Institut für Pflanzenzüchtung, Saatgutforschung und Populationsgenetik

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/13

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DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics
(2021) Kienbaum, Lydia; Correa Abondano, Miguel; Blas, Raul; Schmid, Karl
Background: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy (r = 0.99). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.
Genetic architecture underlying the expression of eight α-amylase trypsin inhibitors
(2021) El Hassouni, Khaoula; Sielaff, Malte; Curella, Valentina; Neerukonda, Manjusha; Leiser, Willmar; Würschum, Tobias; Schuppan, Detlef; Tenzer, Stefan; Longin, C. Friedrich H.
Amylase trypsin inhibitors (ATIs) are important allergens in baker’s asthma and suspected triggers of non-celiac wheat sensitivity (NCWS) inducing intestinal and extra-intestinal inflammation. As studies on the expression and genetic architecture of ATI proteins in wheat are lacking, we evaluated 149 European old and modern bread wheat cultivars grown at three different field locations for their content of eight ATI proteins. Large differences in the content and composition of ATIs in the different cultivars were identified ranging from 3.76 pmol for ATI CM2 to 80.4 pmol for ATI 0.19, with up to 2.5-fold variation in CM-type and up to sixfold variation in mono/dimeric ATIs. Generally, heritability estimates were low except for ATI 0.28 and ATI CM2. ATI protein content showed a low correlation with quality traits commonly analyzed in wheat breeding. Similarly, no trends were found regarding ATI content in wheat cultivars originating from numerous countries and decades of breeding history. Genome-wide association mapping revealed a complex genetic architecture built of many small, few medium and two major quantitative trait loci (QTL). The major QTL were located on chromosomes 3B for ATI 0.19-like and 6B for ATI 0.28, explaining 70.6 and 68.7% of the genotypic variance, respectively. Within close physical proximity to the medium and major QTL, we identified eight potential candidate genes on the wheat reference genome encoding structurally related lipid transfer proteins. Consequently, selection and breeding of wheat cultivars with low ATI protein amounts appear difficult requiring other strategies to reduce ATI content in wheat products.
Hybrid transcriptome sequencing approach improved assembly and gene annotation in Cynara cardunculus (L.)
(2020) Puglia, Giuseppe D.; Prjibelski, Andrey D.; Vitale, Domenico; Bushmanova, Elena; Schmid, Karl J.; Raccuia, Salvatore A.
Background: The investigation of transcriptome profiles using short reads in non-model organisms, which lack of well-annotated genomes, is limited by partial gene reconstruction and isoform detection. In contrast, long-reads sequencing techniques revealed their potential to generate complete transcript assemblies even when a reference genome is lacking. Cynara cardunculus var. altilis (DC) (cultivated cardoon) is a perennial hardy crop adapted to dry environments with many industrial and nutraceutical applications due to the richness of secondary metabolites mostly produced in flower heads. The investigation of this species benefited from the recent release of a draft genome, but the transcriptome profile during the capitula formation still remains unexplored. In the present study we show a transcriptome analysis of vegetative and inflorescence organs of cultivated cardoon through a novel hybrid RNA-seq assembly approach utilizing both long and short RNA-seq reads. Results: The inclusion of a single Nanopore flow-cell output in a hybrid sequencing approach determined an increase of 15% complete assembled genes and 18% transcript isoforms respect to short reads alone. Among 25,463 assembled unigenes, we identified 578 new genes and updated 13,039 gene models, 11,169 of which were alternatively spliced isoforms. During capitulum development, 3424 genes were differentially expressed and approximately two-thirds were identified as transcription factors including bHLH, MYB, NAC, C2H2 and MADS-box which were highly expressed especially after capitulum opening. We also show the expression dynamics of key genes involved in the production of valuable secondary metabolites of which capitulum is rich such as phenylpropanoids, flavonoids and sesquiterpene lactones. Most of their biosynthetic genes were strongly transcribed in the flower heads with alternative isoforms exhibiting differentially expression levels across the tissues. Conclusions: This novel hybrid sequencing approach allowed to improve the transcriptome assembly, to update more than half of annotated genes and to identify many novel genes and different alternatively spliced isoforms. This study provides new insights on the flowering cycle in an Asteraceae plant, a valuable resource for plant biology and breeding in Cynara and an effective method for improving gene annotation.
Optimum breeding strategies using genomic and phenotypic selection for the simultaneous improvement of two traits
(2021) Marulanda, Jose J.; Mi, Xuefei; Utz, H. Friedrich; Melchinger, Albrecht E.; Würschum, Tobias; Longin, C. Friedrich H.
Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package “selectiongain” from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔGa) for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategy GSrapid (one-stage GS followed by one-stage phenotypic selection) achieved higher ΔGa than classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith–Hazel or base index delivered higher ΔGa for net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lower ΔGa in net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package “selectiongain” with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.

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