The genetic architecture of flowering time changes in pea from wild to crop.
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ABSTRACT:
SUBMITTER: Williams O
PROVIDER: S-EPMC9238443 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cereal crop species including bread wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), rice (Oryza sativa L.), and maize (Zea mays L.) provide the bulk of human nutrition and agricultural products for industrial use. These four cereals are central to meet future demands of food supply for an increasing world population under a changing climate. A prerequisite for cereal crop production is the transition from vegetative to reproductive and grain-filling phases starting with flower initiation, a key developmental switch tightly regulated in all flowering plants. Although studies in the dicotyledonous model plant Arabidopsis thaliana build the foundations of our current understanding of plant phenology genes and regulation, the availability of genome assemblies with high-confidence sequences for rice, maize, and more recently bread wheat and barley, now allow the identification of phenology-associated gene orthologs in monocots. Together with recent advances in next-generation sequencing technologies, QTL analysis, mutagenesis, complementation analysis, and RNA interference, many phenology genes have been functionally characterized in cereal crops and conserved as well as functionally divergent genes involved in flowering were found. Epigenetic and other molecular regulatory mechanisms that respond to environmental and endogenous triggers create an enormous plasticity in flowering behavior among cereal crops to ensure flowering is only induced under optimal conditions. In this review, we provide a summary of recent discoveries of flowering time regulators with an emphasis on four cereal crop species (bread wheat, barley, rice, and maize), in particular, crop-specific regulatory mechanisms and genes. In addition, pleiotropic effects on agronomically important traits such as grain yield, impact on adaptation to new growing environments and conditions, genetic sequence-based selection and targeted manipulation of phenology genes, as well as crop growth simulation models for predictive crop breeding, are discussed.
| S-EPMC5165254 | biostudies-literature
Project description:The genetic basis of flowering time changes across environments, and pleiotropy may limit adaptive evolution of populations in response to local conditions. However, little information is known about how genetic architecture changes among environments. We used genome-wide association studies (GWAS) in Boechera stricta (Graham) Al-Shehbaz, a relative of Arabidopsis, to examine flowering variation among environments and associations with climate conditions in home environments. Also, we used molecular population genetics to search for evidence of historical natural selection. GWAS found 47 significant quantitative trait loci (QTLs) that influence flowering time in one or more environments, control plastic changes in phenology between experiments, or show associations with climate in sites of origin. Genetic architecture of flowering varied substantially among environments. We found that some pairs of QTLs showed similar patterns of pleiotropy across environments. A large-effect QTL showed molecular signatures of adaptive evolution and is associated with climate in home environments. The derived allele at this locus causes later flowering and predominates in sites with greater water availability. This work shows that GWAS of climate associations and ecologically important traits across diverse environments can be combined with molecular signatures of natural selection to elucidate ecological genetics of adaptive evolution.
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Project description:The number of leaves and their distributions on plants are critical factors determining plant architecture in maize (Zea mays), and leaf number is frequently used as a measure of flowering time, a trait that is key to local environmental adaptation. Here, using a large set of 866 maize-teosinte BC2 S3 recombinant inbred lines genotyped by using 19,838 single nucleotide polymorphism markers, we conducted a comprehensive genetic dissection to assess the genetic architecture of leaf number and its genetic relationship to flowering time. We demonstrated that the two components of total leaf number, the number of leaves above (LA) and below (LB) the primary ear, were under relatively independent genetic control and might be subject to differential directional selection during maize domestication and improvement. Furthermore, we revealed that flowering time and leaf number are commonly regulated at a moderate level. The pleiotropy of the genes ZCN8, dlf1 and ZmCCT on leaf number and flowering time were validated by near-isogenic line analysis. Through fine mapping, qLA1-1, a major-effect locus that specifically affects LA, was delimited to a region with severe recombination suppression derived from teosinte. This study provides important insights into the genetic basis of traits affecting plant architecture and adaptation. The genetic independence of LA from LB enables the optimization of leaf number for ideal plant architecture breeding in maize.
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Project description:BackgroundCultivated crops have repeatedly faced new climatic conditions while spreading from their site of origin. In Sweden, at the northernmost fringe of Europe, extreme conditions with temperature-limited growth seasons and long days require specific adaptation. Pea (Pisum sativum L.) has been cultivated in Sweden for millennia, allowing for adaptation to the local environmental conditions to develop. To study such adaptation, 15 Swedish pea landraces were chosen alongside nine European landraces, seven cultivars and three wild accessions. Number of days to flowering (DTF) and other traits were measured and the diversity of the flowering time genes HIGH RESPONSE TO PHOTOPERIOD (HR), LATE FLOWERING (LF) and STERILE NODES (SN) was assessed. Furthermore, the expression profiles of LF and SN were obtained.ResultsDTF was positively correlated with the length of growing season at the site of origin (GSO) of the Swedish landraces. Alleles at the HR locus were significantly associated with DTF with an average difference of 15.43 days between the two detected haplotypes. LF expression was found to have a significant effect on DTF when analysed on its own, but not when HR haplotype was added to the model. HR haplotype and GSO together explained the most of the detected variation in DTF (49.6 %).ConclusionsWe show local adaptation of DTF, primarily in the northernmost accessions, and links between genetic diversity and diversity in DTF. The links between GSO and genetic diversity of the genes are less clear-cut and flowering time adaptation seems to have a complex genetic background.
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Project description:There is growing interest in the conservation and utilization of crop wild relatives (CWR) in international food security policy and research. Legumes play an important role in human health, sustainable food production, global food security, and the resilience of current agricultural systems. Pea belongs to the ancient set of cultivated plants of the Near East domestication center and remains an important crop today. Based on genome-wide analysis, P. fulvum was identified as a well-supported species, while the diversity of wild P. sativum subsp. elatius was structured into 5 partly geographically positioned clusters. We explored the spatial and environmental patterns of two progenitor species of domesticated pea in the Mediterranean Basin and in the Fertile Crescent in relation to the past and current climate. This study revealed that isolation by distance does not explain the genetic structure of P. sativum subsp. elatius in its westward expansion from its center of origin. The genetic diversity of wild pea may be driven by Miocene-Pliocene events, while the phylogenetic diversity centers may reflect Pleisto-Holocene climatic changes. These findings help set research and discussion priorities and provide geographical and ecological information for germplasm-collecting missions, as well as for the preservation of extant diversity in ex-situ collections.
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Project description:While addition of managed honeybees (Apis mellifera) improves pollination of many entomophilous crops, it is unknown if it simultaneously suppresses the densities of wild insects through competition. To investigate this, we added 624 honeybee hives to 23 fields of oilseed rape (Brassica napus L.) over 2 years and made sure that the areas around 21 other fields were free from honeybee hives. We demonstrate that honeybee addition depresses the densities of wild insects (bumblebees, solitary bees, hoverflies, marchflies, other flies, and other flying and flower-visiting insects) even in a massive flower resource such as oilseed rape. The effect was independent of the complexity of the surrounding landscape, but increased with the size of the crop field, which suggests that the effect was caused by spatial displacement of wild insects. Our results have potential implications both for the pollination of crops (if displacement of wild pollinators offsets benefits achieved by adding honeybees) and for conservation of wild insects (if displacement results in negative fitness consequences).
| S-EPMC5136583 | biostudies-literature
Project description:Regulation of floral transition and inflorescence development is crucial for plant reproductive success. FLOWERING LOCUS T (FT) is one of the central players in the flowering genetic regulatory network, whereas FLOWERING LOCUS D (FD), an interactor of FT and TERMINAL FLOWER 1 (TFL1), plays significant roles in both floral transition and inflorescence development. Here we show the genetic regulatory networks of floral transition and inflorescence development in Medicago truncatula by characterizing MtFTa1 and MtFDa and their genetic interactions with key inflorescence meristem (IM) regulators. Both MtFTa1 and MtFDa promote flowering; the double mutant mtfda mtfta1 does not proceed to floral transition. RNAseq analysis reveals that a broad range of genes involved in flowering regulation and flower development are up- or downregulated by MtFTa1 and/or MtFDa mutations. Furthermore, mutation of MtFDa also affects the inflorescence architecture. Genetic analyses of MtFDa, MtFTa1, MtTFL1, and MtFULc show that MtFDa is epistatic to MtFULc and MtTFL1 in controlling IM identity. Our results demonstrate that MtFTa1 and MtFDa are major flowering regulators in M. truncatula, and MtFDa is essential both in floral transition and secondary inflorescence development. The study will advance our understanding of the genetic regulation of flowering time and inflorescence development in legumes.
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Project description:Flowering time adaptation is a major breeding goal in the allopolyploid species Brassica napus. To investigate the genetic architecture of flowering time, a genome-wide association study (GWAS) of flowering time was conducted with a diversity panel comprising 523 B. napus cultivars and inbred lines grown in eight different environments. Genotyping was performed with a Brassica 60K Illumina Infinium SNP array. A total of 41 single-nucleotide polymorphisms (SNPs) distributed on 14 chromosomes were found to be associated with flowering time, and 12 SNPs located in the confidence intervals of quantitative trait loci (QTL) identified in previous researches based on linkage analyses. Twenty-five candidate genes were orthologous to Arabidopsis thaliana flowering genes. To further our understanding of the genetic factors influencing flowering time in different environments, GWAS was performed on two derived traits, environment sensitivity and temperature sensitivity. The most significant SNPs were found near Bn-scaff_16362_1-p380982, just 13 kb away from BnaC09g41990D, which is orthologous to A. thaliana CONSTANS (CO), an important gene in the photoperiod flowering pathway. These results provide new insights into the genetic control of flowering time in B. napus and indicate that GWAS is an effective method by which to reveal natural variations of complex traits in B. napus.
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Project description:BACKGROUND:Accurate prediction of crop flowering time is required for reaching maximal farm efficiency. Several models developed to accomplish this goal are based on deep knowledge of plant phenology, requiring large investment for every individual crop or new variety. Mathematical modeling can be used to make better use of more shallow data and to extract information from it with higher efficiency. Cultivars of chickpea, Cicer arietanum, are currently being improved by introgressing wild C. reticulatum biodiversity with very different flowering time requirements. More understanding is required for how flowering time will depend on environmental conditions in these cultivars developed by introgression of wild alleles. RESULTS:We built a novel model for flowering time of wild chickpeas collected at 21 different sites in Turkey and grown in 4 distinct environmental conditions over several different years and seasons. We propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data. By using a combination of Grammatical Evolution and Differential Evolution Entirely Parallel method, we have identified a model that reflects the influence of effects of day length, temperature, humidity and precipitation and has a coefficient of determination of R2=0.97. CONCLUSIONS:We used our model to test two important hypotheses. We propose that chickpea phenology may be strongly predicted by accession geographic origin, as well as local environmental conditions at the site of growth. Indeed, the site of origin-by-growth environment interaction accounts for about 14.7% of variation in time period from sowing to flowering. Secondly, as the adaptation to specific environments is blueprinted in genomes, the effects of genes on flowering time may be conditioned on environmental factors. Genotype-by-environment interaction accounts for about 17.2% of overall variation in flowering time. We also identified several genomic markers associated with different reactions to climatic factor changes. Our methodology is general and can be further applied to extend existing crop models, especially when phenological information is limited.
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Project description:BACKGROUND:Measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties. RESULTS:Here we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly estimate plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this "breeder-trained" network to classify wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering time) traits, we demonstrate high heritability and very high accuracy against the "ground-truth" values from visual scoring. Using the traits predicted by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control for this trait. CONCLUSIONS:We demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly measure morphological and developmental phenotypes in genetic populations from field-based imaging. The deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping because it can potentially estimate any trait in any plant species for which the combination of breeder scores and high-resolution images can be obtained, capturing the expert knowledge from breeders, geneticists, pathologists, and physiologists to train the networks.
| S-EPMC6862935 | biostudies-literature