Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP’s Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics.

对不确定的外显子组/基因组测序数据的重新分析可以提高罕见疾病患者的诊断率。然而，再分析所需的成本和工作量阻碍了其在研究和临床环境中的常规实施。 Solve-RD 项目旨在揭示未确诊罕见疾病的分子原因。目标之一是实施创新方法，重新分析数千个经过充分研究的未确诊病例的外显子组和基因组。原始基因组数据通过 RD-Connect 基因组-表型分析平台 (GPAP) 与标准化表型和谱系数据一起提交给 Solve-RD。我们开发了一个程序化工作流程来重新分析基因组-表型数据。它使用 RD-Connect GPAP 的应用程序编程接口 (API)，并依赖于构建系统的大数据技术。我们应用了该工作流程，对 4411 例未确诊病例中罕见的已知致病变异进行优先排序。这些查询平均返回每个病例 1.45 个变异，这些变异首先由疾病专家小组进行批量评估，然后由每个病例的提交者进行专门评估。共有 120 个指示病例（优先病例的 21.2%，所有外显子组/基因组阴性样本的 2.7%）已得到解决，其他病例正在调查中。按照美国医学遗传学学院的建议，此处描述的解决方案的实施提供了技术框架，可以在临床环境中定期进行病例级数据重新评估。