These studies provide an explanation for what seems like a paradox: on the one hand, twin studies show that autism is very strongly genetic (identical twins are much more likely to share a diagnosis than fraternal twins) – on the other, many cases are sporadic, with no one else in the family affected. How can the condition be “genetic” but not always run in the family? The explanation is that many cases are caused by new mutations – ones that arise in the germline of the parents. (This is similar to conditions like Down syndrome). The studies reported in Nature are trying to find those mutations and see which genes are affected.

They are only possible because of the tremendous advances in our ability to sequence DNA. The first genome cost three billion dollars to sequence and took ten years – we can do one now for a couple thousand dollars in a few days. That means you can scan through the entire genome in any affected individual for mutated genes. The problem is we each carry hundreds of such mutations, making it difficult to recognise the ones that are really causing disease.

The solution is to sequence the DNA of large numbers of people with the same condition and see if the same genes pop up multiple times. That is what these studies aimed to do, with samples of a couple hundred patients each. They also concentrated on families where autism was present in only one child and looked specifically for mutations in that child that were not carried by either parent – so-called de novo mutations, that arise in the generation of sperm or eggs. These are the easiest to detect because they are likely to be the most severe. (Mutations with very severe effects are unlikely to be passed on because the people who carry them are far less likely to have children).

There is already strong evidence that de novo mutations play an important role in the etiology of autism – first, de novo copy number variants (deletions or duplications of chunks of chromosomes) appear at a significantly higher rate in autism patients compared to controls (in 8% of patients compared to 2% of controls). Second, it has been known for a while that the risk of autism increases with paternal age – that is, older fathers are more likely to have a child with autism. (Initial studies suggested the risk was up to five-fold greater in fathers over forty – these figures have been revised downwards with increasing sample sizes, but the effect remains very significant, with risk increasing monotonically with paternal age). This is also true of schizophrenia and, in fact, of dominant Mendelian disorders in general (those caused by single mutations). The reason is that the germ cells generating sperm in men continue to divide throughout their lifetime, leading to an increased chance of a mutation having happened as time goes on.

The three studies in Nature were looking for a different class of mutation – point mutations or changes in single DNA bases. They each provide a list of genes with de novo mutations found in specific patients. Several of these showed a mutation in more than one (unrelated) patient, providing strong evidence that these mutations are likely to be causing autism in those patients. The genes with multiple hits include CHD8, SCN2A, KATNAL2 and NTNG1. Mutations in the last of these, NTNG1, were only found in two patients but have been previously implicated as a rare cause of Rett syndrome. This gene encodes the protein Netrin-G1, which is involved in the guidance of growing nerves and the specification of neuronal connections. CHD8 is a chromatin-remodeling factor and is involved in Wnt signaling, a major neurodevelopmental pathway, as well as interacting with p53, which controls cell growth and division. SCN2A encodes a sodium channel subunit; mutations in this gene are involved in a variety of epilepsies. Not much is known about KATNAL2, except by homology – it is related to proteins katanin and spastin, which sever microtubules – mutations in spastin are associated with hereditary spastic paraplegia. How the specific mutations observed in these genes cause the symptoms of autism in these patients (or contribute to them) is not clear – these discoveries are just a starting point, but they will greatly aid the quest to understand the biological basis of this disorder.

The fact that these studies only got a few repeat hits also means that there are probably many hundreds or even thousands of genes that can cause autism when mutated (if there were only a small number, we would see more repeat hits). Some of these will be among the other genes on the lists provided by these studies and will no doubt be recognisable as more patients are sequenced. Interestingly, many of the genes on the lists are involved in aspects of nervous system development or function and encode proteins that interact closely with each other – this makes it more likely that they are really involved.

These studies reinforce the fact that autism is not one disorder – not clinically and not genetically either. Like intellectual disability or epilepsy or many other conditions, it can be caused by mutations in any of a very large number of genes. The ones we know about so far make up around 30% of cases – these new studies add to that list and also show how far we have to go to complete it.

We should recognise too that the picture will also get more complex – in many cases there may be more than one mutation involved in causing the disease. De novo mutations are likely to be the most severe class and thus most likely to cause disease with high penetrance themselves. But many inherited mutations may cause autism only in combination with one or a few other mutations.

These complexities will emerge over time, but for now we can aim to recognise the simpler cases where a mutation in a particular gene is clearly implicated. Each new gene discovered means that the fraction of cases we can assign to a specific cause increases. As we learn more about the biology of each case, those genetic diagnoses will have important implications for prognosis, treatment and reproductive decisions. We can aim to diagnose and treat the underlying cause in each patient and not just the symptoms.

The fact that the adult brain is very plastic is often held up as evidence against the idea that many psychological, cognitive or behavioural traits are innately determined. At first glance, there does indeed appear to be a paradox. On the one hand, behavioural genetic studies show that many human psychological traits are strongly heritable and thus likely determined, at least in part, by innate biological differences. On the other, it is very clear that even the adult brain is highly plastic and changes itself in response to experience.

The evidence on both sides is very strong. In general, for traits like intelligence and personality characteristics such as extraversion, neuroticism or conscientiousness, among many others, the findings from genetic studies are remarkably consistent. Just as for physical traits, people who are more closely related resemble each other for psychological traits more than people with a more distant relationship. Twin study designs get around the obvious objection that such similarities might be due to having been raised together. Identical twins tend to be far more like each other for these traits than fraternal twins, though the family environment is shared in both cases. Even more telling, identical twins who are raised apart tend to be pretty much as similar to each other as pairs who are raised together. Clearly, we come fairly strongly pre-wired and the family environment has little effect on these kinds of traits.

Yet we know the brain can “change itself”. You could say that is one of its main jobs in fact – altering itself in response to experience to better adapt to the conditions in which it finds itself. For example, as children learn a language, their auditory system specialises to recognise the typical sounds of that language. Their brains become highly expert at distinguishing those sounds and, in the process, lose the ability to distinguish sounds they hear less often. (This is why many Japanese people cannot distinguish between the sounds of the letters “l” and “r”, for example, and why many Westerners have difficulty hearing the crucial tonal variations in languages like Cantonese). Learning motor skills similarly improves performance and induces structural changes in the relevant brain circuits. In fact, most circuits in the brain develop in an experience-dependent fashion, summed up by two adages: “cells that fire together, wire together” and “use it or lose it”.

Given the clear evidence for brain plasticity, the implication would seem to be that even if our brains come pre-wired with some particular tendencies, that experience, especially early experience, should be able to override them.

I would argue that the effect of experience-dependent development is typically exactly the opposite – that while the right kind of experience can, in principle, act to overcome innate tendencies, in practice, the effect is reversed. The reason is that our innate tendencies shape the experiences we have, leading us to select ones that tend instead to reinforce or even amplify these tendencies. Our environment does not just shape us – we shape it.

A child who is naturally shy – due to innate differences in the brain circuits mediating social behaviour, general anxiety, risk-aversion and other parameters – will tend to have less varied and less intense social experience. As a result, they will not develop the social skills that might make social interaction more enjoyable for them. A vicious circle emerges – perhaps intense practice in social situations would alter the preconfigured settings of a shy child’s social brain circuits but they tend not to get that experience, precisely because of those settings. In contrast, their extroverted classmates may, by constantly seeking out social interactions, continue to develop this innate faculty.

This circle may be most vicious in children with autism, most of whom have a reduced level of innate interest in other people. They tend, for example, not to find faces as intrinsically fascinating as other infants. This may contribute to a delay in language acquisition, as they miss out on interpersonal cues that strongly facilitate learning to speak.

A similar situation may hold for children who have difficulties in reading or with mathematics. Dyslexia seems to be caused by an innate difficulty in associating the sounds and shapes of letters. This can be traced to genetic effects during early development of the brain, which may cause interruptions in long-range connections between brain areas. This innate disadvantage is cruelly amplified by the typical experience of many dyslexics. Learning to read is hard enough and requires years of practice and active instruction. For children who have basic difficulties in recognising letters and words, reading remains effortful for far longer and they will therefore tend to read less, missing out on the intensive practice that would help their brain circuitry specialise for reading.

Though less widely known, dyscalculia (a selective difficulty in mathematics) is equally common and shares many characteristics with dyslexia. The initial problem is in innate number sense – the ability to estimate and compare small numbers of objects. This faculty is present in very young infants and even shared with many other animal species, notably crows. Formal mathematical instruction is required to build on this innate number sense but also crucially relies on it. As with reading, mathematics requires hard work to learn and if numbers are inherently mysterious then this will change the nature of the child’s experience, lessen interest and reduce practice. At the other end of the spectrum, those with strong mathematical talent may gravitate towards the subject, further amplifying the differences between these two groups.

Thus, while a certain type of experience can alter the innate tendency, the innate tendency makes getting that experience far less likely. Brain plasticity tends instead to amplify initial differences.

That sounds rather fatalistic, but the good news is that this vicious circle can be broken if innate difficulties are recognised early enough – by actively changing the nature of early experience. There is good evidence that intense early intervention in children with autism (such as Applied Behaviour Analysis) allows them to compensate for innate deficits and lead to improvements in cognitive, communication and adaptive skills. Similarly intense intervention in children with dyslexia has also proven effective. Thus, even if it is not possible to reverse whatever neurodevelopmental differences lead to these kinds of deficits, it should at least be possible to prevent their being amplified by subsequent experience.

Duff FJ, & Clarke PJ (2011). Practitioner Review: Reading disorders: what are the effective interventions and how should they be implemented and evaluated? Journal of child psychology and psychiatry, and allied disciplines, 52 (1), 3-12 PMID: 21039483

Vismara, L., & Rogers, S. (2010). Behavioral Treatments in Autism Spectrum Disorder: What Do We Know? Annual Review of Clinical Psychology, 6 (1), 447-468 DOI: 10.1146/annurev.clinpsy.121208.131151 ]]> http://www.gnxp.com/new/2011/10/01/does-brain-plasticity-trump-innateness/feed/ 19 http://www.gnxp.com/new/2011/08/11/split-brains-autism-and-schizophrenia/ http://www.gnxp.com/new/2011/08/11/split-brains-autism-and-schizophrenia/#comments Thu, 11 Aug 2011 08:34:06 +0000 http://www.gnxp.com/wp/?p=1380 A new study suggests that a gene known to be causally linked to schizophrenia and other psychiatric disorders is involved in the formation of connections between the two hemispheres of the brain. DISC1 is probably the most famous gene in psychiatric genetics, and rightly so. It was discovered in a large Scottish pedigree, where 18 members were affected by psychiatric disease.
The diagnoses ranged from schizophrenia and bipolar disorder to depression and a range of “minor” psychiatric conditions. It was found that the affected individuals had all inherited a genetic anomaly – a translocation of genetic material between two chromosomes. This basically involves sections of two chromosomes swapping with each other. In the process, each chromosome is broken, before being spliced back to part of the other chromosome. In this case, the breakpoint on chromosome 1 interrupted a gene, subsequently named Disrupted-in-Schizophrenia-1, or DISC1.

That this discovery was made using classical “cytogenetic” techniques (physically looking at the chromosomes down a microscope) and in a single family is somehow pleasing in an age where massive molecular population-based studies are in vogue. (A win for “small” science).

The discovery of the DISC1 translocation clearly showed that disruption of a single gene could lead to psychiatric disorders like schizophrenia. This was a challenge to the idea that these disorders were “polygenic” – caused by the inheritance in each individual of a large number of genetic variants. As more and more mutations in other genes are being found to cause these disorders, the DISC1 situation can no longer be dismissed as an exception – it is the norm.

It also was the first example of a principle that has since been observed for many other genes – namely that the effects of the mutation can manifest quite variably – not as one specific disorder, but as different ones in different people. Indeed, DISC1 has since been implicated in autism as well as adult-onset disorders. It is now clear from this and other evidence that these apparently distinct conditions are best thought of as variable outcomes that arise, in many cases at least, from disturbances of neurodevelopment.

Since the initial discovery, major research efforts of a growing number of labs have been focused on the next obvious questions: what does DISC1 do? And what happens when it is mutated? What happens in the brain that can explain why psychiatric symptoms result?

We now know that DISC1 has many different functions. It is a cytoplasmic protein – localised inside the cell – that interacts with a very large number of other proteins and takes part in diverse cellular functions, including cell migration, outgrowth of nerve fibres, the formation of dendritic spines (sites of synaptic contact between neurons), neuronal proliferation and regulation of biochemical pathways involved in synaptic plasticity. Many of the proteins that DISC1 interacts with have also been implicated in psychiatric disease.

This new study adds another possible function, and a dramatic and unexpected one at that. This function was discovered from an independent angle, by researchers studying how the two hemispheres of the brain get connected – or more specifically, why they sometimes fail to be connected. The cerebral hemispheres are normally connected by millions of axons which cross the midline of the brain in a structure called the corpus callosum (or “tough body” – (don’t ask)). Very infrequently, people are born without this structure – the callosal axons fail to cross the midline and the two hemispheres are left without this major route of communication (though there are other routes, such as the anterior commissure).

The frequency of agenesis of the corpus callosum has been estimated at between 1 in 1,000 and 1 in 6,000 live births – thankfully very rare. It is associated with a highly variable spectrum of other symptoms, including developmental delay, autistic symptoms, cognitive disabilities extending into the range of mental retardation, seizures and other neurological signs.

Elliott Sherr and colleagues were studying patients with this condition, which is very obvious on magnetic resonance imaging scans (see Figure). They initially found a mother and two children with callosal agenesis who all carried a deletion on chromosome 1, at position 1q42 – exactly where DISC1 is located. They subsequently identified another patient with a similar deletion, which allowed them to narrow down the region and identify DISC1 as a plausible candidate (among some other genes in the deleted region). Because the functions of proteins can be affected not just by large deletions or translocations but also by less obvious mutations that change a single base of DNA, they also sequenced the DISC1 gene in a cohort of callosal agenesis patients and found a number carrying novel mutations that are very likely to disrupt the function of the gene.

While not rock-solid evidence that it is DISC1 that is responsible, these data certainly point to it as the strongest candidate to explain the callosal defect. This hypothesis is strongly supported by findings from DISC1 mutant mice (carrying a mutation that mimics the effect of the human translocation), which also show defects in formation of the corpus callosum. In addition, the protein is strongly expressed in the axons that make up this structure at the time of its development.

The most obvious test of whether disruption of DISC1 really causes callosal agenesis is to look in the people carrying the initial translocation. Remarkably, it is not known whether the original patients in the Scottish pedigree who carry the DISC1 translocation show this same obvious brain structural phenotype. They have, very surprisingly, never been scanned.

This new paper raises the obvious hypothesis that the failure to connect the two hemispheres results in the psychiatric or cognitive symptoms, which variously include reduced intellectual ability, autism and schizophrenia. This seems like too simplistic an interpretation, however. All we have now is a correlation. First, the implication of DISC1 in the acallosal phenotype is not yet definitive – this must be nailed down and replicated. But even if it is shown that disruption of DISC1 causes both callosal agenesis and schizophrenia (or other psychiatric disorders or symptoms), this does not prove a causal link. DISC1 has many other functions and is expressed in many different brain areas (ubiquitously in fact). Any, or indeed, all of these functions may in fact be the cause of psychopathology.

One prediction, if it were true that the lack of connections between the two hemispheres is causal, is that we would expect the majority of patients with callosal agenesis to have these kinds of psychiatric symptoms. In fact, the rates are indeed very high – in different studies it has been estimated that up to 40% of callosal agenesis patients have an autism diagnosis, while about 8% have the symptoms of schizophrenia or bipolar disorder. (Of course, these patients may have other, less obvious brain defects as well, so even this is not definitive).

Conversely, we might naively expect a high rate of callosal agenesis in patients with autism or schizophrenia. However, we know these disorders are extremely heterogeneous and so it is much more likely that this phenotype might be apparent in only a specific (possibly very small) subset of patients. This may indeed be the case – callosal agenesis has been observed in about 3 out of 200 schizophrenia patients (a vastly higher rate than in the general population). Another study, just published, has found that mutations in a different gene – ARID1B – are also associated with callosal agenesis, mental retardation and autism. More generally, there may be subtle reductions in callosal connectivity in many schizophrenia or autism patients (including some autistic savants).

Whether this defect can explain particular symptoms is not yet clear. For the moment, the new study provides yet another possible function of DISC1, and highlights an anatomical phenotype that is apparently present in a subset of autism and schizophrenia cases and that can arise due to mutation in many different genes (of which DISC1 and ARID1B are only two of many known examples).

One final note: formation of the corpus callosum is a dramatic example of a process that is susceptible to developmental variation. What I mean is this: when patients inherit a mutation that results in callosal agenesis, this phenotype occurs in some patients but not all. This is true even in genetically identical people, like monozygotic twins or triplets (or in lines of genetically identical mice). Though the corpus callosum contains millions of nerve fibres, the initial events that establish it involve very small numbers of cells. These cells, which are located at the medial edge of each cerebral hemisphere, must contact each other to enable the fusion of the two hemispheres, forming a tiny bridge through which the first callosal fibres can cross. Once these are across, the rest seem able to follow easily. Because this event involves very few cells at a specific time in development, it is susceptible to random “noise” – fluctuations in the precise amounts of various proteins in the cells, for example. These are not caused by external forces – the noise is inherent in the system. The result is that, in some people carrying such a mutation the corpus callosum will not form at all, while in others it forms apparently completely normally (see figure of triplets, one on left with normal corpus callosum, the other two with it absent). So, an all-or-none effect can arise, without any external factors involved.

This same kind of intrinsic developmental variation may also explain or at least contribute to the variability in phenotypic outcome at the level of psychiatric symptoms when these kinds of neurodevelopmental mutations are inherited. Even monozygotic twins are often discordant for psychiatric diagnoses (concordance for schizophrenia is about 50%, for example). This is often assumed to be due to non-genetic and therefore “environmental” or experiential factors. If these disorders really arise from differences in brain wiring, which we know are susceptible to developmental variation, then differences in the eventual phenotype could actually be completely intrinsic and innate.

Osbun N, Li J, O’Driscoll MC, Strominger Z, Wakahiro M, Rider E, Bukshpun P, Boland E, Spurrell CH, Schackwitz W, Pennacchio LA, Dobyns WB, Black GC, & Sherr EH (2011). Genetic and functional analyses identify DISC1 as a novel callosal agenesis candidate gene. American journal of medical genetics. Part A, 155 (8), 1865-76 PMID: 21739582

Halgren C, Kjaergaard S, Bak M, Hansen C, El-Schich Z, Anderson CM, Henriksen KF, Hjalgrim H, Kirchhoff M, Bijlsma EK, Nielsen M, den Hollander NS, Ruivenkamp CA, Isidor B, Le Caignec C, Zannolli R, Mucciolo M, Renieri A, Mari F, Anderlid BM, Andrieux J, Dieux A, Tommerup N, & Bache I (2011). Corpus Callosum Abnormalities, Mental Retardation, Speech Impairment, and Autism in Patients with Haploinsufficiency of ARID1B. Clinical genetics PMID: 21801163

The genome sequence (a simple linear code made up of 3 billion bases of DNA in precise order, chopped up onto different chromosomes) is peppered with these polymorphic positions – about 1 in every 1,250 bases. That makes about 2,400,000 polymorphisms in each genome (and we each carry two copies of the genome). That certainly seems like plenty of raw material, with limitless combinations that could explain the richness of human diversity. This interpretation has fuelled massive scientific projects to try and find which common polymorphisms affect which traits. (Not to mention personal genomics companies who will try to tell you your risk of various diseases based on your profile of such polymorphisms).

The problem with this view is that it is wrong. Or at least woefully incomplete.

The reason is it ignores another source of variation: very rare mutations in those bases that are constant across the vast majority of individuals. There is now very good evidence that it is those kinds of mutations that contribute most to our individuality. Certainly, they are much more likely to affect a protein’s function and much more likely to contribute to genetic disease. We each carry hundreds of such rare mutations that can affect protein function or expression and are much more likely to have a phenotypic impact than common polymorphisms.

Indeed, far from most of the genome being effectively constant, it can be estimated that every position in the genome has been mutated many, many times over in the human population. And each of us carries hundreds of new mutations that arose during generation of the sperm and egg cells that fused to form us. New mutations may spread in the pedigree or population in which they arise for some time, depending in part on whether they have a deleterious effect or not. Ones that do will likely be quickly selected against.

A new paper from the 1000 genomes project consortium shows that:

“the vast majority of human variable sites are rare and that the majority of rare variants exhibit, at most, very little sharing among continental populations”.

This is a much more fluid picture of genetic variation than we are used to. We are not all dealt a genetic hand from the same deck – each population, sub-population, kindred, nuclear family has a distinct set of rare genetic variants. And each of these decks contains a lot of jokers – the new mutations that arise each time a hand is dealt.

Why have such rare mutations generally been ignored while the polymorphic sites have been the focus of intense research? There are several reasons, some practical and some theoretical. Practically, it has until recently been almost impossible to systematically find very rare mutations. To do so requires that we sequence the whole genome, which has only recently become feasible. In contrast, methods to survey which bases you carry at all the polymorphic sites across the genome were developed quite some time ago now and are relatively cheap to use. (They rely on sampling about 500,000 such sites around the genome – because of unevenness in the way different bits of chromosomes get swapped when sperm and eggs are made, this sample actually tells you about most of the variable sites across the whole genome). So, there has been a tendency to argue that polymorphic sites will be major contributors to human phenotypes (especially diseases) because those have been the only ones we have been able to look at.

Unfortunately, the results of genome-wide association studies, which aim to identify common variants associated with traits or diseases, have been disappointing. This is especially true for disorders with large effects on fitness, such as schizophrenia or autism. Some variants have been found but their effects, even in combination are very small. Most of the heritability of most of the traits or diseases examined to date remains unexplained. (There are some important exceptions, especially for diseases that strike only late in life and for things like drug responses, where selective pressures to weed out deleterious alleles are not at play).

In contrast, many more rare mutations causing disease are being discovered all the time, and the pace of such discoveries is likely to increase with technological advances. The main message that emerges from these studies has been called by Mary-Claire King the “Anna Karenina principle”, based on Tolstoy’s famous opening line:

“Happy families are all alike; every unhappy family is unhappy in its own way”

But can such rare variants really explain the “missing heritability” of these disorders? Some people have argued that they cannot, but this seems to me to be based on a pervasive misconception of how the heritability of a trait is measured and what it means. According to this misconception, if a trait is heritable across the population, that heritability cannot be accounted for by rare variants. After all, if a mutation only occurs in one or a few individuals, it could only minimally (nearly negligibly) contribute to heritability across the whole population. That is true. However, heritability is not measured across the population – it is measured in families and then averaged across the population.

In humans, it is usually derived by comparing phenotypes between people of different genetic relatedness (identical versus fraternal twins, siblings, parents, cousins, etc.). The values of these comparisons are then averaged across large numbers of pairs to allow estimates of how much genetic variance affects phenotypic variance – the population heritability. While a specific rare mutation may only affect the phenotype within a single family, such mutations could, collectively, explain all of the heritability. Completely different sets of mutations could be affecting the trait or causing the disease in different families.

The next few years will reveal the true impact of rare mutations. We should certainly expect complex genetic interactions and some real effects of common polymorphisms. But the idea that our traits are determined simply by the combination of variants we inherit from a static pool in the population is no longer tenable. We are each far more unique than that.

(And if your personal genomics company isn’t offering to sequence your whole genome, it’s not personal enough).

Gravel S, Henn BM, Gutenkunst RN, Indap AR, Marth GT, Clark AG, Yu F, Gibbs RA, The 1000 Genomes Project, & Bustamante CD (2011). Demographic history and rare allele sharing among human populations. Proceedings of the National Academy of Sciences of the United States of America, 108 (29), 11983-11988 PMID: 21730125

Walsh CA, & Engle EC (2010). Allelic diversity in human developmental neurogenetics: insights into biology and disease. Neuron, 68 (2), 245-53 PMID: 20955932

McClellan, J., & King, M. (2010). Genetic Heterogeneity in Human Disease Cell, 141 (2), 210-217 DOI: 10.1016/j.cell.2010.03.032

Mirrored from Wiring the Brain

So, do these papers really make a strong case for their major conclusions? The first gives results from a study of twins in California. Twin studies are a classic method to determine whether something is caused by genetic or environmental factors. The method asks, if one twin in a pair is affected by some disorder (autism in this case), with what frequency is the other twin also affected? The logic is very simple: if something is caused by environmental factors, particularly those within a family, then it should not matter whether the twins in question are identical or fraternal – their risk should be the same because their exposure is the same. On the other hand, if something is caused by genetic mutations, and if one twin has the disorder, then the rate of occurrence of the disorder in the other twin should be much higher if they are genetically identical than if they only share half their genes, as fraternal twins do.

Working backwards, if the rate of twin concordance for affected status are about the same for identical and fraternal twins, this is strong evidence for environmental factors. If the rate is much higher in monozygotic twins, this is strong evidence for genetic factors. Now to the new study. What they found was that the rate of concordance for monozygotic (identical) twins was indeed much higher than for dizyogotic (fraternal) twins – about twice as high on average.

For males: MZ: 0.58, DZ: 0.21
For females: MZ: 0.60, DZ: 0.27

Those numbers are for the diagnosis of strict autism. The rate of “autism spectrum disorder”, which encompasses a broader range of disability, showed similar results:

Males: MZ: 0.77, DZ: 0.31
Females: MZ: 0.50, DZ: 0.36.

These numbers fit pretty well with a number of other recent twin studies, all of which have concluded that they provide evidence for strong heritability of the disorder – i.e., that whether or not someone develops autism is largely (though not exclusively) down to genetics.

So, why did these authors reach a different conclusion and should their study carry any more weight than others? On the latter point, the study is significantly larger than many that have preceded it. This study looked at 192 twin pairs, each with at least one affected twin. However, some recent studies have been comparable or even larger: Lichtenstein and colleagues looked at 117 twin pairs and Rosenberg and colleagues looked at 277 twin pairs. These studies found eveidence for very high heritability and negligible shared environmental effects.

Another potentially important difference is in how the sample was ascertained. Hallmayer and colleagues claim that their assessment of affected status was more rigorous than for other studies and this may be true. However, it has previously been found that less rigorous assessments correlate extremely well with the more standardised assessments, so this is unlikely to be a major factor. In addition, there is very strong evidence that disorders like autism, ADHD, epilepsy, intellectual disability, tic disorders and others all share common etiology – having a broader diagnosis is therefore probably more appropriate.

In any case, the numbers they came up with for concordance rates were pretty similar across these studies. So, why did they end up with a different conclusion? That’s not a rhetorical question – I actually don’t know the answer and if anyone else does I would love to hear it. Given the data, I don’t know how they conclude that they provide evidence for shared environmental effects.

The methodology involves some statistical modeling that tries to tease out the sources of variance. However, this modeling is based completely on a multifactorial threshold model for the disorder – the idea that autism arises when the collective burden of individually minor genetic or environmental insults passes some putative threshold. Sounds plausible, but there is in fact no evidence – at all – that this model applies to autism. In fact, it seems most likely that autism really is an umbrella term for a collection of distinct genetic disorders caused by mutations in separate genes, but which happen to cause common phenotypes (or symptoms).

If that is the case, then what the twin concordance rates actually measure is the penetrance of such mutations – if one inherits mutation X, how often does that actually lead to autism? For monozygotic twins, let us assume that the affected proband (the first twin diagnosed) has such a mutation. Because they are genetically identical, the other one must too. The chance that the other twin will develop autism thus depends on the penetrance of the mutation – some mutations are more highly penetrant than others, giving a much higher probability of developing a specific phenotype. If we average across all MZ twin pairs we therefore get an average penetrance across all such putative mutations. Now, if such mutations are dominant, as many of the known ones are, then the chance that a dizygotic twin will inherit it is 50%, while the penetrance should remain the same. So, this model would predict that the rate of co-occurrence in DZ twins should be about half that of MZ twins, exactly as observed. (No stats required).

The conclusions from this study that the heritability is only modest and that a larger fraction of variance (55%!) is caused by shared environment thus seem extremely shaky. This is reinforced by the fact that the confidence intervals for these estimates are extremely wide (for the effect of shared environment the 95% confidence interval ranges from 9% to 81%). Certainly not enough to overturn all the other data from other studies.

What about epidemiological studies that have shown statistical evidence of increased risk of autism associated with a variety of other factors, including maternal diabetes, antidepressant use, season and place of brith? All of these factors have been linked with modest increases in the risk of autism. Don’t these prove there are important environmental factors? Well, first, they don’t prove causation, they provide a statistical evidence for an association between the two factors, which is not at all the same thing. Second, the increase in risk is usually on the order of about two-fold. Twice the risk may sound like a lot, but it’s only a 1% increase (from 1 to 2%), compared with some known mutations, which increase risk by 50-fold or more.

The main problem with these kinds of studies (and especially with how they are portrayed in the media) is that they are correlational and so you cannot establish a causal link directly from them. In some cases, two different correlated parameters (like red hair and freckles, for example) may actually be caused by an unmeasured third parameter. For example, in the recently published study, the use of antidepressants of the SSRI (selective serotonin reuptake inhibitor) class in mothers was associated with modestly increased risk of autism in the progeny. This association could be because SSRIs disrupt neural development in the fetus (perfectly plausible) but could alternatively be due to the known genetic link between risk of depression and risk of autism. Rates of depression are known to be higher in relatives of autistic people, so SSRI use could just be a proxy for that condition. The authors claim to have corrected for that by comparing rates of autism in the progeny of depressed mothers who were not prescribed SSRIs versus those who were but one might imagine that the severity of depression would be higher among those prescribed an antidpressant. In addition, the authors are careful to note that their findings were based on a small number of children exposed and that “Further studies are needed to replicate and extend these findings”. As with many such findings, this association may or may not hold up with additional study.

As for season and place of birth, those findings are better replicated and, interestingly, also found for schizophrenia. There is a theory that these effects may relate to maternal vitamin D levels, which can also affect neural development. This also seems plausible enough. However, the problem in really having confidence in these findings and in knowing how to interpret them is that they are population averages with small effect sizes. Overall, it seems quite possible that the environment – especially the prenatal environment – can play a part in the etiology of autism. At the moment, splashy headlines notwithstanding, genetic factors look much more important and genetic studies much more likely to give us the crucial entry points to the underlying biology.

Mirrored from Wiring the Brain.

Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T, Miller J, Fedele A, Collins J, Smith K, Lotspeich L, Croen LA, Ozonoff S, Lajonchere C, Grether JK, & Risch N (2011). Genetic Heritability and Shared Environmental Factors Among Twin Pairs With Autism. Archives of general psychiatry PMID: 21727249

Lichtenstein P, Carlström E, Råstam M, Gillberg C, & Anckarsäter H (2010). The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. The American journal of psychiatry, 167 (11), 1357-63 PMID: 20686188

Rosenberg, R., Law, J., Yenokyan, G., McGready, J., Kaufmann, W., & Law, P. (2009). Characteristics and Concordance of Autism Spectrum Disorders Among 277 Twin Pairs Archives of Pediatrics and Adolescent Medicine, 163 (10), 907-914 DOI: 10.1001/archpediatrics.2009.98

Croen LA, Grether JK, Yoshida CK, Odouli R, & Hendrick V (2011). Antidepressant Use During Pregnancy and Childhood Autism Spectrum Disorders. Archives of general psychiatry PMID: 21727247

Complex disorders are so named because, while it is clear that they are highly heritable (risk to an individual increases the more closely related they are to someone who has the disorder), their mode of inheritance is far more difficult to discern. Unlike classical Mendelian disorders (such as cystic fibrosis or Huntington’s disease), these disorders do not show simple patterns of segregation within families that would peg them as recessive or dominant, nor can they be linked to mutations in a single gene. This has led people to propose two very different explanations for how they are inherited.

One theory is that such disorders arise due to unfortunate combinations of large numbers of genetic variants that are common in the population. Individually, such variants would have little effect on the phenotype, but collectively, if they surpass some threshold of burden, they could tip the balance into a pathological state. This has been called the common disease/common variant (CD/CV) model.

The alternative model is that these “disorders” are not really single disorders at all – rather they are umbrella terms for collections of a large number of distinct genetic disorders, which happen to result in a similar set of symptoms. Within any individual or family, the disorder may indeed be caused by a particular mutation. Because many of the disorders in question are very severe, with high mortality and reduced numbers of offspring, these mutations will be rapidly selected against in the population. They will therefore remain very rare and many cases of the disorder may arise from new, or de novo, mutations. This has therefore been called the multiple rare variants (MRV) model.

Lately, a number of mixed models have been proposed by various researchers, including myself. Even classical Mendelian disorders rarely show strictly Mendelian inheritance – instead the effects of the major mutations are invariably affected by modifiers in the genetic background. (These are variants with little effect by themselves but which may have a strong effect in combination with some other mutation). If this sounds like a return to the CD/CV model, there are a couple important distinctions to keep in mind. One is the nature of the mutations involved – the mixed model would still invoke some rare mutation that has a large effect on protein function. It may not always cause the disorder by itself (i.e., not every one who carries it will be affected), but could still be called causative in the sense that if the affected individual did not carry it one would expect they would not suffer from the disorder. The other is the number of mutations or variants involved – under the CD/CV model this could number in the thousands (a polygenic architecture), while under the mixed model one could expect a handful to be meaningfully involved (an oligogenic architecture – see diagram from review in Current Opinion in Neurobiology).

The new study, from the lab of Jeff Noebels, aimed to test these models in the context of epilepsy. Epilepsy is caused by an imbalance in excitation and inhibition within brain circuits. This can arise due to a large number of different factors, including alterations in the structural organisation of the brain, which may be visible on magnetic resonance imaging. Many neurodevelopmental disorders are therefore associated with epilepsy as a symptom (usually one of many). But it can also arise due to more subtle changes, not in the gross structure of the brain or the physical wiring of different circuits, but in the way the electrical activity of individual neurons is controlled.

The electrical properties of any neuron – how excitable it is, how long it remains active, whether it fires a burst of action potentials or single ones, what frequency it fires at and many other important parameters – are determined in large part by the particular ion channel proteins it expresses. These proteins form a pore crossing the membrane of the cell, through which electrically charged ions can pass. Different channels are selective for sodium, potassium or calcium ions and can be activated by different types of stimuli – binding a particular neurotransmitter or a change in the cell’s voltage for example. Many channels are formed from multiple subunits, each of which may be encoded by a different gene. There are hundreds of these genes in several large families, so the resultant complexity is enormous.

Many familial cases of epilepsy have been found to be caused by mutations in ion channel genes. However, most epilepsy patients outside these families do not carry these particular mutations. Therefore, despite these findings and despite the demonstrated high heritability, the particular genetic cause of the vast majority of cases of epilepsy has remained unknown. Large genome-wide association studies have looked for common variants that are associated with risk of epilepsy but have turned up nothing of note. The interpretation has been that common variants do not play a major role in the etiology of idiopathic epilepsy (epilepsy without a known cause).

The rare variants model suggests that many of these cases are caused by single mutations in any of the very large number of ion channel genes. A straightforward experiment to test that would be to sequence all these candidate genes in a large number of epilepsy patients. The hope is that it would be possible to shake out the “low hanging fruit” – obviously pathogenic mutations in some proportion of cases. The difficulty lies in recognising such a mutation as pathogenic when one finds it. This generally relies on some statistical evidence – any individual mutation, or such mutations in general, should be more frequent in epilepsy patients than in unaffected controls. The experiment must therefore involve as large a sample as possible and a control comparison group as well as patients.

Klassen and colleagues sequenced 237 ion channel genes in 152 patients with idiopathic epilepsy and 139 healthy controls. What they found was surprising in several ways. They did find lots of mutations in these genes, but they found them at almost equal frequency in controls as in patients. Even the mutations predicted to have the most severe effects on protein function were not significantly enriched in patients. Indeed, mutations in genes already known to be linked to epilepsy were found in patients and controls alike (though 96% of patients had such a mutation, so did 67% of controls). Either these specific mutations are not pathogenic or their effects can be strongly modified by the genetic background.

More interesting results emerged from looking at the occurrence of multiple mutations in these genes in individuals. 78% of patients vs 30% of controls had two or more mutations in known familial epilepsy genes. A similar trend was observed when looking at specific ion channel gene families, such as GABA receptors or sodium channels.

These data would seem to fit with the idea that an increasing mutational load pushes the system over a threshold into a pathological state. The reality seems more complicated, however, and far more nuanced. Though the average load was lower, many controls had a very high load and yet were quite healthy. It seems that the specific pattern of mutations is far more important than the overall number. This fits very well with the known biology of ion channels and previous work on genetic interactions between mutations in these genes.

Though one might expect a simple relationship between number of mutations and severity of phenotype, that is unlikely to be the case for these genes. It is well known that the effects of a mutation in one ion channel gene can be suppressed by mutation in another gene – restoring the electrical balance in the cell, at least to a degree sufficient for performance under normal conditions. The system is so complex, with so many individual components, that these interactions are extremely difficult to predict. This is complicated further by the fact that there are active processes within the system that act to normalise its function. It has been very well documented, especially by Eve Marder and colleagues, that changes to one ion channel in a neuron can be compensated for by homeostatic mechanisms within the cell that aim to readjust the electrical set-points for optimal physiological function. In fact, these mechanisms do not just happen within one cell, but across the circuit.

The upshot of the study is that, though some of the mutations they discovered are indeed likely to be the pathogenic culprits, it is very difficult to discern which ones they are. It is very clear that there is at least an oligogenic architecture for so-called “channelopathies” – the phenotype is determined by several mutations in each individual. (Note that this is not evidence for a highly polygenic architecture involving hundreds or thousands of genetic variants with tiny individual effects). The important insight is that it is not the overall number or mutational load that matters but the pattern of specific mutations in any individual that is crucial. Unfortunately, given how complicated the system is, this means it is currently not possible to predict an individual’s risk, even with this wealth of data. This will likely require a lot more biological information on the interactions between these mutations from experimental approaches and computational modelling.

What are the implications for other complex disorders? Should we expect a similarly complicated picture for diseases like schizophrenia or autism? Perhaps, though I would argue against over-extrapolating these findings. For the reasons described above, mutations in ion channel genes will show especially complex genetic interactions – it is, for example, even possible for two mutations that are individually pathogenic to suppress each other’s effects in combination. This is far less likely to occur for classes of mutations affecting processes such as neurodevelopment, many of which have been implicated in psychiatric disorders. Though by no means unheard of, it is far less common for the effects of one neurodevelopmental mutation to be suppressed by another – it generally just makes things worse. So, while modifying effects of genetic background will no doubt be important for such mutations, there is some hope that the interactions will be more straightforward to elucidate (mostly enhancing, far fewer suppressing). Others may see it differently of course (and I would be pleased to hear from you if you do); similar sequencing efforts currently underway for these disorders may soon tell whether that prediction is correct.

Klassen T, Davis C, Goldman A, Burgess D, Chen T, Wheeler D, McPherson J, Bourquin T, Lewis L, Villasana D, Morgan M, Muzny D, Gibbs R, & Noebels J (2011). Exome sequencing of ion channel genes reveals complex profiles confounding personal risk assessment in epilepsy. Cell, 145 (7), 1036-48 PMID: 21703448

Kasperaviciute, D., Catarino, C., Heinzen, E., Depondt, C., Cavalleri, G., Caboclo, L., Tate, S., Jamnadas-Khoda, J., Chinthapalli, K., Clayton, L., Shianna, K., Radtke, R., Mikati, M., Gallentine, W., Husain, A., Alhusaini, S., Leppert, D., Middleton, L., Gibson, R., Johnson, M., Matthews, P., Hosford, D., Heuser, K., Amos, L., Ortega, M., Zumsteg, D., Wieser, H., Steinhoff, B., Kramer, G., Hansen, J., Dorn, T., Kantanen, A., Gjerstad, L., Peuralinna, T., Hernandez, D., Eriksson, K., Kalviainen, R., Doherty, C., Wood, N., Pandolfo, M., Duncan, J., Sander, J., Delanty, N., Goldstein, D., & Sisodiya, S. (2010). Common genetic variation and susceptibility to partial epilepsies: a genome-wide association study Brain, 133 (7), 2136-2147 DOI: 10.1093/brain/awq130

Mitchell KJ (2011). The genetics of neurodevelopmental disease. Current opinion in neurobiology, 21 (1), 197-203 PMID: 20832285

Mirrored from http://wiringthebrain.blogspot.com

The question of “where morals come from” has exercised philosophers, theologians and many others for millennia. It has lately, like many other questions previously addressed only through armchair rumination, become addressable empirically, through the combined approaches of modern neuroscience, genetics, psychology, anthropology and many other disciplines. From these approaches a naturalistic framework is emerging to explain the biological origins of moral behaviour. From this perspective, morality is neither objective nor transcendent – it is the pragmatic and culture-dependent expression of a set of neural systems that have evolved to allow our navigation of complex human social systems.

“Braintrust”, by Patricia S. Churchland, surveys the findings from a range of disciplines to illustrate this framework. The main thesis of the book is grounded in the approach of evolutionary psychology but goes far beyond the just-so stories of which that field is often accused by offering not just a plausible biological mechanism to explain the foundations of moral behaviour, but one with strong empirical support.

The thrust of her thesis is as follows:

Moral behaviour arose in humans as an extension of the biological systems involved in recognition and care of mates and offspring. These systems are evolutionarily ancient, encoded in our genome and hard-wired into our brains. In humans, the circuits and processes that encode the urge to care for close relatives can be co-opted and extended to induce an urge to care for others in an extended social group. These systems are coupled with the ability of humans to predict future consequences of our actions and make choices to maximise not just short-term but also long-term gain. Moral decision-making is thus informed by the biology of social attachments but is governed by the principles of decision-making more generally. These entail not so much looking for the right choice but for the optimal choice, based on satisfying a wide range of relevant constraints, and assigning different priorities to them.

This does not imply that morals are innate. It implies that the capacity for moral reasoning and the predisposition to moral behaviour are innate. Just as language has to be learned, so do the codes of moral behaviour, and, also like language, moral codes are culture-specific, but constrained by some general underlying principles. We may, as a species, come pre-wired with certain biological imperatives and systems for incorporating them into decisions in social situations, but we are also pre-wired to learn and incorporate the particular contingencies that pertain to each of us in our individual environments, including social and cultural norms.

This framework raises an important question, however – if morals are not objective or transcendent, then why does it feel like they are? This is after all, the basis for all this debate – we seem to implicitly feel things as being right or wrong, rather than just intellectually being aware that they conform to or violate social norms. The answer is that the systems of moral reasoning and conscience tap into, or more accurately emerge from ancient neural systems grounded in emotion, in particular in attaching emotional value or valence to different stimuli, including the imagined consequences of possible actions.

This is, in a way, the same as asking why does pain feel bad? Couldn’t it work simply by alerting the brain that something harmful is happening to the body, which should therefore be avoided? A rational person could then take an action to avoid the painful stimulus or situation. Well, first, that does not sound like a very robust system – what if the person ignored that information? It would be far more adaptive to encourage or enforce the avoidance of the painful stimulus by encoding it as a strong urge, forcing immediate and automatic attention to a stimulus that should not be ignored and that should be given high priority when considering the next action. Even better would be to use the emotional response to also tag the memory of that situation as something that should be avoided in the future. Natural selection would favour genetic variants that increased this type of response and select against those that decoupled painful stimuli from the emotional valence we normally associate with them (they feel bad!).

In any case, this question is approached from the wrong end, as if humans were designed out of thin air and the system could ever have been purely rational. We evolved from other animals without reason (or with varying degrees of problem-solving faculties). For these animals to survive, neural systems are adapted to encode urges and beliefs in such a way as to optimally control behaviour. Attaching varying levels of emotional valence to different types of stimuli offers a means to prioritise certain factors in making complex decisions (i.e., those factors most likely to affect the survival of the organism or the dissemination of its genes).

For humans, these important factors include our current and future place in the social network and the success of our social group. In the circumstances under which modern humans evolved, and still to a large extent today, our very survival and certainly our prosperity depend crucially on how we interact and on the social structures that have evolved from these interactions. We can’t rely on tooth and claw for survival – we rely on each other. Thus, the reason moral choices are tagged with strong emotional valence is because they evolved from systems designed for optimal control of behaviour. Or, despite this being a somewhat circular argument, the reason they feel right or wrong is because it is adaptive to have them feel right or wrong.

Churchland fleshes out this framework with a detailed look at the biological systems involved in social attachments, decision-making, executive control, mind-reading (discerning the beliefs and intentions of others), empathy, trust and other faculties. There are certain notable omissions here: the rich literature on psychopaths, who may be thought of as innately deficient in moral reasoning, receives surprisingly little attention, especially given the high heritability of this trait. As an illustration that the faculty of moral reasoning relies on in-built brain circuitry, this would seem to merit more discussion. The chapter on Genes, Brains and Behavior rightly emphasises the complexity of the genetic networks involved in establishing brain systems, especially those responsible for such a high-level faculty as moral reasoning. The conclusion that this system cannot be perturbed by single mutations is erroneous, however. Asking what does it take, genetically speaking, to build the system is a different question from what does it take to break it. Some consideration of how moral reasoning emerges over time in children would also have been interesting.

Nevertheless, the book does an excellent job of synthesising diverse findings into a readily understandable and thoroughly convincing naturalistic framework under which moral behaviour can be approached from an empirical standpoint. While the details of many of these areas remain sketchy, and our ignorance still vastly outweighs our knowledge, the overall framework seems quite robust. Indeed, it articulates what is likely a fairly standard view among neuroscientists who work in or who have considered the evidence from this field. However, one can presume that jobbing neuroscientists are not the main intended target audience and that both the details of the work in this field and its broad conclusions are neither widely known nor held.

The idea that right and wrong – or good and evil – exist in some abstract sense, independent from humans who only somehow come to perceive them, is a powerful and stubborn illusion. Indeed, for many inclined to spiritual or religious beliefs, it is one area where science has not until recently encroached on theological ground. While the Creator has been made redundant by the evidence for evolution by natural selection and the immaterial soul similarly superfluous by the evidence that human consciousness emerges from the activity of the physical brain, morality has remained apparently impervious to the scientific approach. Churchland focuses her last chapter on the idea that morals are absolute and delivered by Divinity, demonstrating firstly the contradictions in such an idea and, with the evidence for a biological basis of morality provided in the rest of the book, arguing convincingly that there is no need of that hypothesis.

Mirrored from Wiring the Brain.

The emerging picture is that each of these disorders can be caused by mutations in any one of a large number of genes. Strikingly, many of these genes play important roles in neural development, with mutations affecting patterns of cell migration, the guidance of growing nerve fibres and their connectivity to other cells. Even more remarkable has been the observation that most such mutations predispose to not just one specific illness (such as schizophrenia) but to mental illness in general, with a strong overlap in the genetics of schizophrenia, autism, bipolar disorder, epilepsy, mental retardation, attention-deficit hyperactivity disorder and other diagnostic categories. These different categories may thus represent arguably distinct endpoints arising from common origins in neurodevelopmental insults.

What we do not yet know is why. How does a mutation in a gene controlling say, the formation of connections between specific types of nerve cells, ultimately result in someone having paranoid delusions? (While another person carrying the same mutation may develop the quite different symptoms of autism at a much earlier age). Answering such questions will require much greater integration of efforts across a wide range of disciplines.

These efforts must include neurodevelopmental biologists. Over the past couple of decades, tremendous progress has been made in elucidating the molecular mechanisms underlying nervous system development. In many cases, these advances have been made using fairly simply model systems – fruit flies and nematode worms have been favourites in this field, as well as simple parts of the vertebrate nervous system such as the spinal cord and retina. While more and more researchers are trying to figure out how these mechanisms apply in the vastly more complicated mammalian brain, we are still a long way from understanding how this structure develops. This is especially the case as much of the circuitry of the brain is not prespecified by genetic instructions down to the last synapse, but is strongly affected by patterns of electrical activity within developing circuits. Nevertheless, it has been possible to use animals with mutations in particular genes to figure out what the functions of these genes are in the development of specific brain circuits.

The logic of these approaches is fairly straightforward: in order to discover the normal function of Gene X, mutate it, look at what happens to some part of the brain and work backwards to deduce the cellular processes that have been affected. What is needed now, if neurodevelopmental biologists are to make a contribution to the study of mental illness, is a different approach. We must develop an interest in the phenotypes themselves, not just as tools to elucidate the gene’s normal functions. If mutations in Gene X can cause autism, for example, then a mouse with the same mutation becomes a valuable and informative model of disease. It becomes of interest to analyse not just the direct processes affected by the mutation but all of the knock-on consequences. While these questions may start with neurodevelopmental biologists they rapidly require additional expertise to address.

This will entail a framework to link investigations across levels of analysis typically carried out by researchers in quite different disciplines. For example, if the mutation affects formation of synaptic connections between certain types of cells in certain brain regions, then how does this change the function of the circuits involved? If this changes the activity of the circuit, then how does this affect further activity-depdendent development of interconnected regions? How does that affect the information processing capabilities of these networks? What cognitive functions are carried out by these networks and how are they impacted? At what level can we most directly translate findings in animals to humans? Each of these questions requires researchers in different disciplines to work together.

The imperative to do this could not be more stark. Roughly 10% of the world’s population is affected by mental illness at any one time, and over 25% will have some mental health problem over their lifetime. As well as the costs to individuals and their families, the public health and economic burdens from these disorders are massive, as large as that of cancer and cardiovascular disease. In fact, the proportional burden is growing as we are making good progress in treating the latter disorders, while mental illnesses have lagged far behind. This is mainly because we have not been able to apply the tools of molecular genetics to the problem. This is now changing, thanks to the revolutionary advances in psychiatric genetics. The challenge now will be to translate these discoveries into real understanding of disease mechanisms and ideas for novel therapies.

This post is based on a brief article that introduces a thematic series of reviews and primary research papers on the theme of Wiring the Brain. This series will appear across various journal titles of the open access publisher BioMed Central and can be accessed here.

Mitchell KJ (2011). The miswired brain: making connections from neurodevelopment to psychopathology. BMC biology, 9 (1) PMID: 21489316

The heritability of a trait can vary from 0.00 to 1.00, depending on the environments from which research participants are sampled. Because we know that genes play some role in the development of any trait, the precise heritability estimate doesn’t matter in a practical sense.

Heritability depends on the amount of variability in the environmental factors that contribute to a trait. The problem is that our understanding of the factors that contribute to the development of human traits in general — and to IQ in particular — is currently so deficient that we typically do not know if the environmental factors important in the development of a particular trait are stable across testing situations, vary somewhat across those situations, or vary wildly across those situations.

In his conclusion he states:

At the very least, heritability tells us how much of the variation in IQ can be accounted for by variation in genetic factors when development occurs in an exquisitely specific range of environments. However, David S. Moore has argued that even this is not significant when we realize that the magnitude of any heritability statistic reflects the extent of variation in unidentified non-genetic factors that contribute to the development of the trait in question.

(HT: Bryan Caplan)

Through his post, Kaufman constructs a series of paper tigers, tears them down and implies that because the extreme case does not hold, we should be wary of heritability estimates. I did not find much to disagree with in his examples, but the I differed on the conclusions we should draw.

So, where I do not agree – first, the heritability estimate does matter. While I don’t think it is hugely important whether the heritability of IQ in a specific sample is 0.5 or 0.6, it is important whether the measured heritability is 0 or 0.6. As Caplan notes in his post:

My money says, for example, that the average adult IQ heritability estimate published in 2020 will exceed .5.

I think that Caplan is right (although I might have stated some conditions about the relevant sample), and Kaufman’s argument overstates how finely tuned the environment needs to be to get a meaningful heritability estimate. Heritability estimates of a sample of children growing up in extreme poverty might be much lower (or zero) but as is found again and again, once the basic requirements of a child are met, heritability estimates for IQ are consistently above 0.4. We can construct arguments that in each study there are different gene-environment interactions and so on, but if genes weren’t important in variation in IQ and the gene-environment interactions weren’t consistent to some degree, why would such consistent heritability results (and correlation between parent and child IQ) be found?

Further, these results matter. They suggest that poverty is affecting the IQ of some children, and policies could be tailored to cut this disadvantage. For children not subject to deficient environments, the high heritability of IQ should influence policies such as those for education. Children are different and the education system should take this into account.

Implicit in Kaufman’s post was the “its all too complex” argument. Social and biological sciences are complex (which is why I find them interesting). However, if we fully accepted Kaufman’s argument that “our understanding of the factors that contribute to the development of human traits … is currently so deficient that we typically do not know if the environmental factors important in the development of a particular trait are stable across testing situations”, it would put into question most of the data analysis in economics, sociology and biology. Econometrics operates on the idea of all other things being equal.

Fortunately, Kaufman has not taken the Gladwell-esque approach of suggesting that we forget about genetic factors. Kaufman suggests further research into how nature and nurture are intertwined. If it is all too complex, we should start unwinding the complexity. However, I believe that, in the meantime, this complexity does not mean that we should throw out all the results that have previously been obtained.

**This is a cross-post from my blog Evolving Economics.

As an example of the consequences of the transmission of income. Bowles and Gintis cited a paper by Hertz which showed that a son born to someone in the top decile of income had a 22.9 per cent chance of attaining that decile himself, compared to a 1.3 per cent chance for someone born to parents in the bottom decile. Conversely, a child born to parents in the top decile had only a 2.4 per cent chance of finishing in the lowest decile compared to over 31.2 per cent for those born to bottom decile parents.

As Gladwell did, Bowles and Gintis started their examination with IQ. To calculate the inheritance of income through genetically inherited IQ, Bowles and Gintis considered the correlation between parent IQ and income, the heritability of IQ from parent to child and the correlation between IQ and income for the child. Breaking this down, Bowles and Gintis used the following steps and estimates:

1. The correlation between parental income and IQ is 0.266.

2.If the parents’ genotypes are uncorrelated, the genetic correlation between the genotype of the parents and of the child is 0.5. This can be increased with assortive mating (people pairing with people more like themselves) to a maximum of one (clones mating). Bowles and Gintis use 0.6.

3.The heritability of IQ is 0.5.

4. The correlation between child income and IQ is 0.266.

Multiplying these four numbers together gives the intergenerational correlation of income due to genetically based transmission of IQ. I think there is a mistake in the calculations used by Bowles and Gintis, as they find an intergenerational correlation of 0.01, where I calculated 0.02. This leads to genetically inherited IQ variation explaining 5.3 per cent of the observed intergenerational correlation in income. Regardless of the error, this is a low proportion of the income heritability. (After I wrote this post I did a google search to find if someone had spotted this error before – and they had – on a earlier Gene Expression post on this same paper.)

I would have used some slightly higher numbers, but pushing the numbers to the edges of feasible estimates, such as increasing the correlation between income and IQ to 0.4, the genetically based correlation between parent and child IQ to 0.8 and the degree of assortive mating so that parent-child genotype correlation is 0.8 only yields an intergenerational correlation of 0.10. Genetically inherited IQ would account for approximately 26 per cent of the observed intergenerational correlation.

Unlike Gladwell, Bowles and Gintis then asked what role other genetic factors may play. By using twin studies, which provide an estimate of the degree of heritability of income (using the difference in correlation between fraternal and identical twins) and the degree of common environments of each type of twin, Bowles and Gintis estimated that genetic factors explain almost a third (0.12) of the 0.4 correlation between parent and child income. Loosening their assumptions on the degree of shared environments by identical twins compared to fraternal twins (i.e. assuming near identical environments for both identical and fraternal twins) can generate a higher estimate of the genetic basis of almost three-quarters of the variability in income.

From this, it seems that genetic inheritance plays an important role income transmission between generations. The obvious question is what these factors might be. I expect that patience or ability to delay gratification must play a role, although I would expect that there would be a broad suite of relevant personality traits. I would also expect that appearance and physical features would be relevant. Bowles and Gintis do not take their analysis to this point.

The authors finished their analysis with some consideration of other factors, and conclude that race, wealth and schooling are more important than IQ as a transmission mechanism of income across generations (although as the authors noted, they may have overestimated the importance of race by not including a measure of cognitive performance in the regression). That conclusion may be fair, but as they had already noted, there is a substantial unexplained genetic component.

This highlights the paper’s limitation, as once the specific idea that heritability of IQ is a substantial cause of intergenerational income inequality has been dented, the identification of other (but unknown) genetic factors leaves open a raft of questions about income heritability. Using Bowles and Gintis’s conservative estimates, we still have 25 per cent of income heritability being put down to genetic factors without any understanding of what these traits are and the extent of the role they play.

In their conclusion, Bowles and Gintis touch on whether policy interventions might be based on these results. They are somewhat vague in their recommendations, but suggest that rather than seeking zero intergenerational correlation, interventions should target correlations that are considered unfair. They suggest, as examples, that there are large majorities supporting compensation for inherited disabilities while intervention for good looks is not appropriate.

One thing I find interesting in an analysis of heritability such as this is that over a long enough time horizon, to the extent that someone with a trait has a fitness advantage (or disadvantage), the gene(s) behind the trait will move to fixation (or be eliminated) as long as heritability is not zero. The degree of heritability is relevant only to the rate at which this occurs and only in a short-term context. The obvious question then becomes (which is besides the point of this post) whether IQ currently yields a fitness advantage. Over a long enough time period, variation will tend to eliminate itself and Bowles and Gintis would be unable to find any evidence of IQ heritability affecting income across generations.

**This a cross-post from my blog Evolving Economics, which is my usual blogging home.

Bowles, S., & Gintis, H. (2002). The Inheritance of Inequality Journal of Economic Perspectives, 16 (3), 3-30 DOI: 10.1257/089533002760278686

End Update

Over the past few days I’ve been very disturbed…and angry. The reason is that I’ve been reading Misha Angrist and Dr. Daniel MacArthur. First, watch this video:

In the very near future you may be forced to go through a “professional” to get access to your genetic information. Professionals who will be well paid to “interpret” a complex morass of statistical data which they barely comprehend. Let’s be real here: someone who regularly reads this blog (or Dr. Daniel MacArthur or Misha’s blog) knows much more about genomics than 99% of medical doctors. And yet someone reading this blog does not have the guild certification in the eyes of the government to “appropriately” understand their own genetic information. Someone reading this blog will have to pay, either out of pocket, or through insurance, someone else for access to their own information. Let me repeat: the government and professional guilds which exist to defend the financial interests of their members are proposing that they arbitrate what you can know about your genome. A friend with a background in genomics emailed me today: “If they succeed in ramming this through, then you will not be able to access your own damn genome without a doctor standing over your shoulder.” That is my fear. Is it your fear? Do you care?

In the medium term this is all irrelevant. Sequencing will be so cheap that it will be impossible for the government and well-connected self-interested parties to prevent you from gaining access to your own genetic information. Until then, they will slow progress and the potential utility of this business. Additionally, this sector will flee the United States and go offshore, where regulatory regimes are not so strict. BGI should give glowing letters of thanks to Jeffrey Shuren and the A.M.A.! This is a power play where big organizations, the government, corporations, and professional guilds, are attempting to squelch the freedom of the consumer to further their own interests, and also strangle a nascent economic sector of start-ups as a side effect.

You are so much more than your genes. So much more than that 3 billion base pairs. But they are a start, a beginning, and how dare the government question your right to know the basic genetic building blocks of who you are. This is the same government which attempted to construct a database of genetic information on foreign leaders. We know very well then who they think should have access to this data. The Very Serious People with a great deal of Power. People with “clearance,” and “expertise,” have a right to know more about about your own DNA sequence than you do.

What can you do? What can we do? Can we affect change? I don’t know, I can’t predict the future. But this is what I’m going to do.

1) I am going to release my own 23andMe sequence into the public domain soon. I encourage everyone to download it. I would rather have someone off the street know my own genetic information than be made invisible by the government. That is my right. For now that right is not barred by law. I will exercise it.

2) Spread word of this video via social networking websites and twitter. The media needs to get the word out, but they only will if they know you care. Do you care? I hope you do. This is a power grab, this is not about safety or ethics. If it was, I assume that the “interpretative services” would be provided for free. I doubt they will be.

3) Contact your local representative in congress. I’ve never done this myself, but am going to draft a quick note. They need to be aware that people care, that this isn’t just a minor regulatory issue.

4) The online community needs to get organized. We’re not as powerful as a million doctors and a Leviathan government, but we have right on our side. They’re trying to take from us what is ours.

5) Plan B’s. We need to prepare for the worst. Which nations have the least onerous regulatory regimes? Is genomic tourism going to be necessary? How about DIYgenomics? The cost of the technology to genotype and sequence is going to crash. I know that the Los Angeles DIYbio group has a cheap cast-off sequencer. For those who can’t afford to go abroad soon we’ll be able to get access to our information in our homes. Let’s prepare for that day.

This is a call to arms, a start. I’ve been complacent about this issue, focusing more on the fascinating aspects of ancestry inference which are enabled by personal genomics. No more. I’ll be doing a lot of reading today. If you have a blog, post the video. Raise awareness. Let’s make our voices heard. If they take away our rights because we’re silent, we have only ourselves to blame. If they take aware our rights despite our efforts, we’ll set up the infrastructure for the day when we can take back what is ours.

P.S. Feel free to post info and ideas in the comments. I just literally woke up to the urgency of this issue in the past 48 hours.

This is what Dienekes also found. Aggregating various ancestral components together to be analogous to what Zack produced at K = 5, you get the bar plot below from his runs:

I assume that all ancestry analyses will find that I have a substantial minority of East Eurasian ancestry. I have a similar amount of ancestry which is obviously connected to West Eurasia. And the rest of my ancestry is going to fall into the catchall which is “South Asian,” which Reich et al. in Reconstructing Indian History argued was in fact a compound between a West Eurasian-like population (“Ancestral North Indian,” ANI) and a South Eurasian population (“Ancestral South Indian,” ASI) which was more closely related to East Eurasians than West Eurasians, though distantly so at that (modern West Eurasians are interchangeable with ANI, but ASI do not exist in unadmixed form).

Finally, here’s an analysis of chromosome 1 and its affinities to various reference populations. I’ve labelled myself. No surprises:

I am HRP002 in HAP. DOD075 in Dodecad. IN8 in BGA. I am willing to submit to any of these new grassroots ancestry projects if they want me. But I doubt I’ll find anything too surprising now. They converge upon the same rough proportions (as they should).

I’m at the stage where I want to look more deeply into the details of how long ago the “eastern” admixture occurred. It seems to come down from both parents. If it was very recent there should be some linkage disequilibrium detectable because recombination should not have broken down the allelic associations distinctive to each ethnic group yet (this is noticeable in African Americans). But I am not so sure it is recent anymore, as I’d thought. I suspect a Tibeto-Burman and Munda element were absorbed by Bengali peasants in the course of demographic expansion in what became Bangladesh between 1000 and 1500 A.D., and that ancestry is well distributed across the population now.

But even though I won’t find anything out for myself, the reason HAP and projects like it are useful is that we need better coverage of the world’s variation. There are big coarse questions which we’ve tapped out, but there are still lots of gaps to fill. I’m willing to do my part in that (or, more precisely, at this point I’ve drafted my parents into the role, since they aren’t related and so represent two independent data points for Bengal).

Addendum: I know for many people of European ancestry this sort of thing doesn’t tell them anything new. Not so for me. I always suspected East Asian admixture due to the phenotype of my extended family (and to some extent, me. I did not need to shave regularly until my 20s), but I was always curious as to its extent. Additionally, for the reasons of phenotype I had assumed my mother had very little of such ancestry while my father had a great deal. It turns out that in fact my mother may marginally be more “eastern” than my father.