Biology subjects

Flutre, T.

Publications and source records attributed to Flutre, T..

9 recordsLinked to original sources

bioRxiv2026

Joint modeling of social genetic effects in mono- and pluri-specific groups: case study in intercrops

The genetics of interspecific groups remains largely unexplored, despite the central role of social (or indirect) genetic effects in shaping phenotypic expression within communities. Intercropping, i.e. the simultaneous cultivation of multiple crop species in the same field, offers a powerful model to harness these interspecific social effects. Such species mixtures provide well-documented agricultural benefits, yet few breeding frameworks have integrated the genetics of social interactions. Here, we address this gap by extending quantitative genetic theory to interspecific groups, with intercropping as a concrete and applied model case. We propose a quantitative genetic model that jointly analyzes intra and interspecific interactions within a unifying framework. Breeding values are decomposed into a direct component, shared in mono and mixed-crops, an interspecific social component corresponding to the effect of one species on another, and an intraspecific component that captures the social effects within a mono-genotypic stand of cloned plants. Statistically, this consists in simultaneously fitting several linear mixed models, one per stand type, all having direct breeding values in common. As no open-source software can fit such a complex mixed model, we provide such an implementation in C++ available in the R package plantmix. Simulations across various genetic (co)variance structures and sparse experimental designs showed accurate estimation of all genetic (co)variances and breeding values. With an incomplete, yet balanced design combining sole crops and intercrops, genetic gains in both systems were achievable simultaneously, enabling breeding strategies that progressively integrate intercropping into existing, sole-crop-only schemes. More broadly, this framework allows dissecting direct and social genetic effects when genotypes are observed in mono- and mixed-species situations, cultivated or not. Article summaryThe genetics of interspecific groups remains largely unexplored, despite the central role of social genetic effects in shaping phenotypic expression within communities. We address this gap by extending quantitative genetic theory to interspecific groups, with intercropping as an example. We propose a quantitative genetic model that jointly analyzes intra- and interspecific interactions within a unifying framework. Simulations showed accurate estimation of genetic (co)variances and breeding values. This framework supports breeding strategies where breeders want to gradually integrate pluri-specific trials in their mono-specific platforms, and allows dissecting direct and social genetic effects when genotypes are observed in mono- and mixed-species situations.

genetics↗

bioRxiv2024

A multidisciplinary on-farm survey of maize-bean intercropping shows that revival of traditionalagricultural systems requires careful consideration of partner varieties and agronomic practices.

O_LICereal-legume intercropping is a promising strategy for sustainable agroecosystems. The traditional intercropping of maize and bean is experiencing a revival in some modern agricultural settings, such as in southwestern France, where maize hybrids are intercropped with the commercialized Tarbais bean. We conducted on-farm surveys and a field assay to address the following questions: How does the cropping system impact yield, nutrient uptake, and rhizosphere bacterial assemblages? Do positive or negative interactions between maize and beans dominate in intercropping? What is the effect of intercropping on plant transcriptomics? C_LIO_LIWe recorded farming practices, conducted yield and nutrient measurements, and characterized soil bacterial assemblages to compare sole-cropped maize and beans with intercropped plants. A controlled field assay was also established to extend this comparison to plant gene expression differences. C_LIO_LIIntercropping was associated with a trend towards increased bacterial diversity. The cropping system significantly influenced agronomic traits, with frequent farm-by-cropping system interactions underscoring the critical role of farming practices. Competition dominated maize-bean intercropping, with 34 negative correlations among the 47 significant ones between maize and bean traits. This competition affected yield and nutrition, but primarily impacted beans, which produced fewer but bigger/heavier seeds. Transcriptomic results concurred with these findings, revealing no differentially expressed genes in maize but 5,070 in beans under competition. C_LIO_LIOverall, our findings suggest that beneficial interactions between the two crops are hindered under current field conditions, underscoring the importance of carefully considering partner varieties and farming practices to revive traditional agricultural systems. C_LI Societal Impact StatementCereal-legume intercropping is a promising strategy for sustainable agroecosystems that leverages the biological complementarities between plant species, reducing the need for chemical inputs while enhancing field biodiversity. Here, we focused on maize-bean intercropping, which is experiencing a revival in modern conventional agricultural settings. We found a trend towards an increased soil bacterial diversity in intercropping. However, competition between the two crops dominated and primarily impacted yield and gene expression in beans. Despite lower yield, bean seeds exhibited greater size. Our study indicates that implementing variety selection of both species and adapting farming practices is essential to fully harness the potential of intercropping.

plant biology↗

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↗

bioRxiv2022

Contrasted phenotypic plasticities of life-history traits in interacting plants: case of pure vs mixed stands of wheat

ContextMixing cultivars is an agroecological practice of crop diversification, increasingly used for cereals. The yield of such cereal mixtures is higher on average than the mean yield of their components in pure stands, but with a large variance. The drivers of this variance are plant- plant interactions leading to different plant phenotypes in pure and mixed stands, i.e phenotypic plasticity. ObjectivesThe objectives were (i) to quantify the magnitude of phenotypic plasticity for yield in pure versus mixed stands, (ii) to identify the yield components that contribute the most to yield plasticity, and (iii) to link such plasticities to differences in functional traits, i.e. plant height and flowering earliness. MethodsA new experimental design based on a precision sowing allowed phenotyping each cultivar in mixture, at the level of individual plants, for above-ground traits throughout growth. Eight commercial cultivars of Triticum aestivum L. were grown in pure and mixed stands in field plots repeated for two years (2019-2020, 2020-2021) with contrasted climatic conditions and with nitrogen fertilization, fungicide and weed removal management strategies. Two quaternary mixtures were assembled with cultivars contrasted either for height or earliness. ResultsCompared to the average of cultivars in pure stands, the height mixture strongly underyielded over both years (-29%) while the earliness mixture overyielded the second year (+11%) and underyielded the first year (-8%). The second year, the magnitude of cultivars grain weight plasticity, measured as the difference between pure and mixed stands, was significantly and positively associated with their relative yield differences in pure stands (R2=0.51). When grain weight plasticity, measured as the log ratio of pure over mixed stands, was partitioned as the sum of plasticities in each yield component, its strongest contributor was the plasticity in spike number per plant ([~]56% of the sum), driven by even stronger but opposed underlying plasticities in both tiller emission and regression. For both years, the plasticity in tiller emission was significantly, positively associated with the height differentials between cultivars in mixture (R2=0.43 in 2019-2020 and 0.17 in 2020-2021). ConclusionsPlasticity in the early recognition of potential resource competitors is a major component of cultivar strategies in mixtures, as shown here for tillering dynamics. Our results also highlighted a link between plasticity in tiller emission and height differential in mixture. Both height and tillering dynamics displayed plasticities typical of the shade avoidance syndrome. ImplicationsBoth the new experimental design and decomposition of plasticities developed in this study open avenues to better study plant-plant interactions in agronomically-realistic conditions. This study also contributed a unique, plant-level data set allowing the calibration of process-based plant models to explore the space of all possible mixtures.

plant biology↗

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↗

bioRxiv2020

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

Viticulture has to cope with climate change and decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a potential key to meet this challenge, and genomic prediction is a promising tool to accelerate breeding programs, multivariate methods being potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and allowing the identification of positional candidate genes. We applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental grapevine progeny, in order to compare their ability to predict genotypic values and detect QTLs. We used a new denser genetic map, simulated two traits under four QTL configurations, and re-analyzed 14 traits measured in semi-controlled conditions under different watering conditions. Using simulations, we recommend the penalized regression method Elastic Net (EN) as a default for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using experimental data, penalized regression methods proved as very efficient for intra-population prediction whatever the genetic architecture of the trait, with accuracies reaching 0.68. These methods applied on the denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. These methods can be applied to other traits and species.

genetics↗

bioRxiv2020

Genome-wide association and prediction studies using a grapevine diversity panel give insights into the genetic architecture of several traits of interest

To cope with the challenges faced by agriculture, speeding-up breeding programs is a worthy endeavor, especially for perennials such as grapevine, but requires understanding the genetic architecture of target traits. To go beyond the mapping of quantitative trait locus (QTL) in bi-parental crosses, we exploited a diverse panel of 279 Vitis vinifera L. cultivars. This panel planted in five blocks in the vineyard was phenotyped over several years for 127 traits including yield components, organic acids, aroma precursors, polyphenols, and a water stress indicator. The panel was genotyped for 63k single nucleotide polymorphisms (SNPs) by combining an 18K microarray and genotyping-by-sequencing (GBS). The experimental design allowed to reliably assess the genotypic values for most traits. Marker densification via GBS markedly increased the proportion of genetic variance explained by SNPs, and two multi-SNP models identified QTLs not found by a SNP-by-SNP model. Overall, 489 reliable QTLs were detected for 41% more response variables than by a SNP-by-SNP model with microarray-only SNPs, many new ones compared to the results from bi-parental crosses. Prediction accuracy higher than 0.42 was obtained for 50% of the response variables. Our overall approach as well as QTL and prediction results provide insights into the genetic architecture of target traits. New candidate genes and the application in breeding are discussed.

genetics↗

bioRxiv2020

The wild grape genome sequence provides insights into the transition from dioecy to hermaphroditism during grape domestication

Grapevine has a major economical and cultural importance since antiquity. A key step in domestication was the transition from separate sexes (dioecy) in wild Vitis vinifera ssp. sylvestris (V. sylvestris) to hermaphroditism in cultivated Vitis vinifera ssp. vinifera. While the grapevine sex locus is known to be small, its precise boundaries, gene content and the sex-determining genes are unknown. Here we obtained a high-quality de novo reference genome for V. sylvestris and whole-genome resequencing data of a cross. Studying SNP segregation patterns, gene content and expression in wild and cultivated accessions allowed us to build a model for sex determination in grapevine. In this model, up- and down-regulation of a cytokinin regulator is sufficient to cause female sterility and reversal to hermaphroditism, respectively. This study highlights the importance of neo-functionalization of Y alleles in sex determination and provides a resource for studying genetic diversity in V. sylvestris and the genomic processes of grapevine domestication.

plant biology↗

bioRxiv2019

Genetic diversity and structure of sweet chestnut (Castanea sativa Mill.) in France: At the intersection between Spain and Italy

ContextRenewed interest in European chestnut in France is focused on finding locally adapted populations partially resistant to ink disease and identifying local landraces. AimsWe genotyped trees to assess (i) the genetic diversity of wild and cultivated chestnut across most of its range in France, (ii) their genetic structure, notably in relation with the sampled regions, and (iii) relations with its neighbors in Spain and Italy. MethodsA total of 693 trees in 16 sampling regions in France were genotyped at 24 SSRs, and 1,401 trees in 17 sampling regions at 13 SSRs. ResultsGenetic diversity was high in most sampling regions, with redundancy between them. No significant differentiation was found between wild and cultivated chestnut. A genetic structure analysis with no a priori information found a low, yet significant structure, and identified two clusters. One cluster gathers trees from south-east France and Corsica (RPP1) and another cluster gathers trees from all other sampled regions (RPP2). A substructure was detected at 13 SSRs suggesting differentiation within both RPP1 and RPP2. RPP1 was split between south-east France and Corsica. RPP2 was split between north-west France, Aveyron, Pyrenees and a last cluster gathering individuals from several other regions. ConclusionThe genetic structure within and between our sampling regions is likely the result of natural events (recolonization after the last glaciation) and human activities (migration and exchanges). Notably, we provide evidence for a common origin of most French and Iberian chestnut trees, except those from south-east France that were associated with the Italian gene pool. This advances our knowledge of chestnut genetic diversity and structure, will benefit conservation and help our local partners valorization efforts.

genetics↗