Conservation of Arabidopsis thaliana photoperiodic flowering time genes in onion (Allium cepa L.)

The genetics underlying onion development is poorly understood. Here the characterisation of onion homologues of Arabidopsis photoperiodic flowering pathway genes is reported with the end goal of accelerating onion breeding programmes by understanding the genetic basis of adaptation to different latitudes. The expression of onion GI, FKF1 and ZTL homologues under SD and LD conditions was examined using quantitative RT-PCR. The expression of AcGI and AcFKF1 was examined in onion varieties which exhibit different daylength responses. Phylogenetic trees were constructed to confirm the identity of the homologues. AcGI and AcFKF1 showed diurnal expression patterns similar to their Arabidopsis counterparts while AcZTL was found to be constitutively expressed. AcGI showed similar expression patterns in varieties which exhibit different daylength responses whereas AcFKF1 showed differences. It is proposed that these differences could contribute to the different daylength responses in these varieties. Phylogenetic analyses showed that all the genes isolated are very closely related to their proposed homologues. The results presented here show that key genes controlling photoperiodic flowering in Arabidopsis are conserved in onion and a role for these genes in the photoperiodic control of bulb initiation is predicted. This theory is supported by expression and phylogenetic data

Informed Search for Learning Causal Structure

Over the past twenty-five years, a large number of algorithms have been developed to learn the structure of causal graphical models. Many of these algorithms learn causal structures by analyzing the implications of observed conditional independence among variables that describe characteristics of the domain being analyzed. They do so by applying inference rules, data analysis operations such as the conditional independence tests, each of which can eliminate large parts of the space of possible causal structures. Results show that the sequence of inference rules used by PC, a widely applied algorithm for constraint-based learning of causal models, is effective but not optimal. This is because algorithms such as PC ignore the probability of the outcomes of these inference rules. We demonstrate how an alternative algorithm can reliably outperform PC by taking into account the probability of inference rule outcomes. Specifically we show that an informed search that bases the order of causal inference on a prior probability distribution over the space of causal constraints can generate a flexible sequence of analysis that efficiently identifies the same results as PC. This class of algorithms is able to outperform PC even under uniform or erroneous priors