Schizophrenia and bipolar disorder genetics blog

An interesting New Yorker article on the history of the DSM manual for diagnosing psychiatric illnesses - and the man most responsible for its creation.

The challenge
I’m open to comments from those that think the following isn’t correct…..
So you have a lab and DNA samples donated with consent from individuals diagnosed with a psychiatric illness and a matched group without illness. How would you go about telling us something about the genetics of psychiatric illness? You’d think the strategy would be already well worked out…a simple recipe of experimentation and analysis which would give you the answer.

To borrow from W. Somerset Maugham:

“There are three rules for cloning complex disease genes. Unfortunately, no one knows what they are.”

The problem is circular…until we have a few of these genes in the bag we won’t know the best way to get them. Here, ‘in the bag’ means proper mutations, not inferred involvement.

In this post, I will tell you what I think you should NOT do (the horse-flogging bit) and some suggestions on what you should do (the horses for courses bit) given the nature of your starting materials.

Don’t try this at home (or in the lab)

Genome-wide linkage studies….or more precisely, multiple family genome-wide linkage studies. In essence, linkage looks for regions of chromosomes which seem to always be found in family members who have the particular condition being studied. The implication is that these regions contain the faulty gene. The snag comes when you go beyond looking at a single family and instead ask what regions of chromosomes are linked with the disease in all families, or most…or even a detectable proportion. It hasn’t and doesn’t work when you go beyond the single family. An Aesop fable may help you see why (although it should be blindingly obvious).

The King of Clonia loved his orchard and the bountiful fruit it produced each year. It covered a sizeable area of his palace grounds and consisted of a multitude of fruiting tree species collected and nurtured from all over his Kingdom. One day, in a fit of ill-advised enthusiasm, he issued an edict to his court alchemists.

“Identify the purest essence of each individual fruit so that I might bottle them all as a gift to the Queen”.

The Alchemists debated over this task and finally came up with this solution. They would collect one example of each fruit, place it in a single barrel and grind them all up. Then they would extract, filter and fractionate the essences in one glorious process.

“That’s crazy”, cried one dissenting Alchemist, “you are just making the problem more difficult…how will you tell orange essence from apple or from plum?”.

“Fool!”, they replied witheringly, “we have the power to detect all the essences simultaneously…the court arithmetician has decreed it so”.

They failed of course, but as is the wont of those convinced by statistical models, they decided that scaling up was the answer.

“More fruit for the barrel!!” went out the command. “Still more fruit!!”, as they failed again.

The moral of the story is made apparent when the ‘fool’ took the fruit of a single, large tree and extracted the essence as required. Rather than praise his efforts, the other Alchemists accused him of plucking low-hanging fruit and producing an essence that was not relevant to the orchard as a whole.

“Your essence looks nothing like ours…and doesn’t even look like that you got from looking at another big tree”, they said.

“Exactly! That’s the point! Isn’t it good!”, he exclaimed excitedly.

“Actually, we think that means you have made a mistake. Or the problem is intractable. Or that fruit beetles must be invoked as the guardians of the essence and must be factored into future extractions”, the Alchemists said without a hint of irony.

The King placed all of them in a specially commissioned barrel and left them to stew in their own juices.
The end.

Are families bad? A graphical answer

No! They are good. But you have to know how to use them correctly to get any meaningful genetic information from them. It all comes down to my favourite phrase, ‘genetic architecture’. How many mutations are there for a disease in the population, how common are they in the population, how strong is each one’s effect and do they run in families or are they ’sporadic’? The graphs below are my attempt to illustrate a genetic model that tries to explain that all of these questions are, in fact, just flip-sides of the same coin….and what is more, this genetic model can tell us what analysis techniques we should use when given a particular DNA sample set.

This first graph shows that the amount of each mutation in the population (its frequency) is directly related to how ’strong’ its effect is (names like Odds Ratios are just genetics terms for measuring mutation effect strength). This is the critical concept which helps explain the rest. Common mutations have weak effects and rare mutations often have strong effects….and every combination in between - as shown by the wide yellow band.

The pretty obvious fact (you would think) is that if a mutation has a strong effect then it will be apparent in most individuals it is passed on to. Hence, it will be recorded as evidence of a family history. In terms of ascertainment bias (shiny things grab our attention), it will highlight a family, catching the eye of a physician collecting DNA samples for a gene-hunting exercise. Weak effect mutations will probably have to work together to push an individual down the route of illness - but that’s OK as they are generally common and likely to be inherited together by chance in unlucky people (note the use of inherited here…it is not a process any different from the nominally familial individuals). But these individuals won’t be part of the families - they will appear as ’sporadic’ cases, caused by the random convergence of population risk factors.

Methods are horses and DNA sets are courses

So this figure is the crunch. We have this model but how do we apply it? Well each experimental technique/method of data analysis has its strengths and weaknesses. I think that researchers should be very, very careful when looking at the DNA sample set in front of them and deciding what they want to do. If it is a familial heavy set (left-hand side of the graph) then you would want to do single large family genome-wide linkage studies, the related technique of following the co-segregation of candidate gene alleles and illness through a family or, finally, deep resequencing. This final technique is not really applied much as it requires the selection of a candidate gene and then sequencing this gene in many DNA samples from individuals with a family history. The idea is that you may be lucky and hit a rare, strong effect (’highly penetrant’ in genetic-speak) mutation.

If, on the other hand (right of the graph), you have a predominantly sporadic DNA sample set than this opens up the possibility of case-control association studies. This technique compares the frequency of candidate gene alleles (’flavours’ of the same gene) between people with illness and unaffected people. If a difference is seen then that gene allele indicates the nearby presence of a mutation. This approach requires the alleles to be reasonably common in the population otherwise they will not be detectable. Hence, case-control association studies and familial (rare allele) samples shouldn’t be combined…..if you want anything out the other end. This is entirely analagous to the multiple family genome-wide linkage problem….here the issue is the number of different fruit types combined in the barrel - you won’t taste the bad apple in your fruit smoothie.
We are entering the age of the whole-genome case-control association study where big-science does away with subjective choice of candidate genes and just screens everything. While the rather poor coverage of each gene (and its constituent LD blocks) might sometimes work against the aims of the experiment - a story for another time, perhaps - let’s hope that nobody applies the wrong DNA sample set and then looks puzzled when results are lacking……..

Schizophrenia and bipolar disorder are described as complex genetic disorders. This means that a single gene is not responsible for all (or even most) cases - as is the case for cystic fibrosis, for example.

The definition of a complex disorder pretty much stops there. No-one really knows what the big picture is when it comes to the underlying ‘genetic architecture‘ of these disorders - so many questions remain. How many genes are involved? Do you have to have several genes damaged at once to get the illness? Do peoples in different parts of the world have a different set of damaged genes? Does the same gene mutation affect different people to different extents? Are there some, rare gene mutations with big effects and some common gene mutations with less pronounced effects? Do some gene mutations actually protect people from psychiatric illness?
etc. etc.

One of the key aspects of the complex genetic disorder is that the outcome of these varied gene changes is a single disease state. But this is quite amazing if you stop to think about it. OK, clinicians can separate psychotic illness into schizophrenia and bipolar disorder and then, furthermore, subdivide these diagnoses into schizoaffective disorder, bipolar I and bipolar II and so on. However, the overlapping symptoms seem to suggest that whatever the myriad genetic or other causes of these conditions they all seem to result in a related psychiatric state.

So that’s why this post is entitled ‘all roads lead to Rome’.
There have been several attempts to group the causative biological/physiological processes that go wrong in schizophrenia and bipolar disorder. Here are a few of them:

1. Failures in embryonic brain development. The movement of brain cells around the developing brain as they seek to establish correct connections is proposed to go wrong. Some reports from brain imaging or post mortem microscope studies suggest certain brain regions (e.g. the hippocampus) are particularly affected.
2. Dopamine is a chemical messenger used by some brain cells (neurons) to communicate with each other. Certain antipsychotic drugs seem to affect dopamine activity suggesting that this system might be at fault.
3. Glutamate is a chemical messenger used by some brain cells (neurons) to communicate with each other. Certain drugs inhibiting glutamate activity give rise to psychosis-like symptoms suggesting that this system might be at fault.
4. Certain brain cells called glial cells nourish and ensheath the neurons in the brain. It has been suggested that poorly functioning glial cells might cause psychaitric illness.

…and there are several other theories too.

Our lab identified a good candidate gene, DISC1 (disrupted in schizophrenia 1), by studying a rare instance of chromosomal damage (we will cover this gene in future posts for sure). Another cause of schizophrenia seems to from the deletion (loss) of a part of chromosome 22. Other forms seem to be dependent on the inheritance of particular forms of parts of certain chromosomes in certain families.

What is certain is that no psychiatrist could interview a schizophrenic patient and say ‘ah yes, a definite DISC1 symptomatology’ or ‘definite evidence of glial failure’ or ‘clear glutamate dysfunction’. Schizophrenia is schizophrenia, whatever the cause.

We genetic researchers all seek reductionist answers: how gene X affects this little part of how a cell works. That’s how we do science. That’s all we can realistically manage.
However, looking at the roadmap for routes north from Naples is only going to help our understanding of how Rome works a little.

So where should we be looking for this ‘big picture’ or ‘unifying’ answer?

Look at this flow chart:-
Damage to Gene X > affects some process in a brain cell > this process affects the way the cell interacts with other brain cells > these cells are now altered and perturb a particular subregion of the brain > this subregion normally processes information from A and sends the result to B > this brain system is now not working properly > this affects higher cognitive functions such as memory, sensory perception and processing, consciousness, personality, mood etc. etc. > schizophrenia is the diagnostic outcome.

I actually think we are doing vital work hunting for Genes X, Y and Z and I think those working on functional imaging (e.g. fMRI) of the brain in certain thought states are telling us interesting things from the other end of the equation. The problem is the middle part of the flow chart. I don’t have any ideas on how to directly interrogate these parts of the brain’s function but it strikes me that these must be the places where all the disparate molecular and cellular causes of schizophrenia converge before funneling into the consistently altered (diagnostic) higher aspects of brain function.

My sneaking suspicion (and this isn’t my original idea) is that the way neurons alter and strengthen their communication with each other - described in a reasonably well-characterised process called long-term potentiation (LTP) - and perhaps the effect that this has on subsystems of the brain - are going to be the levels at which we will find the unifying errors of schizophrenia. In fact, I think LTP is a mechanism in search of a disease (and you can quote me on that!). So far LTP , for practical, technical reasons has only been studied in the context of the function of the hippocampus and to a lesser extent, the cerebellum. Methods to examine it in other brain regions would be invaluable.

Finally, ponder this. What might be the equivalent brain systems at fault in autism, aspergers, depression and ADHD/OCD given that they too are complex genetic disorders?

I’ll be mentioning the suspicions that people have when it comes to the genetic aspects of mental illness in future posts. I suspect that much of these arise from the somewhat messy output of a research field that is going through some painful growth spurts.
Let’s put things in perspective. Any scientific field can be measured in terms of its research publications - the scientific papers. OK, it may not represent the full scope of the work going on but it certainly reflects the emerging focal points and areas considered worthy of investment. I entered ’schizophrenia gene’ into the literature searching website PubMed (I suspect similar trends, but fewer results would be found for bipolar disorder) and counted the number of publications over the last two decades. This is the result:-

What this shows is that from a handful of papers produced per year, we are now seeing more than one a day. I think this reflects greater numbers of researchers in this field and, I hope, a greater insight into the underlying problems (these two factors obviously feed off each other). To put this in context, however, the number of papers pulled up for 2005 with a ‘diabetes gene’ search was 1610 and for ‘cancer gene’ was 3299. Clearly, we seem to be in an exponential phase of growth in the field and who knows where the levelling of will occur…….in terms of funding for mental illness genetics, we will never be near cancer.

For all this growth, can we see any visible dividends? In the first instance, what researchers have aimed for is the identification of the genes contributing to psychiatric illness. The next two graphs show the number of publications relating to some of these genes (N.B. none 100% confirmed as causative genes in the general population):-

These 7 genes are pretty much the most ‘popular’ candidate genes at the moment. Never mind what they do at the moment. I’m sure we’ll be discussing them in future posts. What you should see straight away is that these genes are almost all 21st Century discoveries. Also COMT is bucking the trend in that it was and is a popular gene to work on but perhaps its star is now waning (I could write an essay on the nature of the scientific herd instinct centred on this gene…maybe another day!). There are plenty of bright new things to replace it. Let’s look at them in more detail:-

It’s obvious that 2000 and beyond have been a goldrush in terms of new genes and this can be reasonably attributed solely to the completion of the Human Genome Project. Having said this, look at NOTCH4. This gene was heralded and then disgraced within a 12 month period and the resulting stigma appears to have stuck fast.

What all this tells us is that we are living in interesting times in terms of the discovery of new genes for schizophrenia and bipolar disorder. With any luck, this blog will be able to convey some of the issues and excitement in these formative years.