Diagnostic Interpretation of Array Data Using Public Databases and Internet Sources

• Type of Material: Article
• Authors: Nicole de Leeuw, Radboud University Nijmegen; Trijnie Dijkhuizen, University of Groningen; Jayne Y Hehir-Kwa, Radboud University Nijmegen; Nigel P Carter, Wellcome Trust Sanger Institute; Lars Feuk, Uppsala University; Helen V Firth, Wellcome Trust Sanger Institute; Robert M Kuhn, University of California Santa Cruz; David H Ledbetter, Geisinger Hlth Syst; Christa Martin, Emory University; Conny MA van Ravenswaaij-Arts, University of Groningen
• Language: English
• Date: 2012-06-01
• Publisher: Wiley
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2012 Wiley Periodicals, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/humu.22049
• Title of Journal or Parent Work: Human Mutation
• Volume: 33
• Issue: 6
• Start Page: 930
• End Page: 940
• Grant/Funding Information: DECIPHER is supported by the Wellcome Trust (grant number WT077008).
• Keywords: data interpretation; Life Sciences & Biomedicine; diagnostic; Science & Technology; classification; array; Genetics & Heredity; STANDARDS; VARIANTS; COPY NUMBER; genome wide; GENOME-WIDE ARRAYS; CNV; database
• Research Categories: Biology, Genetics; Health Sciences, Medicine and Surgery

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.