Biology subjects

Papin, V.

Publications and source records attributed to Papin, V..

6 recordsLinked to original sources

bioRxiv2026

Comparison of localGEBV and Optimal Haplotype Stacking Fitness Functions using a Novel R Package: HapSelect

Haplotype-based breeding strategies have emerged as promising approaches to maximize long-term genetic gain by identifying complementary parental combinations while maintaining genetic diversity. However, these methods typically require phased genotypes and more intensive workflow pipelines and skillsets. We developed a novel local genomic estimated breeding value (localGEBV) fitness function with similar intent to the optimal haplotype stacking (OHS) framework fitness function and implemented both in the novel R package, HapSelect. Our aim was to evaluate whether phased haplotypes provide additional benefit over the more easily available dosage-based unphased genotypes in highly inbred crops. A subset of bread wheat nested association mapping (NAM) population comprising 444 lines genotyped with 6,054 DArT-Seq markers was analysed. Marker effects were estimated using rrBLUP, localGEBV and haplotype effects were calculated across linkage disequilibrium-defined haploblocks, and genetic algorithms (GA) were used to identify optimal sets of 30 founders using either a localGEBV derived fitness function with unphased, dosage inputs or the OHS fitness function with phased inputs. Selected parental sets were compared with conventional truncation selection (TS) through 150 generations of forward simulation. The OHS fitness function achieved a marginally greater optimized ultimate GEBV than the localGEBV fitness function during GA optimization, with only 18 of the 30 selected founders overlapped between the two methods. Despite these differences, forward simulations demonstrated nearly identical long-term genetic gain for localGEBV and OHS-selected founders, with both approaches outperforming conventional truncation selection by maintaining greater genetic diversity and delaying the genetic plateau. The minimal difference between localGEBV and OHS is likely attributable to the high homozygosity of the population, where localGEBV and haplotype effects are nearly confounded. These results demonstrate that dosage-based localGEBV provides a practical alternative to phased haplotype approaches for parent selection in inbred crops, substantially simplifying genomic workflows while maintaining long-term breeding performance. Future work should evaluate these methods in more diverse inbred populations and outbred species, where great haplotypic diversity may increase the advantage of true haplotype-based optimizations.

genetics↗

bioRxiv2026

Trait stability of diverse kabuli chickpea germplasm from delayed sowing in a rainfed environment

Structured AbstractO_ST_ABSContext and ObjectiveC_ST_ABSDelayed sowing can expose chickpea crops to stress during the critical period for yield determination, but the effect of yield components and phenology to grain yield variation is not well characterised in diverse germplasm under rainfed conditions. Identifying genetic resources for grain yield improvement requires integration of multi-environment trial and genomic analyses to disentangle direct yield effects from indirect effects of phenology. This study aimed to (1) characterise genotype by environment interaction patterns for grain yield, yield components and phenology across times of sowing and seasons, and (2) identify genomic regions associated with improved grain yield that are present in genebank accessions but absent from current Australian commercial cultivars. MethodsA diversity panel of 141 kabuli chickpea genotypes, including six commercial Australian cultivars and 135 genebank accessions, was evaluated across six rainfed trials at Narrabri, New South Wales over three seasons (2018 to 2020) under typical (MAIN) and delayed (LATE) sowing. Multi-environment trial analyses with factor analytic models partitioned genotype by environment interactions for grain yield, 100-seed weight, seed number, and thermal time to flowering, podding, and maturity. Haplotype block analysis identified high variance blocks associated with seed number, classified by their overlap with high variance thermal time to flowering blocks, to distinguish from effects mediated by phenology. Results and ConclusionsDelayed sowing reduced grain yield by up to 1.04 t ha-{superscript 1}, driven primarily by reductions in seed number rather than 100-seed weight. Accelerated phenology was a key component of adaptation among commercial cultivars. Four haploblocks with high block variance for seed number were identified across all six trials. SignificanceSeed number was the dominant driver of grain yield variation in this diverse kabuli chickpea panel. Targeted introgression of rare superior haplotypes from genebank accessions provides an opportunity to broaden the genetic base of Australian kabuli chickpea and improve yield through higher seed number, with relevance to chickpea production systems facing similar climate variability. HighlightsDelayed sowing reduced grain yield in diverse kabuli chickpea germplasm by up to 1.04 t ha-1 across three years and six trials in northern New South Wales. Seed number, not seed weight, was the dominant driver of grain yield variation, and a shorter phenological duration was associated with higher seed number. Across all six trials, haplotype block analysis identified four genomic regions in high linkage disequilibrium with high variance for seed number and low variance for flowering time. The accession FLIP 94 62C uniquely carried rare superior haplotypes at two chromosome 4 blocks, the haplotype at the 17.0 Mb block was the most superior haplotype in all trials while the haplotype at the 8.5Mb block was most superior only in the most heat stressed environment.

plant biology↗

bioRxiv2025

Local genomic estimates provide a powerful framework for haplotype discovery

Quantitative trait loci (QTL) discovery studies on diversity panels or breeding populations typically use genome-wide association studies (GWAS) to estimate marker effects. For plant and animal breeding applications, researchers increasingly recognize the potential benefits of identifying superior haplotypes (markers in linkage disequilibrium; LD) rather than relying on single markers, as traditional approaches inefficiently account for cumulative signals from incomplete LD with QTL or split effects when multiple markers are in high LD with QTL. Using the genomic prediction framework, the local genomic estimated breeding values (localGEBV) method was developed in animal breeding and has been adopted in crop haplotype mapping studies; however, no study has thoroughly quantified the utility of this method or systematically compared outcomes to traditional GWAS approaches. Here, we characterized a strategy to group markers in chromosomal segments based on LD (haplotype blocks or haploblocks), computed localGEBV as a linear contrast of marker effects within each haploblock, and utilised the variance of localGEBV to enhance QTL discovery compared to traditional GWAS. Marker effects for localGEBV were estimated with ridge-regression best linear unbiased prediction (rrBLUP) and BayesR, with results compared to two common GWAS approaches. Using the barley row-type trait, we demonstrated that localGEBV improved QTL discovery and phenotypic prediction compared to single markers. Furthermore, localGEBV results were robust to the choice of prior marker assumptions and blocking parameters, enabling flexibility in fine or broad-scale QTL mapping. Overall, our findings establish localGEBV as a haplotype-based strategy capable of leveraging localized genomic effects to improve QTL discovery and, potentially, genomic selection.

genetics↗

bioRxiv2024

Unlocking genome-based prediction and selection in conifers: the key role of within-family prediction accuracy

ContextGenomic selection is a promising approach for forest tree breeding. However, its advantage in terms of prediction accuracy over conventional pedigree-based methods is unclear and within-family accuracy is rarely assessed. AimsWe used an pedigree-based model (ABLUP) with corrected pedigree data as a baseline reference for assessing the prediction accuracy of genome-based model (GBLUP) at the global and within-family levels in maritime pine (Pinus pinaster Ait). MethodsWe sampled 39 full-sib families, each comprising 10 to 40 individuals, to constitute an experimental population of 833 individuals. A stochastic simulation model was also developed to explore other scenarios of heritability, training set size and tagging density. ResultsPrediction accuracies with GBLUP and ABLUP were similar and accuracy with GBLUP within-family was on average zero with large variation between families. Simulations revealed that the number of individuals in the training set was the principal factor limiting GBLUP accuracy in our study and likely in many forest tree breeding programmes. Accurate within-family prediction is possible if 40-65 individuals per full-sib family are included in the genomic training set, from a total of 1600-2000 individuals in the training set. ConclusionSuch conditions lead to a significant advantage of GBLUP over ABLUP in terms of prediction accuracy and more clearly justify the switch to genome-based prediction and selection in forest trees.

genomics↗

bioRxiv2023

Integrating environmental gradients into breeding: application of genomic reactions norms in a perennial species

Global warming threatens the productivity of forest plantations. We propose here the integration of environmental information into a genomic evaluation scheme using individual reaction norms, to enable the quantification of resilience in forest tree improvement and conservation strategies in the coming decades. Random regression models were used to fit wood ring series, reflecting the longitudinal phenotypic plasticity of tree growth, according to various environmental gradients. The predictive performance of the models was considered to select the most relevant environmental gradient, namely a gradient derived from an ecophysiological model and combining trunk water potential and temperature. Even if the genotype ranking was preserved over most of the environmental gradient, strong genotype x environment interactions were detected in the extreme unfavorable part of the gradient, which includes environmental conditions that are very likely to increase in the future. Combining genomic information and longitudinal data allowed to predict growth in unobserved environments: considering an equivalent phenotyping effort, the cross-validation scenarios led to predictive performances ranging from 0.25 to 0.59 highlighting the importance of phenotypic data allocation. Genomic reaction norms are useful for the characterization and prediction of the function of genetic parameters and facilitate breeding in a climate change context.

genetics↗

bioRxiv2020

Tomato fruit susceptibility to fungal disease can be uncoupled from ripening by suppressing susceptibility factors

The increased susceptibility of ripe fruit to fungal pathogens poses a substantial threat to crop production and marketability. Here, we coupled transcriptomic analyses with mutant studies to uncover critical processes associated with defenses and susceptibility in tomato (Solanum lycopersicum) fruit. Using unripe and ripe fruit inoculated with three fungal pathogens, we identified common pathogen responses reliant on chitinases, WRKY transcription factors, and reactive oxygen species detoxification. We established that the magnitude and diversity of defense responses do not significantly impact the interaction outcome, as susceptible ripe fruit mounted a strong defense response to pathogen infection. Then, to distinguish features of ripening that may be responsible for susceptibility, we utilized non-ripening tomato mutants that displayed different susceptibility patterns to fungal infection. Based on transcriptional and hormone profiling, susceptible tomato genotypes had losses in the maintenance of cellular redox homeostasis, while jasmonic acid accumulation and signaling coincided with defense activation in resistant fruit. We identified and validated a susceptibility factor, pectate lyase (PL). CRISPR-based knockouts of PL, but not polygalacturonase (PG2a), reduced susceptibility of ripe fruit by >50%. This study suggests that targeting specific genes that drive susceptibility is a viable strategy to improve the resistance of tomato fruit against fungal disease. HighlightIncreased susceptibility to fungal disease during tomato ripening is driven by the accumulation of susceptibility factors and not the lack of defense responses.

plant biology↗