Biology subjects

Cloutier, S.

Publications and source records attributed to Cloutier, S..

3 recordsLinked to original sources

bioRxiv2026

Genomic selection for seed yield enhances flax breeding efficiency

Genomic selection (GS) is a promising strategy to improve breeding efficiency for complex traits such as seed yield by enabling early selection and reducing reliance on extensive field testing. However, practical deployment of GS remains challenging due to limited training populations sizes and reduced predictive ability when models are applied to true breeding germplasm. In this study, we evaluated GS for flax (Linum usitatissimum L.) seed yield under realistic breeding scenarios, with a focus on across-population prediction (APP) and breeding decision support rather than model benchmarking. Using historical germplasm collections and a newly developed breeding-oriented population as training sets, GS performance was assessed across multiple independent test populations representing contemporary breeding lines evaluated in replicated yield trials. APP predictive abilities ranged from r = 0.67 to 0.84 depending on population relatedness when training and test populations were genetically aligned, supporting routine breeding deployment. Training population composition emerged as a key determinant of prediction success, with breeding-oriented populations consistently outperforming broad germplasm collections for predicting true breeding lines. Check-based selection analyses showed that GS reliably reproduced phenotypic advancement decisions while eliminating 61-91% of low-performing lines, resulting in 48-78% reduction in field evaluation costs for a typical cohort of 300 lines. Marker subsampling analyses further indicated that moderate-density genotyping-by-sequencing panels ([~]2,500-3,000 SNPs) are sufficient to achieve stable predictive abilities. Overall, these results demonstrate that GS for seed yield in flax is ready for routine integration into breeding programs, offering a practical pathway to reduce costs, accelerate breeding cycles, and enhance selection efficiency.

genomics↗

bioRxiv2026

MultiGS: A comprehensive and user-friendly genomic prediction platform Integrating statistical, machine learning, and deep learning models for breeders

Genomic selection (GS) is a core strategy in modern breeding programs, yet the rapid expansion of statistical, machine-learning (ML), and deep-learning (DL) models has made systematic evaluation and practical deployment increasingly challenging. To address these issues, we developed MultiGS, a unified and user-friendly framework that integrates linear, ML, DL, hybrid, and ensemble GS models within a standardized and computationally efficient workflow. MultiGS is implemented through two complementary pipelines: MultiGS-R, a Java/R pipeline implementing 12 statistical and ML models, and MultiGS-P, a Python pipeline integrating 17 models including five linear models, three ML approaches, and nine recently developed DL architectures implemented within the framework. We benchmarked MultiGS using wheat, maize, and flax datasets representing contrasting prediction scenarios. Wheat and maize were evaluated using random training-test splits within the same population, reflecting suitable conditions for assessing model capacity and scalability. Under these scenarios, several DL, hybrid, and ensemble models achieved prediction accuracies comparable to RR-BLUP and consistently exceeded those of GBLUP. In contrast, the flax dataset represented a true across-population prediction scenario with limited training set size and strong population structure. In this challenging context, classical linear models provided stable baselines, while a subset of DL architectures--particularly graph-based models and BLUP-integrated hybrids--demonstrated comparatively improved generalization across populations. Comparisons with previously published DL tools showed that MultiGS models achieved comparable or improved prediction accuracies while requiring lower computational costs, enabling routine retraining and large-scale evaluation. Overall, MultiGS informs, scenario-specific model selection and provides a practical platform for deploying genomic prediction under realistic breeding conditions. The software is freely available on GitHub (https://github.com/AAFC-ORDC-Crop-Bioinfomatics/MultiGS).

bioinformatics↗

bioRxiv2024

Phyllobacterium meliloti sp. nov. a novel non-symbiotic bacterium isolated from root nodules of Melilotus albus (white sweet clover) grown in Canada

Two novel bacterial strains isolated from root-nodules of white sweet clover (Melilotus albus) plants grown at a Canadian site were previously characterized and placed in the genus Phyllobacterium. Here we present phylogenomic and phenotypic data to support the description of strain T1293T as representative of a novel species and present the first complete closed genome sequence of a bacterial strain (T1018) representing the species P. pellucidum. Phylogenetic analysis of genome sequences as well as analysis of 53 core genes placed novel strain T1293T in a highly supported cluster of strains distinct from named Phyllobacterium species with P. myrsinacearum and P. calauticae as closest relatives. The highest average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values of genome sequences of T1293T compared to closest species type strains (84.1% and 26.5%, respectively) are well below the threshold values for bacterial species circumscription. The genome of strain T1293T has a size of 5074034 bp with a DNA G+C content of 55 mol% and possesses three plasmids with sizes of 397619 bp, 476847 bp and 519835 bp. Detected in the genome were Type III and Type VI secretion system genes, implicated in plant-microbe and microbe-microbe interactions, but key nodulation, nitrogen-fixation and photosystem genes were not detected. Further analysis revealed that T1293T, like other Phyllobacterium species, possesses key genes encoding an enzyme complex implicated in the degradation of glyphosate, a widely used broad-spectrum herbicide that has negative consequences for many microorganisms including the human gut microbiome. A novel prophage (size [~] 41.5 kb) was also detected in the genome of T1293T. Data for multiple phenotypic tests complemented the sequence-based characterization of strain T1293T. The data presented support the description of a new species and the name Phyllobacterium meliloti sp. nov. is proposed with T1293T = LMG32641T = HAMBI 3765T as the species type strain.

microbiology↗