Biology subjects

Segura, V.

Publications and source records attributed to Segura, V..

At least 19 recordsLinked to original sources

bioRxiv2026

The immunogenic 33-mer gliadin peptide attenuates flagellin-induced TLR5/NF-κB signaling

Wheat gluten is the main trigger of gluten-related disorders such as celiac disease, in which the proteolysis-resistant, 33-mer peptide is a key immunodominant factor. It has been reported that the 33-mer forms oligomers and thin-plate superstructures that accumulate in the Caco-2 intestinal epithelial cell line, although their biological effects remain only partially understood. Here, we screened the effects of these nanostructures on Toll-like receptors in Caco-2 cells to investigate their effect on the early innate immune response. Our findings showed a specific upregulation of TLR5 mRNA in response to 33-mer gliadin superstructures, although no inflammatory response was detected based on CXCL1 and CXCL8 levels. Moreover, immunofluorescence analysis revealed partial co-localization with TLR5. We further found that the 33-mer superstructures attenuate flagellin-induced TLR5/NF{kappa}B signaling in reporter cells, similar to a blocking anti-hTLR5-IgA2 antibody. Molecular docking suggested that the 33-mer peptide occupies the concave region of TLR5 as well as 54% of the TLR5-TLR5 dimerization interface. The interference with the TLR5 dimerization domain could explain the experimental attenuation of flagellin-mediated TLR5 activation. Given that TLR5 contributes not only to inflammation but also to intestinal homeostasis, our findings suggest that the formation of 33-mer nanostructures upon its accumulation may increase the local concentration of 33-mer, interfering with TLR5-mediated mucosal immune responses. These findings provide a rationale for investigating the role of TLR5 and 33-mer aggregates in clinical samples from patients with celiac disease and other gluten-related disorders.

immunology↗

bioRxiv2026

Genetic control of leaf carbon- and water-related traits in grapevine under greenhouse and field conditions

Understanding the genetic architecture of ecophysiological traits related to carbon gain and water loss is crucial to develop cultivars tolerant to abiotic stresses and that maintain productivity in the context of climate change. This can be achieved both through experiments in the greenhouse on potted plants under controlled conditions and in the field. However, the stability of genetic determinism of ecophysiological traits across environmental conditions has rarely been evaluated. We studied a grapevine diversity panel grown both in a greenhouse and in the field, to investigate the impact of growing conditions on leaf ecophysiological traits related to leaf morphology, fluorescence, gas exchange and water use efficiency. The correlation structure between traits was conserved within each environment even though, for most traits, cultivars ranking was not conserved between both environments. However, a set of cultivars, sharing the same functioning pattern in terms of gas exchange and fluorescence in both environments, was identified. Single- and multi-environment Genome-Wide Association Studies (GWAS), based on a dense genotyping matrix, were used to dissect the genetic architecture of leaf traits. The Quantitative Trait Loci (QTLs) identified through single-environment analyses differed substantially from those detected using multi-environment ones. Multi-environment approaches highlighted shared QTLs across environments for gas exchange and fluorescence traits. Candidate genes associated with carbon- and water-related functions were further identified in these genomic regions. Despite the weak correlation between greenhouse and field trait values, combining both environments enabled the identification of robust genetic regions associated with ecophysiological traits of interest.

plant biology↗

bioRxiv2026

INCREASING PHENOMIC PREDICTION EFFICIENCY USING A PRINCIPAL COMPONENT ANALYSIS BASED PRE-PROCESSING OF NEAR INFRARED SPECTRA

Phenomic prediction (PP) is a genetic value prediction method based on near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, the choice of pre-processing is usually done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative, orthogonal and interpretable features of spectra instead of many correlated, uninformative wavelengths. We tested this pre-processing method on five datasets representing four plant species (maize, rice, sorghum and grapevine). Results show that estimating genetic values on components of raw spectra, that are not weighted by their eigenvalues, performs as well as doing it on spectra pre-processed with the best classical chemometrics methods in most cases, while requiring less parameter optimization. Moreover, this SVD step opens up possibilities for better understanding and selecting parts of the spectral information that are relevant for PP.

genetics↗

bioRxiv2026

Development of the 4TREE SNP array, a forest multispecies array to enhance European Breeding and conservation programs in pine, poplar and ash.

Within the framework of the European Adaptive BREEDING for Better FORESTs project (B4EST, https://b4est.eu/), we have developed genotyping tools for Poplar, Ash, and Pine forest tree species. SNP arrays are attractive genotyping tools because of the user-friendly genotype calling system and the robust transferability among laboratories. Here we describe the development of an Axiom SNP array for Pinus pinaster (13,407 SNPs), Pinus pinea (5,671 SNPs), Poplar spp. (13,408 SNPs), and Fraxinus spp. (13,407 SNPs) based on a two-step process. We first assembled a high-density (>100,000 SNPs/species) screening array that served to test a large panel of candidate SNPs on a diversity panel involving at least 120 individual trees per species or species group. In the second step, we selected and combined the most informative SNPs to build the final 50,000 SNP 4TREE array. This approach resulted in high genotyping success rates, including for species lacking previously validated high-quality SNP resources. The 4TREE SNP array provides a valuable and transferable genomic tool to support genomic prediction, breeding, and adaptive management of forest tree species.

genomics↗

bioRxiv2026

Deciphering the genetic basis of phytoplankton traits through genome-wide association studies

Recently, an inventory of genes in phytoplankton was conducted through expeditions such as TARA Oceans. Approximately 1.5 million genes were identified, of which at least three-quarters have unknown function. Presently, a several research programmes are engaged in the sequencing of marine biodiversity, resulting in a rapid expansion of genomic databases. Access to the genomic sequences of these organisms will soon be readily accessible to the scientific community. Although analysing this data is promising, the characterization of genes or genomes, on the other hand, is progressing very slowly and remains a major challenge for scientists. The aim of this study was to use GWAS approaches to decipher genomic loci without a priori assumptions. The microalga Tisochrysis lutea was selected as a case study due to its economic importance and the extensive knowledge accumulated over the years. Particular attention was paid to pigment and lipid metabolism due to their high commercial value. To implement the GWAS approach, a collection of algal lineages was established (100 lineages) from available polyclonal strains (15 strains). This collection was then phenotyped under two different culture conditions. Of the 31 phenotypic traits investigated, 18 met the requirements for GWAS analysis. Concurrently, each algal lineage was genotyped by whole genome sequencing to inventory all genetic polymorphisms. A mixed model was applied, revealing 13 significant associations between phenotypic traits and alleles. These associations highlight previously unsuspected genomic loci that play a major role in pigment or lipid content. Genes identified at these loci may have a direct or indirect role in these metabolic pathways. Nevertheless, elucidating the molecular mechanisms of the associated genes remains limited without the implementation of functional approaches. Despite the complexity of the process, we conclude that the GWAS approach was effective for deciphering phytoplankton genomes, particularly for quantitative traits of interest. Ideally, this approach should be combined with other functional methods to progressively decode marine genomes.

genetics↗

bioRxiv2025

Factors Influencing Phenomic Prediction: A Case Study on a Large Sorghum BCNAM Population

Plant breeding efficiency is crucial to develop varieties able to cope with climate change and support food and feed value chains. Genomic prediction (GP) has been a major step in increasing this efficiency and is now routinely used in breeding programs. Recently, phenomic prediction (PP) has gained attention as a promising complementary approach to GP, further increasing the breeding programs efficiency. Factors impacting the predictive ability (PA) of PP have been studied on many species but are not fully clarified. In this context, we studied the impacts of spectra pre-processing, prediction methods, population structure, training set size, NIRS acquisition environment and wavelength selection on a large multi-parental sorghum population including 2498 genotypes. Our results show that PP can compete with GP, that it is less affected by population structure, and can reach its maximal PA with smaller training sets than GP, but its performances are trait dependant. We also show that NIRS can be acquired in a reference environment to perform prediction in other environments and that it is possible to randomly select as little as 10 wavelengths to perform predictions. Finally, we show that spectra pre-processing, and statistical methods have a limited and unclear impact on PA. Our study confirms that PP is a relevant trait prediction method that deserves attention to optimize breeding schemes. The main challenges for the future will be to better understand the information contained in the spectra and disentangle their genetic and proxy components to optimize the use of PP in breeding programs. Key messagePhenomic prediction is promising for sorghum breeding. Geneticists methods may not be suited to optimally extract spectral information.

genomics↗

bioRxiv2025

Robustness of high-throughput prediction of leaf ecophysiological traits using near infra-red spectroscopy and poro-fluorometry

Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large-scale phenotyping remains a bottleneck. This study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits in a grapevine diversity panel grown in pots under well-watered outdoor conditions and under three contrasting soil water treatments in a greenhouse. We found a certain complementarity between measuring devices. Spectrometers could accurately predict leaf mass per area, water content, and water quantity (R{superscript 2} > 0.58), while the poro-fluorometer was efficient for predicting net CO2 assimilation (R{superscript 2} > 0.72), regardless of the water treatment. The prediction of leaf mass per area using spectrometers appeared to be quite robust across both outdoor and greenhouse experiments, while the prediction of water use efficiency was dependent on the water treatment, with much better predictions under moderate (R{superscript 2} > 0.73) than severe water deficit. Calibrated models were then applied to the full diversity panel using only high-throughput measurements to estimate trait values and their broad-sense heritability. Leaf mass per area, also measured directly, showed similar heritability whether based on observed or predicted data. Heritability estimates for predicted traits reached up to 0.5. Overall, our findings support the use of spectroscopy and poro-fluorometry as reliable, non-destructive tools for high-throughput phenotyping, enabling genetic studies on drought-related traits in grapevine.

plant biology↗

bioRxiv2024

Near-infrared real time non-destructive monitoring of sugar accumulation reveals that single berries ripen two times faster than previously documented on standard asynchronous samples

Understanding how climate change impacts berry ripening physiology is essential for selecting genotypes that balance sugars and acids under warming conditions. In this context, we used a portable near-infrared spectrometer in the vineyard, to monitor sugar and acid evolution in individual berries from 10 grapevine varieties over two years. Spectra were periodically acquired on the same berries all along ripening, and a subset of these berries was also collected for sugars and organic acids quantification by HPLC, to train partial least square regression models. Prediction models for glucose, fructose, and malic acid concentrations were characterized by validation R{superscript 2} of 0.71, 0.64, and 0.55, respectively. We further used these models to follow sugar accumulation in individual berries and observed that single berries ripen two times faster than found in samples composed of multiple berries. Our results pave avenues toward precise quantitative approaches on sugar and acid fluxes in berry ripening studies.

plant biology↗

bioRxiv2024

Natural variation in chalcone isomerase defines a major locus controlling growth variation between Populus nigra populations

Poplar is a promising resource for wood production and the development of lignocellulosic biomass, but currently available varieties have not been optimized for these purposes. Therefore, it is critical to investigate the genetic variability and mechanisms underlying traits that affect biomass yield. Previous studies have shown that target traits in different poplar species are complex, with a small number of genetic factors having relatively low effects compared to medium to high heritability. In this study, a systems biology approach was implemented, combining genomic, transcriptomic, and phenotypic information from a large collection of individuals from natural populations of black poplar from Western Europe. Such an approach identified a QTL and a gene, coding for chalcone isomerase (CHI), as a candidate for controlling radial growth. Additionally, analysis of the structure and diversity of traits as well as CHI gene expression revealed a high allelic fixation index, linked to the geographical origin of the natural populations under study. These findings provide insights into how adaptive traits arise, are selected, and maintained in the populations. Overall, this study contributes to enhancing the use of poplar as a valuable resource for sustainable biomass production.

genomics↗

bioRxiv2024

Investigating the genetic architecture of biotic stress response in stone fruit tree orchards under natural infections with a multi-environment GWAS approach

The mapping and introduction of sustainable plant immunity to pests and diseases in fruit tree is still a major challenge in modern breeding. This study aims at deciphering the genetic architecture underlying resistance or tolerance across environments for major pests and diseases in peach (P. persica) and apricot (P. armeniaca). We set up a multi-environment trial (MET) approach by studying two core collections of 206 peach and 150 apricot accessions deployed under low phytosanitary conditions in respectively three and two environmentally contrasted locations in South-East of France. To capture the complex dynamics of pest and disease spread in naturally infected orchards, visual scoring of symptoms was repeated within and between 3 years, for five and two pests and diseases respectively for peach and apricot, resulting in the maximum of damage score and the AUDPC. These traits were used as phenotypic inputs in our genome-wide association studies (GWAS) strategy, and leading to the identification of: i) non-additive genotype-phenotype associations, ii) environment-shared QTLs iii) environment-specific QTLs, and iv) interactive QTLs which changes in direction ( antagonist) or intensity ( differential) according to the environment. By conducting GWAS with multiple methods, we successfully identified a total of 60 high confidence QTLs, leading to the identification of 87 candidate genes, the majority belonging to the Leucine-rich repeat containing receptors (LRR-CRs) family gene. Finally, we provided a comparative analysis of our results on peach and apricot, two closely related species. The present results contribute to the development of genomics-assisted breeding to improve biotic resilience in Prunus varieties.

plant biology↗

bioRxiv2024

Performance of phenomic selection in rice: effects of population size and genotype-environment interactions on predictive ability

Phenomic prediction (PP), a novel approach utilizing Near Infrared Spectroscopy (NIRS) data, offers an alternative to genomic prediction (GP) for breeding applications. In PP, a hyperspectral relationship matrix replaces the genomic relationship matrix, potentially capturing both additive and non-additive genetic effects. While PP boasts advantages in cost and throughput compared to GP, the factors influencing its accuracy remain unclear and need to be defined. This study investigated the impact of various factors, namely the training population size, the multi-environment information integration, and the incorporations of genotype x environment (GxE) effects, on PP compared to GP. We evaluated the prediction accuracies for several agronomically important traits (days to flowering, plant height, yield, harvest index, thousand-grain weight, and grain nitrogen content) in a rice diversity panel grown in four distinct environments. Training population size and GxE effects inclusion had minimal influence on PP accuracy. The key factor impacting the accuracy of PP was the number of environments included. Using data from a single environment, GP generally outperformed PP. However, with data from multiple environments, using genotypic random effect and relationship matrix per environment, PP achieved comparable accuracies to GP. Combining PP and GP information did not significantly improve predictions compared to the best model using a single source of information (e.g., average predictive ability of GP, PP, and combined GP and PP for grain yield were of 0.44, 0.42, and 0.44, respectively). Our findings suggest that PP can be as accurate as GP when all genotypes have at least one NIRS measurement, potentially offering significant advantages for rice breeding programs. Authors SummaryThis study explores the interest of phenomic selection within the context of rice breeding. Unlike genomic selection, phenomic selection utilizes near-infrared spectroscopic (NIRS) technology to predict genotypes performance. The importance of this methodology lies in its capacity to reduce the costs and enhance the genetic gains of breeding programs, particularly in developing countries where genomic information is not always easily accessible (cost, availability, ease of use). Also, NIRS technology is often already available, even in resource-constrained breeding programs. By focusing the study on rice, a staple food for billions, our research aims to demonstrate the applicability of phenomic selection compared to genomic selection. By investigating the influence of various factors on phenomic prediction accuracy (training population size, incorporation of multiple environment information, consideration of genotype x environment effects in the prediction models), we are contributing to the optimization of this novel breeding method, which could potentially lead to significant improvements in agricultural productivity and food security.

plant biology↗

bioRxiv2024

The single-berry metabolomic clock paradigm reveals new stages and metabolic switches during grapevine berry development

O_LIAsynchronous development of berries causes metabolic chimerism in usual samples. We thus revisited the developmental changes in the metabolome of the Vitis vinifera single berries from anthesis to over-ripening. C_LIO_LIA dataset of 9,256 ions obtained by non-targeted ultra-performance liquid chromatography coupled to high-resolution mass spectrometry was submitted to an analysis workflow combining classification and dimension reduction tools, to reveal the dynamics of metabolite composition without phenological a priori. C_LIO_LIThis approach led to a metabolome-based definition of developmental stages, as well as the clustering of metabolites into 12 specific kinetic patterns. The single berry intrinsic metabolomic clock alleviates constitutive asynchronicity biases in the usual combination of phenological scales and observer clock. Such increase in temporal resolution enabled the identification of metabolite clusters annunciative of the onset of ripening since the herbaceous plateau. In particular, these clusters included transient lipidic changes and the start of ABA accumulation. We also highlighted a cluster of stilbenes that accumulate after sugar loading stops, during fruit shriveling. C_LIO_LIThis non-targeted approach enables a more precise and unbiased characterization of grapevine berry development through the metabolomic clock paradigm. The discovery of new metabolic milestones of berry development paves the way towards an unbiased assessment of berry physiological stages. C_LI

plant biology↗

bioRxiv2024

Genome-wide association analysis of flowering date in a collection of cultivated olive tree

1.Flowering date in perennial fruit trees is an important trait for fruit production. Depending on the winter and spring temperatures, flowering of olive may be advanced, delayed, or even suppressed. Deciphering the genetic control of flowering date is thus key to help selecting cultivars better adapted to the current climate context. Here, we investigated the genetic determinism of full flowering date stage in cultivated olive based on capture sequencing data of 318 genotypes from the worldwide olive germplasm bank of Marrakech, Morocco. The genetic structure of this collection was organized in three clusters that were broadly attributed to eastern, central, and western Mediterranean regions, based on the presumed origin of genotypes. Flowering dates, collected over seven years, were used to estimate the genotypic best linear unbiased predictors, which were then analyzed in a genome-wide association study. Loci with small effects were significantly associated with the studied trait, by either a single- or a multi-locus approach. The three most robust loci were located on chromosomes 01 and 04, and on a scaffold, and explained 7.1%, 6.2%, and 6.5 % of the trait variance, respectively. A significantly higher accuracy in the best linear unbiased predictors of flowering date prediction was reported with Ridge-compared to LASSO-based genomic prediction model. Along with genomic association results, this suggests a complex polygenic determinism of flowering date, as seen in many other fruit perennials. These results and the screening of associated regions for candidate genes open perspectives for further studies and breeding programs targeting flowering date.

genetics↗

bioRxiv2023

Enhancing grapevine breeding efficiency through genomic prediction and selection index

Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20 to 25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on two existing grapevine breeding programs: Rose wine and Cognac production. In these programs, several families were created through crosses of emblematic and inter-specific resistant varieties to powdery and downy mildews. 30 traits were evaluated for each program, using two genomic prediction methods: GBLUP (Genomic Best Linear Unbiased Predictor) and LASSO (Least Absolute Shrinkage Selection Operator). The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector.

genetics↗

bioRxiv2021

Interest of phenomic prediction as an alternative to genomic prediction in grapevine

Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. We found that the co-inertia between spectra and genomic data was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of -0.2, thus suggesting that phenomic prediction could be applied for any trait.

genetics↗

bioRxiv2021

eQTLs are key players in the integration of genomic and transcriptomic data for phenotype prediction

Multi-omics represent a promising link between phenotypes and genome variation. Few studies yet address their integration to understand genetic architecture and improve predictability. Our study used 241 poplar genotypes, phenotyped in two common gardens, with their xylem and cambium RNA sequenced at one site, yielding large phenotypic, genomic and transcriptomic datasets. For each trait, prediction models were built with genotypic or transcriptomic data and compared to concatenation integrating both omics. The advantage of integration varied across traits and, to understand such differences, we made an eQTL analysis to characterize the interplay between the genome and the transcriptome and classify the predicting features into CIS or TRANS relationships. A strong and significant negative correlation was found between the change in predictability and the change in predictor importance for eQTLs (both TRANS and CIS effects) and CIS regulated transcripts, and mostly for traits showing beneficial integration and evaluated in the site of transcriptomic sampling. Consequently, beneficial integration happens when redundancy of predictors is decreased, leaving the stage to other less prominent but complementary predictors. An additional GO enrichment analysis appeared to corroborate such statistical output. To our knowledge, this is a novel finding delineating a promising way to explore data integration. One-sentence summarySuccessful multi-omics integration when predicting phenotypes makes redundant the predictors that are linked to ubiquitous connections between the omics, according to biological and statistical approaches

genetics↗

bioRxiv2021

Across-population genomic prediction in grapevine opens up promising prospects for breeding

Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

genetics↗

bioRxiv2021

Genomic signatures of a major adaptive event in the pathogenic fungus Melampsora larici-populina

BackgroundThe recent availability of genome-wide sequencing techniques has allowed systematic screening for molecular signatures of adaptation, including in non-model organisms. Host-pathogen interactions constitute good models due to the strong selective pressures that they entail. We focused on an adaptive event which affected the poplar rust fungus Melampsora larici-populina when it overcame a resistance gene borne by its host, cultivated poplar. Based on 76 virulent and avirulent isolates framing narrowly the estimated date of the adaptive event, we examined the molecular signatures of selection. ResultsUsing an array of genome scan methods, we detected a single locus exhibiting a consistent pattern suggestive of a selective sweep in virulent individuals (excess of differentiation between virulent and avirulent samples, linkage disequilibrium, genotype-phenotype statistical association and long-range haplotypes). Our study pinpoints a single gene and further a single amino acid replacement which may have allowed the adaptive event. Although the selective sweep occurred only four years earlier, it does not seem to have affected genome diversity further than the immediate vicinity of the causal locus. ConclusionsOur results suggest that M. larici-populina under-went a soft selective sweep and possibly a prominent effect of outbreeding and recombination, which we speculate have increased the efficiency of selection.

evolutionary biology↗