Gene copy number variations at the within-host population level modulate gene expression in a multipartite virus.

Multipartite viruses have a segmented genome, with each segment encapsidated separately. In all multipartite virus species for which the question has been addressed, the distinct segments reproducibly accumulate at a specific and host-dependent relative frequency, defined as the 'genome formula'. Here, we test the hypothesis that the multipartite genome organization facilitates the regulation of gene expression via changes of the genome formula and thus via gene copy number variations. In a first experiment, the faba bean necrotic stunt virus (FBNSV), whose genome is composed of eight DNA segments each encoding a single gene, was inoculated into faba bean or alfalfa host plants, and the relative concentrations of the DNA segments and their corresponding messenger RNAs (mRNAs) were monitored. In each of the two host species, our analysis consistently showed that the genome formula variations modulate gene expression, the concentration of each genome segment linearly and positively correlating to that of its cognate mRNA but not of the others. In a second experiment, twenty parallel FBNSV lines were transferred from faba bean to alfalfa plants. Upon host switching, the transcription rate of some genome segments changes, but the genome formula is modified in a way that compensates for these changes and maintains a similar ratio between the various viral mRNAs. Interestingly, a deep-sequencing analysis of these twenty FBNSV lineages demonstrated that the host-related genome formula shift operates independently of DNA-segment sequence mutation. Together, our results indicate that nanoviruses are plastic genetic systems, able to transiently adjust gene expression at the population level in changing environments, by modulating the copy number but not the sequence of each of their genes.

Evolution of flowering time in a selfing annual plant: Roles of adaptation and genetic drift.

Resurrection studies are a useful tool to measure how phenotypic traits have changed in populations through time. If these trait modifications correlate with the environmental changes that occurred during the time period, it suggests that the phenotypic changes could be a response to selection. Selfing, through its reduction of effective size, could challenge the ability of a population to adapt to environmental changes. Here, we used a resurrection study to test for adaptation in a selfing population of Medicago truncatula, by comparing the genetic composition and flowering times across 22 generations. We found evidence for evolution toward earlier flowering times by about two days and a peculiar genetic structure, typical of highly selfing populations, where some multilocus genotypes (MLGs) are persistent through time. We used the change in frequency of the MLGs through time as a multilocus fitness measure and built a selection gradient that suggests evolution toward earlier flowering times. Yet, a simulation model revealed that the observed change in flowering time could be explained by drift alone, provided the effective size of the population is small enough (<150). These analyses suffer from the difficulty to estimate the effective size in a highly selfing population, where effective recombination is severely reduced.

f-Statistics estimation and admixture graph construction with Pool-Seq or allele count data using the R package poolfstat.

By capturing various patterns of the structuring of genetic variation across populations, f -statistics have proved highly effective for the inference of demographic history. Such statistics are defined as covariances of SNP allele frequency differences among sets of populations without requiring haplotype information and are hence particularly relevant for the analysis of pooled sequencing (Pool-Seq) data. We here propose a reinterpretation of the F (and D ) parameters in terms of probability of gene identity and derive from this unified definition unbiased estimators for both Pool-Seq data and standard allele count data obtained from individual genotypes. We implemented these estimators in a new version of the R package poolfstat, which now includes a wide range of inference methods: (i) three-population test of admixture; (ii) four-population test of treeness; (iii) F 4 -ratio estimation of admixture rates; and (iv) fitting, visualization and (semi-automatic) construction of admixture graphs. A comprehensive evaluation of the methods implemented in poolfstat on both simulated Pool-Seq (with various sequencing coverages and error rates) and allele count data confirmed the accuracy of these approaches, even for the most cost-effective Pool-Seq design involving relatively low sequencing coverages. We further analysed a real Pool-Seq data made of 14 populations of the invasive species Drosophila suzukii, which allowed refining both the demographic history of native populations and the invasion routes followed by this emblematic pest. Our new package poolfstat provides the community with a user-friendly and efficient all-in-one tool to unravel complex population genetic histories from large-size Pool-Seq or allele count SNP data.

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest.

Simulation-based methods such as approximate Bayesian computation (ABC) are well-adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. Random Forest allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user-friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of pseudo-observed and real data sets corresponding to pool-sequencing and individual-sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories.

Preliminary insights into the genetics of bank vole tolerance to Puumala hantavirus in Sweden.

Natural reservoirs of zoonotic pathogens generally seem to be capable of tolerating infections. Tolerance and its underlying mechanisms remain difficult to assess using experiments or wildlife surveys. High-throughput sequencing technologies give the opportunity to investigate the genetic bases of tolerance, and the variability of its mechanisms in natural populations. In particular, population genomics may provide preliminary insights into the genes shaping tolerance and potentially influencing epidemiological dynamics. Here, we addressed these questions in the bank vole Myodes glareolus, the specific asymptomatic reservoir host of Puumala hantavirus (PUUV), which causes nephropathia epidemica (NE) in humans. Despite the continuous spatial distribution of M. glareolus in Sweden, NE is endemic to the northern part of the country. Northern bank vole populations in Sweden might exhibit tolerance strategies as a result of coadaptation with PUUV. This may favor the circulation and maintenance of PUUV and lead to high spatial risk of NE in northern Sweden. We performed a genome-scan study to detect signatures of selection potentially correlated with spatial variations in tolerance to PUUV. We analyzed six bank vole populations from Sweden, sampled from northern NE-endemic to southern NE-free areas. We combined candidate gene analyses (Tlr4, Tlr7, and Mx2 genes) and high-throughput sequencing of restriction site-associated DNA (RAD) markers. Outlier loci showed high levels of genetic differentiation and significant associations with environmental data including variations in the regional number of NE human cases. Among the 108 outliers that matched to mouse protein-coding genes, 14 corresponded to immune-related genes. The main biological pathways found to be significantly enriched corresponded to immune processes and responses to hantavirus, including the regulation of cytokine productions, TLR cascades, and IL-7, VEGF, and JAK-STAT signaling. In the future, genome-scan replicates and functional experimentations should enable to assess the role of these biological pathways in M. glareolus tolerance to PUUV.

Measuring Genetic Differentiation from Pool-seq Data.

The advent of high throughput sequencing and genotyping technologies enables the comparison of patterns of polymorphisms at a very large number of markers. While the characterization of genetic structure from individual sequencing data remains expensive for many nonmodel species, it has been shown that sequencing pools of individual DNAs (Pool-seq) represents an attractive and cost-effective alternative. However, analyzing sequence read counts from a DNA pool instead of individual genotypes raises statistical challenges in deriving correct estimates of genetic differentiation. In this article, we provide a method-of-moments estimator of [Formula: see text] for Pool-seq data, based on an analysis-of-variance framework. We show, by means of simulations, that this new estimator is unbiased and outperforms previously proposed estimators. We evaluate the robustness of our estimator to model misspecification, such as sequencing errors and uneven contributions of individual DNAs to the pools. Finally, by reanalyzing published Pool-seq data of different ecotypes of the prickly sculpin Cottus asper, we show how the use of an unbiased [Formula: see text] estimator may question the interpretation of population structure inferred from previous analyses.

Identifying genomic hotspots of differentiation and candidate genes involved in the adaptive divergence of pea aphid host races.

Identifying the genomic bases of adaptation to novel environments is a long-term objective in evolutionary biology. Because genetic differentiation is expected to increase between locally adapted populations at the genes targeted by selection, scanning the genome for elevated levels of differentiation is a first step towards deciphering the genomic architecture underlying adaptive divergence. The pea aphid Acyrthosiphon pisum is a model of choice to address this question, as it forms a large complex of plant-specialized races and cryptic species, resulting from recent adaptive radiation. Here, we characterized genomewide polymorphisms in three pea aphid races specialized on alfalfa, clover and pea crops, respectively, which we sequenced in pools (poolseq). Using a model-based approach that explicitly accounts for selection, we identified 392 genomic hotspots of differentiation spanning 47.3 Mb and 2,484 genes (respectively, 9.12% of the genome size and 8.10% of its genes). Most of these highly differentiated regions were located on the autosomes, and overall differentiation was weaker on the X chromosome. Within these hotspots, high levels of absolute divergence between races suggest that these regions experienced less gene flow than the rest of the genome, most likely by contributing to reproductive isolation. Moreover, population-specific analyses showed evidence of selection in every host race, depending on the hotspot considered. These hotspots were significantly enriched for candidate gene categories that control host-plant selection and use. These genes encode 48 salivary proteins, 14 gustatory receptors, 10 odorant receptors, five P450 cytochromes and one chemosensory protein, which represent promising candidates for the genetic basis of host-plant specialization and ecological isolation in the pea aphid complex. Altogether, our findings open new research directions towards functional studies, for validating the role of these genes on adaptive phenotypes.

Inferring sex-specific demographic history from SNP data.

The relative female and male contributions to demography are of great importance to better understand the history and dynamics of populations. While earlier studies relied on uniparental markers to investigate sex-specific questions, the increasing amount of sequence data now enables us to take advantage of tens to hundreds of thousands of independent loci from autosomes and the X chromosome. Here, we develop a novel method to estimate effective sex ratios or ESR (defined as the female proportion of the effective population) from allele count data for each branch of a rooted tree topology that summarizes the history of the populations of interest. Our method relies on Kimura's time-dependent diffusion approximation for genetic drift, and is based on a hierarchical Bayesian model to integrate over the allele frequencies along the branches. We show via simulations that parameters are inferred robustly, even under scenarios that violate some of the model assumptions. Analyzing bovine SNP data, we infer a strongly female-biased ESR in both dairy and beef cattle, as expected from the underlying breeding scheme. Conversely, we observe a strongly male-biased ESR in early domestication times, consistent with an easier taming and management of cows, and/or introgression from wild auroch males, that would both cause a relative increase in male effective population size. In humans, analyzing a subsample of non-African populations, we find a male-biased ESR in Oceanians that may reflect complex marriage patterns in Aboriginal Australians. Because our approach relies on allele count data, it may be applied on a wide range of species.

Author Correction: Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time.

In the version of this Article previously published, there was a typographical error ('4' instead of '2') in the equations relating F ST and effective population size (N e) in the Methods section 'Genome-wide scan for selection based on temporal differentiation'. The correct equations are given below.[Formula: see text] [Formula: see text].

Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time.

Rapid phenotypic evolution of quantitative traits can occur within years, but its underlying genetic architecture remains uncharacterized. Here we test the theoretical prediction that genes with intermediate pleiotropy drive adaptive evolution in nature. Through a resurrection experiment, we grew Arabidopsis thaliana accessions collected across an 8-year period in six micro-habitats representative of that local population. We then used genome-wide association mapping to identify the single-nucleotide polymorphisms (SNPs) associated with evolved and unevolved traits in each micro-habitat. Finally, we performed a selection scan by testing for temporal differentiation in these SNPs. Phenotypic evolution was consistent across micro-habitats, but its associated genetic bases were largely distinct. Adaptive evolutionary change was most strongly driven by a small number of quantitative trait loci (QTLs) with intermediate degrees of pleiotropy; this pleiotropy was synergistic with the per-trait effect size of the SNPs, increasing with the degree of pleiotropy. In addition, weak selection was detected for frequent micro-habitat-specific QTLs that shape single traits. In this population, A. thaliana probably responded to local warming and increased competition, in part mediated by central regulators of flowering time. This genetic architecture, which includes both synergistic pleiotropic QTLs and distinct QTLs within particular micro-habitats, enables rapid phenotypic evolution while still maintaining genetic variation in wild populations.

rehh 2.0: a reimplementation of the R package rehh to detect positive selection from haplotype structure.

Identifying genomic regions with unusually high local haplotype homozygosity represents a powerful strategy to characterize candidate genes responding to natural or artificial positive selection. To that end, statistics measuring the extent of haplotype homozygosity within (e.g. EHH, iHS) and between (Rsb or XP-EHH) populations have been proposed in the literature. The rehh package for r was previously developed to facilitate genome-wide scans of selection, based on the analysis of long-range haplotypes. However, its performance was not sufficient to cope with the growing size of available data sets. Here, we propose a major upgrade of the rehh package, which includes an improved processing of the input files, a faster algorithm to enumerate haplotypes, as well as multithreading. As illustrated with the analysis of large human haplotype data sets, these improvements decrease the computation time by more than one order of magnitude. This new version of rehh will thus allow performing iHS-, Rsb- or XP-EHH-based scans on large data sets. The package rehh 2.0 is available from the CRAN repository (http://cran.r-project.org/web/packages/rehh/index.html) together with help files and a detailed manual.

Microevolution of bank voles (Myodes glareolus) at neutral and immune-related genes during multiannual dynamic cycles: Consequences for Puumala hantavirus epidemiology.

Understanding how host dynamics, including variations of population size and dispersal, may affect the epidemiology of infectious diseases through ecological and evolutionary processes is an active research area. Here we focus on a bank vole (Myodes glareolus) metapopulation surveyed in Finland between 2005 and 2009. Bank vole is the reservoir of Puumala hantavirus (PUUV), the agent of nephropathia epidemica (NE, a mild form of hemorrhagic fever with renal symptom) in humans. M. glareolus populations experience multiannual density fluctuations that may influence the level of genetic diversity maintained in bank voles, PUUV prevalence and NE occurrence. We examine bank vole metapopulation genetics at presumably neutral markers and immune-related genes involved in susceptibility to PUUV (Tnf-promoter, Tlr4, Tlr7 and Mx2 gene) to investigate the links between population dynamics, microevolutionary processes and PUUV epidemiology. We show that genetic drift slightly and transiently affects neutral and adaptive genetic variability within the metapopulation. Gene flow seems to counterbalance its effects during the multiannual density fluctuations. The low abundance phase may therefore be too short to impact genetic variation in the host, and consequently viral genetic diversity. Environmental heterogeneity does not seem to affect vole gene flow, which might explain the absence of spatial structure previously detected in PUUV in this area. Besides, our results suggest the role of vole dispersal on PUUV circulation through sex-specific and density-dependent movements. We find little evidence of selection acting on immune-related genes within this metapopulation. Footprint of positive selection is detected at Tlr-4 gene in 2008 only. We observe marginally significant associations between Mx2 genotype and PUUV genogroups. These results show that neutral processes seem to be the main factors affecting the evolution of these immune-related genes at a contemporary scale, although the relative effects of neutral and adaptive forces could vary temporally with density fluctuations. Immune related gene polymorphism may in turn partly influence PUUV epidemiology in this metapopulation.

Maximum-likelihood inference of population size contractions from microsatellite data.

Understanding the demographic history of populations and species is a central issue in evolutionary biology and molecular ecology. In this work, we develop a maximum-likelihood method for the inference of past changes in population size from microsatellite allelic data. Our method is based on importance sampling of gene genealogies, extended for new mutation models, notably the generalized stepwise mutation model (GSM). Using simulations, we test its performance to detect and characterize past reductions in population size. First, we test the estimation precision and confidence intervals coverage properties under ideal conditions, then we compare the accuracy of the estimation with another available method (MSVAR) and we finally test its robustness to misspecification of the mutational model and population structure. We show that our method is very competitive compared with alternative ones. Moreover, our implementation of a GSM allows more accurate analysis of microsatellite data, as we show that the violations of a single step mutation assumption induce very high bias toward false contraction detection rates. However, our simulation tests also showed some limits, which most importantly are large computation times for strong disequilibrium scenarios and a strong influence of some form of unaccounted population structure. This inference method is available in the latest implementation of the MIGRAINE software package.

Detecting and measuring selection from gene frequency data.

The recent advent of high-throughput sequencing and genotyping technologies makes it possible to produce, easily and cost effectively, large amounts of detailed data on the genotype composition of populations. Detecting locus-specific effects may help identify those genes that have been, or are currently, targeted by natural selection. How best to identify these selected regions, loci, or single nucleotides remains a challenging issue. Here, we introduce a new model-based method, called SelEstim, to distinguish putative selected polymorphisms from the background of neutral (or nearly neutral) ones and to estimate the intensity of selection at the former. The underlying population genetic model is a diffusion approximation for the distribution of allele frequency in a population subdivided into a number of demes that exchange migrants. We use a Markov chain Monte Carlo algorithm for sampling from the joint posterior distribution of the model parameters, in a hierarchical Bayesian framework. We present evidence from stochastic simulations, which demonstrates the good power of SelEstim to identify loci targeted by selection and to estimate the strength of selection acting on these loci, within each deme. We also reanalyze a subset of SNP data from the Stanford HGDP-CEPH Human Genome Diversity Cell Line Panel to illustrate the performance of SelEstim on real data. In agreement with previous studies, our analyses point to a very strong signal of positive selection upstream of the LCT gene, which encodes for the enzyme lactase-phlorizin hydrolase and is associated with adult-type hypolactasia. The geographical distribution of the strength of positive selection across the Old World matches the interpolated map of lactase persistence phenotype frequencies, with the strongest selection coefficients in Europe and in the Indus Valley.

When history repeats itself: exploring the genetic architecture of host-plant adaptation in two closely related lepidopteran species.

The genus Ostrinia includes two allopatric maize pests across Eurasia, namely the European corn borer (ECB, O. nubilalis) and the Asian corn borer (ACB, O. furnacalis). A third species, the Adzuki bean borer (ABB, O. scapulalis), occurs in sympatry with both the ECB and the ACB. The ABB mostly feeds on native dicots, which probably correspond to the ancestral host plant type for the genus Ostrinia. This situation offers the opportunity to characterize the two presumably independent adaptations or preadaptations to maize that occurred in the ECB and ACB. In the present study, we aimed at deciphering the genetic architecture of these two adaptations to maize, a monocot host plant recently introduced into Eurasia. To this end, we performed a genome scan analysis based on 684 AFLP markers in 12 populations of ECB, ACB and ABB. We detected 2 outlier AFLP loci when comparing French populations of the ECB and ABB, and 9 outliers when comparing Chinese populations of the ACB and ABB. These outliers were different in both countries, and we found no evidence of linkage disequilibrium between any two of them. These results suggest that adaptation or preadaptation to maize relies on a different genetic architecture in the ECB and ACB. However, this conclusion must be considered in light of the constraints inherent to genome scan approaches and of the intricate evolution of adaptation and reproductive isolation in the Ostrinia spp. complex.

The joint evolution of dispersal and dormancy in a metapopulation with local extinctions and kin competition.

Dispersal and dormancy are two strategies that allow recolonization of empty patches and escape from kin competition. Because they presumably respond to similar evolutionary forces, it is tempting to consider that these strategies may substitute for each other. Yet in order to predict the outcome of the evolution of dispersal and dormancy, and to characterize the emerging covariation between both traits, it is necessary to consider models where dispersal and dormancy evolve jointly. Here, we analyze the evolution of dispersal and dormancy as a function of direct fitness costs, environmental variation, and competition among relatives. We consider two scenarios depending on whether the rates of dormancy for philopatric and dispersed individuals are constrained to be the same (unconditional dormancy) or allowed to be different (conditional dormancy). We show that only philopatric individuals should enter dormancy, at a rate increasing with increasing rates of local extinction and decreasing population sizes. When dormancy and dispersal evolve jointly, we observe a wide range of evolutionary outcomes. In particular, we find that the pattern of covariation between the evolutionarily stable rates of dispersal and dormancy is molded by the rate of extinction and the local population size.

Positive selection of protective variants for type 2 diabetes from the Neolithic onward: a case study in Central Asia.

The high prevalence of type 2 diabetes and its uneven distribution among human populations is both a major public health concern and a puzzle in evolutionary biology. Why is this deleterious disease so common, while the associated genetic variants should be removed by natural selection? The 'thrifty genotype' hypothesis proposed that the causal genetic variants were advantageous and selected for during the majority of human evolution. It remains, however, unclear whether genetic data support this scenario. In this study, we characterized patterns of selection at 10 variants associated with type 2 diabetes, contrasting one herder and one farmer population from Central Asia. We aimed at identifying which alleles (risk or protective) are under selection, dating the timing of selective events, and investigating the effect of lifestyle on selective patterns. We did not find any evidence of selection on risk variants, as predicted by the thrifty genotype hypothesis. Instead, we identified clear signatures of selection on protective variants, in both populations, dating from the beginning of the Neolithic, which suggests that this major transition was accompanied by a selective advantage for non-thrifty variants. Combining our results with worldwide data further suggests that East Asia was particularly prone to such recent selection of protective haplotypes. As much effort has been devoted so far to searching for thrifty variants, we argue that more attention should be paid to the evolution of non-thrifty variants.

Inferring population histories using genome-wide allele frequency data.

The recent development of high-throughput genotyping technologies has revolutionized the collection of data in a wide range of both model and nonmodel species. These data generally contain huge amounts of information about the demographic history of populations. In this study, we introduce a new method to estimate divergence times on a diffusion time scale from large single-nucleotide polymorphism (SNP) data sets, conditionally on a population history that is represented as a tree. We further assume that all the observed polymorphisms originate from the most ancestral (root) population; that is, we neglect mutations that occur after the split of the most ancestral population. This method relies on a hierarchical Bayesian model, based on Kimura's time-dependent diffusion approximation of genetic drift. We implemented a Metropolis-Hastings within Gibbs sampler to estimate the posterior distribution of the parameters of interest in this model, which we refer to as the Kimura model. Evaluating the Kimura model on simulated population histories, we found that it provides accurate estimates of divergence time. Assessing model fit using the deviance information criterion (DIC) proved efficient for retrieving the correct tree topology among a set of competing histories. We show that this procedure is robust to low-to-moderate gene flow, as well as to ascertainment bias, providing that the most distantly related populations are represented in the discovery panel. As an illustrative example, we finally analyzed published human data consisting in genotypes for 452,198 SNPs from individuals belonging to four populations worldwide. Our results suggest that the Kimura model may be helpful to characterize the demographic history of differentiated populations, using genome-wide allele frequency data.

Large-scale candidate gene scan reveals the role of chemoreceptor genes in host plant specialization and speciation in the pea aphid.

Understanding the drivers of speciation is critical to interpreting patterns of biodiversity. The identification of the genetic changes underlying adaptation and reproductive isolation is necessary to link barriers to gene flow to the causal origins of divergence. Here, we present a novel approach to the genetics of speciation, which should complement the commonly used approaches of quantitative trait locus mapping and genome-wide scans for selection. We present a large-scale candidate gene approach by means of sequence capture, applied to identifying the genetic changes underlying reproductive isolation in the pea aphid, a model system for the study of ecological speciation. Targeted resequencing enabled us to scale up the candidate gene approach, specifically testing for the role of chemosensory gene families in host plant specialization. Screening for the signature of divergence under selection at 172 candidate and noncandidate loci, we revealed a handful of loci that show high levels of differentiation among host races, which almost all correspond to odorant and gustatory receptor genes. This study offers the first indication that some chemoreceptor genes, often tightly linked together in the genome, could play a key role in local adaptation and reproductive isolation in the pea aphid and potentially other phytophagous insects. Our approach opens a new route toward the functional genomics of ecological speciation.

DETSEL: an R-package to detect marker loci responding to selection.

In the new era of population genomics, surveys of genetic polymorphism ("genome scans") offer the opportunity to distinguish locus-specific from genome-wide effects at many loci. Identifying presumably neutral regions of the genome that are assumed to be influenced by genome-wide effects only, and excluding presumably selected regions, is therefore critical to infer population demography and phylogenetic history reliably. Conversely, detecting locus-specific effects may help identify those genes that have been, or still are, targeted by natural selection. The software package DETSEL has been developed to identify markers that show deviation from neutral expectation in pairwise comparisons of diverging populations. Recently, two major improvements have been made: the analysis of dominant markers is now supported, and the estimation of empirical P-values has been implemented. These features, which are described below, have been incorporated into an R package, which replaces the stand-alone DETSEL software package.