Danielle Posthuma - Searching for genes

Kat - You're listening to the Naked Genetics podcast with me, Dr Kat Arney. Still to come, we'll be meeting our bold and brave gene of the month. But now it's time to return to the Genetics Society autumn meeting, where we heard an intriguing talk from Danielle Posthuma, from the University of Amsterdam in the Netherlands. For many years, scientists have been searching for genes involved in psychiatric diseases, such as schizophrenia and depression, but although many gene variations have been found, each of them seems to have a tiny effect on the risk of developing an illness. Yet we know that a significant chunk of the risk for these conditions must be in our DNA somewhere. Danielle's trying to look at this problem in new ways, by studying whole networks of genes rather than single suspects, and by using an intriguing new technique based on reprogrammed stem cells made from adult cells, known as induced pluripotent stem cells.

Danielle - This all started I think 30, 40 years ago when we said, if traits are heritable then we should be able to find genes for a trait. And then so, we are selecting one gene and then trying to find association with that gene and a trait.

Kat - So, kind of the usual suspects.

Danielle - Right.

Kat - This should be important, let's see if it links up to this behaviour.

Danielle - Yeah, exactly. That didn't really yield a lot of reliable associations and then a couple of years ago, we were finally able to do this at a genome wide scale - thanks to the development of micro-arrays. So, we started searching for genes for various disorders by scanning the whole genome. So, that's what many people have been doing for the last 8 years or so.

Kat - I see quite a lot of headlines, scientist find a hundred genes for autism, they find a hundred genes for Schizophrenia, these kind of studies.

Danielle - Yeah. So, that's what's really happening. I mean, that's all very exciting, but what we have to keep in mind is that the effects of those genes are very small and that together, they explain very little of those heritability of traits. So, that means that there are more genes that we still have to discover and those genes will have smaller effects than the genes that we currently have identified.

Kat - We've actually found the biggest ones already and they're not very big.

Danielle - Yeah, so it will be increasingly smaller, the effect sizes of the genes.

Kat - Are there missing genes or are we just thinking about how these genes work in the wrong way?

Danielle - Well, I think both. So, we are missing genetic variants that are very rare for example because these are very difficult to detect and they might have a large effect. But we need different strategies and different genotyping for that. But there also might be different statistical strategies that we have to employ in order to find common variants such as gene set analysis for example.

Kat - What do you mean by gene set analysis?

Danielle - Yeah, so what we've been doing mostly up until now is to determine the effects of every single SNP, every genetic variant at a time.

Kat - So, each single one.

Danielle - Yeah. What we would like to do is to say, "Well, we're not really interested in the single variant effects, but we are interested in all of the variants that are related to this and this particular pathway." For example, the dopamine pathway, we have a certain idea which genes are involved in that pathway. So, we can select all the variants that are important for that pathway and then test the effect of those variants as a group instead of looking at the single effects. And that will increase our effect size and it will also be more easy to interpret.

Kat - So, instead of just going, okay, this is single thing here, single thing here, you're saying, as a whole, all the genes involved in this kind of thing, are they important? What do you find when you take that kind of approach?

Danielle - So, we and others have taken this approach and we were able to find certain synaptic pathways that were associated with Schizophrenia for example, and we also found a specific gene set associated with IQ. We weren't able to detect any of those genes if we hadn't done a gene set analysis. So by themselves, these genes were not strongly enough associated with the trait. But when we looked at them in their context, in their functional genetic context, we were able to associate them with the trait.

Kat - So, apart from doing maybe bigger and bigger ever studies or this kind of analysis of lots of genes all bundled together, how else can we try and understand perhaps what some of these variations actually do? Because sometimes it feels to me, we've collected a lot of stamps, but we don't know how they work.

Danielle - Yeah. I think that's an important next step to find out how does it actually work. And so, gene set analysis might be one step in the right direction, but it might point us towards the important pathways, but it doesn't really tell us how things work. So, what we need is functional genomic follow up studies and we need molecular biologists who will look at our findings and to design experiments where they can actually manipulate the gene or the set of genes and look at their effect on a cellular level.

Kat - I guess the problem with some of these variations that we found is they're not necessarily in genes. Also, they're in humans, a lot of molecular biologists work in animal models. Tell me about the way that you're starting to move towards trying to understand these in cells?

Danielle - Yeah, so that's true. I mean, not all the results that we get are directly available for use in functional genomics experiments. So, one of the recent developments in biology is IPSC - induced pluripotent stem cells.

Kat - These are the 'turn back the clock' cells.

Danielle - Yeah. So, those are cells that you can take for example from a hair or from the skin and you reprogramme into an embryonic state, and then you can differentiate them to any kind of cell you like.

Kat - They're like magic!

Danielle - Yeah. I also think it's magic because if I can't do it myself, my colleagues do this. So, you can reprogramme cells and differentiate them from patients and controls and then you can for example, select patients that have a whole bunch of risk factors for Schizophrenia or another disease. So, you don't need one gene and you don't even have to know the function of the genes, and your findings don't even have to be inside genes. You simply select people based on their genotypic array and you do this for patients and controls. And then you differentiate their cells into neurons and use cellular assays to look at different phenotypes of the cell. And then hopefully, you'll find some differences which will tell you something about how the cells function in patients and controls.

Kat - So, you're almost making a model organism from an individual patient.

Danielle - Yeah.

Kat - What are some of the things that these studies are starting to show? It seems so exciting to me.

Danielle - Yeah, I think it's a very exciting era that we live in. I really like being part of this although I can do part of this myself and the other part, I need other people to collaborate with. So, science at least in my field is no longer something that's very individual. You have to collaborate with people from your own field to increase your sample size, but also, from other fields to increase your knowledge of what you're investigating. So, I think it's very nice.

Kat - And with these reprogrammed cells, what sort of results have come out so far? I'm aware it's still at the very early stages.

Danielle - Yeah, so there have been some initial studies and these used one or two patients or one or two controls. So, this is a very small scale but they were published in a very good journal because these were the first ones to do this. For Schizophrenia for example, they found differences in synaptic pathways between cells from patients and controls. But these studies, they do need replication because it's N or one or N of two studies. So, we do need larger samples for these kinds of studies.

Kat - That was Danielle Posthuma from the University of Amsterdam.