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One-step genetic sequence identified monogenic obesity, diabetes
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A one-step polymerase chain reaction-based enrichment used with genetic sequencing detected causal mutations leading to the diagnosis of monogenic diabetes or obesity and appeared to be cost-effective, according to data recently published in Diabetes Care.
With the exception of one variant, 98% of all causal mutations were identified among 40 patients, according to Amélie Bonnefond, PhD, of the European Genomic Institute for Diabetes in France, and colleagues.
“We believe that the assessment of all known susceptibility genes for a disease in only one step will bring a less biased analysis of the causes of the disease and an obvious benefit for the patient (and possibly his/her family if several putatively deleterious mutations are present in the same pedigree),” researchers wrote.
The novel one-step sequencing method is cost-effective compared with whole-exome sequencing ($330 vs. $1,630) and Sanger sequencing ($330 vs. $935,000), according to researchers.
“Moreover, the present method is very fast: for one sample, the microdroplet-based [polymerase chain reaction] enrichment, the sequencing and the analysis can be performed in 2 weeks only,” they added.
The researchers concluded that this method has the potential to simplify the current genetic diagnosis of severe forms of diabetes and obesity.
The method is based on polymerase chain reaction enrichment via microdroplets (RainDance Technologies) and next-generation sequencing using HiSeq 2000 (Illumina), researchers wrote.
Disclosure: The researchers report no relevant financial disclosures.
Perspective
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Robert D. Blank, MD, PhD
This is an interesting report that describes the application of a hybrid candidate gene/high throughput sequencing strategy to identify potentially pathogenic genetic variants responsible for monogenic diabetes and obesity phenotypes. Rather than performing whole genome or whole exome sequencing, the method relies on amplifying candidate genes by PCR and sequencing the amplified DNA. The authors demonstrate proof of concept by applying the hybrid approach in a blinded fashion to 40 specimens in which potential pathogenic sequence variants had previously been identified, correctly identifying 39/40 of the variants and additionally identifying three previously unrecognized potentially pathogenic variants. The low cost and generalizability of the strategy to other disease states make this a promising development in genetic diagnosis. Its main limitations are that few of the putative pathogenic variants have been rigorously validated for functional importance and that the candidate gene list is limited to loci that have already been implicated in monogenic diabetes and obesity.
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