Two Algorithms for Biospecimen Comparison and Differentiation using SNP Genotypes

Abstract

Aims: Biobanks are frequently required to verify specimen relationships. We present two algorithms to compare SNP genotype patterns that provide an objective, high-throughput tool for verification. Methods: The first algorithm allows for comparison of all holdings within a biobank, and is well suited to construct sample relationships de novo for comparison with assumed relationships. The second algorithm is tailored to oncology, and allows one to confirm that paired DNAs from malignant and normal tissues are from the same individual in the presence of copy number variations. To evaluate both algorithms, we used an internal training data set (n = 1504) and an external validation data set (n = 1457). Results: In comparison with the results from manual review and a priori knowledge of patient relationships, we identified no errors in interpreting sample relationships within our validation data set. Conclusion: We provide an efficient and objective method of automated data analysis that is currently lacking for establishing and verifying specimen relationships in biobanks.

Original submitted 11 October 2012; Revision submitted 25 January 2013

KEYWORDS::

• biobanking
• distance algorithm
• human identification
• loss of heterozygosity
• SNP

Acknowledgements

The authors would like to acknowledge the Biospecimen Core Resource at the Nationwide Children‘s Hospital for contributing data to the publication. All genotype data used in this study were collected under Contract No. HHSN261201000048C for The Cancer Genome Atlas Project.

Disclaimer

These algorithms are not patented and are freely accessible to the scientific community. Sequenom, Inc. may include components of these tools in consumer software. The International Genomics Consortium and its partners utilize these tools in fee-for-service biobanking.

Financial & competing interests disclosure

S Morris, JV Smith, JD Paulauskis and R Penny are employees of the International Genomics Consortium. D van den Boom and P Oeth are employees/sharesholders of Sequenom, Inc. ES Gel is an employee of Arizona State University. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.