Department of Molecular Biology and Biochemistry, University of Malaga, 29071, Malaga, Spain.
CIBER de Enfermedades Raras, ISCIII, Madrid, Spain.
Eur J Hum Genet. 2018 Oct;26(10):1451-1461. doi: 10.1038/s41431-018-0139-x. Epub 2018 Jun 26.
Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8-9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. Despite the last advances in genomic sequencing and diagnosis, the pathological effects of many rare genetic variations remain unresolved, largely due to the low number of patients available for these cases, making it difficult to identify consistent patterns of genotype-phenotype relationships. We aimed to improve the identification of statistically consistent genotype-phenotype relationships by integrating all the genetic and clinical data of thousands of patients with rare genomic disorders (obtained from the DECIPHER database) into a phenotype-patient-genotype tripartite network. Then we assessed how our network approach could help in the characterization and diagnosis of novel cases in clinical genetics. The systematic approach implemented in this work is able to better define the relationships between phenotypes and specific loci, by exploiting large-scale association networks of phenotypes and genotypes in thousands of rare disease patients. The application of the described methodology facilitated the diagnosis of novel clinical cases, ranking phenotypes by locus specificity and reporting putative new clinical features that may suggest additional clinical follow-ups. In this work, the proof of concept developed over a set of novel clinical cases demonstrates that this network-based methodology might help improve the precision of patient clinical records and the characterization of rare syndromes.
拷贝数变异（CNVs）是基因组结构变异（缺失、重复或易位），占健康个体人类基因组变异的 4.8-9.5%。在某些情况下，CNVs 也可能导致疾病，是许多已知罕见遗传/基因组疾病的病因。尽管基因组测序和诊断技术取得了最新进展，但许多罕见遗传变异的病理影响仍未得到解决，主要是因为这些病例可用于研究的患者数量较少，难以确定基因型-表型关系的一致模式。我们的目标是通过将数千名患有罕见基因组疾病的患者的所有遗传和临床数据（从 DECIPHER 数据库中获得）整合到一个表型-患者-基因型三方网络中，来改善对统计学上一致的基因型-表型关系的识别。然后，我们评估了我们的网络方法如何帮助临床遗传学中对新病例的特征描述和诊断。本工作中实施的系统方法通过利用数千例罕见疾病患者的表型和基因型的大规模关联网络，能够更好地定义表型与特定基因座之间的关系。所描述方法的应用有助于对新的临床病例进行诊断，根据基因座特异性对表型进行排序，并报告可能提示进一步临床随访的新的临床特征。在这项工作中，一组新的临床病例的概念验证表明，这种基于网络的方法可能有助于提高患者临床记录的精确性和罕见综合征的特征描述。
