Aurélien Beugnot, Tristan Mary-Huard, Cyril Bauland, Valerie Combes, Delphine Madur, Bernard Lagardère, Carine Palaffre, Alain Charcosset, Laurence Moreau & Julie B. Fievet

Theoretical and Applied Genetics; 10 October 2023; vol.136; Article no. 219

Key message

An original GWAS model integrating the ancestry of alleles was proposed and allowed the detection of background specific additive and dominance QTLs involved in heterotic group complementarity and hybrid performance.

Abstract

Maize genetic diversity is structured into genetic groups selected and improved relative to each other. This process increases group complementarity and differentiation over time and ensures that the hybrids produced from inter-group crosses exhibit high performances and heterosis. To identify loci involved in hybrid performance and heterotic group complementarity, we introduced an original association study model that disentangles allelic effects from the heterotic group origin of the alleles and compared it with a conventional additive/dominance model. This new model was applied on a factorial between Dent and Flint lines and a diallel between Dent-Flint admixed lines with two different layers of analysis: within each environment and in a multiple-environment context. We identified several strong additive QTLs for all traits, including some well-known additive QTLs for flowering time (in the region of Vgt1/2 on chromosome 8). Yield trait displayed significant non-additive effects in the diallel panel. Most of the detected Yield QTLs exhibited overdominance or, more likely, pseudo-overdominance effects. Apparent overdominance at these QTLs contributed to a part of the genetic group complementarity. The comparison between environments revealed a higher stability of additive QTL effects than non-additive ones. Several QTLs showed variations of effects according to the local heterotic group origin. We also revealed large chromosomic regions that display genetic group origin effects. Altogether, our results illustrate how admixed panels combined with dedicated GWAS modeling allow the identification of new QTLs that could not be revealed by a classical hybrid panel analyzed with traditional modeling.

See https://link.springer.com/article/10.1007/s00122-023-04431-w

Fig.3: Generalization of the locus biological effects of the Fisher’s model (Falconer et al. (1996) representation) in the case of structured populations. a Genetic value description using an SNP-based reference model. b Adding genetic background information in the SNP-based model. c Decomposition of the SNP-origin model according to the different genetic backgrounds

Fig.5: Manhattan plots of additive (blue, left graphs) and dominance (green, right graphs) P values for the Gad-MONO and Gso-MONO models in SOU17 for FloF in Het2. Origin tests were added as elements of comparison in red (on the top left graph) and purple (on the top right graph). Vertical black lines and the letters A to F indicate the position of the representative markers of the Gso model effects, detailed in Fig. 6 (color figure online)