Biology subjects

Moreau, L.

Publications and source records attributed to Moreau, L..

17 recordsLinked to original sources

bioRxiv2026

Phenomic selection enables nursery screening for field performance in maize and pea

Due to economical constraints, breeding programs eliminate most selection candidates at early stages, before their productivity can be evaluated in field trials. Genomic selection partially overcomes this limitation by enabling early prediction of field performance, but its implementation requires large-scale recurrent genotyping that remains inaccessible for many species and breeding programs. Phenomic selection offers a promising alternative by replacing genotyping with inexpensive high-dimensional phenotyping data. However, previous applications have focused on predicting genotypes already evaluated in field trials. We investigated whether phenomic data acquired by unoccupied aerial vehicles (UAVs) from nursery-grown genotypes can be used to predict their performance in field trials. We validated this nursery-screening approach in maize and pea, two species with contrasting biology and canopy structures, supporting its potential transferability to other species. In the most challenging scenarios, average accuracies for yield-related traits were between 0.36 for maize and 0.40 for pea. Simulations indicated that these accuracies can support substantial nursery culling while limiting the risk of discarding superior genotypes. This proof of concept demonstrates that UAV-based phenomic selection can predict the field performance of nursery-grown genotypes without genotyping the selection candidates.

genetics↗

bioRxiv2026

Dissecting the phenotypic and genetic determinants of maize-bean interactions in intercropping

O_LICereal-legume intercropping is a cornerstone of agroecological systems because interactions between species can enhance agroecosystem resilience. Yet, the mechanisms underlying these interactions remain poorly understood. To address this gap, we investigated the maize-bean association under low-input conditions. C_LIO_LIWe conducted a two-year intercropping experiment with 200 climbing bean lines grown alongside three maize landraces in France and Romania. We evaluated bean phenotypic responses above- and below-ground to 3 maize landraces, treated as distinct biotic environments (E). Direct and indirect genetic effects were assessed by mapping bean and maize phenotypic traits onto the bean genome (G), with G x E interactions tested using contrasts among maize landraces. C_LIO_LICompetitive interactions dominated, maize acting as the stronger competitor. Maize landraces created distinct biotic environments affecting bean traits. Best-performing partners varied across experimental fields, with no evidence of bean local adaptation. The most productive and balanced mixtures were obtained with the traditionally intercropped maize landrace. Genome-wide association analyses identified loci underlying direct and indirect genetic effects, including candidate genes associated with neighbor perception. C_LIO_LIThese findings reveal the genetic complexity of maize-bean interactions and highlight competitive tolerance in bean and reduced aggressiveness in maize as key traits for improving cereal-legume intercrop performance. C_LI

genetics↗

bioRxiv2026

Identification of heterotic group-specific haplotypes and impact of residual inbreeding on grain yield of maize elite hybrids

Modern hybrid maize (Zea mays L.) breeding programs are based on the management of distinct complementary heterotic groups to maximize heterosis in high-performing hybrids. This practice lowers shared genetic segments and increases divergence between groups to limit inbreeding in hybrids. However, most breeding programs have not always enforced strict separation between heterotic groups in the past. Competitor commercial hybrids were notably a common elite germplasm source for inbred development, which would diminish divergence between groups. This study proposes a new haplotype-based approach to assess hybrids residual inbreeding based on parental similarity. The new haplotype method has a stronger significant negative effect on hybrids grain yield than raw SNP data. Evaluation of modern experimental hybrids uncovered related inbreds contributing to superior rates of residual inbreeding. Analysis of these inbreds revealed haplotype transfers between heterotic groups, originating notably from the use of a Stiff Stalk-Iodent commercial hybrid as breeding starts material in both Stiff Stalk and Non-Stiff Stalk breeding populations. The introduction of this intergroup parent generated heterotic-group-specific haplotype migration between crossing pools. These fragments caused significant genome-wide residual inbreeding in experimental hybrids across selection cycles. This study highlights the necessity for accurate evaluation of external sources of diversity to minimize haplotype transfers and admixture between crossing pools. We demonstrate the consequences of using commercial hybrids in inbred development, particularly regarding residual inbreeding, and their effects on hybrid performance. Insights from these results can assist breeders in optimizing the choice of parents for introducing genetic diversity in a reciprocal recurrent selection scheme. KEY MESSAGEHaplotype-based hybrids parental similarity better predicts grain yield than marker-based identity-by-state. Utilization of commercial hybrids as breeding start material resulted in higher hybrid residual inbreeding even after several selection cycles

genetics↗

bioRxiv2026

Cross Potential Selection for Multiple Traits Considering the Progeny Distribution of Future Inbred Lines in Plant Breeding Programs

In plant breeding, it is often necessary to improve a target trait while maintaining other essential traits within desirable ranges. When genetic relationships exist among these traits, improvements in the target trait may lead to undesirable changes in essential traits, complicating cross selections. In such cases, it is critical to select cross-pairs that are expected to produce progeny that satisfy the requirements for all traits. The progeny distribution of each crossing pair can be predicted using the estimated genotypic values and genetic (co)variances of the target and essential traits. By utilizing this distribution, the probability of generating progeny that satisfy predefined trait requirements can be evaluated, allowing a direct comparison of alternative crosses. In this study, we developed Cross Potential Selection for Multiple Traits (CPS-MT), a breeding strategy designed to improve a target trait while maintaining one or more essential traits within desirable ranges. CPS-MT extends the original Cross Potential Selection (CPS) framework to explicitly handle trade-offs between traits under genetic correlations. We evaluated the performance of CPS-MT through simulations involving four types of genetic relationships and two genetic causal factors between traits, resulting in seven scenarios. Across all scenarios, CPS-MT consistently improved the likelihood of obtaining desirable progeny, indicating that CPS-MT provides a practical and effective framework for cross selection under multi-trait constraints in breeding programs. Article SummaryThis study developed Cross Potential Selection for Multiple Traits (CPS-MT), a new breeding strategy designed to improve a target trait while maintaining one or more essential traits within desirable ranges. CPS-MT evaluates crossing pairs by predicting progeny distributions based on estimated genotypic values and genetic covariances, enabling direct comparison of alternative crosses under multi-trait constraints. Through simulations incorporating four types of genetic relationships and two causal factors (seven scenarios), CPS-MT consistently increased the likelihood of obtaining progeny that satisfied the predefined trait requirement. These results indicate that CPS-MT provides a practical, robust framework for target trait improvement under trait constraints.

genetics↗

bioRxiv2026

Predicting agronomic performance of maize landraces in various and future environments by combining genomic prediction and ecogenetics

Forecasting vulnerability of cultivated and wild species to climate changes is highly challenging. Evolutionary genomic models enable the prediction of mal-adaptation (genomic offset - GO) across environments and future climates under the assumption that populations are currently locally adapted but do not predict but the resulting phenotypic changes. To do so, we developed a new genomic prediction model (GP) integrating both genomic offset (GO) and within-population gene diversity (Hs) to capture genotype by environment interaction and inbreeding effects, respectively (GP-HO-Hs). As proof of concept, we applied this GP-GO-Hs model to a collection of 397 maize populations (landraces) evaluated across 25 environments in Europe using high-throughput DNA pool genotyping. GP-GO-Hs model accurately predicted yield, plant height and flowering time. It increased by 13% the predictive abilities of GP model for predicting yield of new landraces in new environments. GP-GO-Hs model also predicted that the more diverse the landrace, the more stable its agronomic performance across environments. GP-GO-Hs model generated phenotypic adaptive landscapes for each landrace in future climatic scenarios, enabling the identification of landraces with enhanced potential to adapt to future or emerging cultivation conditions. This GP-GO-Hs model could be easily applied to other wild and cultivated species. TeaserIdentify promising landrace adapted to new and future environments by combining genomic selection and offset

genomics↗

bioRxiv2025

Unravelling the Genetic Architecture of Field Traits through Multi-Omics Platform Data integration

BackgroundIdentifying the genes and regulatory regions underlying a complex trait is a long-standing challenge. GWAS is generally used, but it suffers from insufficient power, lack of resolution and inefficiency to systematically screen for epistasis. We propose a systems genetics approach integrating multi-omics to overcome these limits. It was applied to a panel of maize with the objective to analyze the genetic determinism of yield in a multi-environment trials by combining genomics with transcriptomics measured on a platform. ResultsDespite the contrasted conditions between the platform and the fields, transcriptomics could be used to identify candidate genes. A presence-absence variation was in particular detected, and the transcripts allowed the identification of causal genes, increasing resolution in comparison to GWAS. In total, 47 genes were identified along the genome, and we could characterize their contrasted effect on yield according to environmental covariates. We demonstrated that the cis- and also the trans-eQTLs of these genes had an important contribution to genetic variance, suggesting a key role of epistatic interactions. In terms of predictive ability, the cis-eQTL resulted in an increase of 39 to 52% on average across the environments, in comparison to random SNP sets. ConclusionsBy efficiently combining multi-omics, it is possible to considerably increase our understanding of genetic architecture in comparison to standard GWAS. We demonstrated that omics data even measured on a phenotyping platform can be used for the analysis of field traits, opening the way for their routine use in plant breeding both for marker-assisted selection and bio-informed predictions.

genetics↗

bioRxiv2025

Rainwash eDNA to monitor tropical rainforest biodiversity

Tropical rainforest canopies shelter an under-explored reservoir of biodiversity. In recent years, the amplification and sequencing of taxonomically informative DNA fragments from environmental samples (i.e. eDNA) has revolutionized biomonitoring. Here, we explore the potential of DNA contained in canopy throughfall water to sample the biological diversity of rainforest canopies. By sampling rainwash eDNA in two 1ha-plots, one mature Amazonian forest and a nearby tree plantation, we were able to detect 170 plant taxa, 72 vertebrate taxa including mammals, birds, and amphibians, and 313 insect taxa including e.g. mosquitoes, ants, beetles. The taxonomic composition retrieved in these two plots reflected their different disturbance status, and the plantation exhibited a much lower diversity. We also found that rainwash eDNA persists over ten days in passive eDNA collectors while providing a local picture of the diversity. This suggests that this method can be scaled up for a cost-effective environmental management of rainforest, and more generally of all forests canopies. TeaserRainwash DNA reveals the identity of plants, animals, and insects diversity, offering a practical tool for monitoring canopy biodiversity.

ecology↗

bioRxiv2024

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions

The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plants response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in routine field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, with and without pedo-climatic data, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a hybrid panel of 244 maize Dent lines crossed to a Flint tester. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV1), and in the second one, partially observed hybrids are predicted (CV2). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. This highlights their efficiency in capturing regulation processes in relation to the growth conditions. For the field traits, we observed that only the additive components of omics were useful and only slightly improved predictive abilities for predicting new hybrids (CV1, model MegaGAO) and for predicting partially observed hybrids (CV2, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest for predicting productivity, if the omics costs were to drop significantly. Our study confirms the potential of omics to predict additive and non-additive genetic effects, resulting in a potentially high increase in predictive abilities compared to standard genomic prediction models. Key MessageTranscriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits.

genetics↗

bioRxiv2023

Dynamics of the DYNLL1/MRE11 complex regulates DNA end resection and recruitment of the Shieldin complex to DSBs

Extent and efficacy of DNA end resection at DNA double strand break (DSB)s determines the choice of repair pathway. Here we describe how the 53BP1 associated protein DYNLL1 works in tandem with Shieldin and the CST complex to protect DNA ends. DYNLL1 is recruited to DSBs by 53BP1 where it limits end resection by binding and disrupting the MRE11 dimer. The Shieldin complex is recruited to a fraction of 53BP1-positive DSBs hours after DYNLL1 predominantly in the G1 cells. Shieldin localization to DSBs is dependent on MRE11 activity and is regulated by the interaction of DYNLL1 with MRE11. BRCA1-deficient cells rendered resistant to PARP inhibitors by the loss of Shieldin proteins can be re-sensitized by the constitutive association of DYNLL1 with MRE11. These results define the temporal and functional dynamics of the 53BP1-centric DNA end resection factors in cells.

molecular biology↗

bioRxiv2022

Detecting directional and non-directional epistasis in bi-parental populations using genomic data

Epistasis, commonly defined as interaction effects between alleles of different loci, is an important genetic component of the variation of phenotypic traits in natural and breeding populations. In addition to its impact on variance, epistasis can also affect the expected performance of a population and is then referred to as directional epistasis. Before the advent of genomic data, the existence of epistasis (both directional and non-directional) was investigated based on complex and expensive mating schemes involving several generations evaluated for a trait of interest. In this study, we propose a methodology to detect the presence of epistasis based on simple inbred bi-parental populations, both genotyped and phenotyped, ideally along with their parents. Thanks to genomic data, parental proportions as well as shared parental proportions between inbred individuals can be estimated. They allow the evaluation of epistasis through a test of the expected performance for directional epistasis or the variance of genetic values. This methodology was applied to two large multi-parental populations, i.e., the American maize and soybean nested association mapping populations, evaluated for different traits. Results showed significant epistasis, especially for the test of directional epistasis, e.g., the increase in anthesis to silking interval observed in most maize inbred progenies or the decrease in grain yield observed in several soybean inbred progenies. In general, the effects detected suggested that shuffling allelic assocations of both elite parents had a detrimental effect on the performance of their progeny. This methodology is implemented in the EpiTest R-package and can be applied to any bi-/multi-parental inbred population evaluated for a trait of interest.

genetics↗

bioRxiv2021

REV7/FANCV Binds to CHAMP1 and Promotes Homologous Recombination Repair

A critical determinant of DNA repair pathway choice is the HORMA protein REV7, a small abundant adaptor which binds to various DNA repair proteins through its C-terminal seatbelt domain. The REV7 seatbelt binds to the REV3 polymerase to form the Polymerase {zeta} complex, a positive regulator of translesion synthesis (TLS) repair. Alternatively, the REV7 seatbelt binds to SHLD3 in the Shieldin complex, a positive regulator of NHEJ repair. Recent studies have identified another novel REV7 seatbelt-binding protein, CHAMP1 (Chromosome Alignment-Maintaining Phosphoprotein, though its role in DNA repair is unknown. Here, we show that the REV7-CHAMP1 complex promotes homologous recombination (HR) repair by sequestering REV7 from the Shieldin complex. CHAMP1 competes directly with the SHLD3 subunit of the Shieldin complex for a limited pool of C-REV7, thereby inhibiting the REV7-mediated recruitment of the SHLD2 and SHLD1 effector subunits to DNA double strand breaks. CHAMP1 thereby channels DNA repair away from error-prone NHEJ and towards the competing error-free HR pathway. Similarly, CHAMP1 competes with the REV3 component of the POL{zeta} complex, thereby reducing the level of mutagenic TLS repair. CHAMP1 interacts with POGZ in a heterochromatin complex further promoting HR repair. Importantly, in human tumors, CHAMP1 overexpression promotes HR, confers PARP inhibitor resistance, and correlates with poor prognosis. Thus, by binding to either REV3, SHLD3, or CHAMP1 through its seatbelt, the REV7 protein can promote either TLS repair, NHEJ repair, or HR repair respectively.

molecular biology↗

bioRxiv2021

Age-Related Differences in Reward-Based Modulation of Sequential Reaching Performance

Reward can increase the speed and accuracy of movements in both simple and sequential reaching tasks. Two mechanisms are thought to be responsible for this: an increase in maximum velocity, due to increased muscle stiffness, resulting in faster, but energetically inefficient, individual movements; or coarticulation - the blending of sub-movements into single, smoother, more energetically efficient movements. Older adults have shown reduced sensitivity to reward in decision paradigms, but there is little research relating reward and motor performance in older adults. Using a novel online sequential reaching task, we compared the effects of reward on motor performance between young (18-23 years) and older (65-79 years) participants. We found that movement time decreased across training in all groups, and reward invigorated this decrease in both age groups. This suggests that reward is a viable facilitator of motor performance to compensate for age-related motor decline and has the potential for use in the design of rehabilitation programmes for age-related motor deficits or disease.

neuroscience↗

bioRxiv2020

Genetic basis of phenotypic plasticity and genotype x environment interaction in a multi-parental population

Deciphering the genetic basis of phenotypic plasticity and genotype x environment interaction (GxE) is of primary importance for plant breeding in the context of global climate change. Tomato is a widely cultivated crop that can grow in different geographical habitats and which evinces a great capacity of expressing phenotypic plasticity. We used a multi-parental advanced generation intercross (MAGIC) tomato population to explore GxE and plasticity for multiple traits measured in a multi-environment trial (MET) design comprising optimal cultural conditions and water deficit, salinity and heat stress over 12 environments. Substantial GxE was observed for all the traits measured. Different plasticity parameters were estimated through the Finlay-Wilkinson and factorial regression models and used together with the genotypic means for quantitative trait loci (QTL) mapping analyses. Mixed linear models were further used to investigate the presence of interactive QTLs (QEI). The results highlighted a complex genetic architecture of tomato plasticity and GxE. Candidate genes that might be involved in the occurrence of GxE were proposed, paving the way for functional characterization of stress response genes in tomato and breeding for climate-adapted crop. HighlightThe genetic architecture of tomato response to several abiotic stresses is deciphered. QTL for plasticity and QTL x Environment were identified in a highly recombinant MAGIC population.

plant biology↗

bioRxiv2019

Optimized breeding strategies to harness Genetic Resources with different performance levels

The narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy. We compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses.

genetics↗

bioRxiv2019

Improving short and long term genetic gain by accounting for within family variance in optimal cross selection

The implementation of genomic selection in recurrent breeding programs raised several concerns, especially that a higher inbreeding rate could compromise the long term genetic gain. An optimized mating strategy that maximizes the performance in progeny and maintains diversity for long term genetic gain on current and yet unknown future targets is essential. The optimal cross selection approach aims at identifying the optimal set of crosses maximizing the expected genetic value in the progeny under a constraint on diversity in the progeny. Usually, optimal cross selection does not account for within family selection, i.e. the fact that only a selected fraction of each family serves as candidate parents of the next generation. In this study, we consider within family variance accounting for linkage disequilibrium between quantitative trait loci to predict the expected mean performance and the expected genetic diversity in the selected progeny of a set of crosses. These predictions rely on the method called usefulness criterion parental contribution (UCPC). We compared UCPC based optimal cross selection and optimal cross selection in a long term simulated recurrent genomic selection breeding program considering overlapping generations. UCPC based optimal cross selection proved to be more efficient to convert the genetic diversity into short and long term genetic gains than optimal cross selection. We also showed that using the UCPC based optimal cross selection, the long term genetic gain can be increased with only limited reduction of the short term commercial genetic gain.

genetics↗

bioRxiv2019

Genomic footprints of local adaptation along elevation gradients associate with present phenotypic variation in teosintes

Local adaptation across species range is widespread. Yet, much has to be discovered on its environmental drivers, the underlying functional traits and their molecular determinants. Because elevation gradients display continuous environmental changes at a short geographical scale, they provide an exceptional opportunity to investigate these questions. Here, we used two common gardens to phenotype 1664 plants from 11 populations of annual teosintes. These populations were sampled across two elevation gradients in Mexico. Our results point to a syndrome of adaptation to altitude with the production of offspring that flowered earlier, produced less tillers, and larger, longer and heavier grains with increasing elevation. We genotyped these plants for 178 outlier single nucleotide polymorphisms (SNPs), which had been chosen because they displayed excess of allele differentiation and/or correlation with environmental variables in six populations with contrasted altitudes. A high proportion of outlier SNPs associated with the phenotypic variation of at least one trait. We tested phenotypic pairwise correlations between traits, and found that the higher the correlation, the greater the number of common associated SNPs. In addition, allele frequencies at 87 of the outlier SNPs correlated with an environmental component best summarized by altitudinal variation on a broad sample of 28 populations. Chromosomal inversions were enriched for both phenotypically-associated and environmentally-correlated SNPs. Altogether, our results are consistent with the set-up of an altitudinal syndrome promoted by local adaptation of teosinte populations in the face of gene flow. We showed that pleiotropy is pervasive and potentially has constrained the evolution of traits. Finally, we recovered variants underlying phenotypic variation at adaptive traits. Because elevation mimics climate change through space, these variants may be relevant for future maize breeding. Author summaryAcross their native range, species encounter a diversity of habitats promoting local adaptation of geographically distributed populations. While local adaptation is widespread, much has yet to be discovered about the conditions of its emergence, the targeted traits, their molecular determinants and the underlying ecological drivers. Here we employed a reverse ecology approach, combining phenotypes and genotypes, to mine the determinants of local adaptation of teosinte populations distributed along two steep altitudinal gradients in Mexico. Evaluation of 11 populations in two common gardens located at mid-elevation pointed to the set-up of an altitudinal syndrome, in spite of gene flow. We scanned genomes to identify loci with allele frequencies shifts along elevation. Interestingly, variation at these loci was commonly associated to variation of phenotypes. Because elevation mimics climate change through space, these variants may be relevant for future maize breeding.

evolutionary biology↗

bioRxiv2018

Usefulness Criterion and post-selection Parental Contributions in Multi-parental Crosses: Application to Polygenic Trait Introgression

Predicting the usefulness of crosses in terms of expected genetic gain and genetic diversity is of interest to secure performance in the progeny and to maintain long-term genetic gain in plant breeding. A wide range of crossing schemes are possible including large biparental crosses, backcrosses, four-way crosses, and synthetic populations. In silico progeny simulations together with genome-based prediction of quantitative traits can be used to guide mating decisions. However, the large number of multi-parental combinations can hinder the use of simulations in practice. Analytical solutions have been proposed recently to predict the distribution of a quantitative trait in the progeny of biparental crosses using information of recombination frequency and linkage disequilibrium between loci. Here, we extend this approach to obtain the progeny distribution of more complex crosses including two to four parents. Considering agronomic traits and parental genome contribution as jointly multivariate normally distributed traits, the usefulness criterion parental contribution (UCPC) enables to (i) evaluate the expected genetic gain for agronomic traits, and at the same time (ii) evaluate parental genome contributions to the selected fraction of progeny. We validate and illustrate UCPC in the context of multiple allele introgression from a donor into one or several elite recipients in maize (Zea mays L.). Recommendations regarding the interest of two-way, three-way, and backcrosses were derived depending on the donor performance. We believe that the computationally efficient UCPC approach can be useful for mate selection and allocation in many plant and animal breeding contexts.

genetics↗