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Finding invisible quantitative trait loci with missing data

Gabur, Iulian ; Chawla, Harmeet S. ; Liu, Xiwei ; Kumar, Vinod ; Faure, Sebastien ; von Tiedemann, Andreas ; Jestin, Christophe ; Dryzska, Emmanuelle ; Volkmann, Susann ; Breuer, Frank ; Delourme, Regine ; Snowdon, Rod ; Obermeier, Christian

Originalveröffentlichung: (2018) Plant Biotechnology Journal 16(12):2102-2112 doi: 10.1111/pbi.12942
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URN: urn:nbn:de:hebis:26-opus-145796

Freie Schlagwörter (Englisch): Brassica napus , quantitative resistance , presenceabsence variation , single nucleotide absence polymorphism , SNaP
Sammlung: Open Access - Publikationsfonds
Universität Justus-Liebig-Universit√§t Gie√üen
Institut: Department of Plant Breeding
Fachgebiet: Agrarwissenschaften und Umweltmanagement
DDC-Sachgruppe: Landwirtschaft
Dokumentart: Aufsatz
Sprache: Englisch
Erstellungsjahr: 2018
Publikationsdatum: 20.05.2019
Kurzfassung auf Englisch: Summary Evolutionary processes during plant polyploidization and speciation have led to extensive presence-absence variation (PAV) in crop genomes, and there is increasing evidence that PAV associates with important traits. Today, high-resolution genetic analysis in major crops frequently implements simple, cost-effective, high-throughput genotyping from single nucleotide polymorphism (SNP) hybridization arrays; however, these are normally not designed to distinguish PAV from failed SNP calls caused by hybridization artefacts. Here, we describe a strategy to recover valuable information from single nucleotide absence polymorphisms (SNaPs) by population-based quality filtering of SNP hybridization data to distinguish patterns associated with genuine deletions from those caused by technical failures. We reveal that including SNaPs in genetic analyses elucidate segregation of small to large-scale structural variants in nested association mapping populations of oilseed rape (Brassica napus), a recent polyploid crop with widespread structural variation. Including SNaP markers in genomewide association studies identified numerous quantitative trait loci, invisible using SNP markers alone, for resistance to two major fungal diseases of oilseed rape, Sclerotinia stem rot and blackleg disease. Our results indicate that PAV has a strong influence on quantitative disease resistance in B. napus and that SNaP analysis using cost-effective SNP array data can provide extensive added value from missing data. This strategy might also be applicable for improving the precision of genetic mapping in many important crop species.
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