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Methods to infer parent numbers from offspring genotypes either determine the minimum number of parents required to explain alleles and multilocus genotypes detected in the offspring or use models to incorporate information on population allele frequencies and allele segregation. Disparate results by different approaches suggest that one or perhaps all methods are subject to bias. Here, we investigate the performance of minimum parent number estimates, maximum likelihood, and Bayesian analyses (programs COLONY and PARENTAGE) with respect to marker information content in simulated data sets without knowledge of parental genotypes. Offspring families of different sizes were assumed to share one parent and to be sired by 1 or 5 additional parents. All methods committed large errors in terms of underestimation (minimum value) and overestimation (COLONY), or both (PARENTAGE) of parent numbers, unless the data were highly informative, and their relative performances depended on full-sib group sizes and sire numbers. Increasing the number of markers with low gene diversity (H(e) &lt; or = 0.68) yielded only slow improvement of the results, but all 3 methods performed well with 5-7 markers of H(e) = 0.84. We emphasize the importance of high marker polymorphism for inferring parent numbers and individual parent contributions, as well as for the detection of monogamous reproduction.

Assessing Parent Numbers from Offspring Genotypes: The Importance of Marker Polymorphism