Tuesday, January 7, 2014

On genetic causality: forwards and backwards

Genetics is getting more complicated. Previously clear and strong links between particular mutations and particular diseases are becoming muddied and weaker with increasing knowledge. Such mutations were usually initially identified in families with a heavy burden of illness, where the mutation segregated clearly with illness. But with our increasing ability to sequence large numbers of people, we are now seeing that many such mutations have a much more variable presentation.

Even classically “Mendelian” mutations, such as those causing cystic fibrosis and Huntington’s disease, are subject to modifying effects in the genetic background. The same mutation in one person may not cause the same symptoms or disease progression in another. And for more complex “disorders”, such as autism, epilepsy or schizophrenia, these effects are far more endemic. Even in cases where a primary mutation is identifiable, there may often be additional genetic factors that strongly influence the phenotype (not to mention intrinsic developmental variation, environmental factors and personal experiences, which may all also have a very large influence). Many such mutations can often be found in individuals without any clinical diagnosis. And in many cases, a disease may emerge due to non-additive interactions between multiple mutations, none of which can be said to be primary.

Given these complexities (even for Mendelian disorders), several commentators, including Anne Buchanan and Ken Weiss, here, and Gholson Lyon, here, have recently questioned the validity of the whole idea of making definitive, categorical genetic diagnoses based on single mutations. Both pieces make excellent and valid points.
Buchanan and Weiss have argued, convincingly, that the highly variable effects of many specific mutations make them almost useless for prediction of disease based on genotype. While I agree completely about the inherent complexity of relating single genotypes to phenotypes (as discussed here), I think it is important not to throw the baby out with the bathwater. In particular, a clear distinction should be drawn between explanation and prediction, as the probability relationships are entirely different in these two directions.

This can be illustrated with a couple of examples of specific mutations that increase risk of neurodevelopmental disorders. Most mutations associated with these conditions show “incomplete penetrance” – that simply means that not everyone who carries the mutation develops the disease (or, more accurately, not all carriers are given the diagnosis). For example, about 30% of carriers of a chromosomal deletion at 22q11.2 develop psychosis and would meet criteria for a diagnosis of schizophrenia. This is a hugely increased risk over the baseline population rate of ~1%, but obviously still far from a majority of carriers.

[As an aside, it is important to note that the value determined for the penetrance depends entirely on what phenotype we are assessing. If it is whether the individual has been given a diagnosis of schizophrenia, then it is around 30% for 22q11.2 deletions. But if it includes clinically determined intellectual disability, developmental delay or autism, then the penetrance approaches 100%. Indeed, a recent study found general effects on cognition even in clinically “unaffected” carriers of this and many other recurrent chromosomal aberrations only sometimes associated with frank disease].

What can we say, based on these numbers? For prediction, we are asking, given the presence of mutation X, what is the likelihood of disease Y? The only thing we can currently base that on is the frequency of disease in carriers of a given mutation. To follow the example above, given the presence of a 22q11.2 deletion, the risk of developing schizophrenia is 30%. Other known mutations associated with neurodevelopmental disorders have differing penetrance – for example, only ~6% of carriers of a NRXN1 deletion develop schizophrenia and only a third are clinically affected overall (versus nearly 100% of 22q11 deletion carriers).

Those numbers make predictions of the prognosis of individual mutation-carriers pretty fuzzy. With a disease like schizophrenia, this kind of prediction is clinically important as there may be methods to intervene during pre-morbid or prodromal phases of the illness, prior to the onset of frank psychosis and the full clinical syndrome. But current medical interventions in individuals at high risk of developing psychosis employ the crude hammer of antipsychotic medication, with all the attendant downsides and potentially serious side-effects – not something to be taken lightly or administered without strong justification. 

On the other hand, risks of the magnitude referred to above may well represent actionable information in terms of prenatal screening and reproductive decisions.
Nevertheless, predictions based on genetic information will remain drastically underpowered until we reach a point where the risk associated with an individual’s entire genome-type, and not just with a single mutation, can be assessed. Making predictions is hard, especially about the future (Niels Bohr or Yogi Berra, depending on who you ask).

But what about going in the opposite direction? This is really a very different situation. If we find an individual with disease Y and with mutation X, can we infer that the mutation is the cause of the disease? Here, we start with two givens (two rare events) and want to infer the likely relationship between them (based on their known contingency). So, if we have a patient with schizophrenia and a test shows they carry a 22q11.2 deletion, how strongly can we infer that that deletion is the primary cause of their illness?

I suppose there is a fancier statistical way to do this, but naïvely, we can say that if that person did not have that mutation, their likelihood of having schizophrenia would only have been ~1% (given no other relevant information). So, I think it right to say, intuitively, that it is 30-fold more likely that their disease was caused by the 22q11 deletion than by some other, unknown factor. We can put more definite numbers on this as follows:

Likelihood of causality = (P(Disease|Mutation) – P(Disease|No information)) /P(Disease|Mutation)

The P(A|B) notation means the probability of A, given B, which we are going to compare to the prior probability of A, given no knowledge of B. Because we take the presence of the mutation as a given, these calculations should be independent of the frequency of the mutation (I think). For 22q11 deletions, this odds ratio comes to 29/30, which corresponds to about a 96.7% probability. For NRXN1 deletions, the penetrance is much lower – 6.4% vs 1% baseline – but the inference of causality still comes out to 84.4%. (Another way to word this is, if we take 1000 individuals with NRXN1 deletions, we would expect 64 to have schizophrenia. But 10 of those would be expected anyway, so we can say the increased burden in this group, which we can equate to the likelihood of causality of the NRXN1 mutation in any individual is 54/64 = 84.4%).

I feel like I may have just committed some egregious statistical sin with the way that last statement is worded, but it’s not that important. Those calculations are very naïve (and not something any clinical geneticist actually carries out), but I think they capture the general intuition – if the known penetrance of a mutation for a particular disease is higher, then the inference of causality is stronger when you find someone with both the disease and the mutation. They also illustrate a surprising result: even in cases where predictive power is quite low (only about 6%), post hoc explanatory power may still be quite high – because now we’re given the presence of disease, an otherwise rare event.

[This is somewhat analogous to interpreting medical tests in a Bayesian framework, by comparing the false positive rate to the underlying prevalence of the condition being screened for (the prior probability) – see here for a great example of this counter-intuitive effect, in the context of autism].

Now, when we use a word like “cause” we are wading into some treacherous philosophical waters. When I use it here, I do not mean that the presence of the mutation is a sufficient cause of the illness, nor is it a complete explanation of the person’s phenotype. But calculations of the type shown above give a value to the strength of the inference that a particular mutation was a necessary condition for the emergence of illness in that individual. They allow us to assign a probability to the idea that, of all the factors and events that led to illness in this person, the presence of the mutation was a difference-maker. It was the main culprit, even if there were multiple accomplices.

This is not causality in a reductive sense (where a single cause fully explains the entire phenotype), but in a counterfactual sense (where a single difference explains a difference in the phenotype – in this case, developing disease versus not developing it). It says, if cause X had not been the case, then phenotype Y would not have arisen. For cases like cystic fibrosis and Huntington’s disease, this inference is rock solid – these disorders do not arise without mutations in the CFTR gene or the Htt gene (even if the disease symptoms and progression can be affected by modifying mutations in other genes). For examples like the mutations listed above that lead to common neurodevelopmental disorders, where there are multiple causes across the population, the best we can do is assign a probability of causal involvement for any particular potentially pathogenic mutation discovered, based on rates of illness across many carriers of that mutation, compared to the baseline rate.
At least, that’s usually the best we can do for humans – we can do a lot better in animal models that are amenable to experimental manipulation. When worm or fly or mouse geneticists map and identify a mutation that they think is causing a particular phenotype, they can do two different experiments to test that hypothesis. First, they can introduce the same mutation into a different animal and see if it reproduces the phenotype. And second, they can repair the mutation in the initial line of animals and see if it rescues the phenotype.

Obviously we can’t do those kinds of things in humans, but we can approach those kinds of experimental tests of causality in two ways. First, we can introduce the putatively causal mutation into an animal and see if it recapitulates known aspects of the disease phenotype (in an animal sense). This is very indirect and suffers from many caveats (especially in knowing which phenotypes to look for and in interpreting negative results) but a positive result in some validated assay does give some confidence that the suspect mutation is having an important and relevant effect.

The second approach relies on two fairly new technologies – the first is the development of induced pluripotent stem cells (iPS cells) from human patients. These can be differentiated in a dish into many different cell types and tissues, which can be tested for cellular-level phenotypes relevant to the function of the damaged gene. This system is obviously highly simplified and far from ideal, especially for disorders that manifest at a physiological or even psychological level, but even in those cases, they must arise initially from changes in the way cells function and these may be definable if we can assay the right cell types in the right ways.

Testing causality of a particular mutation for any such phenotype in a patient’s cells can now be achieved using an even newer technology: the CRISPR method of genome editing. This uses an RNA guide molecule to direct an enzyme to cut the DNA in the genome at a specific position (with astonishingly, game-changingly high efficiency). If a non-mutant template is supplied, this break will be repaired in such a way as to change the sequence of DNA in that region, providing the means to revert a mutation to the “wild-type” version. Then one can determine whether it was really that single mutation that led to the cellular phenotype or, alternatively, if it was not involved at all or only one of many factors contributing. (Exciting proof of principle of this approach was recently provided in a mouse model of cataracts and in cultured intestinal stem cells from cystic fibrosis patients).
Now, for most diseases, we don’t currently have good animal models or proxies at the cellular level. But there is an analogous approach to the rescue experiment that can be performed in humans for some conditions – that is to treat with a medication that targets the candidate pathogenic molecular mechanism. If the patient improves, then we can conclude that that mutation was in fact making a major contribution to their illness. This is the “House, M.D.” method of confirming a diagnosis (it’s never lupus).

Of course, for most mutations, no such specifically tailored medication currently exists. But there are a few exceptions for neurodevelopmental disorders. Fragile X syndrome is one – this condition is a common cause of autism, accounting for 2-3% of cases. Research over several decades has established the nature of the molecular defect in Fragile X patients and the cellular consequences in how nerve cell synapses work, and is beginning to elucidate the emergent physiological consequences on neural networks and brain systems. This detailed knowledge has led to the identification of candidate cellular components that can be targeted to restore the balance of the biochemical pathway affected by the Fragile X mutation. This approach shows great promise in animal models of the disorder and is currently in clinical trials.

Tuberous sclerosis is another genetic condition also often associated with symptoms of autism. It is caused by mutations in either one of two other genes, which also encode proteins that function in synapses. However, when these genes are mutated the biochemical defect is the opposite of that when the Fragile X gene is mutated. It turns out that if this pathway is either too active or not active enough, the functions of neural synapses are impaired, especially in how they change in response to activity. Either situation can lead to autism. In mice, crossing Fragile X mutants with tuberous sclerosis mutants actually restores the balance of this pathway and the resultant double mutants are much more normal than either single mutant alone.

So, if a child comes into a clinic with symptoms of autism, it is important to know if they have mutations in Fragile X or the tuberous sclerosis genes because the medication that may prove beneficial for Fragile X patients would be likely to exacerbate symptoms in those with tuberous sclerosis mutations. (And, of course, there are hundreds of other potential causes of autistic symptoms that may also respond differently or not at all).

But even for cases where no targeted medication exists, the identification of a putatively pathogenic mutation can still inform clinical treatment. Once a large enough database is generated, clinicians will be able to ask how patients with different mutations respond to currently available medications. Perhaps schizophrenic people with 22q11.2 deletions respond better to typical antipsychotics than people with NRXN1 deletions. Or maybe some medications should be avoided in the presence of certain mutations – that is the case for mutations in a sodium channel gene, which are associated with Dravet syndrome, a common form of epilepsy. Patients with these mutations should not be treated with traditional anticonvulsants as this is known to worsen their seizures.

To avoid semantic arguments, we should just probably not use the term “genetic diagnosis” and replace it with “genetic information”. I agree completely that a genetic diagnosis will often be too categorical and definitive, conferring a label based only on one component of a person’s genetic make-up, which may in turn be only one factor in their disease. But despite these complexities, the identification of major mutations still provides very useful genetic information that will often be relevant to the patient’s prognosis and treatment.

With thanks to Dan Bradley, John McGrath (@John_J_McGrath), Gholson Lyon (@GholsonLyon), Svetlana Molchanova (@Svetadotfi) and Shane McKee (@shanemuk) for useful and stimulating discussions.

The following articles have some interesting philosophical discussion of causality, especially in relation to genetics:

Mackie, J.L. (1965) Causes and conditions. American Philosophical Quarterly, vol 2, no. 4.

Meehl (1997) Specific Etiology and Other Forms of Strong Influence: Some
Quantitative Meanings. The Journal of Medicine and Philosophy, 1977, vol. 2, no. 1.

Waters, C.K. (2007) Causes that make a difference. Journal of Philosophy 104 (11):551-579

Kendler, K.S. (2012) The dappled nature of causes of psychiatric illness: replacing the organic–functional/hardware–software dichotomy with empirically based pluralism Apr;17(4):377-88.