Identifying suitable tester for evaluating Striga resistant lines using DArTseq markers and agronomic traits

A desirable tester that elicits greater genetic difference in Striga resistance among test crosses in a breeding program has not been reported. Therefore, this study was conducted to characterize 30 Striga resistant yellow endosperm maize inbred lines and three testers with varying resistance levels to Striga using DArTseq SNP markers and agronomic traits to identify a suitable tester for resistance hybrid breeding. Marker-based and agronomic trait-based genetic distances were estimated for yellow endosperm maize inbred lines and testers with varying resistance levels to Striga. The Marker-based cluster analysis separated the Striga resistant lines and testers into two distinct groups. Although the susceptible tester (T3) was the most distantly related to the 30 Striga resistant inbred lines, it exhibited a narrower range in genetic distance estimates and poor agronomic performance under Striga infestation in crosses with the resistant lines. In contrast, the resistant tester (T2) showed a broader range in genetic distance estimates in pairs with the 30 resistant lines. Also, it formed many high yielding hybrids with desirable traits under parasite pressure. Furthermore, the most significant positive association between agronomic trait-based and marker-based distance estimates (r = 0.389, P = 0.01) was observed when T2 has paired with the Striga resistant maize inbred lines. It thus appears that T2 may be used as a suitable tester to determine the breeding value of lines in hybrid maize resistance breeding programs. T2 was the most suitable tester, with a tolerant tester (T1) as an alternative tester to characterize the combining ability of Striga resistant maize inbred lines. This result can also encourage other breeders to investigate testers relative discriminating ability with varying levels of resistance in hybrid breeding for resistance to diseases, pests, and other parasitic plants.

A heterochromatic knob reducing the flowering time in maize

Maize flowering time is an important agronomic trait, which is associated with variations in the genome size and heterochromatic knobs content. We integrated three steps to show this association. Firstly, we selected inbred lines varying for heterochromatic knob composition at specific sites in the homozygous state. Then, we produced heterozygous hybrids for knobs, which allow us to carry out genetic mapping. Second, we measured the genome size and flowering time for all materials. Knob composition did not affect the genome size. Finally, we developed an association study and identified a knob marker on chromosome 9 showing the strongest association with flowering time. Indeed, modeling allele substitution and dominance effects could offer only one heterochromatic knob locus that could affect flowering time, making it earlier rather than the knob composition.

Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices

The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.

Agronomic Traits in Oilseed Rape (Brassica napus) Can Predict Foraging Resources for Insect Pollinators

Mass-flowering crops, such as oilseed rape (OSR; Brassica napus), provide pulses of nectar and pollen, helping to support pollinators and their pollination services in agricultural landscapes. Despite their value to declining pollinators, varietal in-field OSR testing focusses on agronomic traits, with floral resources being largely overlooked. OSR has a high varietal turnover, and consequently, floral resource data collected for a specific variety quickly become redundant. Here, we explore the potential to predict floral resource availability using agronomic trait data routinely collected in varietal trials. To build predictive models, we investigated the relationships between agronomic traits and pollen and nectar availability in 19 OSR varieties. Nectar quality was positively influenced by early vigour, as well as winter hardiness in conventional varieties and stem stiffness in hybrid varieties. Pollen quantity was driven by different traits, with early maturation having a negative impact in conventional varieties and resistance to lodging having a positive impact in hybrid varieties. Our study highlights the potential to predict floral resources using agronomic trait data, enabling the rapid assessment of these key resources in future OSR varieties without costly sampling. Agronomic traits relating to increased nectar quality were also agronomically favourable, indicating benefits to both pollinators and growers. The inclusion of modelled floral resource data in recommended varietal lists would enable growers to make informed decisions about varietal selection based on local pollinator populations.

Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression

Rice yield is not only influenced by factors of varieties and managements, but also by environmental factors. In this study, agronomic trait data of rice and climate data in eastern China were collected, and rice yields were predicted using a variety of algorithms, including the non-linear tool of feed-forward backpropagation neural networks (FFBN) and the linear model of partial least squares regression (PLSR). The results showed that both the agronomic traits and the climate data were significantly related with rice yield. The PLSR model showed that covariates occurred among the parameters, and modifications should be considered for climate data-based modelling. The FFBN model demonstrated better prediction performance than that of PLSR, in which the relation coefficient (R2) and root mean square error (RMSE) were 0.611 vs. 0.374 and 0.578 vs. 0.865 ton/ha using climate data, respectively; and 0.742 vs. 0.689 and 0.556 vs. 0.608 using agronomic trait data, respectively. When using fused data the R2 and RMSE improved to 0.843 vs. 0.746 and 0.440 vs. 0.549, respectively. The optimum architecture of the FFBN consisted of one hidden layer with 29 neurons. Therefore, the FFBN algorithm is an effective option for the prediction of rice yield in complex systems of rice production.

Inheritance of seed size in watermelon populations

Seed size is an important agronomic trait and is applicable to different abilities. Small seeds guarantee the greater use of the pulp, while larger seeds facilitate sowing. However, there is little work on the genetic control of this characteristic in watermelon. The objective of this work was to study the seed size inheritance in watermelon populations by crossing contrasting genotypes, seeking to gain information to provide technical support during the selection of seed size for the development of new watermelon genotypes. The seed lengths of six populations, P1, P2, F1, F2, BC1 and BC2, were measured using the GENES software segregating and nonsegregating generations procedure. This trait is controlled by two genes with incomplete dominance. In addition, depending on the populations studied, inheritance for the characteristic in question may behave differently. Nevertheless, the selection of superior individuals within populations can be performed based on this phenotype, which allows the exploitation of these individuals within breeding programs to develop lines or hybrids.