BackgroundRecent progress in selective breeding of maize (Zea mays L.) towards adaptation to temperate climate has allowed the production of inbred lines withstanding cold springs with temperatures below 8 °C or even close to 0 °C, indicating that despite its tropical origins maize is not inherently cold-sensitive.ResultsHere we studied the acclimatory response of three maize inbred lines of contrasting cold-sensitivity selected basing on multi-year routine field data. The field observations were confirmed in the growth chamber. Under controlled conditions the damage to the photosynthetic apparatus due to severe cold treatment was the least in the cold-tolerant line provided that it had been subjected to prior moderate chilling, i.e., acclimation. The cold-sensitive lines performed equally poorly with or without acclimation. To uncover the molecular basis of the attained cold-acclimatability we performed comparative transcriptome profiling of the response of the lines to the cold during acclimation phase by means of microarrays with a statistical and bioinformatic data analysis.ConclusionsThe analyses indicated three mechanisms likely responsible for the cold-tolerance: acclimation-dependent modification of the photosynthetic apparatus, cell wall properties, and developmental processes. Those conclusions supported the observed acclimation of photosynthesis to severe cold at moderate chilling and were further confirmed by experimentally showing specific modification of cell wall properties and repression of selected miRNA species, general regulators of development, in the cold-tolerant line subjected to cold stress.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2453-4) contains supplementary material, which is available to authorized users.

Maize is a cold-sensitive species, but selective breeding programs have recently succeeded in producing plants strikingly well adapted to the cold springs of a temperate climate, showing the potential for improved cold tolerance. The aim of the present study was to determine whether the adaptation of some inbred lines to spring chills is due to their increased true cold tolerance or whether it only represents an avoidance mechanism, which was the sole mode of adaptation during early stages of agricultural dispersal of maize towards higher latitudes. By characterizing numerous physiological features of several lines of different cold sensitivity, we show that a combination of both avoidance and tolerance is involved. A novel avoidance mechanism was found that favored unhindered development of the photosynthetic apparatus through protection of the shoot apex below soil level due to a shortened mesocotyl. It seems to be mediated by increased seedling photosensitivity at early growth stages. True tolerance involved improved protection of the cell membrane against cold injury at temperatures close to 0 °C and stimulation of light-induced processes (accumulation of anthocyanins, carotenoids, and chlorophyll, proper development of chloroplasts) at temperatures in the range of 10–14 °C, likely also related to the increased photosensitivity and mediated by gibberellin signaling.

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome wide association studies conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.

Maize is a cold-sensitive plant whose physiological reactions to sub-optimal temperatures are well understood, but their molecular foundations are only beginning to be deciphered. In an attempt to identify key genes involved in these reactions, we surveyed several independent transcriptomic studies addressing the response of juvenile maize to moderate or severe cold. Among the tens of thousands of genes found to change expression upon cold treatment less than 500 were reported in more than one study, indicating an astonishing variability of the expression changes, likely depending on the experimental design and plant material used. Nearly all these “common” genes were specific to either moderate or to severe cold and formed distinct interaction networks, indicating fundamentally different responses. Moreover, down-regulation of gene expression dominated strongly in moderate cold and up-regulation prevailed in severe cold. Very few of these genes have ever been mentioned in the literature as cold-stress–related, indicating that most response pathways remain poorly known at the molecular level. We posit that the genes identified by the present analysis are attractive candidates for further functional studies and their arrangement in complex interaction networks indicates that a re-interpretation of the present state of knowledge on the maize cold-response is justified.

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

SUMMARY Maize (Zea mays ssp. mays) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole‐genome resequencing strategies, identifying millions of segregating single‐nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high‐confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering‐time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.

Summary Photoprotection against excess light via nonphotochemical quenching (NPQ) is indispensable for plant survival. However, slow NPQ relaxation under low light conditions can decrease yield of field‐grown crops up to 40%. Using semi‐high‐throughput assay, we quantified the kinetics of NPQ and photosystem II operating efficiency (ΦPSII) in a replicated field trial of more than 700 maize (Zea mays) genotypes across 2 yr. Parametrized kinetics data were used to conduct genome‐wide association studies. For six candidate genes involved in NPQ and ΦPSII kinetics in maize the loss of function alleles of orthologous genes in Arabidopsis (Arabidopsis thaliana) were characterized: two thioredoxin genes, and genes encoding a transporter in the chloroplast envelope, an initiator of chloroplast movement, a putative regulator of cell elongation and stomatal patterning, and a protein involved in plant energy homeostasis. Since maize and Arabidopsis are distantly related, we propose that genes involved in photoprotection and PSII function are conserved across vascular plants. The genes and naturally occurring functional alleles identified here considerably expand the toolbox to achieving a sustainable increase in crop productivity.