genetic) factors also influence these traits, such as malnutrition that can cause stunted growth.

The architecture of the brain is influenced by a large numer of genes, of which we still know very little. To investigate this, researchers combined genetic data of over 50.000 individuals with MRI-data. MRI-scans can show in detail the thickness of the outer layer of the brain, where all the brain cells are (also called the grey matter). They can also be used to measure how much this layer is folded, which gives information about the total surface of this outerlayer. This brain architecture is unique to every individual. The extent of the folds and the thickness of the outer layer have previously (in other research studies) been linked to cognitive abilities and various neurological and psychiatric disorders, such as Alzheimer’s disease, schizophrenia, depression, autism, and ADHD. It is therefore helpful to understand the genetics of this architecture, because it will help us to better understand the genetic mechanisms of these conditions.

The findings from this research study are also explained in this video:

This important research can only be done by combining a lot of data and collaborating with a large number of scientists and institutes. The ENIGMA consortium has been set upt to facilitate this kind of world-wide collaboration. The research that has now been published is the combined effort of more than 360 scientists from 296 departments across 184 different institutions and universities. They also made their results downloadable so that everyone who is interested can have a closer look.

The full publication can be found here:

See also our previous blogposts about these topics:




Food & mental health: the Eat2beNICE project

We all know that a healthy lifestyle is beneficial for our health. But many of us forget that eating healthy, exercising regularly and getting enough sleep is also important for good mental health. In the Eat2beNICE research project a large team of researchers is investigating the link between food and mental health, specifically impulsivity, compulsivity and aggression. To share this knowledge with the rest of the world, they work together with food consultant Sebastian Lege.

The Eat2beNICE project just released a video to explain what the research is about and why it’s important. In this video Sebastian Lege visits the project coordinator Alejandro Arias-Vasquez, en several other researchers in the consortium.

More information about the Eat2beNICE project can be found at






“No I do not have ADHD, I am just busy!”, but still very interesting for genetic studies!

Do you sometimes find it difficult to pay attention? Can you be very disorganized at times, or very rigid and inflexible? Although difficulties with attention, organization and rigidity are symptoms of psychiatric disorders, these traits are not unique to people with a diagnosis. And that is very useful for studying the genetics of psychiatric disorders.

Being easily distracted, liking things to go in a certain way, having a certain order in the way you do things, these might all be things you recognize yourself (or someone you know) in, while you (or they) are not diagnosed with any psychiatric disorder. We actually know that many of these symptoms are indeed found in a range in the general population, with some people showing them a lot, some a little and some not at all. If these symptoms are also present in people without a diagnosis then why should we only study people with a diagnosis to learn more about the biology of symptom-based disorders?

Many psychiatric disorders, like attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are disorders that ‘run in the family’. Using family-based and genetic studies it was found that they are actually highly heritable. However the underlying genetic risk factors turned out to be difficult to find. Enormous samples sizes (comparing more than 20 000 people with the disorder to even more individuals without the disorder) were needed to robustly find just a few genetic risk factors, although we know that many more genetic factors contribute. Even though these disorders are highly prevalent, collecting genetic data on psychiatric patients for research is still challenging. Using population-based samples – that include all varieties of people from the general population – can be a good alternative to reach large sample sizes for powerful genetic studies.

Taking together the fact that psychiatric-like symptoms are also, to a certain degree, present in the general population, and the fact that genetic studies can benefit from large(r) sample sizes to find genetic associations, it can be very interesting to study psychiatric-like traits in population-based samples. This is indeed what happened in the field of psychiatric genetics. The first proof-of-concept studies were able to show an astonishing overlap in genetic factors of more than 90% between ADHD and ADHD symptoms in the general population. Our own research group was able to show that certain autistic traits, like rigidity, indeed share a genetic overlap with ASD and that genes that were previously linked to ASD show an association to autistic traits in the population. These results show that genetic factors involved in disorder-like traits are overlapping with genetic factors involved in the clinical diagnosis, and therefore can indeed be used to study the biology of psychiatric disorders.

So next time you feel distracted/rigid/disorganized, don’t get discouraged, but consider signing up for a genetic study. Science might need you!

Janita Bralten is a postdoctoral researcher at the department of Human Genetics in the Radboud university medical center, Nijmegen, the Netherlands. Her research focusses on the genetics of psychiatric disorders.

Further reading:

Bralten J, van Hulzen KJ, Martens MB, Galesloot TE, Arias Vasquez A, Kiemeney LA, Buitelaar JK, Muntjewerff JW, Franke B, Poelmans G. Autism spectrum disorders and autistic traits share genetics and biology. Mol Psychiatry. 2018 May;23(5):1205-1212.

Middeldorp CM, Hammerschlag AR, Ouwens KG, Groen-Blokhuis MM, Pourcain BS, Greven CU, Pappa I, Tiesler CMT, Ang W, Nolte IM, Vilor-Tejedor N, Bacelis J, Ebejer JL, Zhao H, Davies GE, Ehli EA, Evans DM, Fedko IO, Guxens M, Hottenga JJ, Hudziak JJ, Jugessur A, Kemp JP, Krapohl E, Martin NG, Murcia M, Myhre R, Ormel J, Ring SM, Standl M, Stergiakouli E, Stoltenberg C, Thiering E, Timpson NJ, Trzaskowski M, van der Most PJ, Wang C; EArly Genetics and Lifecourse Epidemiology (EAGLE) Consortium; Psychiatric Genomics Consortium ADHD Working Group, Nyholt DR, Medland SE, Neale B, Jacobsson B, Sunyer J, Hartman CA, Whitehouse AJO, Pennell CE, Heinrich J, Plomin R, Smith GD, Tiemeier H, Posthuma D, Boomsma DI. A Genome-Wide Association Meta-Analysis of Attention-Deficit/Hyperactivity Disorder Symptoms in Population-Based Pediatric Cohorts. J Am Acad Child Adolesc Psychiatry. 2016 Oct;55(10):896-905.

If you are interested in joining a scientific study see for example:

or (Dutch only)

ADHD and autism – similar or different disorders?

Have you ever thought that ADHD and autism could perhaps be the same disorder? – Or have you thought that they are way too different, two different planets in the psychiatric universe? Researchers do not agree on this. We know that both ADHD and autism are neurodevelopmental conditions with onset in childhood and that they share some common genetic factors, however, they appear with quite different phenotypical characteristics. We also know that people with ADHD or autism have an increased risk of getting other psychiatric disorders, so-called comorbidities, and smaller studies have shown that individuals with ADHD or autism get different psychiatric disorders, and at a different degree.

How can we utilize this knowledge about different psychiatric comorbidities between ADHD and autism? How can we get closer to an answer to this question; are ADHD and autism similar or different conditions? By using large datasets; unique population-based registries in Norway, we wanted to compare the pattern of psychiatric comorbidities in adults diagnosed with ADHD, autism or both disorders. In addition, we wanted to compare the pattern of genetic correlations between ADHD and autism for the same psychiatric traits, and for this, we exploited summary statistics from relevant genome-wide association studies.

In the registries, we identified 39,000 adults with ADHD, 7,500 adults with autism and 1,500 with both ADHD and autism. We compared these three groups with the remaining population of 1.6 million Norwegian adult inhabitants without either ADHD or autism. The psychiatric disorders we studied were anxiety, bipolar, depression, personality disorder, schizophrenia spectrum (schizophrenia) and substance use disorders (SUD).

Interestingly, we found different patterns of psychiatric comorbidities between ADHD and autism, overall and when stratified by sex (Fig.1). These patterns were also reflected in the genetic correlations, however, only two of the six traits showed a significant difference between ADHD and autism (Fig.2).

Figure 1 - Solberg et al. 2019
Figure 1. Prevalence ratios of psychiatric disorders in adults with ADHD, autism or both ADHD and autism, relative to the remaining population, by sex. As can be seen in the figure, schizophrenia is more frequent in autism or ADHD+autism than ADHD alone, while the reverse is true for substance use disorder. There are also significant differences in prevalence between men and women. Figure from Solberg et al. 2019, CC-BY-NC-ND.

Figure 2. Left: The pattern of prevalence ratios of psychiatric comorbidity in adults with ADHD or autism observed in this study (ADHD; n=38,636, autism; n=7,528). Right: genetic correlations (rg) calculated from genome wide association studies. Psychiatric conditions are highly prevalent in both ADHD and ASD, with schizophrenia being most prevalent in ASD and antisocial personality disorders in ADHD. Genetic correlations are also high with both disorders, with especially high correlations between ADHD and alcohol dependence, smoking behavior and anti-social behavoiur. Major depressive disorder has high genetic correlations with both ADHD and autism. Figure from Solberg et al. 2019, CC-BY-NC-ND.

The most marked differences were found for schizophrenia and SUD. Schizophrenia was more common in adults with autism, and SUD more common in adults with ADHD. Associations with anxiety, bipolar and personality disorders were strongest in adults with both ADHD and autism, indicating that this group of adults suffers from more severe impairments than those with ADHD or autism only. The sex differences in risk of psychiatric comorbidities were also different among adults with ADHD and ASD.

In conclusion, our study provides robust and representative estimates of differences in psychiatric comorbidities between adults diagnosed with ADHD, autism or both ADHD and autism. With the results from analyses of genetic correlations, this finding contributes to our understanding of these disorders as being distinct neurodevelopmental disorders with partly shared common genetic factors.

Clinicians should be aware of the overall high level of comorbidity in adults with ADHD, autism or both ADHD and autism, and the distinct patterns of psychiatric comorbidities to detect these conditions and offer early treatment. It is also important to take into account the observed sex differences. The distinct comorbidity patterns may further provide information to etiologic research on biological mechanisms underlying the pathophysiology of these neurodevelopmental disorders.

This study was done at Stiftelsen Kristian Gerhard Jebsen Centre for Neuropsychiatric disorders, University of Bergen, Norway, and published OnlineOpen in Biological Psychiatry, April 2019, with the title:

“Patterns of psychiatric comorbidity and genetic correlations provide new insights into differences between attention-deficit/hyperactivity disorder and autism spectrum disorder”.

Figure 1 and 2 are re-printed by permission

Berit Skretting Solberg is a PhD-candidate at the Department of Biomedicine/Department of Global Health and Primary Care, University of Bergen, Norway. She is also a child- and adolescent psychiatrist/adult psychiatrist. She is affiliated with the CoCa-project, studying psychiatric comorbidities in adults with ADHD or autism, using unique population-based registries in Norway.


Who is the average patient with ADHD?

Is there an ‘average ADHD brain’? Our research group (from the Radboudumc in Nijmegen) shows that the average patient with ADHD does not exist biologically. These findings were recently published in the journal. Psychological Medicine.

Most biological psychiatry research heavily relies on so-called case-control comparisons. In this approach a group of patients with for instance ADHD is compared against a group of healthy individuals on a number of biological variables. If significant group effects are observed those are related to for instance the diagnosis ADHD. This often results in statements such as individuals with ADHD show differences in certain brain structures. While our results are in line with those earlier detected group effects, we clearly show that a simple comparison of these effects disguises individual differences between patients with the same mental disorder.

Modelling individual brains

In order to show this, we developed a technique called ‘normative modelling’ which allows us to map the brain of each individual patient against typical development. In this way we can see that individual differences in brain structure across individuals with ADHD are far greater than previously anticipated. In future, we hope that this approach provides important insights and sound evidence for an individualized approach to mental healthcare for ADHD and other mental disorders.

Individual differences in ADHD

When we studied the brain scans of individual patients, the differences between those were substantial. Only a few identical abnormalities in the brain occurred in more than two percent of patients. Marquand: “The brains of individuals with ADHD deviate so much from the average that the average has little to say about what might be occurring in the brain of an individual.”

Personalized diagnosis of ADHD

The research shows that almost every patient with ADHD has her or his own biological profile. The current method of making a diagnosis of psychiatric disorders based on symptoms is therefore not sufficient, the authors say: “Variation between patients is reflected in the brain, but despite this enormous variation all these people get the same diagnosis. Thus, we cannot achieve a better understanding of the biology behind ADHD by studying the average patient. We need to understand for each individual what the causes of a disorder may be. Insights based on research at group level say little about the individual patient.”

Re-conceptualize mental disorders

The researchers want to make a fingerprint of individual brains on the basis of differences in relation to the healthy range. Wolfers: “Psychiatrists and psychologists know very well that each patient is an individual with her or his own tale, history and biology. Nevertheless, we use diagnostic models that largely ignore these differences. Here, we raise this issue by showing that the average patient has limited informative value and by including biological, symptomatic and demographic information into our models. In future we hope that this kinds of models will help us to re-conceptualize mental disorders such as ADHD.”

Further reading

Wolfers, T., Beckmann, C.F., Hoogman, M., Buitelaar, J.K., Franke, B., Marquand, A.F. (2019). Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models. Psychological Medicine, .

This blog was written by Thomas Wolfers and Andre Marquand from the Radboudumc and Donders Institute for Brain, Cognition and Behaviour in Nijmegen, The Netherlands. On 15 March 2019 Thomas Wolfers will defend his doctoral thesis entitled ‘Towards precision medicine in psychiatry’ at the Radboud university in Nijmegen. You can find his thesis at

A complex genomic jigsaw puzzle

The human genome is not a monolithic entity but has been constantly changing throughout the  evolution of the species. The main reason behind is that when new copies of the genome of each individual are generated during reproduction, replication errors (mutations) introduced by the cellular replication machinery which can occur spuriously and be inherited from the next generation onwards.

From a genomic point of view, the magnitude of the error can range from a simple nucleotide change (called single nucleotide variant or SNV) to duplicating/deleting large fragments of the genome (called copy number variants or CNVs), as well as switching the orientation in the genome or rearranging a genomic fragment in a new genomic positions. The most common type of mutation in the human genome is SNV. However, humans also show extensive CNVs compared to other species.

From a functional point of view, all these types of changes can have important phenotypic consequences in the offspring and, ultimately, in the fate of the species when affecting functional genomic elements such as genes.

From an evolutionary point of view, new mutations that modify the phenotype of an individual are the substrate of natural selection. In the most simplistic model of selection, a mutation that confers a higher fitness to the carrier compared to non-carrier individuals will tend to non-stochastically rise in frequency in the population and, ultimately, reach fixation. Conversely, a mutation  that confers a smaller fitness to the carriers compared to non-carriers will be detrimental and erased from the population. Obviously, much more complex evolutionary patterns exist in nature (i.e. multiple genes contributing to a phenotype, ancient ongoing balancing selection, or selection on standing selection among others). However, detecting the fingerprint of these evolutionary events in the genome is more complex than in a simple selective sweep.

For SNVs, several examples of genomic regions have been reported in the literature (i.e. adulthood lactose tolerance and skin pigmentation among others). Nevertheless, little is known about the selective pressures acting on genomic rearrangements and CNVs and their role in the etiology of current complex phenotypes, including diseases.

In the first instance, the last statement certainly seems a counter-intuitive nonsense. How can something that has been selected for increasing the reproductive fitness and henceforth considered as beneficial for the carriers be associated to a disease? However, when digging a bit in the theory of Natural Selection, this scenario of positively selecting a variant that it is causal of current diseases is more than plausible. We must take into account that natural adaptation is result of genes and environment acting at individual level and mostly before and during reproductive ages. As a consequence, a functional change that increases the reproductive fitness of the carrier but has detrimental effects for the individual after reproductive age would still be under positive selective pressures and increase in frequency in the population. However, this is not a sine qua non condition for a genetic variant under positive selection in the past and showing detrimental effects in the present. Natural Selection does not work following an established master plan, but acting on the available genetic diversity and environmental conditions at the time. This time dimension has dramatic effect in the interpretation of positive selection:

  • A genetic variant that was ascertained in the past for a given environment could be detrimental nowadays due to an environmental change. For example, the thrifty gene hypothesis proposes that genetic variants associated to metabolic efficiency and energy storage increased in frequency across the populations in the past as a response to recurrent famines. However, these variants could be harmful at present  in the rich food energy environment of occidental diet and associated with phenotypes such as obesity or diabetes.


  • By introgression with other related species such as Neanderthals or Denisovans, our ancestors could have incorporated genetic variants specific from these species. Since these species were well adapted to their environments at the time when anatomically modern humans arrived from Africa, humans could have enhanced their adaptation to the new environments by means of this archaic admixture. Nevertheless, although this scenario has been observed for some loci, the archaic hybridization has also a main negative impact in the genome of humans. Most of the introgression has been lost due to purifying selection and it has been shown that some introgressed genetic variants play a role in complex diseases.


  • A supported evolutionary genomic change by natural selection in the past could promote nowadays new disease-associated genomic changes that would unlikely to naturally happen otherwise. This scenario is particularly important in the case of rearrangements and CNVs. For example, a rearrangement allowing increasing or decreasing the dosage of a gene or genes could have been selectively advantageous in the past. However, further favourable modifying the gene dosage modification by the pattern of the rearrangement could have negative side effects lately.


The latest point is the scenario that Nuttle et al reported in

Nuttle and colleagues have recently studied the evolutionary history of the 16p11.2 region in humans and the homologous region in other primate species. In previous studies, it was shown that recurrent copy number variation (CNV) at chromosome 16p11.2 accounts for approximately 1% of cases of autism.

Their analyses show that this region has undergone a large number of complex chromosomal rearrangements and duplications during primate evolution and particularly at the human lineage. In particular, the authors have shown that these rearrangements at the primate lineage have provided the genomic scenario for further human-specific rearrangements and fragment duplications. Interestingly, these human-specific duplications have provided the substratum for the rise of  a CNV region with a block size of 102-kbp cassette, containing a set of genes — BOLA2, SLX1 and SULT1A3 —. involved in autism. The authors have shown that the number of copies of BOLA2 modifies the degree of expression of the gene and protein levels, thus providing evidence of functional involvement for the CNV.

If these results are interesting per se for understanding the evolutionary history of this genomic region, more astonishing information could be concluded  while analyzing the genetic variation present at this locus. Based on the number of copies of BOLA2 in current populations (four or more in 99.8% of humans), the presence of even a higher number of copies in an ancient human sample from ~45,000 years ago, the absence of polyploidy in Neanderthals and Denisovans, the lack of evidence of archaic introgression in this region and the presence of a high frequency of rare variants, Nuttle and colleagues conclude that the presence of such large number of copies in humans is not by a stochastic process, but by the action of positive selection.

What are the implications of these findings for autism risk? According to the authors, human evolution would have directionally promoted the increase in the number of copies of the gene at expenses of creating genomic regions (breakpoints) flanking the CNV of high-identity. A collateral side effect of such high-identity breakpoints would be an increased probability of conducting recurrent unequal crossover during the creation of the gametes and the ultimate creation of microdeletions at the 16p11.2 region that have been associated to autism.

How can we make sense of comorbidity?

Comorbidity, defined as the simultaneous occurrence of more than one disorder in a single patient, is commonplace in psychiatry and somatic medicine. In research, as well as in routine clinical settings.

In March 2016 the new H2020 collaborative project “CoCA” (Comorbidity in adult ADHD) was officially launched, with a 3-day kick-off meeting in Frankfurt, Germany. This ambitious project, which is coordinated by professor Andreas Reif and is co-maintaining this shared blog, will investigate multiple aspects of comorbidity in ADHD.

For instance, CoCA will “identify and validate mechanisms common to the most frequent psychiatric conditions, specifically ADHD, mood and anxiety disorders, and substance use disorders (SUD), as well as a highly prevalent somatic disorder, i.e. obesity”.

As reflected in this bold mission, most scientists trained in the biological sciences agree that studies of overlapping and concurrent phenomena may reveal some underlying common mechanisms, e.g. shared genetic or environmental risk factors.

However, particularly in psychiatry and psychology, the origins of comorbidity have been fiercely debated. Critics have argued that observed comorbidities are “artefacts” of the current diagnostic systems (Maj, Br J Psychiatry, 2005 186: 182–184).

This discussion relates to fundamental questions of how much of our scientific knowledge reflects an independent reality, or is merely a product of our own epistemological traditions. In psychiatry, the DSM and ICD classification systems have been accused of actively producing psychiatric phenomena, including artificial diagnoses and high comorbidity rates, rather than being “true” representations of underlying phenomena.  Thus, the “constructivist” tradition argues that diagnostic systems are projected onto the phenomena of psychiatry, while “realists” acknowledge the presence of an independent reality of psychiatric disorders.

In an attempt to explain these concepts and their implications, psychiatric diagnoses and terminology have been termed “systems of convenience”, rather than phenomena that can be shown to be true or false per se (van Loo and Romeijn, Theor Med Bioeth. 2015, 41-60). It remains to be seen whether such philosophical clarifications will advance the ongoing debate related to the nature of medical diagnoses and their co-occurrence.

CoCA will not resolve these controversies. Neither can we expect that our new data will convince proponents of such opposing perspectives.

It is important to acknowledge the imperfections and limitations of concepts and instruments used in (psychiatric) research.

However, it may provide some comfort that similar fundamental discussions have a long tradition in other scientific disciplines, such as physics and mathematics. Rather  than being portrayed as a weakness or peculiarity of psychiatric research, I consider that an active debate, with questioning and criticism is considered an essential part of a healthy scientific culture.

Hereby, you are invited to join this debate on this blog page!Wooden ruler vector