Individual identification from genetic marker data: developments and accuracy comparisons of methods.

Genetic marker-based identification of distinct individuals and recognition of duplicated individuals has important applications in many research areas in ecology, evolutionary biology, conservation biology and forensics. The widely applied genotype mismatch (MM) method, however, is inaccurate because it relies on a fixed and suboptimal threshold number (TM ) of mismatches, and often yields self-inconsistent pairwise inferences. In this study, I improved MM method by calculating an optimal TM to accommodate the number, mistyping rates, missing data and allele frequencies of the markers. I also developed a pairwise likelihood relationship (LR) method and a likelihood clustering (LC) method for individual identification, using poor-quality data that may have high and variable rates of allelic dropouts and false alleles at genotyped loci. The 3 methods together with the relatedness (RL) method were then compared in accuracy by analysing an empirical frog data set and many simulated data sets generated under different parameter combinations. The analysis results showed that LC is generally one or two orders more accurate for individual identification than the other methods. Its accuracy is especially superior when the sampled multilocus genotypes have poor quality (i.e. teemed with genotyping errors and missing data) and highly replicated, a situation typical of noninvasive sampling used in estimating population size. Importantly, LC is the only method that guarantees to produce self-consistent results by partitioning the entire set of multilocus genotypes into distinct clusters, each cluster containing one or more genotypes that all represent the same individual. The LC and LR methods were implemented in a computer program COLONY for free download from the Internet.