Henshall et al. Genetics Selection Evolution 2010, 42:34 http://www.gsejournal.org/content/42/1/34 several generations before this is detected when another carrier ram is used. In this paper, we have focused on an example from livestock, the self colour black condition in sheep which means we have assumed large half-sib families and restricted ourselves to analyzing these in isolation. These analyses are very quick and the analysis of high- throughput quantitative CNV assay data for half-sib families would be feasible using this method. However, a half-sib family structure is not a requirement for a segregation analysis, and even for family sizes of one or two half-sibs with the sire being also assayed, power was increased to a higher value than when analyzing an indi- vidual alone. Readily available segregation analysis soft- ware makes optimal use of all pedigree links in estimating allele probabilities, and is suitable for copy number allele calling in human pedigrees and other ped- igrees with smaller family sizes. In our simulation study, we assumed that parameters relating to penetrance function were known. Specifically, we assumed allele frequencies and parameters for a nor- mal distribution for the error associated with the quan- titative assay. These were estimated from a sample of unrelated sheep, but could be estimated from the popu- lation of interest. This aspect of segregation analysis was not applied in this study, but software such as Mendel [29] can be used for this purpose. In our study, we also investigated an already known copy number variant, identified from an experimental population designed to uncover the cause of the recessive black condition in Merino sheep. In most cases an experi- mental population will not be available. Using segrega- tion analysis to search for de novo CNV affecting quantitative traits would be similar to using hidden Markov model based approaches [20,21], but from a dif- ferent statistical perspective. To apply segregation analy- sis software for general pedigrees to high-density genomic data would likely be computationally prohibi- tive but software such as that used here for half-sib families might be feasible. Conclusions The precision of copy number allele estimates from quantitative assay data in pedigreed populations is greatly increased if the pedigree information is used in the estimation, and segregation analysis methods based on the peeling algorithm are well suited to this applica- tion. In the case of the Agouti locus and the recessive self-colour black condition, where the purpose of the test is to identify animals to use as parents, the propor- tion of lambs correctly identified (at the 99.9% level) as non-carriers increased from 7% when the lambs were analysed alone to 80% when the lambs were analysed in families. Any segregation analysis software can be used Page 7 of 8 provided that the appropriate penetrance function is specified. Acknowledgements We gratefully acknowledge SheepGenomics, an initiative of Australian Wool Innovation and Meat and Livestock Australia, for providing the DNA samples used to estimate allele frequencies, and both SheepGenomics and the Co- operative Research Centre for the Australian Sheep Industry for supporting research on the genetics of fleece colour in sheep. Author details 1CSIRO Livestock Industries, FD McMaster Laboratory Chiswick, Armidale, 2350, NSW, Australia. 2CSIRO Livestock Industries, Queensland Bioscience Precinct, St Lucia 4067, Queensland, Australia. Authors’ contributions JMH wrote the software for simulating and analysing the datasets, carried out the analyses and drafted the manuscript. VAW conducted the molecular genetic studies and contributed to the manuscript. BJN contributed to the design of the study and to the manuscript. All authors have read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. Received: 28 February 2010 Accepted: 12 August 2010 Published: 12 August 2010 References 1. Feuk L, Carson AR, Scherer SW: Structural variation in the human genome. Nat Rev Genet 2006, 7:85-97. 2. Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, Fiegler H, Shapero MH, Carson AR, Chen WW, et al: Global variation in copy number in the human genome. 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