Predicting Flowering Behavior and Exploring Its Genetic Determinism in an Apple Multi-family Population Based on Statistical Indices and Simplified Phenotyping

Abstract

Irregular flowering over years is commonly observed in fruit trees. The early prediction of tree behavior is highly desirable in breeding programmes. This study aims at performing such predictions, combining simplified phenotyping and statistics methods. Sequences of vegetative vs. floral annual shoots (AS) were observed along axes in trees belonging to five apple related full-sib families. Sequences were analyzed using Markovian and linear mixed models including year and site effects. Indices of flowering irregularity, periodicity and synchronicity were estimated, at tree and axis scales. They were used to predict tree behavior and detect QTL with a Bayesian pedigree-based analysis, using an integrated genetic map containing 6,849 SNPs. The combination of a Biennial Bearing Index (BBI) with an autoregressive coefficient (γg) efficiently predicted and classified the genotype behaviors, despite few misclassifications. Four QTLs common to BBIs and γg and one for synchronicity were highlighted and revealed the complex genetic architecture of the traits. Irregularity resulted from high AS synchronism, whereas regularity resulted from either asynchronous locally alternating or continual regular AS flowering. A relevant and time-saving method, based on a posteriori sampling of axes and statistical indices is proposed, which is efficient to evaluate the tree breeding values for flowering regularity and could be transferred to other species.

Genetic determinism of flowering regularity over years in an apple multi-family population

“…According to BIC values, the best trended model included all fixed (site, memory, year) and the random replication effects (data not shown; see Durand et al, 2017). The trees in Montpellier had lower flowering probability than those in Angers.…”

“…This study revealed a complex genetic architecture of flowering habit in apple. The overview of all loci involved in trait variation led us to assess promising individuals and progenitors (Durand et al, 2017). The pedigree-based approach used herein appears particularly relevant for plant species in which varieties are tightly related to each other, which is the case in most crops.…”

“…They are based on counts of flowering AS at axis and whole-tree scales, whenever possible. The description of the trend and autoregressive models, BBIderived indices and the statistical methodology for classification of genotype habit can be found in Durand et al (2013 and2017). The indices at axis scale, denoted by BBI_res_norm_ax, and  ax , were computed as those defined at tree scale but using the total yearly counts of flowering AS in axes sampled within each tree replicate.…”

Block Level Optimization Based Mapping and Ensemble Classification Based Segmentation for Fruit Flower Detection

“…The experimental results show that the better yield prediction can be done using Cr channel of YCbCr color model. Durand et al [10] incorporated the effect of site and time in yield prediction by using linear and Markovian mixed models. The periodicity, synchronicity and flowering unevenness are measured using tree and axis scale.…”

Two QTL characterized for soft scald and soggy breakdown in apple (Malus × domestica) through pedigree-based analysis of a large population of interconnected families

“…PBA has been made a more attractive approach in apple through guidelines on the composition of the study germplasm (Peace et al 2014), high throughput genomewide genotyping capabilities through SNP arrays (Chagné et al 2012;Bianco et al 2014Bianco et al , 2016, the availability of sets of pedigreed full-sib families (Peace et al 2014), and standardized phenotyping procedures for some major traits (Evans et al 2011a). This approach has been increasingly utilized in a variety of Rosaceous fruit species, including strawberry (Whitaker 2011;Roach et al 2016;Mangandi et al 2017), peach (Fresnedo-Ramírez et al 2015Mora et al 2017), cherry (Rosyara et al 2013;Stegmeir et al 2014;Sandefur et al 2016), and apple (Schmitz et al 2013;Bink et al 2014;Guan et al 2015, Allard et al 2016Durand et al 2017).…”