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Background Homoplasy affects demographic inference estimates. This effect has been recognized and corrective methods have been developed. However, no studies so far have defined what homoplasy metrics best describe the effects on demographic inference, or have attempted to estimate such metrics in real data. Here we study how homoplasy in chloroplast microsatellites (cpSSR) affects inference of population expansion time. cpSSRs are popular markers for inferring historical demography in plants due to their high mutation rate and limited recombination. Results In cpSSRs, homoplasy is usually quantified as the probability that two markers or haplotypes that are identical by state are not identical by descent (Homoplasy index, P). Here we propose a new measure of multi-locus homoplasy in linked SSR called Distance Homoplasy (DH), which measures the proportion of pairwise differences not observed due to homoplasy, and we compare it to P and its per cpSSR locus average, which we call Mean Size Homoplasy (MSH). We use simulations and analytical derivations to show that, out of the three homoplasy metrics analyzed, MSH and DH are more correlated to changes in the population expansion time and to the underestimation of that demographic parameter using cpSSR. We perform simulations to show that Approximate Bayesian Computation (ABC) can be used to obtain reasonable estimates of MSH and DH. Finally, we use ABC to estimate the expansion time, MSH and DH from a chloroplast SSR dataset in Pinus caribaea. To our knowledge, this is the first time that homoplasy has been estimated in population genetic data. Conclusions We show that MSH and DH should be used to quantify how homoplasy affects estimates of population expansion time. We also demonstrate how ABC provides a methodology to estimate homoplasy in population genetic data. Electronic supplementary material The online version of this article (doi:10.1186/s12862-017-1046-4) contains supplementary material, which is available to authorized users.

The parasitoid wasp genus Nasonia has rapidly become a genetic model system for developmental and evolutionary biology. The release of its genome sequence led to the development of high-resolution genomic tools, for both interspecific and intraspecific research, which has resulted in great advances in understanding Nasonia biology. To further advance the utility of Nasonia vitripennis as a genetic model system and to be able to fully exploit the advantages of its fully sequenced and annotated genome, we developed a genetically variable and well-characterized experimental population. In this study, we describe the establishment of the genetically diverse HVRx laboratory population from strains collected from the field in the Netherlands. We established a maintenance method that retains genetic variation over generations of culturing in the laboratory. As a characterization of its genetic composition, we provide data on the standing genetic variation and estimate the effective population size (N-e) by microsatellite analysis. A genome-wide description of polymorphism is provided through pooled resequencing, which yielded 417331 high-quality SNPs spanning all five Nasonia chromosomes. The HVRx population and its characterization are freely available as a community resource for investigators seeking to elucidate the genetic basis of complex trait variation using the Nasonia model system.

Developing genetically diverse core sets is key to the effective management and use of crop genetic resources. Core selection increasingly uses molecular marker-based dissimilarity and clustering methods, under the implicit assumption that markers and genes of interest are genetically correlated. In practice, low marker densities mean that genome-wide correlations are mainly caused by genetic differentiation, rather than by physical linkage. Although of central concern, genetic differentiation per se is not specifically targeted by most commonly employed dissimilarity and clustering methods. Principal component analysis (PCA) on genotypic data is known to effectively describe the inter-locus correlations caused by differentiation, but to date there has been no evaluation of its application to core selection. Here, we explore PCA-based clustering of marker data as a basis for core selection, with the aim of demonstrating its use in capturing genetic differentiation in the data. Using simulated datasets, we show that replacing full-rank genotypic data by the subset of genetically significant PCs leads to better description of differentiation and improves assignment of genotypes to their population of origin. We test the effectiveness of differentiation as a criterion for the formation of core sets by applying a simple new PCA-based core selection method to simulated and actual data and comparing its performance to one of the best existing selection algorithms. We find that although gains in genetic diversity are generally modest, PCA-based core selection is equally effective at maximizing diversity at non-marker loci, while providing better representation of genetically differentiated groups.

Analysis of fine scale genetic structure in continuous populations of outcrossing plant species has traditionally been limited by the availability of sufficient markers. We used a set of 468 SNPs to characterize fine-scale genetic structure within and between two dense stands of the wild ancestor of maize, teosinte (Zea mays ssp. parviglumis). Our analyses confirmed that teosinte is highly outcrossing and showed little population structure over short distances. We found that the two populations were clearly genetically differentiated, although the actual level of differentiation was low. Spatial autocorrelation of relatedness was observed within both sites but was somewhat stronger in one of the populations. Using principal component analysis, we found evidence for significant local differentiation in the population with stronger spatial autocorrelation. This differentiation was associated with pronounced shifts in the first two principal components along the field. These shifts corresponded to changes in allele frequencies, potentially due to local topographical features. There was little evidence for selection at individual loci as a contributing factor to differentiation. Our results demonstrate that significant local differentiation may, but need not, co-occur with spatial autocorrelation of relatedness. The present study represents one of the most detailed analyses of local genetic structure to date and provides a benchmark for future studies dealing with fine scale patterns of genetic diversity in natural plant populations.

International audience; Background Increasing our understanding of the genetic architecture of complex traits, through analyses of genotype-phenotype associations and of the genes/polymorphisms accounting for trait variation, is crucial, to improve the integration of molecular markers into forest tree breeding. In this study, two full-sib families and one breeding population of maritime pine were used to identify quantitative trait loci (QTLs) for height growth and stem straightness, through linkage analysis (LA) and linkage disequilibrium (LD) mapping approaches. Results The populations used for LA consisted of two unrelated three-generation full-sib families (n = 197 and n = 477). These populations were assessed for height growth or stem straightness and genotyped for 248 and 217 markers, respectively. The population used for LD mapping consisted of 661 founders of the first and second generations of the breeding program. This population was phenotyped for the same traits and genotyped for 2,498 single-nucleotide polymorphism (SNP) markers corresponding to 1,652 gene loci. The gene-based reference genetic map of maritime pine was used to localize and compare the QTLs detected by the two approaches, for both traits. LA identified three QTLs for stem straightness and two QTLs for height growth. The LD study yielded seven significant associations (P ≤ 0.001): four for stem straightness and three for height growth. No colocalisation was found between QTLs identified by LA and SNPs detected by LD mapping for the same trait. Conclusions This study provides the first comparison of LA and LD mapping approaches in maritime pine, highlighting the complementary nature of these two approaches for deciphering the genetic architecture of two mandatory traits of the breeding program.

The accessibility of high-throughput genotyping technologies has contributed greatly to the development of genomic resources in non-model organisms. High-density genotyping arrays have only recently been developed for some economically important species such as conifers. The potential for using genomic technologies in association mapping and breeding depends largely on the genome wide patterns of diversity and linkage disequilibrium in current breeding populations. This study aims to deepen our knowledge regarding these issues in maritime pine, the first species used for reforestation in south western Europe. Using a new map merging algorithm, we first established a 1,712 cM composite linkage map (comprising 1,838 SNP markers in 12 linkage groups) by bringing together three already available genetic maps. Using rigorous statistical testing based on kernel density estimation and resampling we identified cold and hot spots of recombination. In parallel, 186 unrelated trees of a mass-selected population were genotyped using a 12k-SNP array. A total of 2,600 informative SNPs allowed to describe historical recombination, genetic diversity and genetic structure of this recently domesticated breeding pool that forms the basis of much of the current and future breeding of this species. We observe very low levels of population genetic structure and find no evidence that artificial selection has caused a reduction in genetic diversity. By combining these two pieces of information, we provided the map position of 1,671 SNPs corresponding to 1,192 different loci. This made it possible to analyze the spatial pattern of genetic diversity (H e ) and long distance linkage disequilibrium (LD) along the chromosomes. We found no particular pattern in the empirical variogram of H e across the 12 linkage groups and, as expected for an outcrossing species with large effective population size, we observed an almost complete lack of long distance LD. These results are a stepping stone for the development of strategies for studies in population genomics, association mapping and genomic prediction in this economical and ecologically important forest tree species.

Since the advent of modern plant breeding in the 1930s, North American maize has undergone a dramatic adaptation to high-input agriculture. Despite the importance of genetic contributions to historical yield increases, little is known about the underlying genomic changes. Here we use high-density SNP genotyping to characterize a set of North American maize lines spanning the history of modern breeding. We provide a unique analysis of genome-wide developments in genetic diversity, ancestry, and selection. The genomic history of maize is marked by a steady increase in genetic differentiation and linkage disequilibrium, whereas allele frequencies in the total population have remained relatively constant. These changes are associated with increasing genetic separation of breeding pools and decreased diversity in the ancestry of individual lines. We confirm that modern heterotic groups are the product of ongoing divergence from a relatively homogeneous landrace population, but show that differential landrace ancestry remains evident. Using a recent association approach, we characterize signals of directional selection throughout the genome, identifying a number of candidate genes of potential agronomic relevance. However, overall we find that selection has had limited impact on genome-wide patterns of diversity and ancestry, with little evidence for individual lines contributing disproportionately to the accumulation of favorable alleles in today's elite germplasm. Our data suggest breeding progress has mainly involved selection and recombination of relatively common alleles, contributed by a representative but limited set of ancestral lines.

Sequence-based typing of a breeding population (G1) consisting of 84 Atlantic salmon individuals revealed the presence of 7 Sasa-DAA and 7 Sasa-DAB expressed alleles. Subsequent typing of 1,182 individuals belonging to 33 families showed that Sasa-DAA and Sasa-DAB segregate as haplotypes. In total seven unique haplotypes were established, with frequencies in the population studied ranging from 0.01 to 0.49. Each haplotype is characterized by a unique minisatellite marker size embedded in the 3' untranslated region of the Sasa-DAA gene. These data corroborate the fact that Atlantic salmon express a single class II locus, consisting of tightly linked class II A and class B genes. The seven haplotypes give rise to 15 genotypes with frequencies varying between 0.01 and 0.23; 21 class II homozygous individuals were present in the G1 population. We also studied the frequency distribution in another breeding population (G4, n=374) using the minisatellite marker. Only one new marker size was present, suggesting the presence of one new class II haplotype. The marker frequency distribution in the G4 population differed markedly from the G1 population. The genomic organizations of two Sasa-DAA and Sasa-DAB alleles were determined, and supported the notion that these alleles belong to the same locus. In contrast to other studies of salmonid class II sequences, phylogenetic analyses of brown trout and Atlantic class II A and class II B sequences provided support for trans-species polymorphism.

Gene flow of transgenes into non-target populations is an important biosafety concern. The case of genetically modified (GM) maize in Mexico has been of particular interest because of the country’s status as center of origin and landrace diversity. In contrast to maize in the U.S. and Europe, Mexican landraces form part of an evolving metapopulation in which new genes are subject to evolutionary processes of drift, gene flow and selection. Although these processes are affected by seed management and particularly seed flow, there has been little study into the population genetics of transgenes under traditional seed management. Here, we combine recently compiled data on seed management practices with a spatially explicit population genetic model to evaluate the importance of seed flow as a determinant of the long-term fate of transgenes in traditional seed systems. Seed flow between farmers leads to a much wider diffusion of transgenes than expected by pollen movement alone, but a predominance of seed replacement over seed mixing lowers the probability of detection due to a relative lack of homogenization in spatial frequencies. We find that in spite of the spatial complexities of the modeled system, persistence probabilities under positive selection are estimated quite well by existing theory. Our results have important implications concerning the feasibility of long term transgene monitoring and control in traditional seed systems.