Henshall et al. Genetics Selection Evolution 2010, 42:34 http://www.gsejournal.org/content/42/1/34 copies in diploid DNA and cannot discriminate between, for example, an individual homozygous for a two-copy allele and a heterozygous individual carrying one- and three-copy alleles. In many cases, this is not an impor- tant limitation as copy number alleles have additive dosage effects, but in other cases it is important, for example when only one copy number allele is associated with disease. To resolve individual alleles, data on related individuals can be analyzed concurrently. Pedi- gree information applies Mendelian constraints to the allowable sets of copy number alleles in related indivi- duals. These constraints have been exploited using Baye- sian graphical models [19] to infer copy number alleles, and hidden Markov model based methods [20,21] to find de novo CNV and infer copy number alleles. The Mendelian constraints underlying analyses such as those noted above have been well studied in the area of segregation analysis. Originally the term “segregation analysis” referred to the determination of the mode of inheritance of a phenotype but in recent decades it has come to include the inference of genotypic probabilities in pedigreed populations. The peeling algorithm of Elston and Stewart [22] is applicable in both cases. In small pedigrees, and in those without inbreeding loops, this algorithm produces unbiased estimates of allelic probabilities. A number of approaches have been described that are computationally feasible for larger pedigrees with inbreeding loops, including iterative peel- ing [23,24], cutting loops [25] and Markov Chain Monte Carlo (MCMC) methods [26,27]. MCMC methods remain an active area of research (e.g. [28]). The data required for single locus segregation analysis are four-fold: 1) a pedigree relating individuals to each other; 2) phenotypes on some individuals in the popula- tion; 3) a penetrance function expressing the probabil- ities of phenotype based on genotype; and 4) estimates of the frequencies of the genotype alleles in the popula- tion. Probabilities of mutation can also be incorporated. Assays that return estimates of the number of copies in diploid DNA can be thought of as a phenotypic mea- surement, and if assumptions are made about the distri- bution of the variation around the expected value of the assay then it is relatively straightforward to derive the appropriate penetrance function. Software to perform computations for segregation analysis is available and may include options to estimate penetrance functions as well as to estimate allelic probabilities (e.g. [29]). In this paper, we demonstrate that segregation analysis is an old solution to a new problem, by applying a segre- gation analysis method to simulated data to explore the inference of copy number alleles at the Agouti locus in sheep. The recessive self-colour black condition in domestic sheep has been studied for many years, with allele Awt, responsible for white fleece, known to be Page 2 of 8 dominant to allele Aa [30,31], responsible for the dark fibre colour. It has been confirmed that the association is due to variation in the agouti region [32] and recently Norris and Whan [13] have shown that a tandem gene duplication/deletion is responsible. Allele Awt is a num- ber of different alleles, having two or more copies of a 190 kb DNA segment including the agouti signalling pro- tein (ASIP) coding region, while allele Aa has a single copy of the region with a non-functional ASIP promoter. Furthermore, Norris and Whan [13] have described an asymmetric competitive PCR copy number assay for the number of copies in diploid DNA. We will use Ac1, Ac2 ... to refer to alleles with one copy, two copies and so on, with allele Aa being equivalent to our allele Ac1 and allele Awt being replaced by our alleles (Ac2, Ac3, Ac4 ...). In this study, we demonstrate that segregation analysis methods are well suited to the inference of copy number alleles, and that if knowledge on the actual allele config- urations is sufficiently important then the utility of a quantitative assay can be greatly improved through the incorporation of relationship data. On small datasets, our methods can be implemented in readily available software such as Mendel [29], accounting for the uncer- tainty in the individual assay results through the pene- trance function. With minor modifications, existing software for large datasets could also be used. While we restrict our analyses to half-sib families, our approach is general and could be applied to any data set with both pedigree and quantitative copy number assay components. Methods Assay parameters and allele frequency estimation The characteristics of the assay are reported in Norris and Whan [13]. In an asymmetric competitive PCR assay, DNA amplified from the junctions between copies is compared to the DNA amplified from the junctions and from the 5’ breakpoint region. For diploid DNA with a copy number count n, the expected value of the assay is the ratio n−2n, as there are n - 2 junctions and two copies of the 5’ breakpoint. As diploid DNA has at least two copies (at least one on each chromosome) this ratio takes values from the series (0/2, 1/3, 2/4, 3/5 ...) which asymptotically approaches unity. It is important to note that since the assay is quantitative, variation occurs around the expected ratios. The magnitude of the variation can differ among laboratories, and even among batches of samples in one laboratory, so we have treated it as unknown and conducted analyses for a range of values. As a lower limit, we chose a CV of 3% (which equates to a standard deviation of 0.01 for the class with an expected ratio of 1/3). This figure is a little smaller than that derived empirically from ranking 111 assay samples and estimating the mean and standard