Philippe Julien, Derhourhi Mehdi, Durand Emmanuelle, Vaillant Emmanuel, Dechaume Aurélie, Rabearivelo Iandry, Dhennin Véronique, Vaxillaire Martine, De Graeve Franck, Sand Olivier, Froguel Philippe, Bonnefond Amélie
CNRS-UMR8199, Lille Pasteur Institute, Lille, France.
Lille University, Lille, France.
PLoS One. 2015 Nov 23;10(11):e0143373. doi: 10.1371/journal.pone.0143373. eCollection 2015.
Molecular diagnosis of monogenic diabetes and obesity is of paramount importance for both the patient and society, as it can result in personalized medicine associated with a better life and it eventually saves health care spending. Genetic clinical laboratories are currently switching from Sanger sequencing to next-generation sequencing (NGS) approaches but choosing the optimal protocols is not easy. Here, we compared the sequencing coverage of 43 genes involved in monogenic forms of diabetes and obesity, and variant detection rates, resulting from four enrichment methods based on the sonication of DNA (Agilent SureSelect, RainDance technologies), or using enzymes for DNA fragmentation (Illumina Nextera, Agilent HaloPlex). We analyzed coding exons and untranslated regions of the 43 genes involved in monogenic diabetes and obesity. We found that none of the methods achieves yet full sequencing of the gene targets. Nonetheless, the RainDance, SureSelect and HaloPlex enrichment methods led to the best sequencing coverage of the targets; while the Nextera method resulted in the poorest sequencing coverage. Although the sequencing coverage was high, we unexpectedly found that the HaloPlex method missed 20% of variants detected by the three other methods and Nextera missed 10%. The question of which NGS technique for genetic diagnosis yields the highest diagnosis rate is frequently discussed in the literature and the response is still unclear. Here, we showed that the RainDance enrichment method as well as SureSelect, which are both based on the sonication of DNA, resulted in a good sequencing quality and variant detection, while the use of enzymes to fragment DNA (HaloPlex or Nextera) might not be the best strategy to get an accurate sequencing.
单基因糖尿病和肥胖症的分子诊断对患者和社会都至关重要，因为它能带来与更好生活相关的个性化医疗，最终还能节省医疗保健支出。目前，基因临床实验室正从桑格测序转向新一代测序（NGS）方法，但选择最佳方案并非易事。在此，我们比较了基于DNA超声处理的四种富集方法（安捷伦SureSelect、RainDance技术公司）或使用酶进行DNA片段化的方法（Illumina Nextera、安捷伦HaloPlex）对43个与单基因形式糖尿病和肥胖症相关基因的测序覆盖度以及变异检测率。我们分析了43个与单基因糖尿病和肥胖症相关基因的编码外显子和非翻译区。我们发现，尚无一种方法能实现对基因靶点的完全测序。尽管如此，RainDance、SureSelect和HaloPlex富集方法对靶点的测序覆盖度最佳；而Nextera方法的测序覆盖度最差。尽管测序覆盖度较高，但我们意外发现，HaloPlex方法遗漏了其他三种方法检测到的20%的变异，Nextera遗漏了10%。文献中经常讨论哪种NGS技术用于基因诊断能产生最高诊断率的问题，答案仍不明确。在此，我们表明，基于DNA超声处理的RainDance富集方法以及SureSelect都能产生良好的测序质量和变异检测效果，而使用酶进行DNA片段化（HaloPlex或Nextera）可能并非获得准确测序的最佳策略。
