Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  Improvement of prediction ability by integrating multi-omic datasets in barley

Wu, P.-Y., Stich, B., Weisweiler, M., Shrestha, A., Erban, A., Westhoff, P., et al. (2022). Improvement of prediction ability by integrating multi-omic datasets in barley. BMC Genomics, 23(1): 200. doi:10.1186/s12864-022-08337-7.

Item is

Basisdaten

einblenden: ausblenden:
Genre: Zeitschriftenartikel

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Wu, Po-Ya1, Autor
Stich, Benjamin1, Autor
Weisweiler, Marius1, Autor
Shrestha, Asis1, Autor
Erban, A.2, Autor           
Westhoff, Philipp1, Autor
Inghelandt, Delphine Van1, Autor
Affiliations:
1external, ou_persistent22              
2Applied Metabolome Analysis, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753338              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2022-03-12
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1186/s12864-022-08337-7
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: BMC Genomics
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 23 (1) Artikelnummer: 200 Start- / Endseite: - Identifikator: ISBN: 1471-2164