An analysis of simulated yield data for pepper shows how genotype × environment interaction in yield can be understood in terms of yield components and their QTLs. Issue 3 (14th March 2021)

• Record Type: Journal Article
• Authors: Rodrigues, Paulo C.; Heuvelink, Ep; Marcelis, Leo F. M.; Chapman, Scott C.; van Eeuwijk, Fred A.
• Is Part Of: Crop science. Volume 61:Issue 3(2021)
• Page Start: 1826
• Page End: 1842
• Publication Date: 2021-03-14
• Subjects: Crop science -- Periodicals; Cultures -- Périodiques; Cultures de plein champ -- Périodiques; Crop science; Nutzpflanzen; Zeitschrift; Pflanzenbau; Periodicals; 633
• DOI: 10.1002/csc2.20476
• Languages: English
• ISSNs: 0011-183X

Abstract

Abstract: Complex traits like yield are those in which phenotypic variation can be modeled as a linear function of a set of quantitative trait loci (QTLs) with environment dependency. This environment dependency can be observed at a phenotypic level as genotype × environment interaction (GEI) for yield itself and at an underlying genetic level as QTL × environment interaction (QEI). We show how GEI in yield may follow from pleiotropic QTLs for yield components that themselves are not environment dependent. We generated synthetic yield data via a crop growth model and analyzed these data by common statistical models for GEI and QEI. The QTLs for yield were pleiotropic with those for yield components. Such pleiotropy offers a path for improvement of yield under GEI. As a model system we used sweet pepper ( Capsicum annuum L.) and developed an eco‐physiological model for yield with seven genotype‐specific inputs or yield components. Synthetic yields were simulated for a back cross population of 500 lines across a factorial combination of four major environmental drivers. The yield components were given a simple QTL basis and produced credible amounts and patterns of GEI for yield. The QEI for yield could be interpreted from the expression of QTLs for yield components and the interaction of these components with the environmental drivers. We see the generation of synthetic yield data via crop growth models followed by an analysis with statistical models for GEI and QEI to quantify the contribution of yield components to GEI as a helpful step in the development of yield prediction models for complex traits across environments that can also serve as a basis for decisions on selection strategies of complex traits.