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Deep Seq For Heterogeneity

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Some diseases are fairly homogeneous — variants associated with them can be found through genome-wide association studies. But many recessive cognitive disorders are more complicated, requiring more sophisticated techniques to find the underlying genetic cause. Recently, researchers in Germany and Iran, led by the Max Planck Institute for Molecular Genetics' Hilger Ropers, applied a deep sequencing approach to study 136 consanguineous families with autosomal recessive intellectual disability. The team found single, probable disease-causing variants in 50 novel genes, and confirmed mutations in 23 genes that had been found previously. Genome Technology's Christie Rizk recently spoke with Ropers about the study, which was published in Nature in October.

Genome Technology: You found variants in 73 genes, 50 of which were novel. Were you surprised by your discovery?

Hilger Ropers: Actually, not really. We had studied several hundred families before, just to find out how many different linkage intervals there were, how many different loci. It turned out during the first study that we published [in Human Genetics in 2007] that in 78 families all the loci were different, indicating that this is a very, very genetically heterogeneous condition. So contrary to, say, X-linked mental handicaps — where we have fragile X that accounts for about 20 percent or so of cases — there's no such thing in autosomal recessive forms of mental handicaps.

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GT: You used a combination of homozygosity mapping, exon enrichment, and next-generation sequencing. Why did you choose these specific methods and not whole-exome or whole-genome sequencing?

HR: Whole-genome sequencing would have been a lot more expensive, and to do it on several members of the family would have been much more [expensive] as well. And if we hadn't done it on several members of the family, we would have lost linkage information.

Linkage information — homozygosity information — turned out to be very important to interpret the data. In many of these cases, by first delimiting the area we scrutinized for mutation by doing linkage studies, we could reduce the complexity by a factor of 1,000 or so. What we dealt with in the end was maybe on the order of 10 megabases. And within these 10 megabases, we focused on the exons — the coding sequences — and that reduced complexity enormously so that in many families just one single change was left over.

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GT: You needed to sequence the patients' family members as well as the patients to do this study?

HR: That is true, and I strongly recommend everybody in this field to try and get any — and each and every — piece of additional information that they can get, to make the task of interpreting the results much easier.

In this case, if we had sequenced the entire genome, it might have taken us a lot of time to really exclude all the changes that we would have found. And it probably would have been impossible to find the defect in, altogether, 78 out of 136 families that we studied. I've seen previous studies where whole-exome sequencing was done without linkage information, and there the success rate is much lower because very often they were left with a few different plausible gene defects and were unable to decide between the two or three or five.

GT: Could this method eventually be used in the clinic to benefit patients?

HR: Yes, of course. Everything that comes out here will eventually be beneficial to patients. Of course, the idea here is to go back to the families, and this is being done. Most of these families are in Iran, and very large families are involved, so this will have a major impact there on health care — in particular because recessive disorders are more frequent there than in our population.

Of course, there is one proviso: Whatever we find and we think we are sure of, we can't be entirely sure until we have really compelling evidence. For example, in some cases, we found allelic mutations that looked good, that were plausible in two different families — so that I would consider as confirmation of the results. Or what you could try to do is generate animal models — for example Drosophila or in the mouse. Now, in the mouse, this is pretty tedious. But in Drosophila, we've already done it, together with a group from Vienna. And the results look very encouraging in quite a significant proportion of Drosophila models with a major impairment of long-term memory.

GT: What has this study shown you about the role of common variants versus rare variants in intellectual disabilities?

HR: I think there are no common variants. These kinds of blunt statements are mostly not completely correct, but in this case, I think any other reply would be plainly wrong. This is an extremely heterogeneous condition, and the bottom line is that it not only has an application for intellectual disability. I would think that this study has bearing on the study of a lot of disorders with very high heritability, but [also] the inability of researchers to find common risk factors by GWAS. In all these cases, I would strongly recommend to take one family at a time and treat them first as if it were a monogenic disorder, and I bet the results would be a lot more productive and better than this futile search for associated markers.

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