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Marshall et al 1998 - delta criterion

Marshall et al 1998 - delta criterion - Molecular...

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Introduction In classical paternity inference one excludes as many as possible of the candidate males from paternity of a partic- ular offspring using the available genetic data. If this pro- cedure yields a single nonexcluded male, paternity is assigned to that male. This is the basic methodology underlying human parentage testing (Chakraborty et al . 1974), and there are several examples of the approach in wild animal populations (e.g. Morin et al . 1994; Hogg & Forbes 1997; Keane et al . 1997). However, exclusion by itself may be insufficient to unambiguously resolve pater- nity in a considerable proportion of paternity tests, even using a series of very polymorphic codominant markers where the probability of excluding an arbitrary unrelated male is very high (Chakraborty et al . 1988). While exclu- sion may be a useful starting point in paternity inference, a statistically based method is needed to assign paternity when multiple males are nonexcluded. The use of likeli- hood (Edwards 1972) for inference of relationships using genetic data was first explored in detail by Thompson (1975, 1976a), who showed that likelihood is an efficient approach to the evaluation of alternative relationships between a given pair of individuals in inference of human Molecular Ecology (1998) 7 , 639–655 © 1998 Blackwell Science Ltd Statistical confidence for likelihood-based paternity inference in natural populations T. C. MARSHALL, J. SLATE, L. E. B. KRUUK and J. M. PEMBERTON Institute of Cell, Animal and Population Biology, University of Edinburgh, Edinburgh, EH9 3JT, UK Abstract Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genet- ically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic for resolving paternity. Using allele frequencies from the study popula- tion in question, a simulation program generates criteria for that permit assignment of paternity to the most likely male with a known level of statistical confidence. The simula- tion takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the inten- sively studied red deer ( Cervus elaphus ) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS †, described in this study is available from the authors.
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