Naslov
Potential of different genomic selection models for predicting wheat dough rheological properties

Autori
Plavšin, Ivana ; Gunjača, Jerko ; Dvojković, Krešimir ; Novoselović, Dario

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Book of Abstracts of 58th Croatian & 18th International Symposium on Agriculture / Carović-Stanko, Klaudija ; Širić, Ivan - Zagreb : Agronomski fakultet Sveučilišta u Zagrebu, 2023, 76-76

Skup
58th Croatian & 18th International Symposium on Agriculture

Mjesto i datum
Dubrovnik, Hrvatska, 11.02.2023. - 17.02.2023

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
wheat ; dough quality ; mixograph ; genomic selection ; prediction models

Sažetak
When breeding for wheat quality traditional methods can be time-consuming and imprecise since the majority of quality traits exhibit complex inheritance and interaction patterns. Therefore, methods based on molecular markers, such as genomic selection, are getting increasingly used in wheat breeding. We examined the potential of five different genomic selection models for predicting five wheat dough quality traits – wet gluten content (WGC) and mixograph traits: optimal dough development time (MPT), dough consistency and stability at the end of mixing (MTW), energy used during the mixing (MTI), and dough strength (MPH). The population comprised of 153 recombinant inbred lines and the experiment was carried out for three years at two locations in Croatia. Genomic predictions were done on the basis of 2231 SNP markers. The highest mean prediction accuracies (rPA) were observed for MPT (0.48– 0.57), followed by MTW (0.48–0.54), WGC (0.32– 0.47), and MPH (0.29–0.42). The lowest rPA were obtained for MTI suggesting that this trait is not an ideal trait for genomic selection. The highest mean prediction accuracies for MPH and MTI were obtained using BayesLASSO model ; for MTW and MPT using Random Forest model ; and for WGC when using the Reproducing Kernels Hilbert Spaces model. All traits had high broad-sense heritabilities (H2 ≥ 0.72). The results showed that genomic selection may be an effective tool for predicting dough quality traits and selecting the best performing lines.

Izvorni jezik
Engleski

Znanstvena područja
Poljoprivreda (agronomija)

POVEZANOST RADA