Weighing in on a method to discriminate maize haploid from hybrid seed

The doubled haploid breeding method can produce maize inbred lines faster than traditional methods, but there are challenges associated with it. Sorting haploid from hybrid seed based on visual colour markers is time consuming and can be difficult due to colour inhibitors that obscure pigmentation needed to distinguish between haploid, hybrid and outcrossed seed. In this study, weight was evaluated as a method to sort haploid from hybrid seed. A first experiment utilized two families for analysis in a preliminary study. Eleven haploid and hybrid kernels from both families were weighed for a total of 44 experimental units. A second experiment was carried out using six families, using the same format as the previous, for 132 experimental units. Hybrid seed weighed significantly more than haploid seed in both experiments. However, the interaction between line and kernel type was significant in the second experiment. In conclusion, efficacy of sorting haploid from hybrid kernels based on weight depends on the genotypes involved

Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry

Rhizoctonia root and crown rot (RRCR) is an important disease in sugar beet production areas, whose assessment and control are still challenging. Therefore, breeding for resistance is the most practical way to manage it. Although the use of spectroscopy methods has proven to be a useful tool to detect soil-borne pathogens through leaves reflectance, no study has been carried out so far applying near-infrared spectroscopy (NIRS) directly in the beets. We aimed to use NIRS on sugar beet root pulp to detect and quantify RRCR in the field, in parallel to the harvest process. For the construction of the calibration model, mainly beets from the field with natural RRCR infestation were used. To enrich the model, artificially inoculated beets were added. The model was developed based on Partial Least Squares Regression. The optimized model reached a Pearson correlation coefficient (R) of 0.972 and a Ratio of Prediction to Deviation (RPD) of 4.131. The prediction of the independent validation set showed a high correlation coefficient (R = 0.963) and a root mean square error of prediction (RMSEP) of 0.494. These results indicate that NIRS could be a helpful tool in the assessment of Rhizoctonia disease in the field

Quantification of Oil Content in Intact Sugar Beet Seed by Near-Infrared Spectroscopy

Sugar beet seed oil reserves play an important role in successful germination and seedling development. The purpose of this study was to establish a non-destructive near-infrared (NIR) methodology with good predictive accuracy to quantify stored seed oil in sugar beet seed. Reflectance NIR spectra were acquired from viable monogerm seeds. Calibration equations were developed using partial least squares. The optimized calibration model reached a Pearson correlation of 0.946; an independent prediction test reached a correlation of 0.919 and a Root Mean Square Error of Prediction of 0.388. The possible role of the outer pericarp in the prediction of oil content was additionally considered. The results indicate that the model is suitable for a rapid and accurate determination of the oil content in both polished and unpolished sugar beet seeds. This NIR application might help to understand the role of seed energy reservoirs in sugar beet germination and further plant growth

Wuerschum_et_al_2011_rawData

Marker names with genetic map positions, P values for all markers for the seven tested biometrical models, shown for all six traits

Weighing in on a method to discriminate maize haploid from hybrid seed

The doubled haploid breeding method can produce maize inbred lines faster than traditional methods, but there are challenges associated with it. Sorting haploid from hybrid seed based on visual colour markers is time consuming and can be difficult due to colour inhibitors that obscure pigmentation needed to distinguish between haploid, hybrid and outcrossed seed. In this study, weight was evaluated as a method to sort haploid from hybrid seed. A first experiment utilized two families for analysis in a preliminary study. Eleven haploid and hybrid kernels from both families were weighed for a total of 44 experimental units. A second experiment was carried out using six families, using the same format as the previous, for 132 experimental units. Hybrid seed weighed significantly more than haploid seed in both experiments. However, the interaction between line and kernel type was significant in the second experiment. In conclusion, efficacy of sorting haploid from hybrid kernels based on weight depends on the genotypes involved.This article is published as Smelser, Andrew, Michael Blanco, Thomas Lübberstedt, Axel Schechert, Adam Vanous, and Candice Gardner. "Weighing in on a method to discriminate maize haploid from hybrid seed." Plant Breeding 134, no. 3 (2015): 283-285.</p

Data from: Comparison of biometrical models for joint linkage association mapping

Joint linkage association mapping (JLAM) combines the advantages of linkage mapping and association mapping, and is a powerful tool to dissect the genetic architecture of complex traits. The main goal of this study was to use a cross-validation strategy, resample model averaging and empirical data analyses to compare seven different biometrical models for JLAM with regard to the correction for population structure and the quantitative trait loci (QTL) detection power. Three linear models and four linear mixed models with different approaches to control for population stratification were evaluated. Models A, B and C were linear models with either cofactors (Model-A), or cofactors and a population effect (Model-B), or a model in which the cofactors and the single-nucleotide polymorphism effect were modeled as nested within population (Model-C). The mixed models, D, E, F and G, included a random population effect (Model-D), or a random population effect with defined variance structure (Model-E), a kinship matrix defining the degree of relatedness among the genotypes (Model-F), or a kinship matrix and principal coordinates (Model-G). The tested models were conceptually different and were also found to differ in terms of power to detect QTL. Model-B with the cofactors and a population effect, effectively controlled population structure and possessed a high predictive power. The varying allele substitution effects in different populations suggest as a promising strategy for JLAM to use Model-B for the detection of QTL and then to estimate their effects by applying Model-C

Genomic characterization of a nematode tolerance locus in sugar beet

Sielemann K, Pucker B, Orsini E, et al. Genomic characterization of a nematode tolerance locus in sugar beet. bioRxiv. 2023.Infection by beet cyst nematodes (BCN, Heterodera schachtii) causes a serious disease of sugar beet, and climatic change is expected to improve the conditions for BCN infection. Yield and yield stability under adverse conditions are among the main breeding objectives. Breeding of BCN tolerant sugar beet cultivars offering high yield in the presence of the pathogen is therefore of high relevance. To identify causal genes providing tolerance against BCN infection, we combined several experimental and bioinformatic approaches. Relevant genomic regions were detected through mapping-by-sequencing using a segregating F2 population. DNA sequencing of contrasting F2 pools and analyses of allele frequencies for variant positions identified a single genomic region which confers nematode tolerance. The genomic interval was confirmed and narrowed down by genotyping with newly developed molecular markers. To pinpoint the causal genes within the nematode tolerance locus, we generated long read-based genome sequence assemblies of the tolerant parental breeding line Strube U2Bv and the susceptible reference line 2320Bv. We analyzed continuous sequences of the locus with regard to functional gene annotation and differential gene expression upon BCN infection. A cluster of genes with similarity to the Arabidopsis thaliana gene encoding nodule inception protein-like protein 7 (NLP7) was identified. Gene expression analyses confirmed transcriptional activity and revealed clear differences between susceptible and tolerant genotypes. Our findings provide new insights into the genomic basis of plant-nematode interactions that can be used to design and accelerate novel management strategies against BCN

Genome assembly, structural and functional annotation, and mRNA coverage/length files for KWS2320ONT_v1.0 and Strube U2BvONT_v1.0

Sielemann K, Pucker B, Orsini E, et al. Genome assembly, structural and functional annotation, and mRNA coverage/length files for KWS2320ONT_v1.0 and Strube U2BvONT_v1.0. Bielefeld University; 2023.For the project 'Genomic characterization of a nematode tolerance locus in sugar beet' two long read-based genome assemblies were generated and annotated (KWS2320ONT_v1.0 and Strube U2BvONT_v1.0). This data publication contains the genome assembly sequences, structural and functional annotation files, as well as mRNA coverage/length files

Genomic characterization of a nematode tolerance locus in sugar beet

Abstract Background Infection by beet cyst nematodes (BCN, Heterodera schachtii) causes a serious disease of sugar beet, and climatic change is expected to improve the conditions for BCN infection. Yield and yield stability under adverse conditions are among the main breeding objectives. Breeding of BCN tolerant sugar beet cultivars offering high yield in the presence of the pathogen is therefore of high relevance. Results To identify causal genes providing tolerance against BCN infection, we combined several experimental and bioinformatic approaches. Relevant genomic regions were detected through mapping-by-sequencing using a segregating F2 population. DNA sequencing of contrasting F2 pools and analyses of allele frequencies for variant positions identified a single genomic region which confers nematode tolerance. The genomic interval was confirmed and narrowed down by genotyping with newly developed molecular markers. To pinpoint the causal genes within the potential nematode tolerance locus, we generated long read-based genome sequence assemblies of the tolerant parental breeding line Strube U2Bv and the susceptible reference line 2320Bv. We analyzed continuous sequences of the potential locus with regard to functional gene annotation and differential gene expression upon BCN infection. A cluster of genes with similarity to the Arabidopsis thaliana gene encoding nodule inception protein-like protein 7 (NLP7) was identified. Gene expression analyses confirmed transcriptional activity and revealed clear differences between susceptible and tolerant genotypes. Conclusions Our findings provide new insights into the genomic basis of plant-nematode interactions that can be used to design and accelerate novel management strategies against BCN

Wuerschum_et_al_2011_rawData

Marker names with genetic map positions, P values for all markers for the seven tested biometrical models, shown for all six traits