Biology subjects

Bouffier, L.

Publications and source records attributed to Bouffier, L..

3 recordsLinked to original sources

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↗

bioRxiv2022

Pollen contamination and mating structure in maritime pine (Pinus pinaster Ait.) clonal seed orchards revealed by SNP markers

Maritime pine (Pinus pinaster Ait.) is a major forest tree species in south-western Europe. In France, an advanced breeding program for this conifer species has been underway since the early 1960s. Open-pollinated seed orchards currently supply more than 90% of maritime pine seedlings for plantation forestry. However, pollen contamination and mating structure have been poorly studied in such seed orchards whereas they could impact genetic gains and diversity. We analyzed these features in three maritime pine clonal seed orchards. We addressed biological (tree genotype, age, flowering phenology) and environmental factors (vicinity with external pollen sources, orchard structure, soil type, climatic conditions) that are expected to determine the genetic composition of seed lots. Genetic analyses were based on an optimized set of 60 SNP markers and performed on 2,552 seedlings with Cervus software (likelihood inference methodology). Pollen contamination rates were highly variable between seed lots (from 20 to 96%), with a mean value of 50%. Interpretative factors included the distance between the seed orchard and external pollen sources, rain during the pollination period, seed orchard age, soil conditions and seed parent identity. All parental genotypes from the seed orchards contributed to the offspring as pollen parents, but differences in paternal reproductive success were detected. Finally, the overall self-fertilization rate was estimated at 5.4%, with considerable variability between genotypes (from 0% to 26%). These findings are useful to formulate recommendations for seed orchard management (seed orchard location, soil and climate optimal conditions, minimum age for commercial seed lots harvesting) and for identifying new research perspectives (exploring links between pollen contamination and climatic data, genetic control of flowering traits).

genetics↗