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We develop a model based on the Dirichlet-compound multinomial distribution (CMD) and Ewens sampling formula to predict the fraction of SNP loci that will appear fixed for alternate alleles between two pooled samples drawn from the same underlying population. We apply this model to next-generation sequencing (NGS) data from Baltic Sea herring recently published by (Corander et al., 2013, Molecular Ecology, 2931-2940), and show that there are many more fixed loci than expected in the absence of genetic structure. However, we show through coalescent simulations that the degree of population structure required to explain the fraction of alternatively fixed SNPs is extraordinarily high and that the surplus of fixed loci is more likely a consequence of limited representation of individual gene copies in the pooled samples, than it is of population structure. Our analysis signals that the use of NGS on pooled samples to identify divergent SNPs warrants caution. With pooled samples, it is hard to diagnose when an NGS experiment has gone awry; especially when NGS data on pooled samples are of low read depth with a limited number of individuals, it may be worthwhile to temper claims of unexpected population differentiation from pooled samples, pending verification with more reliable methods or stricter adherence to recommended sampling designs for pooled sequencing e.g. Futschik & Schlötterer 2010, Genetics, 186, 207; Gautier et al., 2013a, Molecular Ecology, 3766-3779). Analysis of the data and diagnosis of problems is easier and more reliable (and can be less costly) with individually barcoded samples. Consequently, for some scenarios, individual barcoding may be preferable to pooling of samples., (© 2013 John Wiley & Sons Ltd.)

Advances in genotyping that allow tens of thousands of individuals to be genotyped at a moderate number of single nucleotide polymorphisms (SNPs) permit parentage inference to be pursued on a very large scale. The intergenerational tagging this capacity allows is revolutionizing the management of cultured organisms (cows, salmon, etc.) and is poised to do the same for scientific studies of natural populations. Currently, however, there are no likelihood-based methods of parentage inference which are implemented in a manner that allows them to quickly handle a very large number of potential parents or parent pairs. Here we introduce an efficient likelihood-based method applicable to the specialized case of cultured organisms in which both parents can be reliably sampled. We develop a Markov chain representation for the cumulative number of Mendelian incompatibilities between an offspring and its putative parents and we exploit it to develop a fast algorithm for simulation-based estimates of statistical confidence in SNP-based assignments of offspring to pairs of parents. The method is implemented in the freely available software SNPPIT. We describe the method in detail, then assess its performance in a large simulation study using known allele frequencies at 96 SNPs from ten hatchery salmon populations. The simulations verify that the method is fast and accurate and that 96 well-chosen SNPs can provide sufficient power to identify the correct pair of parents from amongst millions of candidate pairs.

Severe declines in megafauna worldwide illuminate the role of top predators in ecosystem structure. In the Antarctic, the Krill Surplus Hypothesis posits that the killing of more than 2 million large whales led to competitive release for smaller krill-eating species like the Antarctic minke whale. If true, the current size of the Antarctic minke whale population may be unusually high as an indirect result of whaling. Here, we estimate the long-term population size of the Antarctic minke whale prior to whaling by sequencing 11 nuclear genetic markers from 52 modern samples purchased in Japanese meat markets. We use coalescent simulations to explore the potential influence of population substructure and find that even though our samples are drawn from a limited geographic area, our estimate reflects ocean-wide genetic diversity. Using Bayesian estimates of the mutation rate and coalescent-based analyses of genetic diversity across loci, we calculate the long-term population size of the Antarctic minke whale to be 670,000 individuals (95% confidence interval: 374,000-1,150,000). Our estimate of long-term abundance is similar to, or greater than, contemporary abundance estimates, suggesting that managing Antarctic ecosystems under the assumption that Antarctic minke whales are unusually abundant is not warranted.

A method for estimating the number of founding chromosomes in an isolated population is introduced. The method assumes that n/2 diploid individuals are sampled from a population and that alleles are identified at L unlinked loci. The population is assumed to have been founded T generations in the past by individuals carrying c chromosomes drawn randomly from a known source population, which has also been sampled. If c is small and the population grew rapidly after it was founded, accurate estimates of c can be obtained and those estimates are not sensitive to details of the history of population sizes. If c is larger or the population remained small after it was founded, then estimates of c depend on the history of population sizes. We test the performance of our method on simulated data and demonstrate its use on data from a rainbow trout (Oncorhynchus mykiss) population.

This article presents an efficient importance-sampling method for computing the likelihood of the effective size of a population under the coalescent model of Berthier et al. Previous computational approaches, using Markov chain Monte Carlo, required many minutes to several hours to analyze small data sets. The approach presented here is orders of magnitude faster and can provide an approximation to the likelihood curve, even for large data sets, in a matter of seconds. Additionally, confidence intervals on the estimated likelihood curve provide a useful estimate of the Monte Carlo error. Simulations show the importance sampling to be stable across a wide range of scenarios and show that the N(e) estimator itself performs well. Further simulations show that the 95% confidence intervals around the N(e) estimate are accurate. User-friendly software implementing the algorithm for Mac, Windows, and Unix/Linux is available for download. Applications of this computational framework to other problems are discussed.

We present a method for detecting haplotype blocks that simultaneously uses information about linkage-disequilibrium decay between the blocks and the diversity of haplotypes within the blocks. By use of phased single-nucleotide polymorphism data, our method partitions a chromosome into a series of adjacent, nonoverlapping blocks. The partition is made by choosing among a family of Markov models for block structure in a chromosomal region. Specifically, in the model, the occurrence of haplotypes within blocks follows a time-inhomogeneous Markov process along the chromosome, and we choose among possible partitions by using the two-stage minimum-description-length criterion. When applied to data simulated from the coalescent with recombination hotspots, our method reliably situates block boundaries at the hotspots and infrequently places block boundaries at sites with background levels of recombination. We apply three previously published block-finding methods to the same data, showing that they either are relatively insensitive to recombination hotspots or fail to discriminate between background sites of recombination and hotspots. When applied to the 5q31 data of Daly et al., our method identifies more block boundaries in agreement with those found by Daly et al. than do other methods. These results suggest that our method may be useful for designing association-based mapping studies that exploit haplotype blocks.

We present a statistical method for identifying species hybrids using data on multiple, unlinked markers. The method does not require that allele frequencies be known in the parental species nor that separate, pure samples of the parental species be available. The method is suitable for both markers with fixed allelic differences between the species and markers without fixed differences. The probability model used is one in which parentals and various classes of hybrids (F(1)'s, F(2)'s, and various backcrosses) form a mixture from which the sample is drawn. Using the framework of Bayesian model-based clustering allows us to compute, by Markov chain Monte Carlo, the posterior probability that each individual belongs to each of the distinct hybrid classes. We demonstrate the method on allozyme data from two species of hybridizing trout, as well as on two simulated data sets.