Diagnostic interpretation of array data using public databases and internet sources

TLDR

Array platforms and analysis software have expanded, making CNV detection easier, yet interpreting CNVs remains challenging due to growing complexity and the need for expert knowledge. The study aims to evaluate which publicly available databases and internet resources are suitable for diagnostic CNV interpretation. The authors review key characteristics of common internet databases and propose a general strategy for interpreting comparative hybridization, comparative intensity, and genotype-based array data.

Abstract

The range of commercially available array platforms and analysis software packages is expanding and their utility is improving, making reliable detection of copy-number variants (CNVs) relatively straightforward. Reliable interpretation of CNV data, however, is often difficult and requires expertise. With our knowledge of the human genome growing rapidly, applications for array testing continuously broadening, and the resolution of CNV detection increasing, this leads to great complexity in interpreting what can be daunting data. Correct CNV interpretation and optimal use of the genotype information provided by single-nucleotide polymorphism probes on an array depends largely on knowledge present in various resources. In addition to the availability of host laboratories' own datasets and national registries, there are several public databases and Internet resources with genotype and phenotype information that can be used for array data interpretation. With so many resources now available, it is important to know which are fit-for-purpose in a diagnostic setting. We summarize the characteristics of the most commonly used Internet databases and resources, and propose a general data interpretation strategy that can be used for comparative hybridization, comparative intensity, and genotype-based array data.