Prediction of flowering time in Brassica oleracea using a quantitative trait loci‐based phenology model

Abstract

Uniformly developing plants with a predictable time to harvest or flowering under unfavourable climate conditions are a major breeding goal in crop species. The main flowering regulators and their response to environmental signals have been identified in Arabidopsis thaliana and homologues of flowering genes have been mapped in many crop species. However, it remains unclear which genes determine within and across genotype flowering time variability in Brassica oleracea and how genetic flowering time regulation is influenced by environmental factors. The goal of this study is model-based prediction of flowering time in a B. oleracea DH-line population using genotype-specific and quantitative trait loci (QTL) model input parameters. A QTL-based phenology model accounting for genotypic differences in temperature responses during vernalisation and non-temperature-sensitive durations from floral transition to flowering was evaluated in two field trials. The model was parameterised using original genotype-specific model input parameters and QTL effects. The genotype-specific model parameterisation showed accurate predictability of flowering time if floral induction was promoted by low temperature (R(2) = 0.81); unfavourably high temperatures reduced predictability (R(2) = 0.65). Replacing original model input parameters by QTL effects reduced the capability of the model to describe across-genotype variability (R(2) = 0.59 and 0.50). Flowering time was highly correlated with a model parameter accounting for vernalisation effects. Within-genotype variability was significantly correlated with the same parameter if temperature during the inductive phase was high. We conclude that flowering time variability across genotypes was largely due to differences in vernalisation response, although it has been shown elsewhere that the candidate FLOWERING LOCUS C (FLC) did not co-segregate with flowering time in the same population. FLC independent vernalisation pathways have been described for several species, but not yet for B. oleracea.

Post-GWAS: where next? More samples, more SNPs or more biology?

“…How should we introduce genetic variation into these models? So far, this integration has relied upon QTL-type inferences (Yin et al, 2003;Uptmoor et al, 2012). The goal was to predict environmental effects on plants using different allele combinations of relevant genes, and to better identify the genetic factors that underlie complex environmentally dependent traits (Reymond et al, 2003;Tardieu, 2003;Hammer et al, 2004;Quilot et al, 2005;Malosetti et al, 2006).…”

Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments

“…Yin et al (1999, 2000) introduced a gene-based model to predict barley flowering time, specific leaf area, and yield from detected QTLs in a bi-parental population. Similar QTL approaches have been used to model maize leaf elongation (Chenu et al , 2009), soybean phenology (Stewart et al , 2003), peach fruit quality (Quilot et al , 2005), barley phenology (Yin et al , 2005), rice phenology (Nakagawa et al , 2005), and Brassica oleracea phenology (Uptmoor et al , 2012). White and Hoogenboom (1996) and Hoogenboom and White (2003) developed a gene-based model, GeneGro, to predict phenology, growth habit, and seed size of soybean (e.g.…”

A simple approach to predict growth stages in winter wheat (Triticum aestivum L.) combining prediction of a crop model and marker based prediction of the deviation to a reference cultivar: A case study in France

“…Two different strategies to obtain parameters values for each genotype may be distinguished in these studies. The first corresponds to the measurement of model parameters for each genotype (Reymond et al, 2003;Nakagawa et al, 2005;Quilot et al, 2005;Yin et al, 2005;Malosetti et al, 2006;Uptmoor et al, 2011) and the second to the optimization of parameters values for each genotype (Messina et al, 2006;White et al, 2008;Zheng et al, 2013;Bogard et al, 2014). The former ensure to get precise and objective parameters value while, in the latter case, these characteristics might not be true depending on the amount of available data for parameters optimization, the relative number of parameters to be optimized and on model structure itself.…”

“…Other studies have been carried out on marker-based modeling for major crops (Reymond et al, 2003;White and Hoogenboom, 2003;Nakagawa et al, 2005;Quilot et al, 2005;Yin et al, 2005;Malosetti et al, 2006;Messina et al, 2006;White et al, 2008;Uptmoor et al, 2011;Zheng et al, 2013;Bogard et al, 2014). All of these consisted in finding a statistical model relating directly parameters values of the crop model to genes or genetic markers.…”