Nav

Monday, February 27, 2012

Nerves of a feather, wire together

Finding your soulmate, for a neuron, is a daunting task. With so many opportunities for casual hook-ups, how do you know when you find “the one”?

In the early 1960’s Roger Sperry proposed his famous “chemoaffinity theory” to explain how neural connectivity arises. This was based on observations of remarkable specificity in the projections of nerves regenerating from the eye of frogs to their targets in the brain. His first version of this theory proposed that each neuron found its target by expression of matching labels on their respective surfaces. He quickly realised, however, that with ~200,000 neurons in the retina, the genome was not large enough to encode separate connectivity molecules for each one. This led him to the insight that a regular array of connections of one field of neurons (like the retina) across a target field (the optic tectum in this case) could be readily achieved by gradients of only one or a few molecules.

The molecules in question, Ephrins and Eph receptors, were discovered thirty-some years later. They are now known to control topographic projections of sets of neurons to other sets of neurons across many areas of the brain, such that nearest-neighbour relationships are maintained (e.g., neurons next to each other in the retina connect to neurons next to each other in the tectum). In this way, the map of the visual world that is generated in the retina is transmitted intact to its targets. Actually, maintenance of nearest-neighbour topography seems to be a general property of projections between any two areas, even ones that do not obviously map some external property across them.

But the idea of matching labels was not wrong – they do exist and they play a very important part in an earlier step of wiring – finding the correct target region in the first place. This is nicely illustrated by a beautiful paper studying projections of retinal neurons in the mouse, which implicates proteins in the Cadherin family in this process.

In the retina, photoreceptor cells sense light and transmit this information, through a couple of relays, to retinal ganglion cells (RGCs). These are the cells that send their projections out of the retina, through the optic nerve, to the brain. But the tectum is not the only target of these neurons. There are, in fact, at least 20 different types of RGCs with distinct functions that project from the retina to various parts of the brain.

In mammals, “seeing” is mediated by projections to the visual centre of the thalamus, which projects in turn to the primary visual cortex. But conscious vision is only one thing we use our eyes for. The equivalent of the tectum, called the superior colliculus in mammals, is also a target for RGCs, and mediates reflexive eye movements, head turns and shifts of attention. (It might even be responsible for blindsight – subconscious visual responsiveness in consciously blind patients). Other RGCs send messages to regions controlling circadian rhythms (the suprachiasmatic nuclei) or pupillary reflexes (areas of the midbrain called the olivary pretectal nuclei).

These RGCs express a photoresponsive pigment (melanopsin) and respond to light directly. This likely reflects the fact that early eyes contained both ciliated photoreceptors (like current rods and cones) and rhabdomeric photoreceptors (possibly the ancestors of RGCs and other retinal cells).

So how do these various RGCs know which part of the brain to project to? This was the question investigated by Andrew Huberman and colleagues, who looked for inspiration to the fly eye. It had previously been shown that a member of the Cadherin family of proteins was involved in fly photoreceptor axons choosing the right layer to project to in the optic lobe. Cadherins are “homophilic” adhesion molecules – they are expressed on the surface of cells and like to bind to themselves. Two cells expressing the same Cadherin protein will therefore stick to each other. This stickiness may be used as a signal to make a synaptic connection between a neuron and its target.

The protein implicated in flies, N-Cadherin, is widely expressed in mammals and thus unlikely to specify connections to different targets of the retina. But Cadherins comprise a large family of proteins, suggesting that other members might play more specific roles. This turns out to be the case – a screen of these proteins revealed several expressed in distinct regions of the brain receiving inputs from subtypes of RGCs. One in particular, Cadherin-6, is expressed in non-image-forming brain regions that receive retinal inputs – those controlling eye movements and pupillary reflexes, for example. The protein is also expressed in a very discrete subset of RGCs – specifically those that project to the Cadherin-6-expressing targets in the brain.

The obvious hypothesis was that this matching protein expression allowed those RGCs to recognise their correct targets by literally sticking to them. To test this, they analysed these projections in mice lacking the Cadherin-6 molecule. Sure enough, the projections to those targets were severely affected – the axons spread out over the general area of the brain but failed to zero in on the specific subregions that they normally targeted.

These results illustrate a general principle likely to be repeated using different Cadherins in different RGC subsets and also in other parts of the brain. Indeed, a paper published at the same time shows that Cadherin-9 may play a similar function in the developing hippocampus. In addition, other families of molecules, such as Leucine-Rich Repeat proteins may play a similar role as synaptic matchmakers by promoting homophilic adhesion between neurons and their targets. (Both Cadherins and LRR proteins also have important “heterophilic” interactions with other proteins).

The expansion of these families in vertebrates could conceivably be linked to the greater complexity of the nervous system, which presumably requires more such labels to specify it. But these molecules may be of more than just academic interest in understanding the molecular logic and evolution of the genetic program that specifies brain wiring. Mutations in various members of the Cadherin (and related protocadherin) and LRR gene families have also been implicated in neurodevelopmental disorders, including autism, schizophrenia, Tourette’s syndrome and others. Defining the molecules and mechanisms involved in normal development may thus be crucial to understanding the roots of neurodevelopmental disease.

Osterhout, J., Josten, N., Yamada, J., Pan, F., Wu, S., Nguyen, P., Panagiotakos, G., Inoue, Y., Egusa, S., Volgyi, B., Inoue, T., Bloomfield, S., Barres, B., Berson, D., Feldheim, D., & Huberman, A. (2011). Cadherin-6 Mediates Axon-Target Matching in a Non-Image-Forming Visual Circuit Neuron, 71 (4), 632-639 DOI: 10.1016/j.neuron.2011.07.006

Williams, M., Wilke, S., Daggett, A., Davis, E., Otto, S., Ravi, D., Ripley, B., Bushong, E., Ellisman, M., Klein, G., & Ghosh, A. (2011). Cadherin-9 Regulates Synapse-Specific Differentiation in the Developing Hippocampus Neuron, 71 (4), 640-655 DOI: 10.1016/j.neuron.2011.06.019

Tuesday, February 7, 2012

I’ve got your missing heritability right here…

A debate is raging in human genetics these days as to why the massive genome-wide association studies (GWAS) that have been carried out for every trait and disorder imaginable over the last several years have not explained more of the underlying heritability. This is especially true for many of the so-called complex disorders that have been investigated, where results have been far less than hoped for. A good deal of effort has gone into quantifying exactly how much of the genetic variance has been “explained” and how much remains “missing”.

The problem with this question is that it limits the search space for the solution. It forces our thinking further and further along a certain path, when what we really need is to draw back and question the assumptions on which the whole approach is founded. Rather than asking what is the right answer to this question, we should be asking: what is the right question?

The idea of performing genome-wide association studies for complex disorders rests on a number of very fundamental and very big assumptions. These are explored in a recent article I wrote for Genome Biology (referenced below; reprints available on request). They are:

1) That what we call complex disorders are unitary conditions. That is, clinical categories like schizophrenia or diabetes or asthma are each a single disease and it is appropriate to investigate them by lumping together everyone in the population who has such a diagnosis – allowing us to calculate things like heritability and relative risks. Such population-based figures are only informative if all patients with these symptoms really have a common etiology.

2) That the underlying genetic architecture is polygenic – i.e., the disease arises in each individual due to toxic combinations of many genetic variants that are individually segregating at high frequency in the population (i.e., “common variants”).

3) That, despite the observed dramatic discontinuities in actual risk for the disease across the population, there is some underlying quantitative trait called “liability” that is normally distributed in the population. If a person’s load of risk variants exceeds some threshold of liability, then disease arises.

All of these assumptions typically go unquestioned – often unmentioned, in fact – yet there is no evidence that any of them is valid. In fact, the more you step back and look at them with an objective eye, the more outlandish they seem, even from first principles.

First, what reason is there to think that there is only one route to the symptoms observed in any particular complex disorder? We know there are lots of ways, genetically speaking, to cause mental retardation or blindness or deafness – why should this not also be the case for psychosis or seizures or poor blood sugar regulation? If the clinical diagnosis of a specific disorder is based on superficial criteria, as is especially the case for psychiatric disorders, then this assumption is unlikely to hold.

Second, the idea that common variants could contribute significantly to disease runs up against the effects of natural selection pretty quickly – variants that cause disease get selected against and are therefore rare. You can propose models of balancing selection (where a specific variant is beneficial in some genomic contexts and harmful in others), but there is no evidence that this mechanism is widespread. In general, the more arcane your model has to become to accommodate contradictory evidence, the more inclined you should be to question the initial premise.

Third, the idea that common disorders (where people either are or are not affected) really can be treated as quantitative traits (with a smooth distribution in the population, as with height) is really, truly bizarre. The history of this idea can be traced back to early geneticists, but it was popularised by Douglas Falconer, the godfather of quantitative genetics (he literally wrote the book).

In an attempt to demonstrate the relevance of quantitative genetics to the study of human disease, Falconer came up with a nifty solution. Even though disease states are typically all-or-nothing, and even though the actual risk of disease is clearly very discontinuously distributed in the population (dramatically higher in relatives of affecteds, for example), he claimed that it was reasonable to assume that there was something called the underlying liability to the disorder that was actually continuously distributed. This could be converted to a discontinuous distribution by further assuming that only individuals whose burden of genetic variants passed an imagined threshold actually got the disease. To transform discontinuous incidence data (mean rates of disease in various groups, such as people with different levels of genetic relatedness to affected individuals) into mean liability on a continuous scale, it was necessary to further assume that this liability was normally distributed in the population. The corollary is that liability is affected by many genetic variants, each of small effect. Q.E.D.

This model – simply declared by fiat – forms the mathematical basis for most GWAS analyses and for simulations regarding proportions of heritability explained by combinations of genetic variants (e.g., the recent paper from Eric Lander’s group). To me, it is an extraordinary claim, which you would think would require extraordinary evidence to be accepted. Despite the fact that it has no evidence to support it and fundamentally makes no biological sense (see Genome Biology article for more on that), it goes largely unquestioned and unchallenged.

In the cold light of day, the most fundamental assumptions underlying population-based approaches to investigate the genetics of “complex disorders” can be seen to be flawed, unsupported and, in my opinion, clearly invalid. More importantly, there is now lots of direct evidence that complex disorders like schizophrenia or autism or epilepsy are really umbrella terms, reflecting common symptoms associated with large numbers of distinct genetic conditions. More and more mutations causing such conditions are being identified all the time, thanks to genomic array and next generation sequencing approaches.

Different individuals and families will have very rare, sometimes even unique mutations. In some cases, it will be possible to identify specific single mutations as clearly causal; in others, it may require a combination of two or three. There is clear evidence for a very wide range of genetic etiologies leading to the same symptoms. It is time for the field to assimilate this paradigm shift and stop analysing the data in population-based terms. Rather than asking how much of the genetic variance across the population can be currently explained (a question that is nonsensical if the disorder is not a unitary condition), we should be asking about causes of disease in individuals:

- How many cases can currently be explained (by the mutations so far identified)?

- Why are the mutations not completely penetrant?

- What factors contribute to the variable phenotypic expression in different individuals carrying the same mutation?

- What are the biological functions of the genes involved and what are the consequences of their disruption?

- Why do so many different mutations give rise to the same phenotypes?

- Why are specific symptoms like psychosis or seizures or social withdrawal such common outcomes?


These are the questions that will get us to the underlying biology.


Mitchell, K. (2012). What is complex about complex disorders? Genome Biology, 13 (1) DOI: 10.1186/gb-2012-13-1-237

Manolio, T., Collins, F., Cox, N., Goldstein, D., Hindorff, L., Hunter, D., McCarthy, M., Ramos, E., Cardon, L., Chakravarti, A., Cho, J., Guttmacher, A., Kong, A., Kruglyak, L., Mardis, E., Rotimi, C., Slatkin, M., Valle, D., Whittemore, A., Boehnke, M., Clark, A., Eichler, E., Gibson, G., Haines, J., Mackay, T., McCarroll, S., & Visscher, P. (2009). Finding the missing heritability of complex diseases Nature, 461 (7265), 747-753 DOI: 10.1038/nature08494

Zuk, O., Hechter, E., Sunyaev, S., & Lander, E. (2012). The mystery of missing heritability: Genetic interactions create phantom heritability Proceedings of the National Academy of Sciences, 109 (4), 1193-1198 DOI: 10.1073/pnas.1119675109
s;o