Biology subjects

Persa, R.

Publications and source records attributed to Persa, R..

3 recordsLinked to original sources

bioRxiv2024

Genomic prediction by modelling genotype by environment interaction for yield in groundnut

Multi-environment trials (METs) are routinely conducted in plant breeding to capture the genotype-by-environment (GxE) interaction effects. We assessed the potential of using genomic prediction (GP) in four groundnut traits (pod yield[PY], shelling percentage[SP], seed weight[SW] and seed weight of 100 seeds[SW100]) observed in four environments. Three prediction models (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) were evaluated under four cross-validation (CV) schemes that simulate realistic problems that breeders face at different stages of their programs. CV2 predicts lines tested in sparse multi-location trials; CV1 predicts newly developed lines; CV0 predicts tested lines in unobserved environments by leaving one environment out; CV00 predicts untested lines in unobserved environments. Under CV2, the average predictive ability (PA) range was 0.6-0.68 (PY), -0.26-0.05 (SP), 0.5-0.59 (SW) and 0.79-0.86 (SW100). For CV1, the PA range was -0.12-0.51 (PY), -0.08-0.35 (SP), -0.10-0.47 (SW) and -0.05-0.47 (SW100). In CV0, the PA range was 0.27-0.35 (PY), -0.18--0.17 (SP), 0.24-0.32 (SW) and 0.59-0.66 (SW100). In CV00, the PA range was 0.01-0.23 (PY), -0.03-0.07 (SP), 0.01-0.19 (SW) and -0.02-0.38 (SW100). In all the four CV schemes, including marker data improved the PA. Incorporating GxE in M3 model improved PA in CV2 and CV1 schemes and also reduced the residual and environment variances. The high PA in CV2 imply that sparse testing could be implemented. The low PA recorded in CV00 shows that it is difficult to predict untested lines in new environments.

genomics↗

bioRxiv2024

Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcanes yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (GxE) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6 x 186 = 1,116 phenotypes). Our study employed three predictive models, including environment and genotype as main effects, as well as the GxE interaction to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for GxE pattern prediction. Results demonstrate that maximum accuracy for SA ({rho} = 0.611) and for TCH ({rho} = 0.341) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72 = 12 x 6) being the most convenient cost-benefit combination.

genomics↗

bioRxiv2019

Coupling Day Length Data and Genomic Prediction tools for Predicting Time-Related Traits under Complex Scenarios

Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; however, when the traits have intermediate optima (phenology stages) this implementation might not be the most convenient. GS might deliver high rank correlations but incurring in serious bias. Days to heading (DTH) is a crucial development stage in rice for regional adaptability with significant impact in yield potential. The objectives of this research consisted in the development of a method that accurately predicts time related traits like DTH in unobserved environments. For this, we developed an implementation that incorporate day length information (DL) in the prediction process for two relevant scenarios: CV0, predicting tested genotypes in unobserved environments (C method); and CV00, predicting untested genotypes in unobserved environments (CB method). The use of DL has advantages over weather data since it can be determined in advance just by knowing the location and planting date. The proposed methods showed that DL information significantly helps to improve the predictive ability of DTH in unobserved environments. Under CV0, the C method returned an root-mean-square-error (RMSE) of 3.9 days, a Pearson Correlation (PC) of 0.98 and the differences between the predicted and observed environmental means (EMD) ranged between -4.95 and 4.67 days. For CV00, the CB method returned an RMSE of 7.3 days, a PC of 0.93 and the EMD ranged between -6.4 and 4.1 days while the conventional GS implementation produced an RMSE of 18.1 days, a PC of 0.41 and the EMD ranged between -31.5 and 28.7 days.

genomics↗