Publikationen im Open Access gefördert durch die UB

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Auflistung Publikationen im Open Access gefördert durch die UB nach Autor:in "Abbadi, Amine"

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    Chromosome-Scale Assembly of Winter Oilseed Rape Brassica napus
    (2020) Lee, HueyTyng; Chawla, Harmeet Singh; Obermeier, Christian; Dreyer, Felix; Abbadi, Amine; Snowdon, Rod
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    haploMAGIC: accurate phasing and detection of recombination in multiparental populations despite genotyping errors
    (2024) Montero-Tena, Jose A; Abdollahi Sisi, Nayyer; Kox, Tobias; Abbadi, Amine; Snowdon, Rod J; Golicz, Agnieszka A
    Recombination is a key mechanism in breeding for promoting genetic variability. Multiparental populations (MPPs) constitute an excellent platform for precise genotype phasing, identification of genome-wide crossovers (COs), estimation of recombination frequencies, and construction of recombination maps. Here, we introduce haploMAGIC, a pipeline to detect COs in MPPs with single-nucleotide polymorphism (SNP) data by exploiting the pedigree relationships for accurate genotype phasing and inference of grandparental haplotypes. haploMAGIC applies filtering to prevent false-positive COs due to genotyping errors (GEs), a common problem in high-throughput SNP analysis of complex plant genomes. Hence, it discards haploblocks not reaching a specified minimum number of informative alleles. A performance analysis using populations simulated with AlphaSimR revealed that haploMAGIC improves upon existing methods of CO detection in terms of recall and precision, most notably when GE rates are high. Furthermore, we constructed recombination maps using haploMAGIC with high-resolution genotype data from 2 large multiparental populations of winter rapeseed (Brassica napus). The results demonstrate the applicability of the pipeline in real-world scenarios and showed good correlations in recombination frequency compared with alternative software. Therefore, we propose haploMAGIC as an accurate tool at CO detection with MPPs that shows robustness against GEs.
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    How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding
    (2020) Werner, Christian R.; Gaynor, R. Chris; Gorjanc, Gregor; Hickey, John M.; Kox, Tobias; Abbadi, Amine; Leckband, Gunhild; Snowdon, Rod; Stahl, Andreas
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    Phenomic Selection for Hybrid Rapeseed Breeding
    (2024) Roscher-Ehrig, Lennard; Weber, Sven E.; Abbadi, Amine; Malenica, Milka; Abel, Stefan; Hemker, Reinhard; Snowdon, Rod J.; Wittkop, Benjamin; Stahl, Andreas
    Phenomic selection is a recent approach suggested as a low-cost, high-throughput alternative to genomic selection. Instead of using genetic markers, it employs spectral data to predict complex traits using equivalent statistical models. Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted. However, for hybrid breeding, the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation. Here, we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding. We performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models. We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height, even when spectral data was collected at single locations, while being less affected by population structure. Furthermore, we demonstrate that phenomic prediction across generations is feasible, and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection. We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.
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    Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP
    (2023) Heilmann, Philipp Georg; Frisch, Matthias; Abbadi, Amine; Kox, Tobias; Herzog, Eva
    Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high.