Biology subjects

Welcker, C.

Publications and source records attributed to Welcker, C..

8 recordsLinked to original sources

bioRxiv2026

How to be dispensable: genomic and transcriptomic determinants in maize genes

BackgroundPlant genomes harbour a substantial proportion of dispensable genes - present only in a subset of individuals - that differ from ubiquitously shared core genes in multiple genomic and expression features. While these differences have been repeatedly documented, the factors shaping gene dispensability remain poorly understood. ResultsWe assembled a pan-gene set from eight maize inbred lines from American and European germplasms, together with their transcriptomic profile across 22 tissues/conditions, revealing the genomic and transcriptomic determinants of maize gene dispensability. Multivariate analysis demonstrates that gene expression level and purifying selection - rather than gene size - are the primary factors distinguishing core from dispensable genes. Dispensable genes overlap Helitrons at 4.6 times the rate of core genes, implicating Helitron-mediated gene capture as a major mechanism of dispensable gene formation. Classifying genes into stably expressed, variably expressed, and on-off categories shows that all three classes contain dispensable genes, though in different proportions than for core genes. Contrary to previous assumptions, we show that dispensable genes can participate in basal biological functions just as core genes, and that gene duplication likely provides only a partial mechanism for functional complementation of accessory genes absence. ConclusionsOur results provide novel insights into the molecular and evolutionary factors distinguishing core from dispensable genes and into the biological mechanisms shaping gene dispensability in maize, and demonstrate that classifying genes by transcriptional patterns provides a powerful framework for understanding the biological functions and evolutionary dynamics of both core and dispensable genes.

plant biology↗

bioRxiv2024

A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation

Understanding how plant phenotypes are shaped by their environments is crucial for addressing questions about crop adaptation to new environments. This study focused on analyzing the genetic variability underlying genotype-by-environment interactions and adaptation for flowering time in maize. We present a physiological reaction norm for flowering time plasticity (PRN-FTP), modeled from multi-environment trial networks and decomposed into its physiological components. We show how genotype-specific differences in developmental responses to temperature fluctuations condition differences in photoperiod perceived among genotypes. This occurs not only across but also within common environments, as the perception of photoperiod is altered by variation in rates of development and durations for becoming sensitized to photoperiod. Using a new metric for envirotyping sensed photoperiods for maize, it was found that, at high latitudes, different genotypes in the same environment can experience up to hours-long differences in photoperiod. This emphasizes the importance of considering genotype-specific differences in the experienced environment when investigating plasticity. Modeling the PRN-FTP for globally representative breeding material showed that tropical and temperate germplasm occupy distinct territories of the trait space for PRN-FTP parameters. Placed in the historical context of maize, our findings suggest that the geographical spread and breeding of maize was mediated by a specific modality of ecophysiological adaptation of flowering time. Our study has implications for understanding crop adaptation and for future crop improvement efforts. Article SummaryMaize adjusts its flowering time across environments using genetic pathways tuned to cues like temperature and day length. This study models how different varieties perceive and respond to these factors, revealing that genotypes can perceive the same environment in distinct ways. By disentangling the ecophysiological basis of genotype-environment interactions, the research explores maize diversity and highlights how adaptation and breeding have shaped distinct strategies for flowering time regulation in tropical and temperate varieties.

plant biology↗

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

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↗

bioRxiv2021

EARBOX: An open tool for high-throughput measurements of the spatial organization of maize ears and inference of novel traits

BackgroundCharacterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. The spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. ResultsWe developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. ConclusionsThe proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorization. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

plant biology↗

bioRxiv2021

High-throughput phenotyping reveals a link between transpiration efficiency and transpiration restriction under high evaporative demand and new loci controlling water use-related traits in African rice, Oryza glaberrima Steud.

Because water availability is the most important environmental factor limiting crop production, improving water use efficiency, the amount of carbon fixed per water used, is a major target for crop improvement. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand, remain unknown. These traits were measured in a panel of 147 African rice Oryza glaberrima genotypes, known as potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly differentiated genotypes confirmed these associations and suggested that the root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency and its residuals, that links to genes involved in water transport and cell wall patterning. Our data suggest that root shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.

physiology↗

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↗

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↗