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. 2020 Feb 21;9(2):282.
doi: 10.3390/plants9020282.

Exploring Heterosis in Melon (Cucumis melo L.)

Marco Napolitano  1 , Niccolò Terzaroli  2 , Subash Kashyap  1 , Luigi Russi  2 , Elen Jones-Evans  1 , Emidio Albertini  2
Affiliations

Exploring Heterosis in Melon (Cucumis melo L.)

Marco Napolitano et al. Plants (Basel). .

Abstract

Heterosis is the superiority of an F1 hybrid over its parents. Since this phenomenon is still unclear in melon, a half diallel experiment based on eight genetically distant breeding lines was conducted in six environments of Central Italy, assessing commercially important traits: yield, total soluble solids (TSS), and days to ripening (DTR). To estimate the additive (general combining ability; GCA) and the non-additive gene effects (specific combining ability; SCA), yield was analyzed by Griffing's methods two and four, and the results were compared to the GGE (Genotype plus Genotype by Environment interaction) biplot methodology; TSS and earliness were evaluated only by Griffing's method four. Overall, GCAs were significantly more relevant than SCAs for all examined traits. Least square means (LsM), mid-parent heterosis (MPH), best-parent heterosis (BPH), as well as Euclidean and Mahalanobis' distances were calculated and compared with the genetic distance (GD). As a few correlations were found statistically significant (only for TSS), it was difficult to predict the value of a hybrid combination only by knowing the genetic distance of its parents. Despite this, heterosis was observed, indicating either the presence of epistatic effects (additive × additive interactions) and/or an underestimate of SCAs embedded within Griffing's method. The significant Env × Entries source of variation suggests development of hybrids in specific environments. The results are discussed with a breeding perspective.

Keywords: GCA; GGE biplot; Griffing’s method; SCA; breeding; diallel; epistasis; genetic distance; heritability.

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Conflict of interest statement

Marco Napolitano, Subash Kashyap and Elen Jones-Evans are Bayer employees. The funder Bayer had no role in designing the study, in the collection, analyses, or interpretation of data and in writing the manuscript.

Figures

Figure 1
Figure 1
GGE biplot for yield per plant in every single environment. (a) Latina 2014, (b) Perugia 2014, (c) Latina 2015, (d) Perugia 2015, (e) Latina 2016, and (f) Perugia 2016. Parents used as entries/testers are indicated with black/blue numbers (1 = Vedrantais; 2 = Ita1; 3 = Ogen; 4 = Top Mark; 5 = Magyar Kincs; 6 = Hale’s Best Jumbo; 7 = PI414723; 8 = PI161375). Entries GCA effects are approximated by their projections on the average tester coordinates (ATC) abscissa indicated by the arrow. SCA effects are orthogonal to GCA; therefore, the projections of the entries onto ordinates of ATC must approximate their SCA effects with all the testers. If entries and testers are on the same side of the ATC abscissa, their interaction is positive, and it is negative otherwise.
Figure 2
Figure 2
GGE biplot (polygon view) for yield per plant in every single environment. (a) Latina 2014, (b) Perugia 2014, (c) Latina 2015, (d) Perugia 2015, (e) Latina 2016, and (f) Perugia 2016. Parents used as entries/testers are indicated with green/blue numbers (1 = Vedrantais; 2 = Ita1; 3 = Ogen; 4 = Top Mark; 5 = Magyar Kincs; 6 = Hale’s Best Jumbo; 7 = PI414723; 8 = PI161375).
See this image and copyright information in PMC

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