Accurate partition of individuals into full-sib families from genetic datawithout parental information

Two Markov chain Monte Carlo algorithms are proposed that allow the partiti oning of individuals into full-sib groups using single-locus genetic marker data when no parental information is available. These algorithms present a method of moving through the sibship configuration space and locating the configuration that maximizes an overall score on the basis of pairwise like lihood ratios of being full-sib or unrelated or maximizes the full joint li kelihood of the proposed family structure. Using these methods, up to 757 o ut of 759 Atlantic salmon were correctly classified into 12 full-sib famili es of unequal size using four microsatellite markers. Large-scale simulatio ns were performed to assess the sensitivity of the procedures to the number of loci and number of alleles per locus, the allelic distribution type, th e distribution of families, and the independent knowledge of population all elic frequencies. The number of loci and the number of alleles per locus ha d the most impact on accuracy. Very good accuracy can be obtained with as f ew as four loci when they have at least eight alleles. Accuracy decreases w hen using allelic frequencies estimated in small target samples with skewed family distributions with the pairwise likelihood approach. We present an iterative approach that partly corrects that problem. The full likelihood a pproach is less sensitive to the precision of allelic frequencies estimates but did not perform as well with the large data set or when little informa tion was available (e.g., four loci with four alleles).