Linking genetic maps and simulation to optimize breeding for wheat flowering time in current and future climates. Issue 2 (30th April 2020)

• Record Type: Journal Article
• Authors: Bogard, Matthieu; Biddulph, Ben; Zheng, Bangyou; Hayden, Matthew; Kuchel, Haydn; Mullan, Dan; Allard, Vincent; Gouis, Jacques Le; Chapman, Scott C.
• Is Part Of: Crop science. Volume 60:Issue 2(2020)
• Page Start: 678
• Page End: 699
• Publication Date: 2020-04-30
• Subjects: Crop science -- Periodicals; Cultures -- Périodiques; Cultures de plein champ -- Périodiques; Crop science; Nutzpflanzen; Zeitschrift; Pflanzenbau; Periodicals; 633
• DOI: 10.1002/csc2.20113
• Languages: English
• ISSNs: 0011-183X

Abstract

Abstract: In Australian wheat ( Triticum aestivum L.) production, optimizing wheat phenology is essential for yield potential and to avoid stress, especially around flowering. Breeding could be accelerated by identifying key loci and developing models to predict genotype flowering times under different pedoclimatic scenarios. Here, association genetics for heading date, earliness components (photoperiod sensitivity [PS]; vernalization requirement [VR]; earliness per se [EPS]) and simulation model (APSIM) phenology parameters from a panel of Australian cultivars and breeding lines identified loci with stable, repeatable effects. Major chromosomal regions with stable effects included the Ppd‐D1 region on chromosome 2D for PS and EPS, one region on 5B for PS, one region on 6B for EPS, and the Vrn‐A1 region on 5A for VR. Regions with stable, smaller effects were detected on 1A and 2D for PS, on 5A and 6B for EPS, and on 1A and 5D for VR. Other regions with stable effects on heading date and earliness components were located on 1A, 2B, 4B, 5B, 6B and 7B (PS and EPS), 2A, 3A and 7A (EPS and VR). Quantitative trait loci (QTL)–based model parameters were used to simulate heading dates across the Australian wheat belt for set of independent genotypes. Comparisons of average observed and predicted heading dates for four main regions of the Australian wheat belt showed good performance in prediction of independent lines from QTL information alone ( r 2 = .61–.83). The model allows testing of putative genotypes under various pedoclimatic scenarios including for adaptation to anticipated climate changes.