Henshall et al. Genetics Selection Evolution 2010, 42:34 http://www.gsejournal.org/content/42/1/34 R E S E A R C H G e n e t i c s Se l e c t i o n Ev o l u t i o n Open Access Reconstructing CNV genotypes using segregation analysis: combining pedigree information with CNV assay John M Henshall1*, Vicki A Whan2, Belinda J Norris2 Abstract Background: Repeated blocks of genome sequence have been shown to be associated with genetic diversity and disease risk in humans, and with phenotypic diversity in model organisms and domestic animals. Reliable tests are desirable to determine whether individuals are carriers of copy number variants associated with disease risk in humans and livestock, or associated with economically important traits in livestock. In some cases, copy number variants affect the phenotype through a dosage effect but in other cases, allele combinations have non-additive effects. In the latter cases, it has been difficult to develop tests because assays typically return an estimate of the sum of the copy number counts on the maternally and paternally inherited chromosome segments, and this sum does not uniquely determine the allele configuration. In this study, we show that there is an old solution to this new problem: segregation analysis, which has been used for many years to infer alleles in pedigreed populations. Methods: Segregation analysis was used to estimate copy number alleles from assay data on simulated half-sib sheep populations. Copy number variation at the Agouti locus, known to be responsible for the recessive self- colour black phenotype, was used as a model for the simulation and an appropriate penetrance function was derived. The precision with which carriers and non-carriers of the undesirable single copy allele could be identified, was used to evaluate the method for various family sizes, assay strategies and assay accuracies. Results: Using relationship data and segregation analysis, the probabilities of carrying the copy number alleles responsible for black or white fleece were estimated with much greater precision than by analyzing assay results for animals individually. The proportion of lambs correctly identified as non-carriers of the undesirable allele increased from 7% when the lambs were analysed alone to 80% when the lambs were analysed in half-sib families. Conclusions: When a quantitative assay is used to estimate copy number alleles, segregation analysis of related individuals can greatly improve the precision of the estimates. Existing software for segregation analysis would require little if any change to accommodate the penetrance function for copy number assay data. Background With the increasing resolution at which genomes can be examined has come the recognition that variation in genome structure is common and affects more nucleo- tides per genome than the sequence variation found in single nucleotide polymorphisms (SNP) [1-3]. Copy number variation (CNV) in DNA, defined as insertions, deletions and duplications larger than 1 kb, is an impor- tant component of this structural variation. Recent * Correspondence: John.Henshall@csiro.au 1CSIRO Livestock Industries, FD McMaster Laboratory Chiswick, Armidale, 2350, NSW, Australia Full list of author information is available at the end of the article publications document the contribution of CNV to genetic diversity in humans [2,4-6] and human disease [7-9]. CNV has been shown to contribute to phenotype in model organisms [9-11] and to important production and disease traits in domesticated livestock species [12-15]. Current technologies to assay a CNV genotype (genome copy number) and its corresponding alleles have limitations [8,16]. Distinguishing among genomes that have multiple DNA copies (> 4-5 copies) is impre- cise while SNP that might ‘tag’ copy number alleles through linkage disequilibrium are usually only found for relatively simple diallelic CNV [2,3,6,17,18]. Typi- cally, assays attempt to quantify the total number of © 2010 Henshall et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.