Open Access Highly Accessed Methodology article

Copy number variation signature to predict human ancestry

Melissa Pronold12, Marzieh Vali1, Roger Pique-Regi3 and Shahab Asgharzadeh1*

Author Affiliations

1 Department of Pediatrics, Children’s Hospital Los Angeles and The Saban Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

2 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

3 Department of Clinical and Translational Science, School of Medicine, Wayne State University, Detroit, MI, USA

For all author emails, please log on.

BMC Bioinformatics 2012, 13:336  doi:10.1186/1471-2105-13-336

Published: 27 December 2012

Abstract

Background

Copy number variations (CNVs) are genomic structural variants that are found in healthy populations and have been observed to be associated with disease susceptibility. Existing methods for CNV detection are often performed on a sample-by-sample basis, which is not ideal for large datasets where common CNVs must be estimated by comparing the frequency of CNVs in the individual samples. Here we describe a simple and novel approach to locate genome-wide CNVs common to a specific population, using human ancestry as the phenotype.

Results

We utilized our previously published Genome Alteration Detection Analysis (GADA) algorithm to identify common ancestry CNVs (caCNVs) and built a caCNV model to predict population structure. We identified a 73 caCNV signature using a training set of 225 healthy individuals from European, Asian, and African ancestry. The signature was validated on an independent test set of 300 individuals with similar ancestral background. The error rate in predicting ancestry in this test set was 2% using the 73 caCNV signature. Among the caCNVs identified, several were previously confirmed experimentally to vary by ancestry. Our signature also contains a caCNV region with a single microRNA (MIR270), which represents the first reported variation of microRNA by ancestry.

Conclusions

We developed a new methodology to identify common CNVs and demonstrated its performance by building a caCNV signature to predict human ancestry with high accuracy. The utility of our approach could be extended to large case–control studies to identify CNV signatures for other phenotypes such as disease susceptibility and drug response.