Sense in sensitivity: difference in the meaning of photoperiod insensitivity between wheat and barley.

The description of long photoperiod sensitivity in wheat and barley is a cause of confusion for researchers working with these crops, usually accustomed to free exchange of physiological and genetic knowledge of such similar crops. Indeed, wheat and barley scientists customarily quote studies of either crop species when researching one of them. Among their numerous similarities, the main gene controlling the long photoperiod sensitivity is the same in both crops (PPD1; PPD-H1 in barley and PPD-D1 in hexaploid wheat). However, the photoperiod responses are different: (i) the main dominant allele inducing shorter time to anthesis is the insensitive allele in wheat (Ppd-D1a) but the sensitive allele in barley (Ppd-H1) (i.e. sensitivity to photoperiod produces opposite effects on time to heading in wheat and barley); (ii) the main 'insensitive' allele in wheat, Ppd-D1a, does confer insensitivity, whilst that of barley reduces the sensitivity but still responds to photoperiod. The different behaviour of PPD1 genes in wheat and barley is put in a common framework based on the similarities and differences of the molecular bases of their mutations, which include polymorphism at gene expression levels, copy number variation, and sequence of coding regions. This common perspective sheds light on a source of confusion for cereal researchers, and prompts us to recommend accounting for the photoperiod sensitivity status of the plant materials when conducting research on genetic control of phenology. Finally, we provide advice to facilitate the management of natural PPD1 diversity in breeding programmes and suggest targets for further modification through gene editing, based on mutual knowledge on the two crops.

Photoperiod sensitivity of Ppd-H1 and ppd-H1 isogenic lines of a spring barley cultivar: exploring extreme photoperiods.

Barley is a long-day plant with a major gene (PPD-H1) that determines its photoperiod sensitivity. Under long days (i.e. 16 h), flowering occurs earlier in sensitive (Ppd-H1) than in insensitive (ppd-H1) genotypes, while under short days (i.e. 12 h) both flower late and more or less simultaneously. We hypothesized that (i) the sensitive line should flower later than the insensitive line under very short days (<12 h), and (ii) both the sensitive and insensitive lines should have similar phenology under very long days (>18 h). When comparing a pair of spring isogenic lines for sensitive and insensitive PPD-H1 alleles (introgressing the PPD-H1 allele into the barley cultivar 'WI4441'), we found responses fully in line with expectations for the commonly explored range from 12 to 16-18 h. When the responses were extended to very short days, sensitivity increased noticeably, and time to flowering of the sensitive line was longer than that of the insensitive one. Under very long days, the sensitive line did not respond further (it seemed to have reached its minimum time to flowering under a 16 h period), while the insensitive line continued shortening its time to flowering until c. 21 h. Consequently, both lines flowered similarly under very long days, which opens opportunities to easily test for differences in earliness per se, as in wheat.

Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data.

BACKGROUND: Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. RESULTS: We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. CONCLUSIONS: The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.