Flowering time plasticity and global adaptation in rice

by Aalt DJ van Dijk 
Wageningen University

Both genetic and physical characteristics of plants are nowadays studied at large scale using automated approaches. In order to understand how genotype and phenotype are related, computational approaches are indispensable. An important caveat of such analyses is that to obtain a meaningful connection between genotype and phenotype, the environment has to be taken into account. This certainly holds for flowering time related traits, given the strong impact of environmental signals on flowering. Hence, it is of major importance to consider the interplay between genes, phenotypes, and the environment when modelling the connection between genotype and flowering related traits.

This is both of fundamental interest and of practical relevance. Plant breeding has been very successful in creating and selecting genotypes that are well-adapted. Plant adaptation depends on the genotype, the environment, and on how sensitive the genotype is to the environmental conditions. This effect is known under the name of ‘genotype by environment interaction’: genotypes showing different sensitivities to the environment. This genotype by environment interaction can cause that the best genotype for one condition might not be the best for another condition, complicating the selection of superior individuals.

A paper by Guo et al. (2020) presents a study of flowering time adaptation to different temperature zones. In doing so, they provide an example of how to integrate data from genomics, phenotype measurements, and quantification of the environment. Guo et al. studied a rice genetic mapping population in nine different natural environments across Asia. To characterize the different environment, a so-called environmental index was defined. To that end, a measure called ‘growing degree days (GDD) from 9 to 50 days after planting’ was chosen. This basically represents the amount of heat accumulation in a certain timeframe (here, day 9-50). This specific index was chosen based on how well it correlated with the mean flowering time in the different environments. (see Fig. 1, left panel).


Fig. 1. Integrating genotype, environment, and flowering time. (Left) Environmental index: Growing degree days (GDD) is used as an index to characterize the environment. (Right) Joint genomic regression analysis: Regression of flowering time on GDD for each genotype results in intercept and slope parameter for each genotype. This is visualized for three out of 174 genotypes. These parameters were subsequently linked to genomic markers.

The idea of this index is that in subsequent analyses, each environment can be represented by a single number. This allows to predict flowering time between different environments, in a similar way as we can predict between different genotypes using genotypic information. These two sources of information (genotype and environment) were then combined in a so-called ‘joint genomic regression analysis’. This was performed in two scenarios, of which I will only describe one (see Fig. 1, right panel); the alternative approach basically contains the same two steps but in a different order. (1) In the first step of this analysis, flowering-time observations for each genotype were regressed on values of the environmental index (GDD). For each genotype, this results in two numbers. The first is an intercept, which quantifies the expected flowering time when GDD equals zero, i.e. without temperature effect. The other is the slope of the regression line, quantifying the sensitivity of flowering time to temperature change; in other words, how much does flowering time change when GDD is increased by a given amount. (2) In the second step of the analysis, the environment-related parameters obtained for each genotype in step 1 (intercept and slope) were now linked to the genotype. A model was fitted to predict these parameters using genotypic markers. The result is a model that can predict for unseen genotypes and/or unseen environments what the flowering time would be. In other words, this is a model that includes rice flowering time plasticity at the genotype level.

Subsequently, to zoom in on specific genes potentially involved in flowering time plasticity, Guo et al. (2020) focused in detail on four known rice flowering time genes: Hd1, Hd2, Hd5, and Hd6. Part of the motivation to focus on these came from a QTL mapping experiment which I will not describe here in detail. In choosing those genes, Guo and colleagues made use of the fact that the molecular mechanisms underlying the timing of the transition from vegetative to reproductive growth have been well-studied in rice. This allowed to select these four genes, known for their role in photoperiod response, as putative causal genes underlying QTLs detected in this study. In the mapping population, for each of the different haplotype combinations for those four genes, slopes of a model regressing flowering time to GDD were obtained, in a similar way as described above.

Next, the results were placed in a genomic context by analyzing the roughly 3000 available genomes from cultivated rice. From these, haplotypes were obtained and designated as wild type and non-wildtype. This allowed to categorize the haplotype combinations in 16 groups (2*2*2*2). The question then was if and how geographic distribution of these haplotypes would correspond with temperature response behaviour. This question was addressed by looking at the slopes obtained for each haplotype combination in the biparental population. It turned out that regions with lower mean annual temperature were dominated by haplotypes sensitive to temperature (large slope) and regions with higher temperature mostly had haplotypes less sensitive to temperature (smaller slope).

The research by Guo and colleagues allows to conceptualize the relationship between environment, flowering time, and either genotype or haplotype. It will be exciting to see further developments in this area. In particular, one could imagine that a next step could be to expand the representation of the genotype by taking into account known molecular modes of action. This could, for example, involve knowledge on how the different genes included are involved in a network of interactions. This would potentially allow further improvement in prediction performance but in particular would serve to enable further interpretation of the relationship between genes, environment, and flowering.


Guo T, Mu Q, Wang J, Vanous A, Onogi A, Iwata H, Li X and Yu J. 2020. Dynamic effects of interacting genes underlying rice flowering-time phenotypic plasticity and global adaptation. Genome Research 30, 673-68310. doi: 1101/gr.255703.119


About Flowering Highlights

Flowering Newsletter published by the Journal of Experimental Botany
This entry was posted in flowering, flowering time and tagged , , , , , , . Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s