Durum wheat (Triticum turgidum subsp. durum) is a key crop worldwide, and yet, its improvement and adaptation to emerging environmental threats is made difficult by the limited amount of allelic variation included in its elite pool. New allelic diversity may provide novel loci to international crop breeding through quantitative trait loci (QTL) mapping in unexplored material. Here, we report the extensive molecular and phenotypic characterization of hundreds of Ethiopian durum wheat landraces and several Ethiopian improved lines. We test 81 587 markers scoring 30 155 single nucleotide polymorphisms and use them to survey the diversity, structure, and genome-specific variation in the panel. We show the uniqueness of Ethiopian germplasm using a siding collection of Mediterranean durum wheat accessions. We phenotype the Ethiopian panel for ten agronomic traits in two highly diversified Ethiopian environments for two consecutive years and use this information to conduct a genome-wide association study. We identify several loci underpinning agronomic traits of interest, both confirming loci already reported and describing new promising genomic regions. These loci may be efficiently targeted with molecular markers already available to conduct marker-assisted selection in Ethiopian and international wheat. We show that Ethiopian durum wheat represents an important and mostly unexplored source of durum wheat diversity. The panel analysed in this study allows the accumulation of QTL mapping experiments, providing the initial step for a quantitative, methodical exploitation of untapped diversity in producing a better wheat.
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http://dx.doi.org/10.1111/pbi.12538 | DOI Listing |
J Plant Physiol
December 2024
Waite Research Institute, School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, Urrbrae, SA 5064, Australia; Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University-University of Adelaide Joint Centre for Agriculture and Health, State Key Laboratory of Hybrid Rice, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Krishi Vigyan Kendra, Siwan, Dr. RPCAU, Pusa, Bihar, India.
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Sub Campus T.T Singh, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan.
The current study was designed to evaluate the effect of particle size (PS) and inclusion level of wheat straw (WS) obtained from genetically improved wheat on the performance and feeding behavior of Sahiwal cows. Twelve multiparous, mid-lactating Sahiwal cows (DIM 135 ± 25, mean ± SD; 12.8 ± 1.
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State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road, Beijing, 100193, China.
Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches, particularly kernel-based methods, offer promising solutions to overcome these limitations. In this study, we developed a novel machine learning method, KPRR, which integrated a polynomial kernel into ridge regression to effectively capture nonadditive genetic effects.
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