Diagnostic interpretation of array data using public databases and internet sources

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.

Figure 1

Schematic representation of a general workflow to determine the relevance of an event detected by genome-wide array analysis to the observed phenotype of a patient.

Figure 2

Graph showing the number of laboratories (Y-axis) using the various resources (X-axis) to interpret their array results.

Figure 3

Plot of chromosome 9 showing a terminal loss of the short arm detected by genome-wide SNP array analysis. The deleted region is shown in the UCSC Genome Browser and several tracks are selected to help in interpreting this loss and to determine its clinical relevance. From top to bottom, the following tracks were selected: chromosome bands, DECIPHER, OMIM genes, RefSeq genes, Microarray Probe sets, SNP Genotyping Arrays, DGV, and duplications.

Figure 4

Schematic representation of possible connections between databases and Internet resources that can be used to optimize the quality and speed of array data interpretation. See text for details on the various resources.