17. In silico SNP array for aiding in interpretation of genomic microarray results: Application to case of mosaic trisomy 9

      Whole genome SNP microarrays are a powerful tool for identifying chromosomal abnormalities. However, the interpretation of SNP data can be difficult, especially in cases with mosaic aberrations which can produce non-intuitive patterns of SNP probes. Here, we describe an in silico SNP array simulation tool which can be used to assist in interpreting unusual array data patterns. This tool simulates the SNP array output from many chromosomal abnormalities including: single copy loss, two copy loss, single copy gain, two copy gain, isodisomy, three haplotype cells, and foreign haplotype contamination (e.g., maternal cell contamination). Additionally, any mosaic combination of aberrations can also be simulated, providing a powerful library for comparison to real-world SNP array data. One example where this tool aided in diagnosis was a case of mosaic trisomy 9. The proband presented at age 2 with mild developmental delay, preauricular tags, an inguinal hernia, mild left ptosis, and unusual swirled areas of hyper- and hypo-pigmentation on the trunk. SNP microarray from a peripheral blood sample was normal, however SNP microarray from a skin biopsy indicated mosaic trisomy 9 at a level of 35% mosaicism. The SNP array analysis was complicated by distal regions on Chr9 with 6 bands visible in the B-Allele Frequency data, while the proximal region showed the 4 bands expected in a mosaic gain. Analysis with the in silico array tool indicated that the 6 band pattern observed can be obtained if there are 3 haplotypes present in the sample. Based on this data, we can infer the original abnormality was likely the result of a Meiosis II error in one of the gametes, followed by a trisomy rescue event early in development. This case demonstrates the clinical utility of our in silico tool for interpreting SNP array data.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic and Personal


      Subscribe to Cancer Genetics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect