Nucleic Acids Research Advance Access originally published online on October 2, 2008
Nucleic Acids Research 2008 36(21):e138; doi:10.1093/nar/gkn641
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Nucleic Acids Research, 2008, Vol. 36, No. 21 e138
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Modeling genetic inheritance of copy number variations
1Department of Genetics, University of Pennsylvania, 2Center for Applied Genomics and Division of Human Genetics, The Children's Hospital of Philadelphia, 3Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104 and 4Department of Mathematics, Georgetown University, Washington, DC 20057, USA
*To whom correspondence should be addressed. Tel: 267 426 2378; Fax: 267 426 0363; Email: wangk{at}chop.edu
Correspondence may also be addressed to Mingyao Li. Tel: 215 746 3916; Fax: 215 573 4865; Email: mingyao{at}mail.med.upenn.edu
Received July 25, 2008. Revised September 5, 2008. Accepted September 16, 2008.
Copy number variations (CNVs) are being used as genetic markers or functional candidates in gene-mapping studies. However, unlike single nucleotide polymorphism or microsatellite genotyping techniques, most CNV detection methods are limited to detecting total copy numbers, rather than copy number in each of the two homologous chromosomes. To address this issue, we developed a statistical framework for intensity-based CNV detection platforms using family data. Our algorithm identifies CNVs for a family simultaneously, thus avoiding the generation of calls with Mendelian inconsistency while maintaining the ability to detect de novo CNVs. Applications to simulated data and real data indicate that our method significantly improves both call rates and accuracy of boundary inference, compared to existing approaches. We further illustrate the use of Mendelian inheritance to infer SNP allele compositions in each of the two homologous chromosomes in CNV regions using real data. Finally, we applied our method to a set of families genotyped using both the Illumina HumanHap550 and Affymetrix genome-wide 5.0 arrays to demonstrate its performance on both inherited and de novo CNVs. In conclusion, our method produces accurate CNV calls, gives probabilistic estimates of CNV transmission and builds a solid foundation for the development of linkage and association tests utilizing CNVs.
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