Biology subjects

Mary-Huard, T.

Publications and source records attributed to Mary-Huard, T..

18 recordsLinked to original sources

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

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↗

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

New resistance to bacterial wilt in heat-stressed tomato is revealed by two-reference Genome Wide Association

Bacterial wilt, caused by bacterial strains of the Ralstonia solanacearum species complex, is one of the most harmful diseases striking many crops including tomato. Its spread is dependent upon temperature and humidity, which are expected to fluctuate strongly due to climate change. Previous results have highlighted that temperature elevation led to an increase in disease severity in commercial cultivars, whose resistance is quantitative and mostly relies on the Quantitative Trait Loci (QTL) bwr-6 and bwr-12. In this study, we focused on temperature-dependent quantitative disease resistance (QDR) to bacterial wilt with the aim to unravel new resistance mechanisms that remain efficient at higher temperatures. For this purpose, a new panel of 189 accessions composed of tomato wild relatives, was assembled and sequenced thus creating a unique genomic resource. Its response to the Ralstonia pseudosolanacearum strain GMI1000 from three- to ten-days post-inoculation at 28{degrees}C and 32{degrees}C was explored. To discover the genetic basis underlying the responses of the panel, Genome-Wide Association (GWA) studies were conducted using the disease symptom scores recorded daily and monitored throughout the kinetics of the infection. To improve QTL detection, we have proposed a new approach using two reference genomes from within the panel. By correcting part of a single reference genome, especially when the only reference genome is a cultivar, this approach may be considered an alternative to pangenomic studies. As panel sequencing was highly resolutive, QTL positions allowed the identification of 44 candidate genes, which seemed to follow a temporal dynamic of activation after pathogen inoculation. Interestingly, no candidate genes were found to be common between the two phenotyping temperatures, highlighting the importance of the experimental design in addressing this type of question. Most of our quantitative disease resistance candidate genes belong to gene families described as being involved in immunity. Moreover, a significant proportion appears to be expressed in roots where bacterial infection occurs. Among them, two candidates are closely linked to the genomic positions of the bwr-6 and bwr-12 QTLs, the main QTLs of bacterial wilt Quantitative Disease Resistance (QDR) studied whose mechanisms of action are still unknown. Author summaryBacterial wilt is a plant disease that affects more than 200 crop species (including tomato, potato and banana) leading to high yield losses. The most efficient way to deal with this disease is still the use of genetic resistance. However, previous studies have shown that numerous sources of resistance are negatively affected when plants face heat stress, which is alarming in a context of global warming. In this study, we have developed a new approach for discovering candidate genes in tomato capable of conferring thermostable resistance to bacterial wilt. Forty-four genes were sequentially detected over time, reflecting different temporal dynamics of induction after inoculation. Even if none were found to be common between the two temperatures, available information on their transcriptional regulation in roots and their involvement in immune processes confirms their relevance. Finally, we provide a short list of the candidate genes identified, some of which are currently undergoing functional validation and will be used in breeding programs to help overcome epidemics in future years.

genetics↗

bioRxiv2025

Ridge Redundancy Analysis for High-Dimensional Omics Data

MotivationRedundancy Analysis (RDA) is a popular reduced rank regression approach for modeling the relationships between two sets of variables. In omics studies, RDA provides a flexible framework for investigating associations between high-dimensional molecular data. However, omics datasets often exhibit strong multicollinearity and suffer from the "large p, small n" problem, where the number of predictor variables p (features) exceeds the number of samples n. Ridge RDA addresses multicollinearity by introducing a ridge penalty in addition to the rank restriction. Despite these advantages, its application to high-dimensional omics data remains challenging due to i) difficulties in manipulating the large coefficient matrix of dimensions p x q, where q represents the number of response variables, and ii) the need to jointly select the rank and the regularization parameter that both influence the performance of the method. ResultsWe propose an efficient computational framework for ridge RDA that overcomes these challenges by leveraging the Singular Value Decomposition of the predictor matrix X. This approach eliminates the need for direct covariance matrix inversion, improving computational efficiency. Furthermore, we introduce a novel strategy that reduces the memory burden associated with the storage of the coefficient matrix from pq to (p + q)r, with r the chosen reduced-rank dimensionality. Our method enables an efficient grid search to jointly select the ridge penalty{lambda} and r via cross-validation. The proposed framework is implemented in the R package rrda, providing a practical and scalable solution for the analysis of high-dimensional omics data. AvailabilityThe R package rrda is available on CRAN: https://CRAN.R-project.org/package=rrda. Scripts and data used for the analysis are available on GitHub: https://github.com/Yoska393/rrda.

bioinformatics↗

bioRxiv2025

Integration of Proxy Intermediate Omics traits into a Nonlinear Two-Step model for accurate phenotypic prediction

Intermediate omics traits, which mediate the effects of genetic variation on phenotypic traits, are increasingly recognised as valuable components of genetic evaluation. In particular, rhizosphere microbiota play a crucial role in plant health and productivity; however, their complex interactions with host genetics remain challenging to model. Although two-step modeling frameworks have been proposed to integrate intermediate omics traits into phenotype prediction, existing approaches do not incorporate nonlinear relationships between different omics layers. To address this, we have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omicsomics nonlinearities. The first step is to predict meta-metabolome traits from genetic and microbial features, thus effectively isolating them from the environmental noise. In this process, intermediate "proxy" omics traits are generated as general biological information to provide robust models. The second step utilises this "proxy" to enhance the accuracy of the phenotype prediction. We compared the linear model (Best Linear Unbiased Prediction, BLUP) and the nonlinear model (Random Forest, RF) at each step, as demonstrated through simulations and empirical analysis of a multi-omics soybean dataset in which nonlinear modeling captures intricate omics interactions. Notably, our approach enables phenotype prediction without requiring the original meta-metabolome data used in model training, thereby reducing reliance on costly omics measurements. This framework integrates intermediate omics traits into genomic prediction to improve prediction accuracy and provide solutions for deeper insights into plant-microbiome interactions.

bioinformatics↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

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

Predicting nonlinear genetic relationships between traits in multi-trait evaluations by using a GBLUP-assisted Deep Learning model

BackgroundGenomic prediction aims to predict the breeding values of multiple complex traits, usually assumed to be normally distributed by the largely used statistical methods, thus imposing linear genetic correlations between traits. While statistical methods are of great value for genomic prediction, these methods do not account for nonlinear genetic relationships between traits. If such relationships exist, although statistical models do perform a fair linear approximation, their prediction accuracy is limited due to the nonlinearity. Deep learning (DL) is a promising methodology for predicting multiple complex traits, in scenarios where nonlinear genetic relationships are present, due to its capacity to capture complex and nonlinear patterns in large data. We proposed a novel hybrid DLGBLUP model which uses the output of the traditional GBLUP, and enhances its PGV by accounting for nonlinear genetic relationships between traits using DL. Using simulated data, we compared the accuracy of the PGV obtained with the proposed hybrid DLGBLUP model, a DL model, and the traditional GBLUP model - the latter being our baseline reference. ResultsWe found that both DL and DLGBLUP models either outperformed GBLUP, or presented equally accurate PGV, with a particular greater accuracy for traits presenting a strongly characterized nonlinear genetic relationship. Overall, DLGBLUP presented the highest prediction accuracy, up to 0.2 points higher than GBLUP, and smallest mean squared error of the PGV for all traits. Additionally, we evolved a base population over seven generations and compared the genetic progress when selecting individuals based on the additive PGV obtained by either DL, DLGBLUP or GBLUP. For all traits with a nonlinear genetic relationship, after the fourth generation, the observed genetic gain when selection was based on the additive PGV from GBLUP was always inferior to the one achieved from either DL or DLGBLUP. ConclusionsThe integration of DL into genomic prediction enables the possibility of modeling nonlinear relationships between traits. Moreover, by identifying these nonlinear genetic relationships, our DL and DLGBLUP models improved prediction accuracy, when compared to GBLUP. The possibility of nonlinear relationships between traits offers a different perspective into multi-trait evaluations and prediction, as well as into the traits evolution over generations, with potential to further improve selection strategies in commercial livestock breeding programs. Moreover, DLGBLUP shows that DL can be used as a complement to statistical methods, by enhancing their performance.

genomics↗

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↗

bioRxiv2024

Development of a novel GWAS method to detect QTL effects interacting with the discrete and continuous population structure

Although GWAS has been a key technology to identify causal genes, the current standard GWAS model still has problems that need to be solved. Among them, the population structure is one of the most severe problems when detecting QTLs in GWAS since the GWAS model is statistically confounded by effects derived from the population structure. Further, the existence of QTLs, whose effects depend on the genetic background, also affects the conventional GWAS results by causing many false positives. Although the model to detect these population-specific QTLs has already been developed, this model requires prior information on the population structure, which may only sometimes be available. Also, the previous model only assumed the situation where QTLs interact with the discrete population structure. However, target populations of GWAS often consist of genetic resources with a more continuous population structure, and there has been no model that can consider such QTLs interacting with the continuous structure. In this study, by explicitly including an interaction term between a SNP/haplotype block and the genetic background in the conventional SNP-based/haplotype block-based GWAS model, we developed two models, named SNPxGB and HBxGB, that can detect QTLs interacting with the discrete and continuous structure. Our developed models were compared to the previous models by a simulation study assuming some types of QTLs, i.e., QTLs with effects common to all the backgrounds, specific to one genetic background, and interacting with polygenes. The simulation study showed that the models assuming the same situation as the simulation settings for each QTL type were suitable for detecting the corresponding QTLs. Primarily, our second HBxGB model could detect QTLs interacting with polygenes, i.e., continuous population structure, better than the previous model utilizing the prior population structure information. Our developed models are expected to help unravel the unknown genetic architecture of many complex traits. Author summaryGWAS aims at detecting candidate genes associated with a target trait via statistical testing. Since a classical GWAS starts with the constitution of a panel of individuals, usually gathered from different populations, many methods have been proposed to control the false positives in large datasets with a strong population structure. However, most methods assume the same QTL effect across populations, which is not always true in the natural biological process. One study has proposed a method to consider population-specific QTL effects by assuming marker effects depend on each subpopulation with prior information on population membership for each individual. This information on the population structure, however, may only sometimes be available, and sometimes the population structure is more continuous rather than discrete, where their methodology cannot be applied. We successfully developed two novel models that do not require prior knowledge of the population structure by explicitly including an interaction term between a SNP/haplotype block of interest and the genetic background in the conventional SNP-based/haplotype block-based GWAS model. The developed models, named SNPxGB and HBxGB, were suitable for capturing gene effects interacting with the discrete and continuous population structure, leading to the clarification of the genetic architecture of complex traits.

genomics↗

bioRxiv2023

A genome-wide segmentation approach for the detection of selection footprints

MotivationIn population genetics, the detection of genomic regions under positive selection is essential to understand the genetic basis of locally adaptive trait variation. We propose a principled approach to detect those regions that combines a robust moment based FST estimator with a segmentation algorithm. ResultsOur approach allows for pairwise comparisons of populations and does not require any prior knowledge about the size of the regions to be detected. The procedure runs within seconds even for large genome datasets with millions of SNPs, and provides a complete landscape of the FST distribution over the chromosome. The procedure comes with a grounded estimator of the baseline FST level, allowing the detection of regions exhibiting high departures from this reference value. The potential of our procedure is illustrated in two applications in animal and human population genetics. We were able to recover in a matter of seconds regions known to be under selection, often with greater precision than what was reported in previous studies. AvailabilityOur approach is implemented in the fst4pg R package available from the CRAN repository. The Sheep dataset is downloadable from the Zenodo repository https://doi.org/10.5281/zenodo.237116. The 1000 Genome dataset is downloadable from ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502

bioinformatics↗

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↗

bioRxiv2022

Fast and accurate joint inference of coancestry parameters for populations and/or individuals

We introduce a fast, new algorithm for inferring jointly the FST parameters describing genetic distances among a set of populations and/or unrelated diploid individuals, and a tree representing their genetic structure, from allele count data. While the inferred tree typically reflects historical processes of splitting and divergence, its aim is to represent the actual genetic variance, with FST values specified by branch lengths. We generalise two major approaches to defining FST, via correlations and mismatch probabilities of sampled allele pairs, which measure shared and non-shared components of genetic variance. A diploid individual can be treated as a population of two gametes, which allows inference of coancestry coefficients for individuals as well as for populations, or a combination of the two. A simulation study illustrates that our fast method-of-moments estimation of FST values, simultaneously for multiple populations/individuals, gains statistical efficiency over pairwise approaches by pooling information about ancestral allele frequencies. We apply our approach to genome-wide genotypes from the 26 worldwide human populations of the 1000 Genomes Project. We first analyse at the population level, then a subset of individuals and in a final analysis we pool individuals from the more homogeneous populations. This flexible analysis approach gives many advantages over traditional approaches to population structure/coancestry, including visual and quantitative assessments of long-standing questions about the relative magnitudes of within- and between-population genetic differences. Author summaryWe propose new ways to measure, and visualise in a tree, the genetic distances among a set of populations using allele frequency data. The two genomes within a diploid individual can be treated as a small population, which allows a flexible framework for investigating genetic variation within and between populations. Genetic structure can be accurately and efficiently represented in a tree with nodes representing either homogeneous populations or genetically diverse individuals, for example due to admixture. We first generalise the long-established measure of genetic distance, FST, to tree-structured populations and individuals, finding that two measures are required for each pair of populations, corresponding to their shared and and non-shared genetic variation. We show using a simulation study that our novel tree-based estimators are more efficient than current pairwise estimators, and we illustrate the potential for novel ways to explore and visualise genetic variation within and between populations using a worldwide human genetic dataset.

genetics↗

bioRxiv2020

Genome-wide SNP genotyping of DNA pools identifies untapped landraces and genomic regions that could enrich the maize breeding pool

Maize landraces preserved in genebanks have a large genetic diversity that is still poorly characterized and underexploited in modern breeding programs. Here, we genotyped DNA pools from 156 American and European landraces with a 50K SNP Illumina array to study the effect of both human selection and environmental adaptation on the genome-wide diversity of maize landraces. Genomic diversity of landraces varied strongly in different parts of the genome and with geographic origin. We detected selective footprints between landraces of different geographic origin in genes involved in the starch pathway (Su1, Waxy1), flowering time (Zcn8, Vgt3, ZmCCT9) and tolerance to abiotic and biotic stress (ZmASR, NAC and dkg genes). Landrace diversity was compared to that of (i) 327 inbred lines representing American and European diversity ("CK lines) and (ii) 103 new lines derived directly from landraces ("DH-SSD lines"). We observed limited diversity loss or selective sweep between landraces and CK lines, except in peri-centromeric regions. However, analysis of modified Rogers distance between landraces and the CK lines showed that most landraces were not closely related to CK lines. Assignment of CK lines to landraces using supervised analysis showed that only a few landraces, such as Reids Yellow Dent, Lancaster Surecrop and Lacaune, strongly contributed to modern European and American breeding pools. Haplotype diversity of CK lines was more enriched by DH-SSD lines that derived from the landraces with no related lines and the lowest contribution to CK lines. Our approach opens an avenue for the identification of promising landraces for pre-breeding. SIGNIFICANCE STATEMENTSMaize landraces are a valuable source of genetic diversity for addressing the challenges of climate change and the requirements of low input agriculture as they have been long selected to be well adapted to local agro-climatic conditions and human uses. However, they are underutilized in modern breeding programs because they are poorly characterized, genetically heterogeneous and exhibit poor agronomic performance compared to elite hybrid material. In this study, we developed a high-throughput approach to identify landraces that could potentially enlarge the genetic diversity of modern breeding pools. We genotyped DNA pools from landraces using 50K array technology, which is widely used by breeders to characterize the genetic diversity of inbred lines. To identify landraces that could enrich the modern maize germplasm, we estimated their contribution to inbred lines using supervised analysis and a new measurement of genetic distance.

genetics↗

bioRxiv2019

Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: an application to maize flowering

When handling a structured population in association mapping, group-specific allele effects may be observed at quantitative trait loci (QTLs) for several reasons: (i) a different linkage disequilibrium (LD) between SNPs and QTLs across groups, (ii) group-specific genetic mutations in QTL regions, and/or (iii) epistatic interactions between QTLs and other loci that have differentiated allele frequencies between groups. We present here a new genome-wide association (GWAS) approach to identify QTLs exhibiting such group-specific allele effects. We developed genetic materials including admixed progeny from different genetic groups with known genome-wide ancestries (local admixture). A dedicated statistical methodology was developed to analyze pure and admixed individuals jointly, allowing one to disentangle the factors causing the heterogeneity of allele effects across groups. This approach was applied to maize by developing an inbred \"Flint-Dent\" panel including admixed individuals that was evaluated for flowering time. Several associations were detected revealing a wide range of configurations of allele effects, both at known flowering QTLs (Vgt1, Vgt2 and Vgt3) and new loci. We found several QTLs whose effect depended on the group ancestry of alleles while others interacted with the genetic background. The existence of directional epistasis was highlighted by comparing admixed with pure individuals and was consistent with epistatic interactions identified at the level of QTLs. Our GWAS approach provides useful information on the stability of QTL effects across genetic groups and can be applied to a wide range of species.\n\nAuthor summaryIdentification of genomic regions involved in genetic architecture of traits has become commonplace in quantitative genetics studies. Genetic structure is a common feature in human, animal and plant species and most current methods target genomic regions whose effects on traits are conserved between genetic groups. However, a heterogeneity of allele effects may be observed due to different factors: a group-specific correlation between the alleles of the tagged marker and those of the causal variant, a group-specific mutation at the causal variant or an epistatic interaction between the causal variant and the genetic background. We propose a new method adapted to structured populations including admixed individuals, which aims to identify these genomic regions and to unravel the previous factors. method was applied to a maize inbred diversity panel including lines from the dent and the flint genetic groups, as well as admixed lines, evaluated for flowering time. Several genomic regions were detected with various configurations of allele effects, with evidence of epistatic interactions between some of the loci and the genetic background.

genetics↗

bioRxiv2018

Novel cytonuclear combinations modify Arabidopsis seed physiology and vigor

The influence of intraspecific variation in cytoplasmic genomes and cytonuclear interactions on key seed traits that can impact adaptation and agriculture has not been thoroughly explored, so far. Here, dormancy, germination performance and longevity of seeds have been assessed in Arabidopsis plants with novel cytonuclear combinations that disrupt coadaptation between natural variants of nuclear and cytoplasmic genomes. Although all three traits were affected by cytonuclear reshuffling, the sensitivity of seed traits to cytoplasmic change was dependent on the nuclear background. Both deleterious and, more surprisingly, favorable effects of novel cytonuclear combinations (in comparison with the nuclear parent) were observed, suggesting suboptimal genetic combinations exist in natural populations for these traits. Significant changes on dormancy and germination performance due to specific cytonuclear interacting combinations mainly occurred in opposite directions, in accordance with the previously proposed dormancy continuum. Consistently, reduced sensitivity to exogenous ABA and faster endogenous ABA decay during germination were observed in a novel cytonuclear combination that also exhibited enhanced longevity and better germination performance, compared to its natural nuclear parent. Cytoplasmic genomes, therefore, represent an additional resource of natural variation for breeding seed vigor traits.\n\nIssue sectionGrowth and development\n\nHighlightNatural variation in Arabidopsis organelles and cytonuclear interactions influence seed dormancy, longevity and germination performance. Enhanced seed vigor was obtained through the creation of novel cytonuclear combinations.

plant biology↗