Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
J Med Genet. 2023 Aug;60(8):810-818. doi: 10.1136/jmg-2022-108618. Epub 2023 Jan 20.
Genomic variant prioritisation is one of the most significant bottlenecks to mainstream genomic testing in healthcare. Tools to improve precision while ensuring high recall are critical to successful mainstream clinical genomic testing, in particular for whole genome sequencing where millions of variants must be considered for each patient.
We developed EyeG2P, a publicly available database and web application using the Ensembl Variant Effect Predictor. EyeG2P is tailored for efficient variant prioritisation for individuals with inherited ophthalmic conditions. We assessed the sensitivity of EyeG2P in 1234 individuals with a broad range of eye conditions who had previously received a confirmed molecular diagnosis through routine genomic diagnostic approaches. For a prospective cohort of 83 individuals, we assessed the precision of EyeG2P in comparison with routine diagnostic approaches. For 10 additional individuals, we assessed the utility of EyeG2P for whole genome analysis.
EyeG2P had 99.5% sensitivity for genomic variants previously identified as clinically relevant through routine diagnostic analysis (n=1234 individuals). Prospectively, EyeG2P enabled a significant increase in precision (35% on average) in comparison with routine testing strategies (p<0.001). We demonstrate that incorporation of EyeG2P into whole genome sequencing analysis strategies can reduce the number of variants for analysis to six variants, on average, while maintaining high diagnostic yield.
Automated filtering of genomic variants through EyeG2P can increase the efficiency of diagnostic testing for individuals with a broad range of inherited ophthalmic disorders.
基因组变异优先级排序是将基因组测试应用于医疗保健的最大瓶颈之一。提高精度同时确保高召回率的工具对于成功的主流临床基因组测试至关重要，特别是对于全基因组测序，每个患者都需要考虑数百万个变体。
我们开发了 EyeG2P，这是一个使用 Ensembl Variant Effect Predictor 的公共数据库和网络应用程序。EyeG2P 专为高效的个体遗传眼病变异优先级排序而设计。我们评估了 EyeG2P 在 1234 名患有各种眼部疾病的个体中的敏感性，这些个体先前通过常规基因组诊断方法获得了明确的分子诊断。对于 83 名前瞻性队列的个体，我们评估了 EyeG2P 与常规诊断方法相比的精度。对于另外 10 名个体，我们评估了 EyeG2P 用于全基因组分析的实用性。
EyeG2P 对通过常规诊断分析先前确定为临床相关的基因组变异具有 99.5%的敏感性（n=1234 名个体）。前瞻性地，与常规测试策略相比，EyeG2P 显著提高了精度（平均提高 35%）（p<0.001）。我们证明，将 EyeG2P 纳入全基因组测序分析策略可以将分析的变体数量平均减少到 6 个，同时保持高诊断产量。
通过 EyeG2P 自动过滤基因组变体可以提高患有各种遗传性眼部疾病的个体的诊断测试效率。
