Biology subjects

Charcosset, A.

Publications and source records attributed to Charcosset, A..

16 recordsLinked to original sources

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↗

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↗

bioRxiv2024

Large-scale composite hypothesis testing for omics analyses.

Composite hypothesis testing using summary statistics is a well-established approach for assessing the effect of a single marker or gene across multiple traits or omics levels. Numerous procedures have been developed for this task and have been successfully applied to identify complex patterns of association between traits, conditions, or phenotypes. However, existing methods often struggle with scalability in large datasets or fail to account for dependencies between traits or omics levels, limiting their ability to control false positives effectively. To overcome these challenges, we present the qch_copula approach, which integrates mixture models with a copula function to capture dependencies between traits or omics, and provides rigorously defined p-values for any composite hypothesis. Through a comprehensive benchmark against eight state-of-the-art methods, we demonstrate that qch_copula controls Type I error rates effectively while enhancing the detection of joint association patterns. Compared to other mixture model-based approaches, our method notably reduces memory usage during the EM algorithm, allowing the analysis of up to 20 traits and 105 - 106 markers. The effectiveness of qch_copula is further validated through two application cases in human and plant genetics. The method is available in the R package qch, accessible on CRAN.

genomics↗

bioRxiv2023

metaGE: Investigating Genotype-by-Environment interactions through meta-analysis

Dissecting the genetic components of Genotype-by-Environment interactions is of key importance in the context of increasing instability and plant competition due to climate change and phytosanitary treatment limitations. It is widely addressed in plants using Multi-Environment Trials (MET), in which statistical modelling for genome-wide association studies (GWAS) is promising but significantly more complex than for single-environment studies. In this context, we introduce metaGE, a flexible and computationally efficient meta-analysis approach for the joint analysis of any MET GWAS experiment. To cope with the specific requirements of the MET context, metaGE accounts for both the heterogeneity of QTL effects across environments and the correlation between GWAS summary statistics acquired on the same or related set(s) of genotypes. Compared to previous GWAS in 3 plant species and a multi-parent population, metaGE identified known and new QTLs. It provided valuable insight into the genetic architecture of several complex traits and the variation of QTL effects conditional to environmental conditions.

plant 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↗

bioRxiv2020

First results on diversity patterns and selective sweeps in a Southeast European panel of maize inbred lines as combined with two West European panels

More than one third of European grain maize is produced in South Eastearn Europe (SEE) and utilization of historical maize material developed in SEE for its favorable alleles and diversity has long been speculated. However, molecular diversity of the SEE maize genetic material is scarce. The objectives of this study were i) to analyze diversity patterns in a large panel of densely genotyped historical accessions from SEE, ii) to compare the data with those obtained from other two European panels, and iii) to identify genomic regions that have undergone selection (selective sweeps) in response to adaptation to SEE conditions. 572 accessions of the historical inbred lines from Maize Research Institute Zemun Polje representing the SEE material were genotyped using the 600k maize genotyping Axiom array. The genotyping results were merged with two European panels DROPS and TUM. Genetic structure and diversity were analyzed using neighbor-joining cladogram, PcoA, Admixture, Structure and sNMF. To detect the selective sweep signals, Tajimas D statistic and RAiSD were employed. The best number of ancestral populations was K=7, whereby one of them is a subpopulation containing inbreds belong exclusively to the SEE panel. The prevalence of inbreds linked to historical US inbred lines Wf9, Oh43, Pa91 and A374 was detected in SEE. Possible soft selective sweep was detected in chromosome 2 in region harboring a gene linked to promotion of flowering FPF1. Additional scan for selective sweeps using the RAiSD methodology yielded four signals in chromosomes 5 and 6, all in gene-rich regions. Several candidates of selection were identified, influencing the plant morphology and adaptation. Our study provides the first step towards the utilization of the SEE genetic materials for use in maize breeding. Phenotypic analysis is needed for assessment of SEE accessions for favorable alleles, and identification of breeding targets.

genetics↗

bioRxiv2020

Genetic diversity of maize landraces from the South-West of France

From the 17th century until the arrival of hybrids in 1960s, maize landraces were cultivated in the South-West of France, a traditional region for maize cultivation. A set of landraces were collected in this region between the 1950s and 1980s and were then conserved ex situ in a germplam collection. Previous studies using molecular markers on approx. twenty landraces fo this region showed that they belonged to a Pyrenees-Galicia Flint genetic group and originated from hybridization between Caribbean and Northern Flint germplasms introduced in Europe. In this study, we assessed the structure and genetic diversity of 194 SWF maize landraces to elucidate their origin, using a 50K SNP array and a bulk DNA approach. We identified two weakly differentiated genetic groups, one in the Western part and the other in the Eastern part. We highlighted the existence of a longitudinal gradient along the SWF area that was probably maintained through the interplay between genetic drifts and restricted gene flows, rather than through differential climatic adaptation. The contact zone between the two groups observed near the Garonne valley may be the result of these evolutionnary forces. We found only few significant cases of hybridization between Caribbean and Northern Flint germplasms in the region. We also found gene flows from various maize genetic groups to SWF landraces. Thus, we assumed that SWF landraces had a multiple origin with a slightly higher influence of Tropical germplasm in the West and preponderance of Northern Flint germplasm in the East.

genetics↗

bioRxiv2020

Deciphering the genetic diversity of landraces with high-throughput SNP genotyping of DNA bulks: methodology and application to the maize 50k array

Genebanks harbor original landraces carrying many original favorable alleles for mitigating biotic and abiotic stresses. Their genetic diversity remains however poorly characterized due to their large within genetic diversity. We developed a high-throughput, cheap and labor saving DNA bulk approach based on SNP Illumina Infinium HD array to genotype landraces. Samples were gathered for each landrace by mixing equal weights from young leaves, from which DNA was extracted. We then estimated allelic frequencies in each DNA bulk based on fluorescent intensity ratio (FIR) between two alleles at each SNP using a two step-approach. We first tested either whether the DNA bulk was monomorphic or polymorphic according to the two FIR distributions of individuals homozygous for allele A or B, respectively. If the DNA bulk was polymorphic, we estimated its allelic frequency by using a predictive equation calibrated on FIR from DNA bulks with known allelic frequencies. Our approach: (i) gives accurate allelic frequency estimations that are highly reproducible across laboratories, (ii) protects against false detection of allele fixation within landraces. We estimated allelic frequencies of 23,412 SNPs in 156 landraces representing American and European maize diversity. Modified Rogers genetic Distance between 156 landraces estimated from 23,412 SNPs and 17 SSRs using the same DNA bulks were highly correlated, suggesting that the ascertainment bias is low. Our approach is affordable, easy to implement and does not require specific bioinformatics support and laboratory equipment, and therefore should be highly relevant for large-scale characterization of genebanks for a wide range of species.

genetics↗

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

Integrating proteomics and genomics into systems genetics provides novel insights into the mechanisms of drought tolerance in maize

The effect of drought on maize yield is of particular concern in the context of climate change and human population growth. However, the complexity of drought-response mechanisms make the design of new drought-tolerant varieties a difficult task that would greatly benefit from a better understanding of the genotype-phenotype relationship. To provide novel insight into this relationship, we applied a systems genetics approach integrating high-throughput phenotypic, proteomic and genomic data acquired from 254 maize hybrids grown under two watering conditions. Using association genetics and protein co-expression analysis, we detected more than 22,000 pQTLs across the two conditions and confidently identified fifteen loci with potential pleiotropic effects on the proteome. We showed that even mild water deficit induced a profound remodeling of the proteome, which affected the structure of the protein co-expression network, and a reprogramming of the genetic control of the abundance of many proteins, notably those involved in stress response. Co-localizations between pQTLs and QTLs for ecophysiological traits, found mostly in the water deficit condition, indicated that this reprogramming may also affect the phenotypic level. Finally, we identified several candidate genes that are potentially responsible for both the co-expression of stress response proteins and the variations of ecophysiological traits under water deficit. Taken together, our findings provide novel insights into the molecular mechanisms of drought tolerance and suggest some pathways for further research and breeding.

systems biology↗

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↗

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↗

bioRxiv2018

Genotyping-by-sequencing and microarrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

BackgroundSingle Nucleotide Polymorphism (SNP) array and re-sequencing technologies have different properties (e.g. calling rate, minor allele frequency profile) and drawback (e.g. ascertainment bias), which lead us to study the complementarity and consequences of using them separately or combined in diversity analyses and Genome-Wide Association Studies (GWAS). We performed GWAS on three traits (grain yield, plant height and male flowering time) measured in 22 environments on a panel of 247 diverse dent maize inbred lines using three genotyping technologies (Genotyping-By-Sequencing, Illumina Infinium 50K and Affymetrix Axiom 600K arrays).\n\nResultsThe effects of ascertainment bias of both arrays were negligible for deciphering global genetic trends of diversity in this panel and for estimating relatedness. We developed an original approach based on linkage disequilibrium (LD) extent in order to determine whether SNPs significantly associated with a trait and that are physically linked should be considered as a single QTL or several independent QTLs. Using this approach, we showed that the combination of the three technologies, which have different SNP distribution and density, allowed us to detect more Quantitative Trait Loci (QTLs, gain in power) and potentially refine the localization of the causal polymorphisms (gain in position).\n\nConclusionsConceptually different technologies are complementary for detecting QTLs by tagging different haplotypes in association studies. Considering LD, marker density and the combination of different technologies (arrays and re-sequencing), the genotypic data presently available were most likely enough to well represent polymorphisms in the centromeric regions, whereas using more markers would be beneficial for telomeric regions.

genomics↗