We present version 6 of the DNA Sequence Polymorphism (DnaSP) software, a new version of the popular tool for performing exhaustive population genetic analyses on multiple sequence alignments. This major upgrade incorporates novel functionalities to analyze large data sets, such as those generated by high-throughput sequencing technologies. Among other features, DnaSP 6 implements: 1) modules for reading and analyzing data from genomic partitioning methods, such as RADseq or hybrid enrichment approaches, 2) faster methods scalable for high-throughput sequencing data, and 3) summary statistics for the analysis of multi-locus population genetics data. Furthermore, DnaSP 6 includes novel modules to perform single- and multi-locus coalescent simulations under a wide range of demographic scenarios. The DnaSP 6 program, with extensive documentation, is freely available at http://www.ub.edu/dnasp.

A number of statistical tests for detecting population growth are described. We compared the statistical power of these tests with that of others available in the literature. The tests evaluated fall into three categories: those tests based on the distribution of the mutation frequencies, on the haplotype distribution, and on the mismatch distribution. We found that, for an extensive variety of cases, the most powerful tests for detecting population growth are Fu's F_S test and the newly developed R₂ test. The behavior of the R₂ test is superior for small sample sizes, whereas F_S is better for large sample sizes. We also show that some popular statistics based on the mismatch distribution are very conservative.

Although many computer programs can perform population genetics calculations, they are typically limited in the analyses and data input formats they offer; few applications can process the large data sets produced by whole-genome resequencing projects. Furthermore, there is no coherent framework for the easy integration of new statistics into existing pipelines, hindering the development and application of new population genetics and genomics approaches. Here, we present PopGenome, a population genomics package for the R software environment (a de facto standard for statistical analyses). PopGenome can efficiently process genome-scale data as well as large sets of individual loci. It reads DNA alignments and single-nucleotide polymorphism (SNP) data sets in most common formats, including those used by the HapMap, 1000 human genomes, and 1001 Arabidopsis genomes projects. PopGenome also reads associated annotation files in GFF format, enabling users to easily define regions or classify SNPs based on their annotation; all analyses can also be applied to sliding windows. PopGenome offers a wide range of diverse population genetics analyses, including neutrality tests as well as statistics for population differentiation, linkage disequilibrium, and recombination. PopGenome is linked to Hudson’s MS and Ewing’s MSMS programs to assess statistical significance based on coalescent simulations. PopGenome’s integration in R facilitates effortless and reproducible downstream analyses as well as the production of publication-quality graphics. Developers can easily incorporate new analyses methods into the PopGenome framework. PopGenome and R are freely available from CRAN (http://cran.r-project.org/) for all major operating systems under the GNU General Public License.

Several tests have been proposed to detect departures of nucleotide variability patterns from neutral expectations. However, very different kinds of evolutionary processes, such as selective events or demographic changes, can produce similar deviations from these tests, thus making interpretation difficult when a significant departure of neutrality is detected. Here we study the effects of demography and recombination upon neutrality tests by analyzing their power under sudden population expansions, sudden contractions, and bottlenecks. We evaluate tests based on the frequency spectrum of mutations and the distribution of haplotypes and explore the consequences of using incorrect estimates of the rates of recombination when testing for neutrality. We show that tests that rely on haplotype frequencies—especially Fs and ZnS, which are based, respectively, on the number of different haplotypes and on the r2 values between all pairs of polymorphic sites—are the most powerful for detecting expansions on nonrecombining genomic regions. Nevertheless, they are strongly affected by misestimations of recombination, so they should not be used when recombination levels are unknown. Instead, class I tests, particularly Tajima's D or R2, are recommended.

The simultaneous analysis of multiple genomic loci is a powerful approach to studying the effects of population history and natural selection on patterns of genetic variation of a species. By surveying nucleotide sequence polymorphism at 334 randomly distributed genomic regions in 12 accessions of Arabidopsis thaliana, we examined whether a standard neutral model of nucleotide sequence polymorphism is consistent with observed data. The average nucleotide diversity was 0.0071 for total sites and 0.0083 for silent sites. Although levels of diversity are variable among loci, no correlation with local recombination rate was observed, but polymorphism levels were correlated for physically linked loci (<250 kb). We found that observed distributions of Tajima's D- and D/D_min- and of Fu and Li's D-, D*- and F-, F*-statistics differed significantly from the expected distributions under a standard neutral model due to an excess of rare polymorphisms and high variances. Observed and expected distributions of Fay and Wu's H were not different, suggesting that demographic processes and not selection at multiple loci are responsible for the deviation from a neutral model. Maximum-likelihood comparisons of alternative demographic models like logistic population growth, glacial refugia, or past bottlenecks did not produce parameter estimates that were more consistent with observed patterns. However, exclusion of highly polymorphic “outlier loci” resulted in a fit to the logistic growth model. Various tests of neutrality revealed a set of candidate loci that may evolve under selection.