Browsing by Person "Schmid, Karl"

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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.
Exploring adaptive genetic variation in exotic barley germplasm with landscape genomics
(2025) Chang, Che-Wei; Schmid, Karl
Understanding genetic variation underlying local adaptation is essential for improving crop resilience to address challenges posed by climate change. Barley (Hordeum vulgare L. ssp. vulgare), one of the most important crops, is suitable for studying local adaptation due to its remarkable adaptability. This PhD dissertation investigated adaptive genetic variation in exotic barley germplasm, including wild barley (Hordeum vulgare ssp. spontaneum) and barley landraces, from diverse environments and explored strategies to improve the use of genebank accessions for harnessing valuable genetic variants. In the first study, local adaptation in wild barley populations from the Southern Lev- ant was explored using landscape genomics approaches, combining genomic data with the climatic and soil properties of geographical origins. Through redundancy analysis (RDA), we found spatial autocorrelation explained 45% of genomic variation, and environmental factors accounted for 15%. Adaptive signatures were identified in the pericentromeric regions by the population-genetics-based scans and genome- environment association (GEA) scans, but they mostly disappeared when the population structure was considered. Our findings overall highlighted the role of nonselective forces in shaping the genetic variation of wild barley even in divergent environments. The second study addressed challenges in passport data quality control for large- scale samples, such as germplasm collections in genebanks. The R package GGoutlieR was developed in this work to tackle the shortcomings of traditional manual data cleaning. It efficiently detects and visualizes samples with unusual geo-genetic patterns by characterizing geography-genotype associations with distance-based statis- tics via K-nearest neighbors and calculating empirical p-values accordingly. By stream- lining data cleaning and quality control, GGoutlieR supports more reliable landscape genomics studies, which is crucial for studying loci involved in local adaptation. The third study explored the use of neural networks to predict geographical origins for genebank accessions lacking passport data, enabling their integration into genome- environment association (GEA) analyses. Neural network models demonstrated high prediction accuracy in cross-validation. Incorporating imputed environmental data (N = 11,032) into GEA, using barley flowering time genes as benchmarks, revealed complementary detection of genomic regions near flowering time genes compared to regular GEA (N = 1,626). Furthermore, simulations of polygenic local adaptation in selfing species showed that GEA power is insensitive to large sample sizes. These findings suggest that GEA with imputed environmental data can be a complementary approach for uncovering novel adaptive loci that might remain undetected in conventional GEA, rather than improving the statistical power of GEA. Overall, this dissertation contributes to understanding the adaptive genetic variation in barley and expanding methodologies in landscape genomics, providing a direction for the future development of GEA approaches to better support allele mining for prebreeding to enhance crop resilience.
Genetic variation for tolerance to the downy mildew pathogen Peronospora variabilis in genetic resources of quinoa (Chenopodium quinoa)
(2021) Colque-Little, Carla; Abondano, Miguel Correa; Lund, Ole Søgaard; Amby, Daniel Buchvaldt; Piepho, Hans-Peter; Andreasen, Christian; Schmöckel, Sandra; Schmid, Karl
Background: Quinoa (Chenopodium quinoa Willd.) is an ancient grain crop that is tolerant to abiotic stress and has favorable nutritional properties. Downy mildew is the main disease of quinoa and is caused by infections of the biotrophic oomycete Peronospora variabilis Gaüm. Since the disease causes major yield losses, identifying sources of downy mildew tolerance in genetic resources and understanding its genetic basis are important goals in quinoa breeding. Results: We infected 132 South American genotypes, three Danish cultivars and the weedy relative C. album with a single isolate of P. variabilis under greenhouse conditions and observed a large variation in disease traits like severity of infection, which ranged from 5 to 83%. Linear mixed models revealed a significant effect of genotypes on disease traits with high heritabilities (0.72 to 0.81). Factors like altitude at site of origin or seed saponin content did not correlate with mildew tolerance, but stomatal width was weakly correlated with severity of infection. Despite the strong genotypic effects on mildew tolerance, genome-wide association mapping with 88 genotypes failed to identify significant marker-trait associations indicating a polygenic architecture of mildew tolerance. Conclusions: The strong genetic effects on mildew tolerance allow to identify genetic resources, which are valuable sources of resistance in future quinoa breeding.
High-density mapping of quantitative trait loci controlling agronomically important traits in quinoa (Chenopodium quinoa Willd.)
(2022) Maldonado-Taipe, Nathaly; Barbier, Federico; Schmid, Karl; Jung, Christian; Emrani, Nazgol
Quinoa is a pseudocereal originating from the Andean regions. Despite quinoa’s long cultivation history, genetic analysis of this crop is still in its infancy. We aimed to localize quantitative trait loci (QTL) contributing to the phenotypic variation of agronomically important traits. We crossed the Chilean accession PI-614889 and the Peruvian accession CHEN-109, which depicted significant differences in days to flowering, days to maturity, plant height, panicle length, and thousand kernel weight (TKW), saponin content, and mildew susceptibility. We observed sizeable phenotypic variation across F2 plants and F3 families grown in the greenhouse and the field, respectively. We used Skim-seq to genotype the F2 population and constructed a high-density genetic map with 133,923 single nucleotide polymorphism (SNPs). Fifteen QTL were found for ten traits. Two significant QTL, common in F2 and F3 generations, depicted pleiotropy for days to flowering, plant height, and TKW. The pleiotropic QTL harbored several putative candidate genes involved in photoperiod response and flowering time regulation. This study presents the first high-density genetic map of quinoa that incorporates QTL for several important agronomical traits. The pleiotropic loci can facilitate marker-assisted selection in quinoa breeding programs.