Biology subjects

Fe, D.

Publications and source records attributed to Fe, D..

3 recordsLinked to original sources

bioRxiv2026

Genomic prediction of single cross families of perennial ryegrass in two nitrogen managements

The performance of a single cross is determined by the average additive effects of the parents, as well as the interactions between them. These quantities can be estimated using an appropriate genetic design, allowing for the estimation of general (GCA) and specific (SCA) combining abilities. The prediction of GCA for new parents and the total genetic value of unrealized crosses can be made when genome-wide marker information is available. Several studies in crops such as maize and rice have demonstrated the potential of genomic-assisted prediction of single-cross performance in economically important crops. However, no study to date has explored its relevance in perennial ryegrass, an obligate allogamous species that is bred in genetically heterogeneous families. In this study, we aimed to estimate genetic parameters and assess the ability of genomic models to predict the performance of F2 families in terms of dry matter yield and nutritive quality traits. We used data from a large partial diallel involving 104 parents from two distinct subpopulations, as inferred by admixture analysis. F2 families were evaluated in multiple environments and under two nitrogen availability conditions. Genotyping-by-sequencing of the parent plants produced 42,145 variants after quality control, which were used to estimate genomic relationships based on identity-by-state. Variance component estimation revealed limited GCA and SCA interactions with the environment, and particularly with nitrogen management. The predictive abilities of two parental models exceeded 0.60 and often surpassed 0.70 for most traits. However, incorporating non-additive effects into the model did not improve predictive ability. We leveraged the genetic diversity among parents to map genomic regions associated with all recorded traits. Genome-wide association studies (GWAS) by genomic best linear unbiased prediction (GBLUP) identified six quantitative trait loci (QTL) regions, with 45 candidate genes within the linkage disequilibrium range, estimated at approximately 92 kb. Our results demonstrate that genomic prediction of single crosses can be performed with high accuracy, especially when both parents are also progenitors of families in the training set.

genomics↗

bioRxiv2022

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce. Therefore, this study aimed to combine multi-omics from grass hybrids by prioritizing omic features based on the reconstruction of interacting networks and assessing their relevance in explaining economically important phenotypes. Transcriptomic and NMR-based metabolomic data were used for sparse estimation via the fused graphical lasso, followed by modularity-based gene expression and metabolite-metabolite network reconstruction, node hub identification, omic-phenotype association via pairwise fitting of a multivariate genomic model, and machine learning-based prediction study. Analyses were jointly performed across two data sets composed of family pools of hybrid ryegrass (Lolium perenne x L. multiflorum) and Festulolium loliaceum (L. perenne x Festuca pratensis), whose phenotypes were recorded for eight traits in field trials across two European countries in 2020/21. Our results suggest substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus relative to across genera. However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled. The use of the graphical lasso method for network reconstruction and identification of biological targets is discussed with an emphasis on forage grass breeding.

genetics↗

bioRxiv2020

Effects of Different Strategies for Exploiting Genomic Selection in Perennial Ryegrass Breeding Programs

Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass (Lolium perenne L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs. Different scenarios were compared to a conventional breeding program. Simulated scenarios differed in the method of selection and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The amount of genetic gain achieved was different across scenarios. Compared to phenotypic scenarios, GS scenarios resulted in a significantly larger genetic gain for the simulated traits. This was mainly due to more efficient selection of plots and single plants based on GEBV. Also, GS allows for reduction in cycle time, which led to at least a doubling or a trebling of genetic gain compared to the traditional program. Reduction in additive genetic variance levels were higher with GS scenarios than with phenotypic selection. The results demonstrated that implementation of GS in ryegrass breeding is possible and presents an opportunity to make very significant improvements in genetic gains.

genetics↗