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Summary1. Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data. Approximate Bayesian computation (ABC) is devoted to these complex models because it bypasses the evaluation of the likelihood function by comparing observed and simulated data. 2. We introduce the R package 'abc' that implements several ABC algorithms for performing parameter estimation and model selection. In particular, the recently developed nonlinear heteroscedastic regression methods for ABC are implemented. The 'abc' package also includes a cross-validation tool for measuring the accuracy of ABC estimates and to calculate the misclassification probabilities when performing model selection. The main functions are accompanied by appropriate summary and plotting tools. 3. R is already widely used in bioinformatics and several fields of biology. The R package 'abc' will make the ABC algorithms available to a large number of R users. 'abc' is a freely available R package under the GPL license, and it can be downloaded at http://cran.r-project.org/web/packages/ abc/index.html.

Knowledge of relatedness between pairs of individuals plays an important role in many research areas including evolutionary biology, quantitative genetics, and conservation. Pairwise relatedness estimation methods based on genetic data from highly variable molecular markers are now used extensively as a substitute for pedigrees. Although the sampling variance of the estimators has been intensively studied for the most common simple genetic relationships, such as unrelated, half-and full-sib, or parent-offspring, little attention has been paid to the average performance of the estimators, by which we mean the performance across all pairs of individuals in a sample. Here we apply two measures to quantify the average performance: first, misclassification rates between pairs of genetic relationships and, second, the proportion of variance explained in the pairwise relatedness estimates by the true population relatedness composition (i.e., the frequencies of different relationships in the population). Using simulated data derived from exceptionally good quality marker and pedigree data from five long-term projects of natural populations, we demonstrate that the average performance depends mainly on the population relatedness composition and may be improved by the marker data quality only within the limits of the population relatedness composition. Our five examples of vertebrate breeding systems suggest that due to the remarkably low variance in relatedness across the population, marker-based estimates may often have low power to address research questions of interest. INFERRING relatedness among pairs of individuals plays a central role in our understanding of many areas of genetics and population biology. For example, the extent of relatedness between individuals is important in the study of social evolution (e.g., Hamilton 1964;Cheverud 1985) and studies incorporating measures of relatedness have influenced our understanding of the mechanism of kin selection in natural populations (e.g., Choe and Crespi 1997). In quantitative genetics the estimation of genetic variance components, allowing estimation of heritability and genetic correlation, requires pairs of individuals with known relatedness (Lynch and Walsh 1998). In conservation biology, knowledge of relatedness is essential in captive management, where the goal is to preserve the genetic variation of the wild population from which the founders were drawn (e.g., Lacy 1994). Relatedness estimates are also used when testing hypotheses about inbreeding avoidance (e.g., Reusch et al. 2001;Richardson et al. 2004) and isolation by distance (e.g., Matocq and Lacey 2004).Relatedness has traditionally been estimated from pedigrees. Given an outbred source population and good recording, laboratory or managed populations can instantly provide pedigree information. However, many relevant ecological and evolutionary questions can only be addressed in free-living populations with the help of molecular marker data (Kruuk 2004). When relatedness estimation can be simp...

Quantitative genetic theory provides a means of estimating the evolutionary potential of natural populations. However, this approach was previously only feasible in systems where the genetic relatedness between individuals could be inferred from pedigrees or experimental crosses. The genomic revolution opened up the possibility of obtaining the realized proportion of genome shared among individuals in natural populations of virtually any species, which could promise (more) accurate estimates of quantitative genetic parameters in virtually any species. Such a 'genomic' quantitative genetics approach relies on fewer assumptions, offers a greater methodological flexibility, and is thus expected to greatly enhance our understanding of evolution in natural populations, for example, in the context of adaptation to environmental change, eco-evolutionary dynamics, and biodiversity conservation.