title: The power of single-nucleotide polymorphisms for large-scale parentage inference.
abstract: Likelihood-based parentage inference depends on the distribution of a likelihood-ratio statistic, which, in most cases of interest, cannot be exactly determined, but only approximated by Monte Carlo simulation. We provide importance-sampling algorithms for efficiently approximating very small tail probabilities in the distribution of the likelihood-ratio statistic. These importance-sampling methods allow the estimation of small false-positive rates and hence permit likelihood-based inference of parentage in large studies involving a great number of potential parents and many potential offspring. We investigate the performance of these importance-sampling algorithms in the context of parentage inference using single-nucleotide polymorphism (SNP) data and find that they may accelerate the computation of tail probabilities >1 millionfold. We subsequently use the importance-sampling algorithms to calculate the power available with SNPs for large-scale parentage studies, paying particular attention to the effect of genotyping errors and the occurrence of related individuals among the members of the putative mother-father-offspring trios. These simulations show that 60-100 SNPs may allow accurate pedigree reconstruction, even in situations involving thousands of potential mothers, fathers, and offspring. In addition, we compare the power of exclusion-based parentage inference to that of the likelihood-based method. Likelihood-based inference is much more powerful under many conditions; exclusion-based inference would require 40% more SNP loci to achieve the same accuracy as the likelihood-based approach in one common scenario. Our results demonstrate that SNPs are a powerful tool for parentage inference in large managed and/or natural populations.
authors.affiliation: Fisheries Ecology Division, Southwest Fisheries Science Center, Santa Cruz, California 95060, USA. eric.anderson@noaa.gov
authors.forename: Eric C
authors.initials: EC
authors.lastname: Anderson
authors.forename: John Carlos
authors.initials: JC
authors.lastname: Garza
mesh_terms: D000465:Algorithms; D000483:Alleles; D000818:Animals; D005260:Female; D005838:Genotype; D016013:Likelihood Functions; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide; D011336:Probability; D012474:Salmon