Polygenic adaptation and negative selection across traits, years and environments in a long-lived plant species (Pinus pinaster Ait., Pinaceae).

A decade of genetic association studies in multiple organisms suggests that most complex traits are polygenic; that is, they have a genetic architecture determined by numerous loci, each with small effect-size. Thus, determining the degree of polygenicity and its variation across traits, environments and time is crucial to understand the genetic basis of phenotypic variation. We applied multilocus approaches to estimate the degree of polygenicity of fitness-related traits in a long-lived plant (Pinus pinaster Ait., maritime pine) and to analyse this variation across environments and years. We evaluated five categories of fitness-related traits (survival, height, phenology, functional, and biotic-stress response) in a clonal common-garden network planted in contrasted environments (over 20,500 trees). Most of the analysed traits showed evidence of local adaptation based on Qst -Fst comparisons. We further observed a remarkably stable degree of polygenicity, averaging 6% (range of 0%-27%), across traits, environments and years. We detected evidence of negative selection, which could explain, at least partially, the high degree of polygenicity. Because polygenic adaptation can occur rapidly, our results suggest that current predictions on the capacity of natural forest tree populations to adapt to new environments should be revised, especially in the current context of climate change.

Evolutionary genomics of gypsy moth populations sampled along a latitudinal gradient.

The European gypsy moth (Lymantria dispar L.) was first introduced to Massachusetts in 1869 and within 150 years has spread throughout eastern North America. This large-scale invasion across a heterogeneous landscape allows examination of the genetic signatures of adaptation potentially associated with rapid geographical spread. We tested the hypothesis that spatially divergent natural selection has driven observed changes in three developmental traits that were measured in a common garden for 165 adult moths sampled from six populations across a latitudinal gradient covering the entirety of the range. We generated genotype data for 91,468 single nucleotide polymorphisms based on double digest restriction-site associated DNA sequencing and used these data to discover genome-wide associations for each trait, as well as to test for signatures of selection on the discovered architectures. Genetic structure across the introduced range of gypsy moth was low in magnitude (FST  = 0.069), with signatures of bottlenecks and spatial expansion apparent in the rare portion of the allele frequency spectrum. Results from applications of Bayesian sparse linear mixed models were consistent with the presumed polygenic architectures of each trait. Further analyses indicated spatially divergent natural selection acting on larval development time and pupal mass, with the linkage disequilibrium component of this test acting as the main driver of observed patterns. The populations most important for these signals were two range-edge populations established less than 30 generations ago. We discuss the importance of rapid polygenic adaptation to the ability of non-native species to invade novel environments.

Population assignment reveals low migratory connectivity in a weakly structured songbird.

Understanding migratory connectivity is essential for determining the drivers behind population dynamics and for implementing effective conservation strategies for migratory species. Genetic markers provide a means to describe migratory connectivity; however, they can be uninformative for species with weak population genetic structure, which has limited their application. Here, we demonstrated a genomic approach to describing migratory connectivity in the prothonotary warbler, Protonotaria citrea, a Neotropical songbird of conservation concern. Using 26,189 single nucleotide polymorphisms (SNPs), we revealed regional genetic structure between the Mississippi River Valley and the Atlantic Seaboard with overall weak genetic differentiation among populations (FST  = 0.0055; 95% CI: 0.0051-0.0059). Genetic variation had a stronger association with geographic rather than environmental factors, with each explaining 14.5% and 8.2% of genetic variation, respectively. By varying the numbers of genomic markers used in population assignment models with individuals of known provenance, we identified a maximum assignment accuracy (89.7% to site, 94.3% to region) using a subset of 600 highly differentiated SNPs. We then assigned samples from nonbreeding sites to breeding region and found low migratory connectivity. Our results highlight the importance of filtering markers for informative loci in models of population assignment. Quantifying migratory connectivity for weakly structured species will be useful for expanding studies to a wider range of migratory species across taxonomic groups and may contribute to a deeper understanding of the evolution of migratory strategies.

The role of hybridization during ecological divergence of southwestern white pine (Pinus strobiformis) and limber pine (P. flexilis).

Interactions between extrinsic factors, such as disruptive selection and intrinsic factors, such as genetic incompatibilities among loci, often contribute to the maintenance of species boundaries. The relative roles of these factors in the establishment of reproductive isolation can be examined using species pairs characterized by gene flow throughout their divergence history. We investigated the process of speciation and the maintenance of species boundaries between Pinus strobiformis and Pinus flexilis. Utilizing ecological niche modelling, demographic modelling and genomic cline analyses, we illustrated a divergence history with continuous gene flow. Our results supported an abundance of advanced generation hybrids and a lack of loci exhibiting steep transition in allele frequency across the hybrid zone. Additionally, we found evidence for climate-associated variation in the hybrid index and niche divergence between parental species and the hybrid zone. These results are consistent with extrinsic factors, such as climate, being an important isolating mechanism. A build-up of intrinsic incompatibilities and of coadapted gene complexes is also apparent, although these appear to be in the earliest stages of development. This supports previous work in coniferous species demonstrating the importance of extrinsic factors in facilitating speciation. Overall, our findings lend support to the hypothesis that varying strength and direction of selection pressures across the long lifespans of conifers, in combination with their other life history traits, delays the evolution of strong intrinsic incompatibilities.

High rate of adaptive evolution in two widespread European pines.

Comparing related organisms with differing ecological requirements and evolutionary histories can shed light on the mechanisms and drivers underlying genetic adaptation. Here, by examining a common set of hundreds of loci, we compare patterns of nucleotide diversity and molecular adaptation of two European conifers (Scots pine and maritime pine) living in contrasted environments and characterized by distinct population genetic structure (low and clinal in Scots pine, high and ecotypic in maritime pine) and demographic histories. We found higher nucleotide diversity in Scots pine than in maritime pine, whereas rates of new adaptive substitutions (ωa ), as estimated from the distribution of fitness effects, were similar across species and among the highest found in plants. Sample size and population genetic structure did not appear to have resulted in significant bias in estimates of ωa . Moreover, population contraction-expansion dynamics for each species did not affect differentially the rate of adaptive substitution in these two pines. Several methodological and biological factors may underlie the unusually high rate of adaptive evolution of Scots pine and maritime pine. By providing two new case studies with contrasting evolutionary histories, we contribute to disentangling the multiple factors potentially affecting adaptive evolution in natural plant populations.

Water availability drives signatures of local adaptation in whitebark pine (Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA.

Patterns of local adaptation at fine spatial scales are central to understanding how evolution proceeds, and are essential to the effective management of economically and ecologically important forest tree species. Here, we employ single and multilocus analyses of genetic data (n = 116 231 SNPs) to describe signatures of fine-scale adaptation within eight whitebark pine (Pinus albicaulis Engelm.) populations across the local extent of the environmentally heterogeneous Lake Tahoe Basin, USA. We show that despite highly shared genetic variation (FST  = 0.0069), there is strong evidence for adaptation to the rain shadow experienced across the eastern Sierra Nevada. Specifically, we build upon evidence from a common garden study and find that allele frequencies of loci associated with four phenotypes (mean = 236 SNPs), 18 environmental variables (mean = 99 SNPs), and those detected through genetic differentiation (n = 110 SNPs) exhibit significantly higher signals of selection (covariance of allele frequencies) than could be expected to arise, given the data. We also provide evidence that this covariance tracks environmental measures related to soil water availability through subtle allele frequency shifts across populations. Our results replicate empirical support for theoretical expectations of local adaptation for populations exhibiting strong gene flow and high selective pressures and suggest that ongoing adaptation of many P. albicaulis populations within the Lake Tahoe Basin will not be constrained by the lack of genetic variation. Even so, some populations exhibit low levels of heritability for the traits presumed to be related to fitness. These instances could be used to prioritize management to maintain adaptive potential. Overall, we suggest that established practices regarding whitebark pine conservation be maintained, with the additional context of fine-scale adaptation.

Combining Genotype, Phenotype, and Environment to Infer Potential Candidate Genes.

Population genomic analysis can be an important tool in understanding local adaptation. Identification of potential adaptive loci in such analyses is usually based on the survey of a large genomic dataset in combination with environmental variables. Phenotypic data are less commonly incorporated into such studies, although combining a genome scan analysis with a phenotypic trait analysis can greatly improve the insights obtained from each analysis individually. Here, we aimed to identify loci potentially involved in adaptation to climate in 283 Loblolly pine (Pinus taeda) samples from throughout the species' range in the southeastern United States. We analyzed associations between phenotypic, molecular, and environmental variables from datasets of 3082 single nucleotide polymorphism (SNP) loci and 3 categories of phenotypic traits (gene expression, metabolites, and whole-plant traits). We found only 6 SNP loci that displayed potential signals of local adaptation. Five of the 6 identified SNPs are linked to gene expression traits for lignin development, and 1 is linked with whole-plant traits. We subsequently compared the 6 candidate genes with environmental variables and found a high correlation in only 3 of them (R2 > 0.2). Our study highlights the need for a combination of genotypes, phenotypes, and environmental variables, and for an appropriate sampling scheme and study design, to improve confidence in the identification of potential candidate genes.

Landscape genomic analysis of candidate genes for climate adaptation in a California endemic oak, Quercus lobata.

PREMISE OF THE STUDY: The ability of California tree populations to survive anthropogenic climate change will be shaped by the geographic structure of adaptive genetic variation. Our goal is to test whether climate-associated candidate genes show evidence of spatially divergent selection in natural populations of valley oak, Quercus lobata, as preliminary indication of local adaptation. METHODS: Using DNA from 45 individuals from 13 localities across the species' range, we sequenced portions of 40 candidate genes related to budburst/flowering, growth, osmotic stress, and temperature stress. Using 195 single nucleotide polymorphisms (SNPs), we estimated genetic differentiation across populations and correlated allele frequencies with climate gradients using single-locus and multivariate models. RESULTS: The top 5% of FST estimates ranged from 0.25 to 0.68, yielding loci potentially under spatially divergent selection. Environmental analyses of SNP frequencies with climate gradients revealed three significantly correlated SNPs within budburst/flowering genes and two SNPs within temperature stress genes with mean annual precipitation, after controlling for multiple testing. A redundancy model showed a significant association between SNPs and climate variables and revealed a similar set of SNPs with high loadings on the first axis. In the RDA, climate accounted for 67% of the explained variation, when holding climate constant, in contrast to a putatively neutral SSR data set where climate accounted for only 33%. CONCLUSIONS: Population differentiation and geographic gradients of allele frequencies in climate-associated functional genes in Q. lobata provide initial evidence of adaptive genetic variation and background for predicting population response to climate change.

A practical guide to environmental association analysis in landscape genomics.

Landscape genomics is an emerging research field that aims to identify the environmental factors that shape adaptive genetic variation and the gene variants that drive local adaptation. Its development has been facilitated by next-generation sequencing, which allows for screening thousands to millions of single nucleotide polymorphisms in many individuals and populations at reasonable costs. In parallel, data sets describing environmental factors have greatly improved and increasingly become publicly accessible. Accordingly, numerous analytical methods for environmental association studies have been developed. Environmental association analysis identifies genetic variants associated with particular environmental factors and has the potential to uncover adaptive patterns that are not discovered by traditional tests for the detection of outlier loci based on population genetic differentiation. We review methods for conducting environmental association analysis including categorical tests, logistic regressions, matrix correlations, general linear models and mixed effects models. We discuss the advantages and disadvantages of different approaches, provide a list of dedicated software packages and their specific properties, and stress the importance of incorporating neutral genetic structure in the analysis. We also touch on additional important aspects such as sampling design, environmental data preparation, pooled and reduced-representation sequencing, candidate-gene approaches, linearity of allele-environment associations and the combination of environmental association analyses with traditional outlier detection tests. We conclude by summarizing expected future directions in the field, such as the extension of statistical approaches, environmental association analysis for ecological gene annotation, and the need for replication and post hoc validation studies.

An empirical evaluation of two-stage species tree inference strategies using a multilocus dataset from North American pines.

BACKGROUND: As it becomes increasingly possible to obtain DNA sequences of orthologous genes from diverse sets of taxa, species trees are frequently being inferred from multilocus data. However, the behavior of many methods for performing this inference has remained largely unexplored. Some methods have been proven to be consistent given certain evolutionary models, whereas others rely on criteria that, although appropriate for many parameter values, have peculiar zones of the parameter space in which they fail to converge on the correct estimate as data sets increase in size. RESULTS: Here, using North American pines, we empirically evaluate the behavior of 24 strategies for species tree inference using three alternative outgroups (72 strategies total). The data consist of 120 individuals sampled in eight ingroup species from subsection Strobus and three outgroup species from subsection Gerardianae, spanning ∼47 kilobases of sequence at 121 loci. Each "strategy" for inferring species trees consists of three features: a species tree construction method, a gene tree inference method, and a choice of outgroup. We use multivariate analysis techniques such as principal components analysis and hierarchical clustering to identify tree characteristics that are robustly observed across strategies, as well as to identify groups of strategies that produce trees with similar features. We find that strategies that construct species trees using only topological information cluster together and that strategies that use additional non-topological information (e.g., branch lengths) also cluster together. Strategies that utilize more than one individual within a species to infer gene trees tend to produce estimates of species trees that contain clades present in trees estimated by other strategies. Strategies that use the minimize-deep-coalescences criterion to construct species trees tend to produce species tree estimates that contain clades that are not present in trees estimated by the Concatenation, RTC, SMRT, STAR, and STEAC methods, and that in general are more balanced than those inferred by these other strategies. CONCLUSIONS: When constructing a species tree from a multilocus set of sequences, our observations provide a basis for interpreting differences in species tree estimates obtained via different approaches that have a two-stage structure in common, one step for gene tree estimation and a second step for species tree estimation. The methods explored here employ a number of distinct features of the data, and our analysis suggests that recovery of the same results from multiple methods that tend to differ in their patterns of inference can be a valuable tool for obtaining reliable estimates.

Genetic signatures of natural selection in response to air pollution in red spruce (Picea rubens, Pinaceae).

One of the most important drivers of local adaptation for forest trees is climate. Coupled to these patterns, however, are human-induced disturbances through habitat modification and pollution. The confounded effects of climate and disturbance have rarely been investigated with regard to selective pressure on forest trees. Here, we have developed and used a population genetic approach to search for signals of selection within a set of 36 candidate genes chosen for their putative effects on adaptation to climate and human-induced air pollution within five populations of red spruce (Picea rubens Sarg.), distributed across its natural range and air pollution gradient in eastern North America. Specifically, we used FST outlier and environmental correlation analyses to highlight a set of seven single nucleotide polymorphisms (SNPs) that were overly correlated with climate and levels of sulphate pollution after correcting for the confounding effects of population history. Use of three age cohorts within each population allowed the effects of climate and pollution to be separated temporally, as climate-related SNPs (n = 7) showed the strongest signals in the oldest cohort, while pollution-related SNPs (n = 3) showed the strongest signals in the youngest cohorts. These results highlight the usefulness of population genetic scans for the identification of putatively nonneutral evolution within genomes of nonmodel forest tree species, but also highlight the need for the development and application of robust methodologies to deal with the inherent multivariate nature of the genetic and ecological data used in these types of analyses.

Multilocus analyses reveal little evidence for lineage-wide adaptive evolution within major clades of soft pines (Pinus subgenus Strobus).

Estimates from molecular data for the fraction of new nonsynonymous mutations that are adaptive vary strongly across plant species. Much of this variation is due to differences in life history strategies as they influence the effective population size (Ne ). Ample variation for these estimates, however, remains even when comparisons are made across species with similar values of Ne . An open question thus remains as to why the large disparity for estimates of adaptive evolution exists among plant species. Here, we have estimated the distribution of deleterious fitness effects (DFE) and the fraction of adaptive nonsynonymous substitutions (α) for 11 species of soft pines (subgenus Strobus) using DNA sequence data from 167 orthologous nuclear gene fragments. Most newly arising nonsynonymous mutations were inferred to be so strongly deleterious that they would rarely become fixed. Little evidence for long-term adaptive evolution was detected, as all 11 estimates for α were not significantly different from zero. Nucleotide diversity at synonymous sites, moreover, was strongly correlated with attributes of the DFE across species, thus illustrating a strong consistency with the expectations from the Nearly Neutral Theory of molecular evolution. Application of these patterns to genome-wide expectations for these species, however, was difficult as the loci chosen for the analysis were a biased set of conserved loci, which greatly influenced the estimates of the DFE and α. This implies that genome-wide parameter estimates will need truly genome-wide data, so that many of the existing patterns documented previously for forest trees (e.g. little evidence for signature of selection) may need revision.

High-resolution mapping and analysis of copy number variations in the human genome: a data resource for clinical and research applications.

We present a database of copy number variations (CNVs) detected in 2026 disease-free individuals, using high-density, SNP-based oligonucleotide microarrays. This large cohort, comprised mainly of Caucasians (65.2%) and African-Americans (34.2%), was analyzed for CNVs in a single study using a uniform array platform and computational process. We have catalogued and characterized 54,462 individual CNVs, 77.8% of which were identified in multiple unrelated individuals. These nonunique CNVs mapped to 3272 distinct regions of genomic variation spanning 5.9% of the genome; 51.5% of these were previously unreported, and >85% are rare. Our annotation and analysis confirmed and extended previously reported correlations between CNVs and several genomic features such as repetitive DNA elements, segmental duplications, and genes. We demonstrate the utility of this data set in distinguishing CNVs with pathologic significance from normal variants. Together, this analysis and annotation provides a useful resource to assist with the assessment of CNVs in the contexts of human variation, disease susceptibility, and clinical molecular diagnostics.

Association genetics of the loblolly pine (Pinus taeda, Pinaceae) metabolome.

The metabolome of a plant comprises all small molecule metabolites, which are produced during cellular processes. The genetic basis for metabolites in nonmodel plants is unknown, despite frequently observed correlations between metabolite concentrations and stress responses. A quantitative genetic analysis of metabolites in a nonmodel plant species is thus warranted. Here, we use standard association genetic methods to correlate 3563 single nucleotide polymorphisms (SNPs) to concentrations of 292 metabolites measured in a single loblolly pine (Pinus taeda) association population. A total of 28 single locus associations were detected, representing 24 and 20 unique SNPs and metabolites, respectively. Multilocus Bayesian mixed linear models identified 2998 additional associations for a total of 1617 unique SNPs associated to 255 metabolites. These SNPs explained sizeable fractions of metabolite heritabilities when considered jointly (56.6% on average) and had lower minor allele frequencies and magnitudes of population structure as compared with random SNPs. Modest sets of SNPs (n = 1-23) explained sizeable portions of genetic effects for many metabolites, thus highlighting the importance of multi-SNP models to association mapping, and exhibited patterns of polymorphism consistent with being linked to targets of natural selection. The implications for association mapping in forest trees are discussed.

Defining the landscape of adaptive genetic diversity.

Whether they are used to describe fitness, genome architecture or the spatial distribution of environmental variables, the concept of a landscape has figured prominently in our collective reasoning. The tradition of landscapes in evolutionary biology is one of fitness mapped onto axes defined by phenotypes or molecular sequence states. The characteristics of these landscapes depend on natural selection, which is structured across both genomic and environmental landscapes, and thus, the bridge among differing uses of the landscape concept (i.e. metaphorically or literally) is that of an adaptive phenotype and its distribution across geographical landscapes in relation to selective pressures. One of the ultimate goals of evolutionary biology should thus be to construct fitness landscapes in geographical space. Natural plant populations are ideal systems with which to explore the feasibility of attaining this goal, because much is known about the quantitative genetic architecture of complex traits for many different plant species. What is less known are the molecular components of this architecture. In this issue of Molecular Ecology, Parchman et al. (2012) pioneer one of the first truly genome-wide association studies in a tree that moves us closer to this form of mechanistic understanding for an adaptive phenotype in natural populations of lodgepole pine (Pinus contorta Dougl. ex Loud.).

Spatially variable natural selection and the divergence between parapatric subspecies of lodgepole pine (Pinus contorta, Pinaceae).

PREMISE OF THE STUDY: Plant populations arrayed across sharp environmental gradients are ideal systems for identifying the genetic basis of ecologically relevant phenotypes. A series of five uplifted marine terraces along the northern coast of California represents one such system where morphologically distinct populations of lodgepole pine (Pinus contorta) are distributed across sharp soil gradients ranging from fertile soils near the coast to podzolic soils ca. 5 km inland. METHODS: A total of 92 trees was sampled across four coastal marine terraces (N = 10-46 trees/terrace) located in Mendocino County, California and sequenced for a set of 24 candidate genes for growth and responses to various soil chemistry variables. Statistical analyses relying on patterns of nucleotide diversity were employed to identify genes whose diversity patterns were inconsistent with three null models. KEY RESULTS: Most genes displayed patterns of nucleotide diversity that were consistent with null models (N = 19) or with the presence of paralogs (N = 3). Two genes, however, were exceptional: an aluminum responsive ABC-transporter with F(ST) = 0.664 and an inorganic phosphate transporter characterized by divergent haplotypes segregating at intermediate frequencies in most populations. CONCLUSIONS: Spatially variable natural selection along gradients of aluminum and phosphate ion concentrations likely accounted for both outliers. These results shed light on some of the genetic components comprising the extended phenotype of this ecosystem, as well as highlight ecotones as fruitful study systems for the detection of adaptive genetic variants.

Development and implementation of a highly-multiplexed SNP array for genetic mapping in maritime pine and comparative mapping with loblolly pine.

BACKGROUND: Single nucleotide polymorphisms (SNPs) are the most abundant source of genetic variation among individuals of a species. New genotyping technologies allow examining hundreds to thousands of SNPs in a single reaction for a wide range of applications such as genetic diversity analysis, linkage mapping, fine QTL mapping, association studies, marker-assisted or genome-wide selection. In this paper, we evaluated the potential of highly-multiplexed SNP genotyping for genetic mapping in maritime pine (Pinus pinaster Ait.), the main conifer used for commercial plantation in southwestern Europe. RESULTS: We designed a custom GoldenGate assay for 1,536 SNPs detected through the resequencing of gene fragments (707 in vitro SNPs/Indels) and from Sanger-derived Expressed Sequenced Tags assembled into a unigene set (829 in silico SNPs/Indels). Offspring from three-generation outbred (G2) and inbred (F2) pedigrees were genotyped. The success rate of the assay was 63.6% and 74.8% for in silico and in vitro SNPs, respectively. A genotyping error rate of 0.4% was further estimated from segregating data of SNPs belonging to the same gene. Overall, 394 SNPs were available for mapping. A total of 287 SNPs were integrated with previously mapped markers in the G2 parental maps, while 179 SNPs were localized on the map generated from the analysis of the F2 progeny. Based on 98 markers segregating in both pedigrees, we were able to generate a consensus map comprising 357 SNPs from 292 different loci. Finally, the analysis of sequence homology between mapped markers and their orthologs in a Pinus taeda linkage map, made it possible to align the 12 linkage groups of both species. CONCLUSIONS: Our results show that the GoldenGate assay can be used successfully for high-throughput SNP genotyping in maritime pine, a conifer species that has a genome seven times the size of the human genome. This SNP-array will be extended thanks to recent sequencing effort using new generation sequencing technologies and will include SNPs from comparative orthologous sequences that were identified in the present study, providing a wider collection of anchor points for comparative genomics among the conifers.

Chromosome 6p22 locus associated with clinically aggressive neuroblastoma.

BACKGROUND: Neuroblastoma is a malignant condition of the developing sympathetic nervous system that most commonly affects young children and is often lethal. Its cause is not known. METHODS: We performed a genomewide association study by first genotyping blood DNA samples from 1032 patients with neuroblastoma and 2043 control subjects of European descent using the Illumina HumanHap550 BeadChip. Samples from three independent groups of patients with neuroblastoma (a total of 720 patients) and 2128 control subjects were then genotyped to replicate significant associations. RESULTS: We observed a significant association between neuroblastoma and the common minor alleles of three consecutive single-nucleotide polymorphisms (SNPs) at chromosome band 6p22 and containing the predicted genes FLJ22536 and FLJ44180 (P=1.71x10(-9) to 7.01x10(-10); allelic odds ratio, 1.39 to 1.40). Homozygosity for the at-risk G allele of the most significantly associated SNP, rs6939340, resulted in an increased likelihood of the development of neuroblastoma (odds ratio, 1.97; 95% confidence interval, 1.58 to 2.45). Subsequent genotyping of the three 6p22 SNPs in three independent case series confirmed our observation of an association (P=9.33x10(-15) at rs6939340 for joint analysis). Patients with neuroblastoma who were homozygous for the risk alleles at 6p22 were more likely to have metastatic (stage 4) disease (P=0.02), amplification of the MYCN oncogene in the tumor cells (P=0.006), and disease relapse (P=0.01). CONCLUSIONS: A common genetic variation at chromosome band 6p22 is associated with susceptibility to neuroblastoma.