Huynh Emily, De Roach John, McLaren Terri, Thompson Jennifer, Montgomery Hannah, Kap Caitlyn, Hoffmann Ling, Lamey Tina
Australian Inherited Retinal Disease Register and DNA Bank, Department of Medical Technology and Physics, Sir Charles Gairdner Hospital, Perth, WA, Australia.
Centre for Ophthalmology and Visual Science, University of Western Australia/Lions Eye Institute, Perth, WA, Australia.
Australas Phys Eng Sci Med. 2016 Mar;39(1):239-45. doi: 10.1007/s13246-015-0420-z. Epub 2016 Jan 4.
The assignment of pathogenicity to variants suspected of causing an inherited retinal disease and the subsequent creation of molecular genetic reports sent to clinical geneticists and ophthalmologists has traditionally been time-consuming and subject to error and ambiguity. The purpose of this paper is to describe a computer-assisted method we have developed for (1) assessment of the predicted pathogenicity of genetic variants identified in patients diagnosed with an inherited retinal disease and (2) the incorporation of these results into the Australian Inherited Retinal Disease Register and DNA Bank's databases, for the production of molecular genetics reports. This method has significantly accelerated the assessment of variant pathogenicity prediction and subsequent patient report generation for the Australian Inherited Retinal Disease Register and DNA Bank, and has reduced the potential for human error. The principles described in this paper may be applied in any situation where genetic variants and patient information are stored in a well-organised database.
传统上，将疑似导致遗传性视网膜疾病的变异判定为致病因素，并随后生成发送给临床遗传学家和眼科医生的分子遗传学报告，这一过程既耗时，又容易出错且存在歧义。本文旨在描述一种我们开发的计算机辅助方法，用于：（1）评估在被诊断患有遗传性视网膜疾病的患者中鉴定出的基因变异的预测致病性；（2）将这些结果纳入澳大利亚遗传性视网膜疾病登记处和DNA库的数据库，以生成分子遗传学报告。该方法显著加快了澳大利亚遗传性视网膜疾病登记处和DNA库对变异致病性预测的评估以及后续患者报告的生成，并减少了人为错误的可能性。本文所述原则可应用于任何将基因变异和患者信息存储在组织良好的数据库中的情况。
