PATRI (PaTeRnity Inference) is a program for paternity analysis of genetic data. The program requires genotypic, diploid data from one or more loci from mother-offspring pairs and from potential fathers. Typical data might include microsatellite markers, Restriction Fragment Length Polymorphisms (RFLPs) or Single Nucleotide Polymorphisms (SNPs). Given such genotypic data, PATRI can calculate posterior probabilities of paternity for all sampled offspring. When behavioral or ecological information can be used to divide the sampled males into different groups, PATRI can perform maximum likelihood analyses of hypotheses regarding the relative reproductive success of those groups. The underlying statistical methodology was described in Nielsen,R., Mattila,D.K., Clapham,P.J. and Palsbøll,P.J. 2001. Statistical Approaches to Paternity Analysis in Natural Populations and Applications to the North Atlantic Humpback Whale. Genetics 157:1673-1682.
For all genotypes, PATRI can estimate the posterior probability that a particular male has sired a particular offspring, assuming a uniform prior among all males in the population. The male population size (N) can either be specified by the user as a fixed value, or uncertainty regarding N can be modeled using a uniform or Gaussian prior. Using a uniform prior corresponds to assuming no prior information regarding the male population size, except that an upper bound can be specified. PATRI can also produce a maximum likelihood estimate of N based solely on the parent-offspring genotypic data. The estimation of N assumes equal fecundity and unbiased sampling of males.
If sampled males can be divided into groups based on behavioral or ecological information, PATRI can be used to evaluate hypotheses regarding the relative reproductive success of these groups. For k groups the user starts with a full model containing k-1 parameters, a