Search

Plants are exposed to combinations of various biotic and abiotic stresses, but stress responses are usually investigated for single stresses only. Here, we investigated the genetic architecture underlying plant responses to 11 single stresses and several of their combinations by phenotyping 350 Arabidopsis thaliana accessions. A set of 214 000 single nucleotide polymorphisms (SNPs) was screened for marker-trait associations in genome-wide association (GWA) analyses using tailored multi-trait mixed models. Stress responses that share phytohormonal signaling pathways also share genetic architecture underlying these responses. After removing the effects of general robustness, for the 30 most significant SNPs, average quantitative trait locus (QTL) effect sizes were larger for dual stresses than for single stresses. Plants appear to deploy broad-spectrum defensive mechanisms influencing multiple traits in response to combined stresses. Association analyses identified QTLs with contrasting and with similar responses to biotic vs abiotic stresses, and below-ground vs above-ground stresses. Our approach allowed for an unprecedented comprehensive genetic analysis of how plants deal with a wide spectrum of stress conditions.

Zinc (Zn) is an essential nutrient for plants, with a crucial role as a cofactor for many enzymes. Approximately one-third of the global arable land area is Zn deficient, leading to reduced crop yield and quality. To improve crop tolerance to Zn deficiency, it is important to understand the mechanisms plants have adopted to tolerate suboptimal Zn supply. In this study, physiological and molecular aspects of traits related to Zn deficiency tolerance were examined in a panel of 19 Arabidopsis thaliana accessions. Accessions showed a larger variation for shoot biomass than for Zn concentration, indicating that they have different requirements for their minimal Zn concentration required for growth. Accessions with a higher tolerance to Zn deficiency showed an increased expression of the Zn deficiency-responsive genes ZIP4 and IRT3 in comparison with Zn deficiency-sensitive accessions. Changes in the shoot ionome, as a result of the Zn treatment of the plants, were used to build a multinomial logistic regression model able to distinguish plants regarding their Zn nutritional status. This set of biomarkers, reflecting the A. thaliana response to Zn deficiency and Zn deficiency tolerance, can be useful for future studies aiming to improve the performance and Zn status of crop plants grown under suboptimal Zn concentrations.

Aphids induce many transcriptional perturbations in their host plants, but the signalling cascades responsible and the effects on plant resistance are largely unknown. Through a genome-wide association (GWA) mapping study in Arabidopsis thaliana, we identified WRKY22 as a candidate gene associated with feeding behaviour of the green peach aphid, Myzus persicae. The transcription factor WRKY22 is known to be involved in pathogen-triggered immunity, andWRKY22 gene expression has been shown to be induced by aphids. Assessment of aphid population development and feeding behaviour on knockout mutants and overexpression lines showed that WRKY22 increases susceptibilityto M. persicae via a mesophyll-located mechanism. mRNA sequencing analysis of aphid-infested wrky22 knockout plants revealed the up-regulation of genes involved in salicylic acid (SA) signalling and down-regulation of genesinvolved in plant growth and cell-wall loosening. In addition, echanostimulation of knockout plants by clip cages up-regulated jasmonic acid (JA)-responsive genes, resulting in substantial negative JA–SA crosstalk. Based on thisand previous studies, WRKY22 is considered to modulate the interplay between the SA and JA pathways in response to a wide range of biotic and abiotic stimuli. Its induction by aphids and its role in suppressing SA and JA signallingmake WRKY22 a potential target for aphids to manipulate host plant defences.

Heritability is a central parameter in quantitative genetics, both from an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within and between genotype variability. This approach estimates broad-sense heritability, and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here, is to use mixed models at individual plant or plot level. Using statistical arguments, simulations and real data we investigate the feasibility of both approaches, and how these affect genomic prediction with G-BLUP and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For GWAS on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.

In this paper we study the semi-parametric problem of the estimation of the long-memory parameter d in a Gaussian long-memory model. Considering a family of priors based on FEXP models, called FEXP priors in Rousseau et al. (2012), we derive concentration rates together with a Bernstein-von Mises theorem for the posterior distribution of d, under Sobolev regularity conditions on the short-memory part of the spectral density. Three different variations on the FEXP priors are studied. We prove that one of them leads to the minimax (up to a logn term) posterior concentration rate for d, under Sobolev conditions on the short memory part of the spectral density, while the other two lead to sub-optimal posterior concentration rates in d. Interestingly these results are contrary to those obtained in Rousseau et al. (2012) for the global estimation of the spectral density.

Count data on a lattice may arise in observational studies of ecological phenomena. In this paper a hierarchical spatial model is used to analyze weed counts. Anisotropy is introduced, and a bivariate extension of the model is presented