QTL detection for bread wheat processing quality in a nested association mapping population of semi-wild and domesticated wheat varieties

Abstract

Background Wheat processing quality is an important factor in evaluating overall wheat quality, and dough characteristics are important when assessing the processing quality of wheat. As a notable germplasm resource, semi-wild wheat has a key role in the study of wheat processing quality. Results In this study, four dough rheological characteristics were collected in four environments using a nested association mapping (NAM) population consisting of semi-wild and domesticated wheat varieties to identify quantitative trait loci (QTL) for wheat processing quality. A total of 49 QTL for wheat processing quality were detected, explaining 0.36–10.82% of the phenotypic variation. These QTL were located on all wheat chromosomes except for 2D, 3A, 3D, 6B, 6D and 7D. Compared to previous studies, 29 QTL were newly identified. Four novel QTL, QMlPH-1B.4, QMlPH-3B.4, QWdEm-1B.2 and QWdEm-3B.2, were stably identified in three or more environments, among which QMlPH-3B.4 was a major QTL. Moreover, eight important genetic regions for wheat processing quality were identified on chromosomes 1B, 3B and 4D, which showed pleiotropy for dough characteristics. In addition, out of 49 QTL, 15 favorable alleles came from three semi-wild parents, suggesting that the QTL alleles provided by the semi-wild parent were not utilized in domesticated varieties. Conclusions The results show that semi-wild wheat varieties can enrich the existing wheat gene pool and provide broader variation resources for wheat genetic research.

Statistical models and tools for QTL mapping in multi-parent populations

“…MPPs, as an intermediate between BPPs and diversity panels, increase genetic diversity by using multiple parents and balancing population structure through experimental crossing schemes (Scott et al 2020;Arrones et al 2020). Genetic studies using MPPs have recently been applied to a wide range of crops and vegetables, such as rice (Kitony et al 2021;Zheng et al 2022;Liang et al 2022), maize (Swarts et al 2021;Odell et al 2022;Michel et al 2022), potato (Amadeu et al 2021), wheat (Rollar et al 2021a;Hu et al 2022), barley (Dang et al 2020;Hautsalo et al 2021;Grieco et al 2022), cowpea (Ravelombola et al 2021(Ravelombola et al , 2022, chickpea (Thudi et al 2014), tomato (Diouf et al 2018;Campanelli et al 2019), eggplant (Gramazio et al 2019).…”

“…To the set of tools shown in Table 1, we add in Chapter 3 the R package statgenMPP that integrates IBD calculation for genetic predictors by a Hidden Markov Model with mixed model approaches for QTL mapping for a wide range of MPP designs. (Verbyla et al 2014a, b) IBD MAGIC (Verbyla et al 2014a, b) WGNAM (Paccapelo et al 2022) IBD NAM (Paccapelo et al 2022) GAPIT (Lipka et al 2012) IBS MAGIC (Islam et al 2016;Abdelraheem et al 2021); NAM (Altendorf et al 2021;Sandhu et al 2021) HAPPY (Mott et al 2000) IBD MAGIC (Kover et al 2009) mpMap (Huang and George 2011) IBD MAGIC (Rollar et al 2021b;Burgos et al 2021) GAPL (Zhang et al 2019) IBD MAGIC (Diaz et al 2021) IciMapping (Meng et al 2015) IBS NAM (Zhao et al 2022;Hu et al 2022) FarmCPU (Liu et al 2016) IBD MAGIC (Satturu et al 2020;Hautsalo et al 2021) BLINK (Huang et al 2019) IBS MAGIC (Hautsalo et al 2021…”