Fabric Genomics Inc., Oakland, CA, USA.
Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
Genome Med. 2021 Oct 14;13(1):153. doi: 10.1186/s13073-021-00965-0.
Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation.
We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed.
GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases.
GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.
在患者表型的背景下对遗传变异进行临床解读，正在成为基于基因组的罕见遗传病诊断中花费和时间最多的部分。人工智能（AI）有望通过将预测方法与日益增长的遗传疾病知识相结合，极大地简化和加快基因组解读。在这里，我们评估了 Fabric GEM 的诊断性能，这是一种新的基于人工智能的临床决策支持工具，用于加速基因组解读。
我们在一个由 119 名先证者组成的回顾性队列中对 GEM 进行了基准测试，这些先证者主要是新生儿重症监护病房的婴儿，他们接受了全基因组或外显子组测序（WGS、WES）。我们在来自五个学术医疗中心的 60 例独立病例中复制了我们的分析。为了比较，我们还使用了当前最先进的变异优先级工具分析了这些病例。包括 trio、duo 和 singleton 病例。支撑诊断的变异涵盖了多种遗传模式和类型，包括结构变异（SV）。通过两种方法从临床记录中提取患者表型：手动和使用自动临床自然语言处理（CNLP）工具。最后，重新分析了 14 个以前未解决的病例。
使用手动或通过 CNLP 衍生的表型描述，GEM 将超过 90%的致病基因排在前或第二位候选基因，并优先进行审查，每个病例平均审查 3 个候选基因。当分析为 singleton 时， trio 和 duo 的排名不变。在 20 个具有诊断性 SV 的病例中，GEM 将致病 SV 识别为首选候选基因，在 20 个病例中均在前 5 位，无论 SV 调用是否由 GEM 提供，还是由 GEM 使用其内部 SV 检测算法根据其自身的 SV 检测算法推断而来。在没有父母基因型的情况下，GEM 表现出相似的性能。对 14 个以前未解决的病例进行分析后，一个病例有了新的发现，3 个病例最终在手动审查后没有进展，10 个病例没有新的发现。
GEM 通过自动提名非常短的候选基因和疾病清单，实现了包括所有变异类型的诊断解读，以供最终审查和报告。与 CNLP 进行深度表型分析相结合，GEM 实现了遗传疾病诊断的大量自动化，可能降低成本并加快病例审查。
