NeuroX: the most cost-effective tool for neurodegenerative diseases


Over the last months we have seen more and more efforts being devoted to sequencing in order to identify genetic factors involved in disease. One of the most recent examples is the creation of a whole sequencing facility to accommodate and analyse the data coming from the Genomics England project (for more on this see here). A truly remarkable endeavour that involves a serious amount of money.

But these incredibly large-scale projects are not suited for every approach to disease. In many cases, we still need inexpensive methods to test medium to large numbers of samples. Sequencing costs have not yet dropped to a point that allow us to perform this.

Genotyping arrays, on the other hand, have been around for a bit longer and are quiet a bit cheaper to use, both in terms of cost of experiments as well as in terms of data analysis.

With this in mind, we have developed, together with Illumina, a genotyping array that is designed towards the study of neurological conditions: NeuroX. We have used as a backbone Illumina’s ExomeChip, which contains rare(ish) coding variants in the genome and we have added approximately 30,000 markers that we know are involved in neurological conditions. These include the very latest GWAS hits, rare disease-specific variants and known disease-causing mutations.

In total, NeuroX provides information on about 280,000 variants for each sample tested. The array is based on a 12-sample format (you can test a minimum of 12 samples in a single experiment) and takes about 3 days from DNA to data. The cost per sample is about $50 (~£30), which, when compared with sequencing costs of an exome (~£500) or a genome (~£1,500), is significantly lower, particularly if you want to test a large cohort of samples.

We think NeuroX has the potential to become the first tool for anyone starting to analyse a new cohort with a neurological disease. The GBLab has actually performed this approach with our study on DLB (for more see here) where we used this new tool to try to understand the genetic basis of DLB.

An obvious side benefit is that we, as a community, will be able to generate large amounts of data on the same platform for a variety of diseases, which will enable true comparison of genetic etiology.